From f9e9cdaf8af021b7bfe0fe2b668e02653ff04938 Mon Sep 17 00:00:00 2001 From: Helen Kershaw Date: Tue, 24 Aug 2021 12:19:26 -0600 Subject: [PATCH 01/16] remove pages (old jekyll website) --- guide/pages/About_Us.md | 86 - guide/pages/CESM_DART_guidelines.md | 463 ----- guide/pages/Diagnostics.md | 1250 ------------ guide/pages/Getting_Started.md | 2811 --------------------------- guide/pages/Miscellany.md | 499 ----- guide/pages/Models.md | 1948 ------------------- guide/pages/Observations.md | 881 --------- guide/pages/Presentations.md | 1359 ------------- guide/pages/Publications.md | 1040 ---------- guide/pages/Research.md | 373 ---- guide/pages/Tutorial.md | 46 - guide/pages/WRF_DART_guidelines.md | 70 - guide/pages/api.md | 18 - guide/pages/dart_lab.md | 60 - guide/pages/dart_mpi.md | 304 --- guide/pages/random_seq_mod.md | 594 ------ guide/pages/release.md | 599 ------ guide/pages/support.md | 27 - guide/pages/time_manager_mod.md | 1011 ---------- 19 files changed, 13439 deletions(-) delete mode 100644 guide/pages/About_Us.md delete mode 100644 guide/pages/CESM_DART_guidelines.md delete mode 100644 guide/pages/Diagnostics.md delete mode 100644 guide/pages/Getting_Started.md delete mode 100644 guide/pages/Miscellany.md delete mode 100644 guide/pages/Models.md delete mode 100644 guide/pages/Observations.md delete mode 100644 guide/pages/Presentations.md delete mode 100644 guide/pages/Publications.md delete mode 100644 guide/pages/Research.md delete mode 100644 guide/pages/Tutorial.md delete mode 100644 guide/pages/WRF_DART_guidelines.md delete mode 100644 guide/pages/api.md delete mode 100644 guide/pages/dart_lab.md delete mode 100644 guide/pages/dart_mpi.md delete mode 100644 guide/pages/random_seq_mod.md delete mode 100644 guide/pages/release.md delete mode 100644 guide/pages/support.md delete mode 100644 guide/pages/time_manager_mod.md diff --git a/guide/pages/About_Us.md b/guide/pages/About_Us.md deleted file mode 100644 index 5752ea8225..0000000000 --- a/guide/pages/About_Us.md +++ /dev/null @@ -1,86 +0,0 @@ ---- -layout: default -title: About Us ---- - -# NCAR's Data Assimilation Research Section - DAReS - -The NCAR Data Assimilation Initiative was founded to create and to then -lead a research community for data assimilation where individuals benefit -from sharing ideas, methodologies, and software tools as well as access to -a data assimilation testbed. NCAR has a large number of researchers for -whom data assimilation is an essential part of their ongoing or planned -research. New developments in theoretical data assimilation and in software -engineering are making collaborations between data assimilation experts, -modelers, observational specialists and statisticians easier and more -productive than was possible in the past. -The maturation of the Initiative resulted in the -**Data Assimilation Research Section (DAReS)**. -The primary goal of DAReS is to continue to advance the theory and -practice of ensemble data assimilation. -Also, DAReS accelerates the progress of many other NCAR -projects by providing a centralized data assimilation expertise which can -be coordinated with existing observational and modeling expertise. - -
-group photo - -from left to right: -Kevin Raeder, -Glen Romine, -Jeff Anderson,\ -Nancy Collins, -Tim Hoar -Jonathan Hendricks, and -Moha El Gharamti.\ -This is an old photo and does not show all of us\! -
- - [](#contact) - -Our small group is comprised of experts in software design, -algorithm development, large-model implementation and execution, -observations and observation operators, and hardware/software portability. -We have given many presentations on DART - our software facility for -ensemble data assimilation, and have held several workshops for young -researchers interested in DA. - - - -Our central email address is **dart@ucar.edu**, which will hit -'everyone' and find its way to the best person. -The categories that follow are not set in stone, everyone has some -expertise in all areas. - - [](#regularstaff) - -DAReS Staff (alphabetically) - - [Jeff Anderson](http://staff.ucar.edu/users/jla), Scientist, algorithms - - [Nancy Collins](http://staff.ucar.edu/users/nancy), Software Engineer, platforms/mpi - - [Moha El Gharamti](http://staff.ucar.edu/users/gharamti), Scientist, algorithms - - [Tim Hoar](http://staff.ucar.edu/users/thoar), Associate Scientist, diagnostics, documentation - - [Ben Johnson](http://staff.ucar.edu/users/johnsonb), Associate Scientist - - [Kevin Raeder](http://staff.ucar.edu/users/raeder), Associate Scientist, CAM - - [Glen Romine](http://staff.ucar.edu/users/romine), Project Scientist, WRF - - [Jeff Steward](http://staff.ucar.edu/users/jsteward), Software Engineer - - [](#supportstaff) - -DAReS Support (alphabetically) - - [Elizabeth Chapin](http://staff.ucar.edu/users/echapin), Senior Business Analyst - - [Lisa Larson](http://staff.ucar.edu/users/larsonl), Administrative Assistant - - [Taysia Peterson](http://staff.ucar.edu/users/taysiana), Administrative Assistant - - [Mary Pronk](http://staff.ucar.edu/users/pronk), Administrator - - [](#shipping) - -### Shipping information: - -| postal address | "overnight" deliveries | electronic methods | -| :----------------------- | :----------------------- | :------------------- | -| Lisa Larson | Lisa Larson | larsonl @ ucar . edu | -| NCAR | NCAR | 303 497 1858 | -| P.O. Box 3000 | 1850 Table Mesa Dr. | 303 497 2483 (FAX) | -| Boulder, CO 80307-3000 | Boulder, CO 80305 | | - ---- diff --git a/guide/pages/CESM_DART_guidelines.md b/guide/pages/CESM_DART_guidelines.md deleted file mode 100644 index 1ecb2b8115..0000000000 --- a/guide/pages/CESM_DART_guidelines.md +++ /dev/null @@ -1,463 +0,0 @@ ---- -title: CESM and DART -layout: default ---- - -# CESM+DART setup - -[SET-UP](#CESM%20MODES) / [CESM+DART MODELS](#CESM+DART%20MODELS) / -[SET-UP](#SETUP) / [INITIAL ENSEMBLE](#INITIALFILES) / -[OUTPUT DIRECTORY](#OUTPUTDIRECTORY) / [HINTS](#HINTS) / -[SPACE](#SPACE) / [FAQ](#cesm_dart_faq) - - - -## CESM and DART Setup Overview - -If you found your way to this file without reading more basic DART help -files, please read those first. `$DART/README.md` is a good place to find -pointers to those files. This document gives specific help in setting up -a CESM+DART assimilation for the first time. Unless you just came from -there, also see the ../{your_model(s)}/model_mod.html documentation -about the code-level interfaces and namelist values. - -#### CESM context - -Most other models are either called by DART (low order models), or are -run by DART via a shell script command (e.g. WRF). In contrast, CESM -runs its forecast, and then calls DART to do the assimilation. The -result is that assimilation set-up scripts for CESM components focus on -modifying the set-up and build of CESM to accommodate DART's needs, such -as multi-instance forecasts, stopping at the assimilation times to run -filter, and restarting with the updated model state. The amount of -modification of CESM depends on which version of CESM is being used. -Later versions require fewer changes because CESM has accommodated more -of DART's needs with each CESM release. This release of DART focuses on -selected CESM versions from 1_2_1 onward, through CESM2 (June, 2017) -and versions to be added later. Using this DART with other CESM versions -will quite possibly fail. - -Since the ability to use DART has not been completely integrated into -CESM testing, it is necessary to use some CESM fortran subroutines which -have been modified for use with DART. These must be provided to CESM -through the SourceMods mechanism. SourceMods for selected versions of -CESM are available from the DART website. They can often be used as a -template for making a SourceMods for a different CESM version. If you -have other CESM modifications, they must be merged with the DART -modifications. - -#### CESM2 - -CESM2 has several helpful improvements, from DART's perspective. - - - Reduced number of subroutines in DART's SourceMods. - - "Multi-instance" capability enables the ensemble forecasts DART - needs. - - Cycling capability, enabling multiple assimilation cycles in a - single job, which reduces the frequency of waiting in the queue. - - Removal of the short term archiver from the run script so that the - MPI run doesn't need to idle while the single task archiver runs. - This significantly reduces the core hours required. - - CESM's translation of the short term archiver to python, and control - of it to an xml file ($caseroot/`env_archive.xml`), so that DART - modifications to the short term archiver are more straight-forward. - - The creation of a new component class, "External Statistical - Processing" ("esp"), of which DART is the first instance, integrates - DART more fully into the CESM development, testing, and running - environment. This is the same as the atm class, which has CAM as an - instance. This will help make DART available in the most recent - tagged CESM versions which have the most recent CESM component - versions. - -These have been exploited most fully in the CAM interfaces to DART, -since the other components' interfaces still use older CESMs. The -`cam-fv/shell_scripts` can be used as a template for updating other -models' scripting. The multi-cycling capability, with the short- and -long-term archivers running as separate jobs at the end, results in -assimilation jobs which rapidly fill the scratch space. Cam-fv's and -POP's *assimilate.csh* scripts have code to remove older and unneeded CESM -restart file sets during the run. All of DART's output and user -selected, restart file sets are preserved. - -DART's Manhattan release includes the change to hard-wired input and -output filenames in filter. Cam-fv's *assimilate.csh* renames these files -into the CESM file format: -$case.$component{_$instance}.$filetype.$date.nc. -DART's hard-wired names are used as new filetypes, just like CESM's -existing "r", "h0", etc. For example, `preassim_mean.nc` from a CAM -assimilation named "Test0" will be renamed -`Test0.cam.preassim_mean.2013-03-14-21600.nc` -The obs_seq files remain an exception to this renaming, since they are -not in NetCDF format (yet). - - - -## CESM Component Combinations - -CESM can be configured with many combinations of its components (CAM, -CLM, POP, CICE, ...) some of which may be 'data' components, which -merely read in data from some external source and pass it to the other, -active, components to use. The components influence each other only -through the coupler. There are several modes of assimilating -observations in this context. - -#### Single-Component Assimilation - -The first, and simplest, consists of assimilating relevant observations -into one active component. Most/all of the rest of the components are -'data'. For example, observations of the oceans can be assimilated into -the POP model state, while the atmospheric forcing of the ocean comes -from CAM reanalysis files, and is not changed by the observations. A -variation of this is used by CAM assimilations. A CAM forecast usually -uses an active land component (CLM) as well as an active atmospheric -component. Atmospheric observations are assimilated only into the CAM -state, while the land state is modified only through its interactions -with CAM through the coupler. Each of these assimilations is handled by -one of $DART/models/{cam-fv, POP, clm, ...} If you want to use an -unusual combination of active and data components, you may need to (work -with us to) modify the setup scripts. - - -
-CAM+DART flowchart -CAM+POP flowchart -
- ---- - -#### Multi-Component Assimilation (aka "weakly coupled") - - - - - - -
Multi-component flowchart -It's also possible to assimilate observations into multiple active components, -but restricting the impact of observations to only "their own" component. -So in a "coupled" CESM with active CAM and POP, atmospheric observations change -only the CAM model state while oceanic observations change only the POP model -state. This mode uses multiple DART models; cam-fv and POP in this example to -make a *filter* for each model. -
- ---- - -#### Cross-Component Assimilation (aka "strongly coupled") - - - - - - -
cross-component flowchart -Work is underway to enable the assimilation of all observations into -multiple active CESM components. So observations of the atmosphere -would directly change the POP state variables and vice versa. -Some unresolved issues include defining the "distance" between an -observation in the atmosphere and a grid point in the ocean -(for localization), and how frequently to assimilate in CAM versus -POP. This mode will use code in this `models/CESM` directory. -
- - -[Go to cam-fv/model_mod page](../../models/cam-fv/model_mod.html) - - - ------ - -## models/*CESM components* organization - -~~~ -SCRIPT NOTES - -$DART/models/**cam-fv**/ A 'model' for each CAM dynamical core (see note below this outline) - model_mod.\* The fortran interface between CAM-FV and DART - shell_scripts/ - no_assimilate.csh,... Independent_of_cesm_version - cesm1_5/ - setup_hybrid,... Dependent on CESM version - cesm2_0/ - setup_hybrid,... Dependent on CESM version - -$DART/models/**POP**/ A 'model' for each ocean model (MOM may be interfaced next) - model_mod.\* The fortran interface between CAM-FV and DART - shell_scripts/ - no_assimilate.csh,... Independent_of_cesm_version - cesm1_5/ - setup_hybrid,... Dependent on CESM version - cesm2_0/ - setup_hybrid,... Dependent on CESM version -~~~ - - -For each CAM dynamical core "model", e.g. "cam-fv", the scripts in `cesm#_#` will handle: -- all CAM variants + vertical resolutions (*dy-core is NOT part of this.*): - - CAM5.5, CAM6, ... - - WACCM4, WACCM6, WACCM-X... - - CAM-Chem, - - ... --all horizontal resolutions of its dy-core: - - 1.9x2.5, 0.9x1.25, ..., for cam-fv - - ne30np4, ne0_CONUS,..., for cam-se - - - - ------ - -## Assimilation Set-up Procedure - -Here is a list of steps to set up an assimilation from scratch, except -that it assumes you have downloaded DART and learned how to use it with -low order models. Some of the steps can be skipped if you have a -suitable replacement, as noted. - -1. Decide which component(s) you want to use as the assimilating - model(s). (The rest of this example assumes that you're building a - cam-fv assimilation.) Look in models/cam-fv/shell_scripts to see - which CESM versions are supported. -2. CESM: locate that version on your system, or check it out from - http://www.cesm.ucar.edu/models/current.html -3. Choose a start date for your assimilation. Choosing/creating the - initial ensemble is a complicated issue. - - It's simpler for CAM assimilations. If you don't have an initial - state and/or ensemble for this date, build a single instance of - CESM (Fxxxx compset for cam-fv) and run it from the default Jan - 1 start date until 2-4 weeks before your start date. Be sure to - set the cam namelist variable `inithist = 'ENDOFRUN'` during the - last stage, so that CAM will write an "initial" file, which DART - needs. - - For ocean and land assimilations,which use an ensemble of data - atmospheres, creating usable initial ensemble is a different - process. -4. Put the entire cam-fv restart file set (including the initial file) - where it won't be scrubbed before you want to use it. Create a - pseudo-ensemble by linking files with instance numbers in them to - the restart file set (which has no instance number) using - CESM/shell_scripts/*link_ens_to_single.csh* -5. Choose the options in $DART/mkmf/mkmf.template that are best for - your assimilation. These will not affect the CESM build, only - filter. -6. In models/cam-fv/work/`input.nml`, be sure to include all of your - required obs_def_${platform}_mod.f90 file names in - `preprocess_nml:input_files`. It's also useful to modify the rest of - `input.nml` to make it do what you want for the first assimilation - cycle. This input.nml will be copied to the $case_root directory - and used by *assimilate.csh*. -7. Build the DART executables using *quickbuild.csh*. -8. Follow the directions in - `models/cam-fv/shell_scripts/cesm#_#/*setup_hybrid*` to set up the - assimilation and build of CESM. We recommend a tiny ensemble to - start with, to more quickly test whether everything is in order. -9. After convincing yourself that the CESM+DART framework is working - with *no_assimilate.csh*, activate the assimilation by changing - CESM's ```env_run.xml:DATA_ASSIMILATION_SCRIPT``` to use - *assimilate.csh*. -10. After the first forecast+assimilation cycle finishes correctly, - change the `input.nml`, `env_run.xml` and `env_batch.xml` to do - additional cycle(s) without the perturbation of the initial state, - and with using the just created restart files. You may also want to - turn on the st_archive program. Instructions are in setup_hybrid - and cam-fv/work/`input.nml`. -11. Finally, build a new case with the full ensemble, activate the - *assimilate.csh* script and repeat the steps in step 10. - - - ------ - -## CAM Initial Ensembles - -Strategies for generating an initial ensemble from which DART can -start. - -1. MINIMAL WORK; Get an ensemble of CAM/CLM/CICE/POP/... initial and - restart files from someone else (DART has a few dates for a few - model cores and resolutions - [here](http://www.image.ucar.edu/pub/DART/Obs_sets). This limits the - investigations you can undertake, but is the fastest and cheapest - way to start assimilating. - -2. MINIMAL CAM COMPUTING; an assimilation can be started from a single - CAM (+CLM\[+CICE\]) initial file. The single model state is randomly - perturbed to make as many ensemble members as are requested in the - *ens_size* variable in the *filter_nml* namelist. ~~Create a - *filter_ic* file from the CAM initial file (dart_to_cam.f90).~~ - Create an *obs_seq.out* file which has a single observation with a - large observational error variance, valid at least a week after the - start date for the spin-up. This will make the ensemble advance long - enough to balance the fields, without being perturbed by the - assimilation of any observations. - -~~~ -&filter_nml - ... - start_from_restart = .false., - restart_in_file_name = "filter_ic", - ... -/ -&model_nml - ... - pert_names = 'T ','US ','VS ' - pert_sd = 1.0d0,2.0d0,2.0d0 - ... -/ -~~~ - -Note that `start_from_restart` is false ("don't start from a -pre-existing \*ensemble\*"), but a restart file (*filter_ic*) is -still needed for filter to have something realistic to perturb. -`pert_names` specifies which fields will be perturbed. CAM field -names are used. `pert_sd` \> 0 allows each point of the pert_names -fields of each ensemble member to be randomly perturbed with a -standard deviation of pert_sd. Other fields can be used, but -moisture variables are tricky because of their variation with height -by orders of magnitude. Regardless of which fields are specified, -the spin-up period will allow the fields to come into balance with -respect to the model, so the perturbations will propagate into all fields. - -3. FULL FUNCTION ENSEMBLE; In order to have, on hand, initial ensembles - of any practical size, for any date of the year, we recommend the - following. ~~Scripts for doing this are available in - .../DART/models/cam/make_ensemble. See the README there for more - details. They are not highly documented or elegent, but offer a - starting point.~~ Make 20 successive 1-year free CAM runs (MPI CAM - highly recommended, NO_LEAP calender), saving the initial files - every 5 days. ? ? Or 5 years, saving every day. Then pull together - all of the, e.g., Jan 6ths (00Z) into a 20 (5) member ensemble - (numbered 1...20(5)). - - When you need an ensemble of, say 60 members for June 1 then - retrieve the 20 members from each of May 26, May 31, and June 5, - renumbering them 1,...,60. \--\> - - - ------ - -## Output Directory - -CESM's short term archiver (st_archive) is controlled by its -*env_archive.xml*. DART's setup scripts modify that file to archive -DART output along with CESM's. -~~(See the [list of RMA changes](../html/rma.html) for a description of -DART's output).~~ -DART's output is archived in -*$arch_dir/dart/{hist,rest,logs,...}*, where arch_dir is defined in -*setup_{hybrid,advanced}*, *hist* contains all of the state space and -observation space output, and *rest* contains the inflation restart -files. - -## Central directory - -User Location of scripts and pass-through point for files during execution. -Typically named according defining characteristics of a \*set\* of -experiments; resolution, model, obs being assimilated, unique model -state variables, etc. \--\> - -The cam-XX assimilate.csh scripts also make a copy of the obs_seq.final -files in a scratch space ($scratch/$case/Obs_seqs) which won't be -removed by CESM's long term archiver, if that is run. - - - ------ - -## Helpful Hints - - - ------ - -### Space Requirements - -Space requirements (Gb per ensemble member) for several CAM resolutions. - -There are, no doubt, things missing from these lists, so don't struggle -too long before contacting dart'at'ucar.edu. - - - ------ - -### The CESM Climate Model and DART Frequently Asked Questions - -> How does DART interact with CESM? - -The CESM climate model comes with its own configuration, build, run, and -archive scripts already. The DART distribution supplies a 'setup' script -that calls the CESM scripts to build a new case, and then add a section -to the CESM run script so the DART code will be run after each CESM -model advance. The DART setup scripts are needed only when building a -new case. At run-time the CESM run scripts are used to start the job. -The CESM "multi-instance" capability is used to run multiple ensemble -members as part of a single job. - -> I want to assimilate with one of the CESM models. Where do I start? - -We use the CESM framework to execute the CESM model components, and then -call the DART assimilation via an addition to the standard CESM run -script. We provide a set of setup scripts in our -`DART/models/XXX/shell_scripts` directories, where `XXX` is currently -one of: `cam`, `POP`, `clm`, or `CESM`. Start with the shell script, set -the options you want there, and then run the script. It calls the -standard CESM 'build_case' scripts, and stages the files that will be -needed for assimilation. See comments in the appropriate setup script -for more details of how to -proceed. - -> I'm getting a mysterious run-time error from CESM about box rearranging. - -Certain versions of CESM (including CESM1_5_alpha02d) won't run with 3 -instances (ensemble members). We are unsure what other instance sizes -fail. The error message is about box rearranging from -box_rearrange.F90. This is a problem in CESM and should be reported via -their Bugzilla process. 4 instances works -fine. - -> I'm getting 'update_reg_list' errors trying to assimilate with POP. - -If you are trying to assimilate with POP and you get this error: - -~~~ -ERROR FROM: - routine: update_reg_list - message: max_reg_list_num ( 80) is too small ... increase -~~~ - -The most likely cause is that the POP-DART model interface code is -trying to read the POP grid information and the default file is in the -wrong kind of binary for this system (big-endian and not little-endian). -At this point the easiest solution is to rebuild the DART executables -with a flag to swap the bytes as it is reading binary files. For the -Intel compiler, see the comments at the top of the mkmf file about -adding '-convert bigendian' to the FFLAGS line. - -> I'm getting asked to confirm removing files when CESM is built. - -If you have the `rm` (remove) command aliased to require you to confirm -removing files, the CESM build process will stop and wait for you to -confirm removing the files. You should reply yes when prompted. - -If you have questions about the DART setup scripts and how they interact -with CESM it is a good idea to set up a standalone CESM case without any -DART scripts or commands to be sure you have a good CESM environment -before trying to add DART. The DART setup script uses CESM scripts and -commands and cannot change how those scripts behave in your environment. - -> I'm getting module errors when CESM is built. - -DART only uses the plain netCDF libraries for I/O. CESM can be -configured to use several versions of netCDF including PIO, parallel -netCDF, and plain netCDF. Be sure you have the correct modules loaded -before you build CESM. If there are questions, try setting up a CESM -case without DART and resolve any build errors or warnings there before -using the DART scripts. - -\[[top](#)\] - ------ diff --git a/guide/pages/Diagnostics.md b/guide/pages/Diagnostics.md deleted file mode 100644 index 3c7639d083..0000000000 --- a/guide/pages/Diagnostics.md +++ /dev/null @@ -1,1250 +0,0 @@ ---- -title: Diagnostics -layout: default ---- - -# Did my experiment work? - -## *filter* ran and produced output files, now what? - -One of the most requested features for DART has been finer control of -what information is written and when. Do you want the input ensemble -spread before the application of prior inflation? After prior inflation -but before the assimilation? After the assimilation but before posterior -inflation? After posterior inflation? etc. There are several namelist -settings that control what files are output. -[Section 16](../tutorial/section_16.pdf) of the DART -tutorial has a more detailed explanation. - -From a computational perspective, there are two broad situations: - -1. *filter* is called for a single assimilation and each ensemble - member is in its own file (both `single_file_in = .false.` and - `single_file_out = .false.`.), and -2. *filter* is called once for multiple assimilation cycles and writes - a single output file containing information for all the ensemble - members (`single_file_out = .true.`) **for each stage\!** Depending - on what you choose to write, this actually results in more than a - single file. - -**`single_file_out = .false.`** :  *filter* is usually called for a single -assimilation cycle by 'large' models or in cases where it is beneficial -to run different number of MPI tasks for the model advances and the -assimilation. In this case, there can be a substantial computational -efficiency to have each ensemble member write its information to a -separate file, and each file can be written simultaneously by different -tasks. The tradeoff (at the moment) is that each of the files can only -have a single timestep in them. Consequently, some files are redundant -and should not be output. -See [Section 16](../tutorial/section_16.pdf) of the DART -tutorial for a more detailed explanation. - -**`single_file_out = .true.`** :  When *filter* is used for a long -assimilation experiment (as in the case for the low-order models), it is -possible to consolidate all the information for a particular stage into -a single file that contains all the ensemble members, the mean, spread, -inflation, etc. This results in far fewer files, and each file may -contain multiple timesteps to encompass the entirety of the experiment. -Since a single task must write each file, there is some computational -overhead. - -Perhaps somewhat paradoxically, *single_file_out* only refers to the -output **for a particular stage**. So even if you set -`single_file_out = .true.` , you can get *several* output files - -one per stage. However, if you set `single_file_out = .false.` , -be prepared for a deluge of files. -Be careful about what stages you choose to write. - -### What output and diagnostic files are produced: - -#### When *single_file_out = .false.* - -| from *perfect_model_obs* | | | -| ------------------------ | ---- | ---- | -| `obs_seq.out` | | the synthetic observations at some predefined times and locations | -| `perfect_output.nc` | 1 timestep | a netCDF file containing the model trajectory - the true state | - -There are some namelist settings that control what files are output. - Depending on the settings for *input.nml&filter_nml:stages_to_write* and others ... - -| from *filter* | | | -| ------------- | ---- | ---- | -| `forecast_member_####.nc` | 1 timestep | the ensemble forecast, each ensemble member is a separate file | -| `forecast_[mean,sd].nc` | 1 timestep | the mean and standard deviation (spread) of the ensemble forecast | -| `forecast_priorinf_[mean,sd].nc` | 1 timestep | the prior inflation information before assimilation | -| `forecast_postinf_[mean,sd].nc` | 1 timestep | the posterior inflation information before assimilation | -| `preassim_member_####.nc` | 1 timestep | the model states after any prior inflation but before assimilation | -| `preassim_[mean,sd].nc` | 1 timestep | the mean and standard deviation (spread) of the ensemble after any prior inflation but before assimilation | -| `preassim_priorinf_[mean,sd].nc` | 1 timestep | the prior inflation information before assimilation | -| `preassim_postinf_[mean,sd].nc` | 1 timestep | the posterior inflation information before assimilation | -| `postassim_member_####.nc` | 1 timestep | the model states after assimilation but before posterior inflation | -| `postassim_[mean,sd].nc` | 1 timestep | the mean and standard deviation (spread) of the ensemble after assimilation but before posterior inflation | -| `postassim_priorinf_[mean,sd].nc` | 1 timestep | the (new) prior inflation information after assimilation | -| `postassim_postinf_[mean,sd].nc` | 1 timestep | the (new) posterior inflation information after assimilation | -| `analysis_member_####.nc` | 1 timestep | the model states after assimilation and after any posterior inflation | -| `analysis_[mean,sd].nc` | 1 timestep | the mean and standard deviation (spread) of the ensemble after assimilation and after posterior inflation | -| `analysis_priorinf_[mean,sd].nc` | 1 timestep | the (new) prior inflation information after assimilation | -| `analysis_postinf_[mean,sd].nc` | 1 timestep | the (new) posterior inflation information after assimilation | -| `output_[mean,sd].nc` | 1 timestep | the mean and spread of the posterior ensemble | -| `output_priorinf_[mean,sd].nc` | 1 timestep | the (new) prior inflation information after assimilation | -| `output_priorinf_[mean,sd].nc` | 1 timestep | the (new) posterior inflation information after assimilation | -| `obs_seq.final` | | the model estimates of the observations (an integral part of the data assimilation process) | - -| from both | | -| --------- | ---- | -| `dart_log.out` | the 'important' run-time output (each run of *filter* appends to this file; remove it or start at the bottom to see the latest values) | -| `dart_log.nml` | the input parameters used for an experiment | - -#### When *single_file_out = .true.* - -All the information for each stage is contained in a single file that -*may* have multiple timesteps. - -| from *perfect_model_obs* | | | -| ------------------------ | ---- | ---- | -| `obs_seq.out` | | the synthetic observations at some predefined times and locations | -| `perfect_output.nc` | N timesteps | a netCDF file containing the model trajectory - the true state | - -There are some namelist settings that control what files are output. Depending on the settings for - *input.nml &filter_nml:stages_to_write* and others ... - -| from *filter* | | | -| -------------- | ---- | ---- | -| `filter_input.nc` | 1 timestep | The starting condition of the experiment. All ensemble members, \[optionally\] the input mean and standard deviation (spread), \[optionally\] the prior inflation values, \[optionally\] the posterior inflation values | -| `forecast.nc` | N timesteps | The ensemble forecast. All ensemble members, the mean and standard deviation (spread), the prior inflation values, the posterior inflation values | -| `preassim.nc` | N timesteps | After any prior inflation but before assimilation. All ensemble members, the mean and standard deviation (spread) of the ensemble, the prior inflation values, the posterior inflation values | -| `postassim.nc` | N timesteps | After assimilation but before posterior inflation. All ensemble members, the mean and standard deviation (spread) of the ensemble, the (new) prior inflation values, the (new) posterior inflation values | -| `analysis.nc` | N timesteps | After assimilation and after any posterior inflation. All ensemble members, the mean and standard deviation (spread) of the ensemble, the (new) prior inflation values, the (new) posterior inflation values | -| `filter_output.nc` | 1 timestep | After assimilation and after any posterior inflation. All ensemble members, the mean and standard deviation (spread) of the ensemble, the (new) prior inflation values, the (new) posterior inflation values | -| `obs_seq.final` | | the model estimates of the observations (an integral part of the data assimilation process) | - - -| from both | | -| ------------- | --- | -| `dart_log.out` | the 'important' run-time output (each run of *filter* appends to this file; remove it or start at the bottom to see the latest values) | -| `dart_log.nml` | the input parameters used for an experiment | - -### First questions to ask - -**NOTE: the remainder of this document is written from the perspective -of an experiment run with `single_file_out=.true.` The permutations -of the file names is just too great to write exhaustive documentation.** - -After *filter* executes without error and produces an `obs_seq.final` -file, a `preassim.nc` file, and an `analysis.nc` file, the first -questions to ask are: - -1. "Is the model state output from *filter* different from the input?", - and -2. "Were any observations successfully assimilated?". - -One way to check if the output model state data was changed by the -assimilation is to use the 'ncdiff' tool to difference the `preassim.nc` -and `analysis.nc` files: - -> ncdiff analysis.nc preassim.nc Innov.nc -> ncview Innov.nc - -If your model can run under *single_file_\[in,out\]* Look at the -ensemble mean variables. If all values are 0, then the assimilation -changed nothing in the state. If your model writes multiple output -files, diff the `preassim_mean.nc` and `output_mean.nc`. - - [](#debugging) - ------ - -#### Debugging hints: - -You may need to rerun *filter* multiple times to diagnose this. The -fastest way to get to the answer is to make *filter* very fast to run. -You can do this by: - -1. make an observation sequence file with only 1 or a just a few observations in - it, and -2. configure a run so *filter* does a single assimilation and exits, - without having to advance the ensemble of models or do other work. - -##### To make an obs file with a single observation, use one of these methods: - -1. run *create_obs_sequence* to make a new, short, observation sequence file -2. Use the *obs_sequence_tool* to cut your existing `obs_seq.out` file - down to just a few obs by selecting only a subset of the types and - setting a very short time window (just a second or two where you - know there are obs). - -##### To make the *filter* program only do an assimilation: - -1. Edit the `input.nml` and in the *&filter_nml* namelist set the - *init_time_days* and *init_time_seconds* to match the - observation time in the truncated observation sequence file. This overrides any - times in the input files and ensures that *filter* will only - assimilate and not try to advance the model. -2. Make sure the truncated observation sequence file contains only 1 obs, a few obs - at the same time, or obs close enough together in time to fit into a - single assimilation window. - -If there are no changes in the model state after assimilation, then -examine the `obs_seq.final` file. There are two ways to do this. 1) If -you are testing with a single observation, just look in the file. If -this file is in binary format, change the namelist so the output -observation sequence file will be written in ascii: - -~~~ -&obs_sequence_nml - write_binary_obs_sequence = .false. - / -~~~ - -and rerun *filter* to regenerate an `obs_seq.final` file in ascii. -[These diagrams](Observations.md#obs_seq_overview) help to understand -an `obs_seq.final` file. 2) If you are using many observations, run the -[obs_diag.f90](../../assimilation_code/programs/obs_diag/threed_sphere/obs_diag.html) -appropriate for your model. There are some -[diagnostic scripts](#mat_obs) to help summarize and explore what is going on. - -If all the prior and posterior mean values are -888888.0 (the DART -"missing data" value), those observations were not assimilated. *Note: -some observations have precomputed values and the posterior values for -these will always be -888888.0, no matter if the observation was -assimilated or not.* If it is not already set, change -*&filter_nml::num_output_obs_members* to be the same as the ensemble size. -This will give you all the forward operator values for all the ensemble -members. You can determine if all ensemble members are failing in the -same way, or if only a few are problematic. - -For the failing observations, the 'DART QC' may indicate the reason. -([How to locate the different values in an `obs_seq.final` file.](Observations.md#obs_seq_overview) -The 'DART QC' field is usually the second of the 2 "quality control" copies.) -A list of all the DART QC values can be found in [this table](#qc_table). - -- If the DART QC values are 4, the forward operators have failed. Look - at the *model_interpolate()* routine in your model_mod.f90 file, - or the forward operator code in - *observations/forward_operators/obs_def_xxx_mod.f90* for your - observation type. A successful forward operator must return a valid - obs_val and an *istatus = 0*. If the forward operator code returns - different istatus values for different error types, you can set - *&filter_nml::output_forward_op_errors = .true.* and rerun - *filter* to see exactly what error istatus codes are being set. See - [the filter webpage](../../assimilation_code/programs/filter/filter.html) - for more information on how to use the 'output_forward_op_errors' - option. Negative istatus values are reserved for the system, - *istatus = 0* is success, and any positive value indicates a failed - forward operator. The code is free to use different positive values - to signal different types of errors. - -- If the DART QC values are 5, those observation types were - intentionally ignored because they were not listed in the - &obs_kind_nml namelist, in the 'assimilate_these_obs_types' - stringlist. - -- If the DART QC values are 6, the data quality control that came with - the original observation data indicates this is a bad quality - observation and it was skipped for this reason. - -- If the DART QC values are 7, the observation value is too far away - from the ensemble mean. Set *&filter_nml::outlier_threshold = -1* - to ignore this for now and rerun. In general, this is - not the optimal strategy as the number of observations inconsistent - with the ensemble is a very powerful indicator of the success or - failure of the assimilation. - -If the prior and posterior values in the `obs_seq.final` are not --888888.0 but are identical, your obs are being assimilated but are -having no impact. - -The most common reasons assimilated obs have no impact on the model -state include: - - - **Zero spread in ensemble members** - Your initial ensemble members must have different values for each - state item. If all members have identical values, the observations - cannot make a change. To diagnose this condition, look at the prior - ensemble spread. This is either in `preassim.nc` or - `preassim_sd.nc`, depending on your model. If all the values are 0, - this is your problem. One way to generate an ensemble with some - spread is to set - *&filter_nml::perturb_from_single_instance = .false.,* - (which will still - require a single filter initial condition file) but then the - *filter* code will add random gaussian perturbations to each state - vector item to generate an initial ensemble with spread. The - magnitude of the gaussian noise added is controlled by the - *&filter_nml::perturbation_amplitude*. It is also - possible to write your own perturbation routine in your - `model_mod.f90` code. - - **Cutoff value too small** - If the localization radius is too small, the observation may not be - 'close enough' to the model grid to be able to impact the model. - Check the localization radius (*&assim_tools_nml::cutoff*). Set it - to a very large number (e.g. 100000) and rerun. If there is now an - impact, the cutoff was restricting the items in the state vector so - your obs had no impact before. Cutoff values are dependent on the - location type being used. It is specified in radians for the - threed_sphere locations module (what most large models use), or in - simple distance (along a unit circle) if using a low order model - (lorenz, ikeda, etc). - - **Obs error values too large (less likely)** - If the observation error is very large, it will have no impact on - the model state. This is less likely a cause than other - possibilities. - - **No correlation (unlikely)** - If there is no correlation between the distribution of the forward - observation values and the state vector values, the increments will - be very tiny. However there are generally still tiny increments - applied, so this is also a low likelyhood case. - - **Errors in forward operator location computations, or - *get_close_obs()*** - If there is an error in the `model_mod.f90` code in either - *get_state_meta_data()*, *model_interpolate()*, or the vertical - conversion code in *get_close_obs()*, it is possible for the - forward operators to appear to be working correctly, but the - distances computed for the separation between the obs and the state - vector values can be incorrect. The most frequent problem is that - the wrong locations are being passed back from - *get_state_meta_data()*. This can result in the increments being - applied in the wrong locations or not at all. This is usually one of - the things to test carefully when developing a new model interface, - and usually why we recommend starting with a single observation at a - known location. - - **Incorrect vertical conversion** - If the model is using 3d coordinates and needs the capability to - convert between pressure, height, and/or model level, the conversion - may be incorrect. The state vector locations can appear to be too - high or too low to be impacted by an observation. Some models have a - height limit built into their model_mod code to avoid trying to - assimilate observations at the model top. The observations cannot - make meaningful changes to the model state there and trying to - assimilate them can lead to problems with the inflation. If the code - in the model_mod is excluding observations incorrectly, or you are - testing with observations at the model top, this can result in no - impact on the model state. - -### How is the output different from the input? - -If you compute the difference between the prior and posterior diagnostic -files by this process: - -> ncdiff analysis.nc preassim.nc Innov.nc -> ncview Innov.nc - -and you see a difference, is it correct? - -If you run with a single observation, you should be able to easily see -the impact - generally it's a mostly spherical or circular ring around -the observation location depending on your vertical localization, you -may or may not see an impact in the vertical. Using - `&location_nml::horiz_dist_only=.true.` is usually a good idea for -a full 3d model to start out, and then add vertical localization once -you believe the horizontal impact makes sense. Without any vertical -localization, the observation should have an impact along the entire -vertical column. (For lower order models this doesn't apply.) If you -change the cutoff distances you should be able to watch the change in -impact on the state and make sure that it's something reasonable. - -Now you can use the observation space diagnostics, and the state space -diagnostics to get more information about what the impact was, and -whether it's doing the right thing or not. - - [](#DidItWork) - ------ - -# Was the Assimilation Effective? - -If your filter run finished, and the Posterior is now different from the -Prior, now what? This section presumes that you have debugged your -model/DART interfaces or are using a model that came with the DART -distribution. A *working* implementation. - -**There is no single metric that can declare success.** - -[The DART Tutorial](dart_tutorial.md) has the -best explanation of what to look for, what to change to improve the next -experiment, etc. DART has an extensive set of diagnostics implemented in -MATLAB®. To use them, make sure you have read the -[Configuring MATLAB®](Getting_Started.md#matlab) section. - - - -### The Observations are the Key. - -My own (Tim's) personal view is that the first thing to check is to see -how many observations are getting rejected by the assimilation in -addition to the RMSE and spread of the ensemble. A natural part of the -DART framework is that these metrics are calculated automatically and -are readily available in the `obs_seq.final` files. Checking the -temporal evolution of the RMSE and observation rejection characteristics -is a first-order metric for determining the health of the assimilation -system. - -1. Use - [obs_diag.f90](../../assimilation_code/programs/obs_diag/threed_sphere/obs_diag.html) - to process the collection of `obs_seq.final` files for regions and - times of interest. Edit the *input.nml:obs_diag_nml* namelist to - reflect the details of your experiment, and then run *obs_diag* to - create a netCDF file `obs_diag_output.nc` that contains the - summaries. -2. Make sure the spread of the ensemble does not collapse. Use - *plot_evolution.m* with *copystring = 'spread';* to explore - `obs_diag_output.nc`. It is normal (and desirable\!) for the - spread to decrease somewhat from the initial value, but it should - not decrease to a small value. Insufficient spread leads to filter - divergence and a large observation rejection rate. - *plot_evolution.m* automatically plots the number of observations - available and the number of observations successfully assimilated. -3. Make sure the RMSE of the ensemble does not collapse. Use - *plot_evolution.m* with *copystring = 'rmse';* to explore - `obs_diag_output.nc`. It is important to interpret the RMSE in - light of the number of observations successfully assimilated. It is - possible to have a very low RMSE if the assimilation system rejects - all of the observations that are in disagreement\! A low RMSE is - desirable and is a much stronger result if most/all of the - observations are being assimilated successfully. Also - the RMSE of - the **prior** is a much stronger result. Any method can overfit the - observations (match them perfectly) - what is important is that the - **forecast** is a good forecast\! -4. Make sure the RMSE of the ensemble does not continually increase. - *plot_evolution.m* with *copystring = 'rmse';* to explore - `obs_diag_output.nc`. It is natural for the RMSE to vary in time - in response to the changing number and location of the observations, - the phenomenon being modeled, etc. ... but it should not generally - increase as the system evolves. Once the system has 'burned in', the - RMSE should be relatively stable. -5. Check to make sure the observations that are getting rejected are - getting rejected for the right reasons. Run *obs_seq_to_netcdf* - to convert the `obs_seq.final` files into netCDF files that can be - explored with *link_obs.m* or *plot_obs_netcdf.m*. Both of these - tools will allow you to isolate the rejected observations and - determine if the observations are being rejected because they are in - disagreement with the ensemble (DART QC = 7) or they were rejected - because of a namelist setting (DART QC = 5), or incoming QC value - (DART QC = 6), or were they simply outside the domain (DART - generally only interpolates, not extrapolate) or ... -6. Check that the ensemble spread captures the 'truth' as determined by - the observations. Use *obs_diag* to create the data for a rank - histogram. [Plot the histograms with ncview](#ncview_histogram) or - [plot_rank_histogram.m](#mat_obs) - -Generally speaking, the observation-space diagnostics provide the first -and best metrics for the assimilation system. We always have -observations, we rarely have the 'truth' in a full state-space -representation. Personally, I rarely see value when comparing to some -other gridded product - as it surely has its own set of deficiencies or -assumptions. Observations aren't perfect - but they are still best. - -**NOTE:** Combining observations from multiple sources can lead to the -same observation type begin defined with *different* vertical coordinate -systems. While this does not pose a problem for assimilation, it *does* -pose a problem for the diagnostics. The current -[obs_diag.f90](../../assimilation_code/programs/obs_diag/threed_sphere/obs_diag.html) -cannot (vertically) bin the same observation type that exploits two -different vertical coordinate systems. If you get a WARNING from -*obs_diag:CheckVertical()* you should know that the observation -triggering the warning uses a different vertical coordinate system that -the first observation encountered of that same type. If the *termlevel* -is set to be 1, this is a fatal error, not a warning. The following -example is one where observation 7113 defined the vertical coordinate -system to be VERTISSURFACE (i.e. -1) and observation 7150 was created -using VERTISPRESSURE. The messages take the form: - -~~~ -WARNING FROM: - routine: CheckVertical - message: obs 7150 type 42 changing from -1 to pressure - def by obs 7113 -~~~ - -### State-Space Diagnostics - -If you're reading this, it means you have an assimilation that is not -rejecting too many observations, the ensemble spread is relatively -constant and is not too small, and presumably the RMSE is stable if not -decreasing. Now it is time to assess the affect the assimilation has on -your model - the whole point of data assimilation. - -DART produces (sometimes many) netCDF files: `preassim.nc` and -`analysis.nc`. Your files many have slightly different names depending -on the namelist settings you have chosen. You should get familiar with -what is available in the files you have created. Please read the -documentation for -[&filter_nml](../../assimilation_code/programs/filter/filter.html) -especially for the variables: *stages_to_write*, *output_members*, -*output_mean*, *output_sd*, and *single_file_out*. You should -experiment and become familiar with **ALL** the namelist variables -controlling what gets written. The ability to explore the ensemble at -various parts of the assimilation process and/or restrict the volume of -data being written has been on of our most-requested -enhancements. - -| unix command | meaning | -| --------------| ------- | -| ncdump -v MemberMetadata preassim.nc | to check which copy you want to explore | -| ncdiff analysis.nc preassim.nc Innov.nc | ncdiff comes from NCO, not DART | -| ncview Innov.nc | ncview is another 'third-party' tool. | - -See the expanded section on -[DART state-space diagnostics](#ss_diagnostics) for more. - - [](#obs_diagnostics) - [](#qc_table) - ------ - -### Observation-Space Diagnostics. - -**The DART QC table is an important piece of information.** - -| QC value | meaning | -| :------- | :------ | -| 0,1 == both Prior and Posterior are good. | -| 0 | Assimilated O.K. | -| 1 | Evaluated O.K., not assimilated because namelist specified evaluate only. | -| 2,3 == Prior OK, but Posterior failed. | -| 2 | Assimilated O.K. BUT posterior forward operator failed. | -| 3 | Evaluated O.K. BUT posterior forward operator failed. | -| 4 or higher == both Prior and Posterior have failed | -| 4 | Prior forward operator failed. | -| 5 | Not used because of namelist control. | -| 6 | Rejected because of incoming data QC higher than namelist control. | -| 7 | Rejected because of outlier threshold test. | -| 8 | Vertical conversion failed. | -| 9 and above | reserved for future use. | - -It is required to post-process the `obs_seq.final` file(s) with -[obs_diag](../../assimilation_code/programs/obs_diag/threed_sphere/obs_diag.html) -to generate a netCDF file containing accumulated diagnostics for -specified regions, etc. Since the experiment information (assimilation -interval, assimilating model, etc.) are not recorded in the -`obs_seq.final` file, the -[obs_diag_nml](../../assimilation_code/programs/obs_diag/threed_sphere/obs_diag.html#Namelist) -namelist has a section that allows specification of the necessary -quantities. - -The following quantities are normally diagnosed: - -| | | -| ------------ | ---------------------------------------------------------------------------------------------------- | -| Nposs | the number of observations for each assimilation period; | -| Nused | the number of observations successfully assimilated each assimilation period; | -| NbigQC | the number of observations that had an undesirable (original) QC value; | -| NbadIZ | the number of observations that had an undesirable Innovation Z; | -| NbadUV | the number of velocity observations that had a matching component that was not assimilated; | -| NbadLV | the number of observations that were above or below the highest or lowest model level, respectively; | -| rmse | the rmse of the ensemble; | -| bias | the bias of the ensemble (forecast-observation); | -| spread | the spread of the ensemble; | -| totalspread | the pooled spread of the observation (knowing its observational error) and the ensemble. | -| NbadDARTQC | the number of observations that had a DART QC value (\> 1 for a prior, \> 3 for a posterior) | -| observation | the mean of the observation values | -| ens_mean | the ensemble mean of the model estimates of the observation values | -| N_DARTqc_0 | the number of observations that had a DART QC value of 0 | -| N_DARTqc_1 | the number of observations that had a DART QC value of 1 | -| N_DARTqc_2 | the number of observations that had a DART QC value of 2 | -| N_DARTqc_3 | the number of observations that had a DART QC value of 3 | -| N_DARTqc_4 | the number of observations that had a DART QC value of 4 | -| N_DARTqc_5 | the number of observations that had a DART QC value of 5 | -| N_DARTqc_6 | the number of observations that had a DART QC value of 6 | -| N_DARTqc_7 | the number of observations that had a DART QC value of 7 | -| N_DARTqc_8 | the number of observations that had a DART QC value of 8 | - - [](#mat_obs) - -The observation-space functions are in the *DART/diagnostics/matlab* -directory. Once you have processed the `obs_seq.final` files into a -single `obs_diag_output.nc`, you can use that as input to your own -plotting routines or use the following DART MATLAB® routines: - -[plot_evolution.m](../../diagnostics/matlab/plot_evolution.m) plots the temporal evolution - of any of the quantities above for each variable for specified levels. - The number of observations possible and used are plotted on the same axis. - -~~~ -fname = 'POP11/obs_diag_output.nc'; % netcdf file produced by 'obs_diag' -copystring = 'rmse'; % 'copy' string == quantity of interest -plotdat = plot_evolution(fname,copystring); % -- OR -- -plotdat = plot_evolution(fname,copystring,'obsname','RADIOSONDE_TEMPERATURE'); -~~~ - -plot evolution - - -[plot_profile.m](../../diagnostics/matlab/plot_profile.m) plots the spatial and temporal - average of any specified quantity as a function of height. The number - of observations possible and used are plotted on the same axis. - -plot profile - -~~~ -fname = 'POP11/obs_diag_output.nc'; % netcdf file produced by 'obs_diag' -copystring = 'rmse'; % 'copy' string == quantity of interest -plotdat = plot_profile(fname,copystring); -~~~ - -[plot_rmse_xxx_evolution.m](../../diagnostics/matlab/plot_rmse_xxx_evolution.m) same as *plot_evolution.m* - but will overlay **rmse** on the same axis. - -[plot_rmse_xxx_profile.m](../../diagnostics/matlab/plot_rmse_xxx_profile.m) same as *plot_profile.m* - with an overlay of **rmse**. - -[plot_bias_xxx_profile.m](../../diagnostics/matlab/plot_bias_xxx_profile.m) same as *plot_profile.m* - with an overlay of **bias**. - -[two_experiments_evolution.m](../../diagnostics/matlab/two_experiments_evolution.m) same as - *plot_evolution.m* but will overlay multiple (more than two, actually) - experiments (i.e. multiple `obs_diag_output.nc` files) on the same - axis. A separate figure is created for each region in the - `obs_diag_output.nc` file. - -~~~ -files = {'POP12/obs_diag_output.nc','POP11/obs_diag_output.nc'}; -titles = {'CAM4','CAM3.6.71'}; -varnames = {'ACARS_TEMPERATURE'}; -qtty = 'rmse'; -prpo = 'prior'; -levelind = 5; -two_experiments_evolution(files, titles,{'ACARS_TEMPERATURE'}, qtty, prpo, levelind) -~~~ - -two evolution - -[two_experiments_profile.m](../../diagnostics/matlab/two_experiments_profile.m) same as - *plot_profile.m* but will overlay multiple (more than two, actually) - experiments (i.e. multiple `obs_diag_output.nc` files) on the same - axis. If the `obs_diag_output.nc` file was created with multiple - regions, there are multiple axes on a single figure. - -~~~ -files = {'POP12/obs_diag_output.nc','POP11/obs_diag_output.nc'}; -titles = {'CAM4','CAM3.6.71'}; -varnames = {'ACARS_TEMPERATURE'}; -qtty = 'rmse'; -prpo = 'prior'; -two_experiments_profile(files, titles, varnames, qtty, prpo) -~~~ -  -two profile - -[plot_rank_histogram.m](../../diagnostics/matlab/plot_rank_histogram.m) will create rank histograms - for any variable that has that information present in `obs_diag_output.nc`. - -~~~ -fname = 'obs_diag_output.nc'; % netcdf file produced by 'obs_diag' -timeindex = 3; % plot the histogram for the third timestep -plotdat = plot_rank_histogram(fname, timeindex, 'RADIOSONDE_TEMPERATURE'); -~~~ - -rank hist -  -You may also convert observation sequence files to netCDF by using -[obs_seq_to_netcdf](../../assimilation_code/programs/obs_seq_to_netcdf/obs_seq_to_netcdf.html). -All of the following routines will work on observation sequences files -AFTER an assimilation (i.e. `obs_seq.final` files that have been -converted to netCDF), and some of them will work on *obs_seq.out*-type -files that have been converted. - - [](#read_obs_netcdf) - [](#plot_obs_netcdf) - -[read_obs_netcdf.m](../../diagnostics/matlab/read_obs_netcdf.m) reads a particular variable - and copy from a netCDF-format observation sequence file and returns a - single structure with useful bits for plotting/exploring. - This routine is the back-end for *plot_obs_netcdf.m* . - -~~~ -fname = 'obs_sequence_001.nc'; -ObsTypeString = 'RADIOSONDE_U_WIND_COMPONENT'; % or 'ALL' ... -region = [0 360 -90 90 -Inf Inf]; -CopyString = 'NCEP BUFR observation'; -QCString = 'DART quality control'; -verbose = 1; % anything > 0 == 'true' -obs = read_obs_netcdf(fname, ObsTypeString, region, CopyString, QCString, verbose); -~~~ -  -[plot_obs_netcdf.m](../../diagnostics/matlab/plot_obs_netcdf.m) creates a 3D scatterplot of - the observation locations, color-coded to the observation values. - A second axis will also plot the QC values if desired. - -~~~ -fname = 'POP11/obs_epoch_011.nc'; -region = [0 360 -90 90 -Inf Inf]; -ObsTypeString = 'AIRCRAFT_U_WIND_COMPONENT'; -CopyString = 'NCEP BUFR observation'; -QCString = 'DART quality control'; -maxgoodQC = 2; -verbose = 1; % > 0 means 'print summary to command window' -twoup = 1; % > 0 means 'use same Figure for QC plot' -bob = plot_obs_netcdf(fname, ObsTypeString, region, CopyString, ... - QCString, maxgoodQC, verbose, twoup); -~~~ -  -plot_obs_netcdf - -[plot_obs_netcdf_diffs.m](../../diagnostics/matlab/plot_obs_netcdf_diffs.m) creates a 3D - scatterplot of the difference between two 'copies' of an observation. - -~~~ -fname = 'POP11/obs_epoch_011.nc'; -region = [0 360 -90 90 -Inf Inf]; -ObsTypeString = 'AIRCRAFT_U_WIND_COMPONENT'; -CopyString1 = 'NCEP BUFR observation'; -CopyString2 = 'prior ensemble mean'; -QCString = 'DART quality control'; -maxQC = 2; -verbose = 1; % > 0 means 'print summary to command window' -twoup = 0; % > 0 means 'use same Figure for QC plot' -bob = plot_obs_netcdf_diffs(fname, ObsTypeString, region, CopyString1, CopyString2, ... - QCString, maxQC, verbose, twoup); -~~~ -  -plot diffs - -[plot_wind_vectors.m](../../diagnostics/matlab/plot_wind_vectors.m) creates a 2D 'quiver' - plot of a wind field. This function is in the *matlab/private* - directory - but if you want to use it, you can move it out. - I find it has very little practical value. - -~~~ -fname = 'obs_epoch_001.nc'; -platform = 'SAT'; % usually 'RADIOSONDE', 'SAT', 'METAR', ... -CopyString = 'NCEP BUFR observation'; -QCString = 'DART quality control'; -region = [210 310 12 65 -Inf Inf]; -scalefactor = 5; % reference arrow magnitude -bob = plot_wind_vectors(fname, platform, CopyString, QCString, ... - 'region', region, 'scalefactor', scalefactor); -~~~ - -plot wind vectors - -[link_obs.m](../../diagnostics/matlab/link_obs.m) creates multiple figures that have linked - attributes. This is my favorite function. Click on the little paintbrush - icon in any of the figure frames and select all the observations with - DART QC == 4 in one window, and those same observations are highlighted - in all the other windows (for example). The 3D scatterplot can be - rotated around with the mouse to really pinpoint exactly where the - observations are getting rejected, for example. All the images are - links to larger versions - the image on the right has the MATLAB® call. - If the data browser (the spreadsheet-like panel) is open, the selected - observations get highlighted there too ... - -link_obs frame2 -link_obs frame1 -link_obs frame0 - -[ObsTimeCheck.m](../../diagnostics/matlab/ObsTimeCheck.m) is an example of a trivial little - script to wrap around *plot_obs_netcdf.m* that allows you to explorex - the spatial distribution of your observation sequences. Since - *obs_seq_to_netcdf* doesn't know anything about assimilation windows; - the idea is to create separate netCDF files for each assimilation window - and then explore a sequence of windows. Since *ObsTimeCheck.m* is - under version control, you should feel free to edit it/modify it to - your heart's desire. If there are no observations of that type in a - particular assimilation window, the MATLAB® Command window will have - a comment to that effect. - -time_check 1 -time_check 2 -time_check 3 -time_check 4 - - [](#obs_diag_output_explanation) - ------ - -#### Understanding what's in `obs_diag_output.nc` - -After you create `obs_diag_output.nc` with -[obs_diag](Manhattan/assimilation_code/programs/obs_diag/threed_sphere/obs_diag.html) -it is important to understand what is contained in -`obs_diag_output.nc`. Remember, this is just a dump of the *header* of the file\! - -~~~ -[work]$ ncdump -v CopyMetaData obs_diag_output.nc -netcdf obs_diag_output { -dimensions: - copy = 21 ; - obstypes = 78 ; - region = 1 ; - surface = 1 ; - undef = 1 ; - mlevel = 11 ; - mlevel_edges = 12 ; - plevel = 11 ; - plevel_edges = 12 ; - hlevel = 11 ; - hlevel_edges = 12 ; - time = UNLIMITED ; // (6 currently) - bounds = 2 ; - stringlength = 32 ; - rank_bins = 97 ; -variables: - int copy(copy) ; - copy:explanation = "see CopyMetaData" ; - int obstypes(obstypes) ; - obstypes:explanation = "see ObservationTypes" ; - int region(region) ; - region:long_name = "model region" ; - region:units = "nondimensional" ; - region:valid_range = 1, 1 ; - int mlevel(mlevel) ; - mlevel:long_name = "model level" ; - mlevel:units = "model level" ; - mlevel:axis = "Z" ; - mlevel:valid_range = 1, 11 ; - float mlevel_edges(mlevel_edges) ; - mlevel_edges:long_name = "model level edges" ; - mlevel_edges:units = "model level" ; - mlevel_edges:axis = "Z" ; - mlevel_edges:valid_range = 0.5, 11.5 ; - float plevel(plevel) ; - plevel:long_name = "pressure bin midpoints" ; - plevel:units = "hPa" ; - plevel:axis = "Z" ; - plevel:valid_range = 100., 1000. ; - float plevel_edges(plevel_edges) ; - plevel_edges:long_name = "pressure bin edges" ; - plevel_edges:units = "hPa" ; - plevel_edges:axis = "Z" ; - plevel_edges:valid_range = 75., 1025. ; - float hlevel(hlevel) ; - hlevel:long_name = "height bin midpoints" ; - hlevel:units = "m" ; - hlevel:axis = "Z" ; - hlevel:valid_range = 1000., 11000. ; - float hlevel_edges(hlevel_edges) ; - hlevel_edges:long_name = "height bin edges" ; - hlevel_edges:units = "m" ; - hlevel_edges:axis = "Z" ; - hlevel_edges:valid_range = 500., 11500. ; - int bounds(bounds) ; - bounds:valid_range = 1, 2 ; - double time(time) ; - time:standard_name = "time" ; - time:long_name = "temporal bin midpoints" ; - time:units = "days since 1601-1-1" ; - time:calendar = "Gregorian" ; - time:axis = "T" ; - time:bounds = "time_bounds" ; - time:valid_range = 148880.5, 148885.5 ; - double time_bounds(time, bounds) ; - time_bounds:long_name = "temporal bin edges" ; - time_bounds:units = "days since 1601-1-1" ; - time_bounds:calendar = "Gregorian" ; - time_bounds:valid_range = 148880.000011574, 148886. ; - char region_names(region, stringlength) ; - region_names:long_name = "region names" ; - char CopyMetaData(copy, stringlength) ; - CopyMetaData:long_name = "quantity names" ; - char ObservationTypes(obstypes, stringlength) ; - ObservationTypes:long_name = "DART observation types" ; - ObservationTypes:comment = "table relating integer to observation type string" ; - int rank_bins(rank_bins) ; - rank_bins:long_name = "rank histogram bins" ; - rank_bins:comment = "position of the observation among the sorted noisy ensemble members" ; - float RADIOSONDE_U_WIND_COMPONENT_guess(time, copy, plevel, region) ; - RADIOSONDE_U_WIND_COMPONENT_guess:_FillValue = -888888.f ; - RADIOSONDE_U_WIND_COMPONENT_guess:missing_value = -888888.f ; - int RADIOSONDE_U_WIND_COMPONENT_guess_RankHi(time, rank_bins, plevel, region) ; -... - float AIRCRAFT_U_WIND_COMPONENT_guess(time, copy, plevel, region) ; - AIRCRAFT_U_WIND_COMPONENT_guess:_FillValue = -888888.f ; - AIRCRAFT_U_WIND_COMPONENT_guess:missing_value = -888888.f ; - int AIRCRAFT_U_WIND_COMPONENT_guess_RankHist(time, rank_bins, plevel, region) ; -... - float RADIOSONDE_U_WIND_COMPONENT_analy(time, copy, plevel, region) ; - RADIOSONDE_U_WIND_COMPONENT_analy:_FillValue = -888888.f ; - RADIOSONDE_U_WIND_COMPONENT_analy:missing_value = -888888.f ; -... - float AIRCRAFT_U_WIND_COMPONENT_analy(time, copy, plevel, region) ; - AIRCRAFT_U_WIND_COMPONENT_analy:_FillValue = -888888.f ; - AIRCRAFT_U_WIND_COMPONENT_analy:missing_value = -888888.f ; -... - float RADIOSONDE_U_WIND_COMPONENT_VPguess(copy, plevel, region) ; - RADIOSONDE_U_WIND_COMPONENT_VPguess:_FillValue = -888888.f ; - RADIOSONDE_U_WIND_COMPONENT_VPguess:missing_value = -888888.f ; -... - float RADIOSONDE_U_WIND_COMPONENT_VPanaly(copy, plevel, region) ; - RADIOSONDE_U_WIND_COMPONENT_VPanaly:_FillValue = -888888.f ; - RADIOSONDE_U_WIND_COMPONENT_VPanaly:missing_value = -888888.f ; -... -... - -// global attributes: - :creation_date = "YYYY MM DD HH MM SS = 2011 03 18 13 37 34" ; - :obs_diag_source = "URL: blah_blah_blah/diagnostics/threed_sphere/obs_diag.html $" ; - :obs_diag_revision = "$Revision$" ; - :obs_diag_revdate = "$Date$" ; - :bias_convention = "model - observation" ; - :horizontal_wind = "vector wind derived from U,V components" ; - :horizontal_wind_bias = "definition : sum[sqrt(u**2 + v**2) - obsspeed]/nobs" ; - :horizontal_wind_rmse = "definition : sqrt(sum[(u-uobs)**2 + (v-vobs)**2]/nobs)" ; - :horizontal_wind_spread = "definition : sqrt(sum[var(u) + var(v)]/nobs)" ; - :DART_QCs_in_histogram = 0, 1, 2, 3 ; - :outliers_in_histogram = "FALSE" ; - :first_bin_center = 2008, 8, 15, 12, 0, 0 ; - :last_bin_center = 2008, 8, 20, 12, 0, 0 ; - :bin_separation = 0, 0, 1, 0, 0, 0 ; - :bin_width = 0, 0, 1, 0, 0, 0 ; - :time_to_skip = 0, 0, 0, 0, 0, 0 ; - :max_num_bins = 1000 ; - :rat_cri = 5000. ; - :input_qc_threshold = 3. ; - :lonlim1 = 0. ; - :lonlim2 = 360. ; - :latlim1 = -90. ; - :latlim2 = 90. ; - :obs_seq_file_001 = "/ptmp/thoar/HuiGPS/08_01/obs_seq.final" ; -... -... -data: - - CopyMetaData = - "Nposs ", - "Nused ", - "NbigQC ", - "NbadIZ ", - "NbadUV ", - "NbadLV ", - "rmse ", - "bias ", - "spread ", - "totalspread ", - "NbadDARTQC ", - "observation ", - "ens_mean ", - "N_DARTqc_0 ", - "N_DARTqc_1 ", - "N_DARTqc_2 ", - "N_DARTqc_3 ", - "N_DARTqc_4 ", - "N_DARTqc_5 ", - "N_DARTqc_6 ", - "N_DARTqc_7 " ; -} -~~~ - -There are **many** more variables in this particular netCDF file - -indicated by the '...' (the ellipsis). -Every observation type is preserved in its own set of variables with the -following suffixes: - -| suffix | description | -| ---------- | ------------------------------------------- | -| \_guess | prior, forecast | -| \_analy | posterior, analysis | -| \_VPguess | vertical profile only - prior, forecast | -| \_VPanaly | vertical profile only - posterior, analysis | -| \_RankHist | rank histogram - prior | - - -What is really important to note is that each observation variable has a - *copy* dimension - and each copy description is contained in the - *CopyMetaData* variable. A dump of that variable provides information - about what quantities are directly available. In the above example, the - *rmse* is copy **7** . You should never assume this information, you - should always check the *CopyMetaData* variable to find the appropriate - copy index. Let's look at the *RADIOSONDE_U_WIND_COMPONENT_guess* - variable in the example. It has 4 dimensions: -\[time, copy, plevel, region\]. Since 'plevel' is one of the -dimensions, the appropriate levels are defined by the coordinate -variable of the same name. The *RADIOSONDE_U_WIND_COMPONENT_VPguess* -variable has 3 dimensions: \[copy, plevel, region\]. The 'time' -dimension has been averaged out such that a single vertical -profile (VP) is defined for the entire timeframe specified in the -namelist. If I were a better man, I'd put the averaging interval in the -variable metadata to have a hope of complying with convention; instead, -it is only in the global metadata for the entire netCDF file as -*first_bin_center, last_bin_center*, and *time_to_skip*. Add the -*time_to_skip* to the *first_bin_center* to derive the start of the -averaging period. This allows one to ignore the effects of spinup. - -The *RADIOSONDE_U_WIND_COMPONENT_guess_RankHi* variable name has -been cut short by the netCDF restriction that variable names can only -contain 40 characters. The *_RankHist* variables are only present if -the input `obs_seq.final` files have multiple ensemble members present. -You cannot derive this information unless the assimilation was performed -with *filter_nml:num_output_obs_members* equal to something like the -ensemble size. - - [](#ncview_histogram) - ------ - -#### Viewing the rank histogram information with 'ncview' - -After you create `obs_diag_output.nc` with *obs_diag* you can view -the rank histograms in the following way: - -> ncview obs_diag_output.nc - -Note that in this particular file, there are forty-seven 3D variables. -Pick one. In this case, I selected *AIRCRAFT_U_WIND_COMPONENT_guess_RankHist* - -xxxx - -Navigate to the time of interest (these are the arrows next to the QUIT -button.) If *ncview* was built with *udunits* support, the actual -calendar dates and times should be shown next to the frame counter. That -should generate something like the graphic on the right below. Since the -default axes are (Y == histogram_bins, X == levels) and there are many -more ensemble members (96) than vertical levels (20) in this netCDF -file, the graphic appears tall and narrow. - -xxxx - -Click anywhere on the graphic and something like the following is -displayed: - -xxxx - -Change the "X Axis:" to "rank_bins" and a new graphic will display the -rank histogram. - -xxxx - -If you continue to click on the "tall,skinny" graphic, the histogram for -that level will be added to the rank histogram window. Remember, levels -are along the X axis on the "tall,skinny" graphic. Viola'\! - - [](#ss_diagnostics) - [](#ss_diagnostics_matlab) - ------ - -### State-Space diagnostics for High-order Models. - -Vary model-by-model. This section is under construction. - -### State-Space diagnostics for Low-order Models. - -#### MATLAB® - -There are a set of MATLAB® functions to help explore the assimilation -performance in state-space, which is very useful for OSSE's (i.e. when -you know the true model state). The general guideline here is that -anything that computes an 'error' requires the truth. There are some -functions that work without a true state. - -In order to use any of these functions, the scripts need to know how to -interpret the layout of the netCDF file - which is usually -model-dependent. See the section on -[Adding MATLAB® support for your own model](Models.md#model_matlab_support) -if you are not using one of the supported DART models. - -The state-space functions are in the *`DART/diagnostics/matlab`* -directory. They all have the expected 'help' file accessible by simply -typing *help* *\[function_of_interest\]* at the Matlab prompt. - -| | | -| --- | --- | -| *plot_total_err.m* | plots the evolution of the error (un-normalized) and ensemble spread of all state variables. | -| *plot_bins.m* | plots the rank histograms for a set of state variables. | -| *plot_ens_time_series.m* | plots the evolution of a set of state variables - all ensemble members, the ensemble mean (and Truth, if available). | -| *plot_ens_mean_time_series.m* | plots the evolution of a set of state variables - just the ensemble mean (and Truth, if available). | -| *plot_ens_err_spread.m* | plots the evolution of the ensemble error and spread for a select set of state variables. | -| *plot_correl.m* | plots the correlation through time of a state variable and the rest of the state. | -| *plot_var_var_correl.m* | plots the correlation between two variables at a particular location. | -| *plot_jeff_correl.m* | plots the correlation through time of a state variable at a particular time and any state variable. | -| *plot_sawtooth.m* | plots the trajectory of any set of state variables highlighting the assimilation 'increment'. | -| *plot_phase_space.m* | plots the trajectory of any two or three state variables. The classic attractor diagram. | -| *plot_smoother_err.m* | plots the error of the ensemble smoother - which uses observations in the future as well as the present. | - -netCDF files are self-describing, so it is imperative that you -understand the structure of the netCDF files and variables. fact that it -might be the ensemble mean, the ensemble spread, ensemble member 43, the -inflation values ... a whole host of possibilities. For instance: - -~~~ -0[1020] swordfish:models/wrf/work % ncdump -v member,MemberMetadata preassim.nc -netcdf preassim { -dimensions: - metadatalength = 64 ; - member = 3 ; - time = UNLIMITED ; // (1 currently) - NMLlinelen = 129 ; - NMLnlines = 203 ; - domain = 1 ; - west_east_d01 = 267 ; - west_east_stag_d01 = 268 ; - south_north_d01 = 215 ; - south_north_stag_d01 = 216 ; - bottom_top_d01 = 35 ; - bottom_top_stag_d01 = 36 ; -variables: - int member(member) ; - member:long_name = "ensemble member or copy" ; - member:units = "nondimensional" ; - member:valid_range = 1, 100 ; - char MemberMetadata(member, metadatalength) ; - MemberMetadata:long_name = "Metadata for each copy/member" ; - ... - float U_d01(time, member, bottom_top_d01, south_north_d01, west_east_stag_d01) ; - ... - member = 1, 2, 3 ; - - MemberMetadata = - "ensemble member 1 ", - "ensemble member 2 ", - "ensemble member 3 ", -} -~~~ - - [](#alt_diagnostics) - [](#ss_diagnostics_nonmatlab) - -#### Non-MATLAB® state-space diagnostics - -The innovations to the model state are easy to derive. Use the -[NCO Operator](http://nco.sourceforge.net/) *ncdiff* to difference the two -DART diagnostic netCDF files to create the innovations. Be sure to check -the *CopyMetaData* variable to figure out what *copy* is of interest. -Then, use *ncview* to explore the innovations or the inflation values or ... - -If the assimilation used state-space inflation, the inflation fields -will be added as additional 'copies'. A sure sign of trouble is if the -inflation fields grow without bound. As the observation network changes, -expect the inflation values to change. - -The only other thing I look for in state-space is that the increments -are 'reasonable'. As the assimilation 'burns in', the increments are -generally larger than increments from an assimilation that has been -cycling for a long time. If the increments keep getting bigger, the -ensemble is continually drifting away from the observation. Not good. In -*ncview*, it is useful to navigate to the copy/level of interest and -re-range the data to values appropriate to the current data and then hit -the '\>\>' button to animate the image. It should be possible to get a -sense of the magnitude of the innovations as a function of time. - -**Example from a model of intermediate complexity: the bgrid model.** -I ran a perfect model experiment with the bgrid model in the -DART-default configuration and turned on some adaptive inflation for -this example. To fully demonstrate the adaptive inflation, it is useful -to have an observation network that changes through time. I created two -observation sequence files: one that had a single -'RADIOSONDE_TEMPERATURE' observation at the surface with an observation -error variance of 1.5 degrees Kelvin - repeated every 6 hours for 6 days -(24 timesteps); and one that had 9 observations locations clustered in -about the same location that repeated every 6 hours for 1.5 days (6 -timesteps). I merged the two observation sequences into one using -[obs_sequence_tool](../../assimilation_code/programs/obs_sequence_tool/obs_sequence_tool.html) -and ran them through *perfect_model_obs* to derive the observation -values and create an *obs_seq.out* file to run through *filter*. -**NOTE:** Other models may have their ensemble means and spreads and -inflation values in separate files. [See the table of possible -filenames.](#FilenameTable) - -~~~ -0[1074] models/bgrid_solo/work % ncdiff analysis.nc preassim.nc Innov.nc -0[1075] models/bgrid_solo/work % ncview preassim.nc & -0[1076] models/bgrid_solo/work % ncview Innov.nc & -0[1077] models/bgrid_solo/work % ncdump -v MemberMetadata preassim.nc -netcdf preassim { -dimensions: - metadatalength = 64 ; - member = 20 ; - time = UNLIMITED ; // (24 currently) - NMLlinelen = 129 ; - NMLnlines = 303 ; - StateVariable = 28200 ; - TmpI = 60 ; - TmpJ = 30 ; - lev = 5 ; - VelI = 60 ; - VelJ = 29 ; -variables: - char MemberMetadata(member, metadatalength) ; - MemberMetadata:long_name = "Metadata for each copy/member" ; - ... - double ps(time, member, TmpJ, TmpI) ; - ps:long_name = "surface pressure" ; - ps:units = "Pa" ; - ps:units_long_name = "pascals" ; - double t(time, member, lev, TmpJ, TmpI) ; - t:long_name = "temperature" ; - t:units = "degrees Kelvin" ; - double u(time, member, lev, VelJ, VelI) ; - u:long_name = "zonal wind component" ; - u:units = "m/s" ; - double v(time, member, lev, VelJ, VelI) ; - v:long_name = "meridional wind component" ; - v:units = "m/s" ; - double ps_mean(time, TmpJ, TmpI) ; The ensemble mean is now a separate variable. - double t_mean(time, lev, TmpJ, TmpI) ; The ensemble spread is now a separate variable. - double u_mean(time, lev, VelJ, VelI) ; If I was using inflation, they would also be separate variables. - double v_mean(time, lev, VelJ, VelI) ; - double ps_sd(time, TmpJ, TmpI) ; - double t_sd(time, lev, TmpJ, TmpI) ; - double u_sd(time, lev, VelJ, VelI) ; - double v_sd(time, lev, VelJ, VelI) ; - -data: - MemberMetadata = - "ensemble member 1 ", - "ensemble member 2 ", - "ensemble member 3 ", - "ensemble member 4 ", - "ensemble member 5 ", - "ensemble member 6 ", - "ensemble member 7 ", - "ensemble member 8 ", - "ensemble member 9 ", - "ensemble member 10 ", - "ensemble member 11 ", - "ensemble member 12 ", - "ensemble member 13 ", - "ensemble member 14 ", - "ensemble member 15 ", - "ensemble member 16 ", - "ensemble member 17 ", - "ensemble member 18 ", - "ensemble member 19 ", - "ensemble member 20 " ; -} -~~~ - -xxxx - -xxxx - -xxxx - -This is an exploration of the `preassim.nc` file. Note that I selected -the '**t**' field, turned the coastlines 'off' under the 'Opts' button, -used the 'Repl' instead of 'Bi-lin' (to more faithfully represent the -model resolution), *navigated to copy 23 of 24 (in this case, the -**inflation mean**)* select the **inflation mean variable of your -choice** and advanced to the last timestep. The image plot is pretty -boring, but does indicate that the inflation values are restricted to -where I put the observations. Right-clicking on the 'Range' button -automatically re-ranges the colorbar to the min/max of the current data. -Clicking on any location generates a time series figure. - -xxxx - -xxxx - -xxxx - -This is an exploration of the `Innov.nc` file as created by *ncdiff*. -Note that the titles are somewhat misleading because they reflect -information from the first file given to *ncdiff*. This time I left the -rendering as 'Bi-lin' (which obfuscates the model resolution), -*navigated to copy 1 of 24 (in this case, the **ensemble mean**)* -selected the **t_mean** variable and advanced to the 6th timestep. -Right-click on the 'Range' button to reset the colorbar. The image plot -confirms that the innovations are restricted to a local region. Clicking -on any location generates a time series. - -xxxx - -xxxx - -xxxx - -This is fundamentally the same as the previous panel except that I have -now selected the '***u***' **u_mean** variable. Despite the fact the -observations were only of '**t**', the assimilation has generated -(rightly so) increments to the '**u**' state variable. - ------ diff --git a/guide/pages/Getting_Started.md b/guide/pages/Getting_Started.md deleted file mode 100644 index eca1b7fe84..0000000000 --- a/guide/pages/Getting_Started.md +++ /dev/null @@ -1,2811 +0,0 @@ ---- -title: Getting Started -layout: default ---- - -[](#top) - -# Getting Started with DART - -The Data Assimilation Research Testbed (DART) is an open-source, freely -available community facility for ensemble data assimilation (DA) developed and -maintained by the -[Data Assimilation Research Section (DAReS)](https://www.image.ucar.edu/DAReS/) -at the [National Center for Atmospheric Research (NCAR)](https://ncar.ucar.edu) -and released under the open-source -[Apache License v2.0](https://www.apache.org/licenses/LICENSE-2.0). -DART provides modelers, observational scientists, and geophysicists with -powerful, flexible DA tools that are easy to use and can be customized to -support efficient and reliable DA applications. DART is primarily oriented for -DA research but has also been used in operational settings. DART is a software -environment that makes it easy to explore a variety of data assimilation -methods and observations with different numerical models and is designed to -facilitate the combination of assimilation algorithms, models, and real (as well -as synthetic) observations to allow increased understanding of all three. DART -includes extensive documentation, a comprehensive tutorial, and a variety of -models and observation sets that can be used to introduce new users or graduate -students to ensemble DA. DART also provides a framework for developing, testing, -and distributing advances in ensemble DA to a broad community of users by -removing the implementation-specific peculiarities of one-off DA systems. - -DART is intended for use by DA beginners as well as experts, students as well -as teachers, national labs and centers as well as university research labs, and -everything in between; thus, different users may have different goals in -"getting started." We have therefore broken the process into the following -sections, each of which can be read independently: - -* [Quick-start instructions for the impatient](#QuickStart) -* [More detailed DART setup instructions](#DetailedSetup) - * [On conventions used within this document](#conventions). - * [Check your system requirements](#system). - * [Determine which F90 compiler is available](#fortran90). - * [Determine the location of (or build) the netCDF library](#netCDFlib). - * [Download the DART software](#download). - * [Building and testing DART](#testing). - * [Verifying the DART installation](#verify). -* [What is data assimilation?](#WhatIsDA) - * [Introduction to ensemble DA](#EnsDAIntro) - * [The Lorenz 63 model: what is it and why should we care?](#Lorenz63) - * [Data assimilation in DART using the Lorenz 63 model](#DAForLorenz63) -* [What is DART?](#WhatIsDART) - * [Why should I use DART for my project?](#whyDart) - * [A brief history of DART](#dartHistory) - * [High-level DA workflows in DART](#dartWorkflow) - * [DART's design philosophy](#dartDesign) - * [Important capabilities of DART](#dartCapabilities) - * [How to cite DART](#citeDart) -* [How do I run DART with my model?](#RunWithMyModel) - * [Required `model_mod` routines](#requiredRoutines) - * [Suggestions for a "simple" model](#simpleModel) - * [Suggestions for a "complex" model](#complexModel) - * [How to test your `model_mod` routines](#howToTestModelMod) -* [How do I add my observations to DART?](#RunWithMyObs) - * [Background on DART observations](#obsBackground) - * [Adding new observation *quantities*](#newQUANTITIES) - * [Adding new observation *types*](#newTYPES) - * [Simple observation definitions](#simpleObs) - * [Obs definitions for observations needing special handling](#complexObs) - * [Example observation definition](#exampleObsDef) - * [Observation sequence file](#obsSeqFile) -* [How would I use DART for teaching students and/or myself?](#DartForEducation) - * [The DART tutorial](#dartTutorial) - * [DART_LAB](#dartLab) -* [How can I contribute to DART?](#ContributeToDart) - ---- - - [](#QuickStart) - -## Quick-start instructions for the impatient - -\[[top](#top)\] - -This section details how to get started with DART as quickly as possible. This -section may be for you if you want to see that DART works before spending a lot -of time learning about the details. However, if this level of detail is too -sparse for you, or if you run into any problems, please refer to -[More detailed DART setup instructions](#DetailedSetup). - -The DART source code is distributed on the GitHub repository -[NCAR/DART](https://github.com/NCAR/DART) -with the documentation served through GitHub Pages at -[https://dart.ucar.edu](https://dart.ucar.edu). - -Go to https://github.com/NCAR/DART and clone the repository or get the ZIP file -according to your preference. Take note of the directory you installed into, -which is referred to as *DART* below. - -:exclamation: *IMPORTANT*: If you are interested in contributing to DART, -see [How can I contribute to DART?](#ContributeToDart) for more information. - -Your `DART` directory will now contain the latest release of DART. - -Go into the `build_templates` subdirectory and copy over the closest `mkmf.template.` file into `mkmf.template`. - -Edit `mkmf.template` to set the `NETCDF` directory location if not in -`/usr/local` or comment it out and set `$NETCDF` in your environment. - -:warning: *WARNING*: Your netCDF library must have been compiled with the -same compiler (including version) that you will use to compile DART and also -must include the F90 interfaces. In practice this means that even if you have a -netCDF distribution on your system, you may need to recompile netCDF in a -separate location to match the compiler you will use for DART. - -If you are interested in running *bgrid_solo*, *cam-fv*, or -testing the *NCEP/prep_bufr* observation converter, you will need to download a -supplemental archive file. There are several large files that are needed to run -some of these DART tests and examples that are not included on Github in order -to keep the repository as small as possible. These files are available at: - -| Release | Size | Filename | -| :-------------- | :----- | :-------- | -| "Manhattan" | 189M | [Manhattan_large_files.tar.gz](https://www.image.ucar.edu/pub/DART/Release_datasets/Manhattan_large_files.tar.gz) | -| "wrf-chem.r13172" | 141M | [wrf-chem.r13172_large_files.tar.gz](https://www.image.ucar.edu/pub/DART/Release_datasets/wrf-chem.r13172_large_files.tar.gz) | -| "Lanai" | 158M | [Lanai_large_files.tar.gz](https://www.image.ucar.edu/pub/DART/Release_datasets/Lanai_large_files.tar.gz) | -| "Kodiak" | 158M | [Kodiak_large_files.tar.gz](https://www.image.ucar.edu/pub/DART/Release_datasets/Kodiak_large_files.tar.gz) | -| "Jamaica" | 32M | [Jamaica_large_files.tar.gz](https://www.image.ucar.edu/pub/DART/Release_datasets/Jamaica_large_files.tar.gz) | -| "Hawaii" | 32M | [Hawaii_large_files.tar.gz](https://www.image.ucar.edu/pub/DART/Release_datasets/Hawaii_large_files.tar.gz) | - -If so desired, download the appropriate tar file and untar it into your -`DART` directory. Ignore any warnings about -``` -tar: Ignoring unknown extended header keyword -``` - -You are now ready to start building the DART code. Go into -`models/lorenz_63/work` and run *quickbuild.csh*. - -> cd models/lorenz_63/work -> ./quickbuild.csh - -If it compiles, *:tada:* If not, see the next section for more information. -Run this series of commands to do a very basic test: - -> ./perfect_model_obs -> ./filter - -If that runs, *:tada:* again! Finally, if you have Matlab installed on -your system add '$DART/diagnostics/matlab' to your matlab search path with -the command - -> addpath('path_to_dart/diagnostics/matlab','-BEGIN') - -replacing `path_to_dart` with your actual DART installation directory. - -In Matlab, run the `plot_total_err` diagnostic script while in the -`models/lorenz_63/work` directory. If the output plots and looks -reasonable (error level stays around 2 and doesn't grow unbounded) -you're great! Congrats. - -If you are planning to run one of the models that uses MPI and want to -use the Lorenz 63 model as a test, run ```./quickbuild.csh -mpi```. -It will build filter and any other MPI-capable executables with MPI. - -:warning: *WARNING*: The underlying compiler used by *mpif90* must match the -same compiler that you use for DART and netCDF. - -If any of these steps fail or you don't know how to do them, you can see -much more detailed instructions in the next section that hopefully -should get you over any bumps you encountered in the process. - ---- - [](#DetailedSetup) - -## More detailed DART setup description - -\[[top](#top)\] - -This section is a more detailed description of the steps in the previous -section, [Quick-start instructions for the impatient](#QuickStart). In -particular, you can refer to these instructions if anything in the previous -section failed or if you want a more complete picture of how DART is configured. - -The getting started process is summarized in the following steps: - -0. [On conventions used within this document](#conventions). -1. [Check your system requirements](#system). -2. [Determine which F90 compiler is available](#fortran90). -3. [Determine the location of (or build) the netCDF library](#netCDFlib). -4. [Download the DART software](#download). -5. [Modify certain DART files to reflect the available F90 compiler and location of the appropriate libraries](#customizations). -6. [Build the executables](#building). -7. [Verify the DART installation](#verify). - ----- - [](#conventions) - -### On conventions used within this document - -This getting started document is intended to be used as a reference by users of -different levels and backgrounds. By design, this page is mostly -"self-contained" as a single page so that users can navigate through the -document links without becoming lost. Some sections contain helpful -information, while others contain step-by-step instructions to run programs -or shell commands. - -In order to make the tutorial-style sections more clear, the following -conventions are used within this document: - -Commands to be **typed at the command line** will appear in *blockquote*. For example: - -> my_command.exe run_it.nml - -The **contents of a file** will be *enclosed in a box* as follows (for some hypothetical namelist file): - -~~~ -&hypothetical_nml - obs_seq_in_file_name = "obs_seq.in", - obs_seq_out_file_name = "obs_seq.out", - init_time_days = 0, - init_time_seconds = 0, - output_interval = 1 -&end -~~~ - -The **names of files** or environment variables will appear in a *code-block* -as in `filename`. - -The *names of executables* will be italicized (although note that italic -will also be used for emphasis where there is no potential for confusion). - -:dart: *NOTE*: "pro-tip" information to note will be written like this. - -:exclamation: *IMPORTANT*: important information will be written like this. - -:warning: *WARNING*: don't miss out on warnings which will be written like this. - ----- - [](#system) - -### System Requirements - -The DART software is intended to compile and run on many different -Unix/Linux operating systems with little to no change. At this point we have no -plans to port DART to Windows machines, although Windows 10 -users may be interested in the free -[Windows Subsystem For Linux](https://docs.microsoft.com/en-us/windows/wsl/about) -which allows developers to "run a GNU/Linux environment — including most -command-line tools, utilities, and applications — directly on Windows, -unmodified, without the overhead of a virtual machine" (see - for more details) - -:warning: *DISCLAIMER:* we have tried to make the DART code as portable as -possible, but we do not have access to all compilers on all platforms, so -unfortunately we cannot guarantee that the code will work correctly on your -particular system. We are genuinely interested in your experience building the -system, so we welcome you to send us an email with your experiences at -dart @ ucar .edu, which we will endeavor to incorporate into future versions of -this guide. - -Minimally, you will need: - -1. [a Fortran90 compiler](#fortran90), -2. the [netCDF libraries](http://www.unidata.ucar.edu/software/netcdf/) - built with the F90 interface, -3. *perl* (just about any version), -4. an environment that understands *csh*, *tcsh*, *sh*, and *ksh* -5. the long-lived Unix build tool *make* -6. and up to 1 Gb of disk space for the DART distribution. - -History has shown that it is a very good idea to remove the stack and heap -limits in your run-time environment with the following terminal commands: - -> limit stacksize unlimited -> limit datasize unlimited - -Additionally, the following tools have proven to be *nice* (but are not -required to run DART): - -1. [ncview](http://meteora.ucsd.edu/~pierce/ncview_home_page.html): a - great visual browser for netCDF files. -2. [the netCDF Operators (NCO)](http://nco.sourceforge.net/): tools to - perform operations on netCDF files like concatenating, slicing, and - dicing -3. Some sort of MPI environment. In other words, DART does not come - with *MPICH*, *LAM-MPI*, or *OpenMPI*, but many users of DART rely on these - MPI distributions to run DART in a distributed-memory parallel setting. In - order to use MPI with DART, please refer to [the DART MPI introduction](dart_mpi.md). -4. If you want to use the DART diagnostic scripts, you will need a - basic MATLAB® installation. No additional toolboxes are required, and no - third-party toolboxes are required. - ----- - - [](#fortran90) - -### Requirements: a Fortran90 compiler - -The DART software is written in standard Fortran 90, with no compiler-specific -extensions. It has been compiled and run with several versions of each of the -following: -* [GNU Fortran Compiler ("gfortran")](http://gcc.gnu.org/fortran) (free) -* [Intel Fortran Compiler for Linux and OSX](http://software.intel.com/en-us/intel-composer-xe) -* [IBM XL Fortran Compiler](http://www-01.ibm.com/software/awdtools/fortran/) -* [Portland Group Fortran Compiler](http://www.pgroup.com/) -* [Lahey Fortran Compiler](http://www.lahey.com/) -* [NAG Fortran compiler](https://www.nag.com/nag-compiler) -* [PathScale Fortran compiler](https://en.wikipedia.org/wiki/PathScale) - -Since recompiling the code is a necessity to experiment with different models, -there are no DART binaries to distribute. If you are unfamiliar with Fortran -and/or wonder why we would choose this language, see -[Why Fortran?](#whyFortran) for more information. - - [](#netCDFlib) - -### Requirements: the netCDF library - -DART uses the [netCDF](https://www.unidata.ucar.edu/software/netcdf/) -self-describing data format for storing the results of assimilation experiments. -These files have the extension *.nc* and can be read by a number of -standard data analysis tools. In particular, DART also makes use of the -F90 netCDF interface which is available through the `netcdf.mod` -and `typesizes.mod` modules and the `libnetcdf` library. Depending on the -version, the `libnetcdff` library is also often required. - -If the netCDF library does not exist on your system, you must build it -(as well as the F90 interface modules). - -:warning: *WARNING*: You must build netCDF -with the same compiler (including version) you plan to use for compiling DART. -In practice this means that even if you have a netCDF -distribution on your system, you may need to recompile netCDF in a separate -location to match the compiler you will use for DART. The library and -instructions for building the library or installing from a package manager may -be found at the netCDF home page: - - -:exclamation: *IMPORTANT*: the normal location for the netCDF Fortran modules -and libraries would be in the `include` and `lib` subdirectories of the netCDF -installation. However, different compilers or package managers sometimes -place the modules and/or libraries into non-standard locations. It is required -that both modules and the libraries be present. - -:dart: *NOTE:* The location of the netCDF library, `libnetcdf.a`, and the -locations of both `netcdf.mod` and `typesizes.mod` will be needed later. -Depending on the version of netCDF and the build options selected, the -Fortran interface routines may be in a separate library named -`libnetcdff.a` (note the two F's). In this case both libraries are -required to build executables. - - [](#download) - -### Download DART - -The DART source code is distributed on the GitHub repository -[NCAR/DART](https://github.com/NCAR/DART) -with the documentation served through GitHub Pages at -[http://dart.ucar.edu](http://dart.ucar.edu). - -Go to https://github.com/NCAR/DART and clone the repository or get the ZIP file -according to your preference. See the -[github help page on cloning](https://help.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) -for more information on how to clone a repository. Take note of the directory -you installed into, which is referred to as *DART* below. - -:exclamation: *IMPORTANT*: If you are interested in contributing to DART, -see [How can I contribute to DART?](#ContributeToDart) for more information. -In short, you will need to be familiar with the -[GitHub workflow](https://guides.github.com/introduction/flow/). - -Unzip or clone the distribution in your desired directory, which we refer to as -"DART" in this document. Compiling the code in this tree (as is usually the -case) may require a large amount of additional disk space (up to the 1 Gb -required for DART), so be aware of any disk quota restrictions before -continuing. - - [](#testing) - -### Building and testing DART - -Now that the DART code has been downloaded and the prerequisites have been -verified, you can now begin building and verifying the DART installation. - - [](#customizations) - -#### Customizing the build scripts — overview - -DART executable programs are constructed using two tools: *mkmf*, and -*make*. The *make* utility is a very commonly used tool that -requires a user-defined input file (a `Makefile`) that records dependencies -between different source files. *make* then performs actions to the source -hierarchy, in order of dependence, when one or more of the source files is -modified. *mkmf* is a *perl* script that generates a *make* input file (named -*Makefile*) and an example namelist `input.nml._default` with -default values. - -*mkmf* (think *"make makefile"*) requires two separate input files. The -first is a template file which specifies the commands required for a specific -Fortran90 compiler and may also contain pointers to directories containing pre- -compiled utilities required by the DART system. **This template file will need to be modified to reflect your system as detailed in the next section**. - -The second input file is a `path_names` file which is -supplied by DART and can be used without modification. An *mkmf* command -is executed which uses the `path_names` file and the mkmf template file -to produce a `Makefile` which is subsequently used by the standard -*make* utility. - -Shell scripts that execute the *mkmf* command for all standard DART -executables are provided with the standard DART distribution. For more -information on the [mkmf](https://github.com/NOAA-GFDL/mkmf) tool please -see the [mkmf documentation](https://extranet.gfdl.noaa.gov/~vb/mkmf.html). - - [](#template) - -#### Building and Customizing the 'mkmf.template' file - -A series of templates for different compilers/architectures can be found in -the *DART/build_templates* directory and have names with extensions -that identify the compiler, the architecture, or both. This is how you -inform the build process of the specifics of your system. **Our intent -is that you copy one that is similar to your system into -`DART/build_templates/mkmf.template` and customize it.** For the -discussion that follows, knowledge of the contents of one of these -templates (e.g. `DART/build_templates/mkmf.template.intel.linux`) is -needed. Note that only the LAST lines of the file are shown here. -The first portion of the file is a large comment block that provides valuable -advice on how to customize the *mkmf* template file if needed. - -~~~ -... -MPIFC = mpif90 -MPILD = mpif90 -FC = ifort -LD = ifort -NETCDF = /usr/local -INCS = -I${NETCDF}/include -LIBS = -L${NETCDF}/lib -lnetcdf -lnetcdff -FFLAGS = -O2 $(INCS) -LDFLAGS = $(FFLAGS) $(LIBS) -~~~ - -| variable | value | -| :------- | :---- | -| FC | the Fortran compiler | -| LD | the name of the loader; typically, the same as the Fortran compiler | -| MPIFC | the MPI Fortran compiler; see [the DART MPI introduction](dart_mpi.md) for more info| -| MPILD | the MPI loader; see [the DART MPI introduction](dart_mpi.md) for more info| -| NETCDF | the location of your root netCDF installation, which is assumed to contain `netcdf.mod` and `typesizes.mod` in the include subdirectory. Note that the value of the NETCDF variable will be used by the "INCS" and "LIBS" variables. | -| INCS | the includes passed to the compiler during compilation. Note you may need to change this if your netCDF includes `netcdf.mod` and `typesizes.mod` are not in the standard location under the `include` subdirectory of NETCDF. | -| LIBS | the libraries passed to "FC" (or "MPIFC") during compilation. Note you may need to change this if the netCDF libraries `libnetcdf` and `libnetcdff` are not in the standard location under the "lib" subdirectory of NETCDF. | -| FFLAGS | the Fortran flags passed to "FC" (or "MPIFC") during compilation. There are often flags used for optimized code versus debugging code. See your particular compiler's documentation for more information. | -| LDFLAGS | the linker flags passed to *LD* during compilation. See your particular linker's documentation for more information. | - - [](#path_names) - -#### Customizing the 'path_names_\*' files - -Several `path_names_*` files are provided in the "work" directory for -each specific model. In this case, the directory of interest is -`DART/models/lorenz_63/work` (see the next section). Since each model comes -with its own set of files, the `path_names_*` files typically need no -customization. However, modifying these files will be required if you wish to -add your model to DART. See [How do I run DART with my model?](#RunWithMyModel) -for more information. - - [](#building) - -### Building the Lorenz_63 DART project. - -In order to get started with DART, here we use the Lorenz 63 model, which is a -simple ODE model with only three variables. DART supports models with many -orders of magnitude more variables than three, but if you can compile and run -the DART code for any ONE of the models, you should be able to compile and run -DART for ANY of the models. For time-dependent filtering known as -**cycling**, where observations are iteratively assimilated at multiple time -steps, DART requires the ability to move the model state forward in time. For -low-order models, this may be possible with a Fortran function call, but for -higher-order models, this is typically done outside of DART's execution -control. However, the assimilation itself is conducted the same way for **all** -models. For this reason, here we focus solely on the Lorenz 63 model. If -so desired, see -[The Lorenz 63 model: what is it and why should we care?](#Lorenz63) for -more information on this simple yet surprisingly relevant model. See -[A high-level workflow of DA in DART](#dartWorkflow) for further information -regarding the DART workflow if you prefer to do so before building the code. - -There are seven separate, stand-alone programs that are typically necessary for -the end-to-end execution of a DART experiment; see below or the -[What is DART?](#WhatIsDART) section for more information on these programs and -their interactions. All DART programs are compiled the same way, and each model -directory has a directory called `work` that has the components necessary to -build the executables. - -:dart: *NOTE:* some higher-order models have many more than seven programs; for -example, the Weather Research and Forecasting (WRF) model, which is run -operationally around the world to predict regional weather, has 28 -separate programs. Nonetheless, each of these programs are built the same way. - -The `quickbuild.csh` in each directory builds all seven programs necessary for -Lorenz 63. Describing what the `quickbuild.csh` script does is useful for -understanding how to get started with DART. - -The following shell commands show how to build two of these seven -programs for the lorenz_63 model: *preprocess* and *obs_diag*. *preprocess* is -a special program that needs to be built and run to automatically generate -Fortran code that is used by DART to support a subset of observations - which -are (potentially) different for every model. Once *preprocess* has been run and -the required Fortran code has been generated, any of the other DART programs may -be built in the same way as *obs_diag* in this example. Thus, the following -runs *mkmf* to make a `Makefile` for *preprocess*, -makes the *preprocess* program, runs *preprocess* to generate the Fortran -observation code, runs *mkmf* to make a `Makefile` for *obs_diag*, then makes the -*obs_diag* program: - -> cd DART/models/lorenz_63/work -> ./mkmf_preprocess -> make -> ./preprocess -> ./mkmf_obs_diag -> make - -The remaining executables are built in the same fashion as *obs_diag*: run -the particular *mkmf* script to generate a Makefile, then execute *make* to -build the corresponding program. - -Currently, DART executables are built in a `work` subdirectory under the -directory containing code for the given model. The Lorenz_63 model has -seven `mkmf_xxxxxx` files for the following programs: - -| Program | Purpose | -| :------ | :------ | -| [preprocess](../../assimilation_code/programs/preprocess/preprocess.html) | creates custom source code for just the observations of interest | -| [create_obs_sequence](../../assimilation_code/programs/create_obs_sequence/create_obs_sequence.html) | specify a (set) of observation characteristics taken by a particular (set of) instruments | -| [create_fixed_network_seq](../../assimilation_code/programs/create_fixed_network_seq/create_fixed_network_seq.html) | specify the temporal attributes of the observation sets | -| [perfect_model_obs](../../assimilation_code/programs/perfect_model_obs/perfect_model_obs.html) | spinup and generate "true state" for synthetic observation experiments | -| [filter](../../assimilation_code/programs/filter/filter.html) | perform data assimilation analysis | -| [obs_diag](../../assimilation_code/programs/obs_diag/threed_sphere/obs_diag.html) | creates observation-space diagnostic files in netCDF format to support visualization and quantification. | -| [obs_sequence_tool](../../assimilation_code/programs/obs_sequence_tool/obs_sequence_tool.html) | manipulates observation sequence files. This tool is not generally required (particularly for low-order models) but can be used to combine observation sequences or convert from ASCII to binary or vice-versa. Since this is a rather specialized routine, we will not cover its use further in this document. | - -As mentioned above, `quickbuild.csh` is a script that will build every -executable in the directory. There is an optional argument that will -additionally build the MPI-enabled versions which will not be covered in this -set of instructions. See [The DART MPI introduction](dart_mpi.md) page for more -information on using DART with MPI. - -Running `quickbuild.csh` will compile all the executables mentioned above for -the lorenz_63 model: - -> cd DART/models/lorenz_63/work -> ./quickbuild.csh - -The result (hopefully) is that seven executables now reside in your work -directory. - -:dart: *NOTE*: The most common problem is that the netCDF libraries and/or -include files were not found in the specified location(s). The second most -common problem is that the netCDF libraries were built with a different -compiler than the one used for DART. Find (or compile) a compatible netCDF -library, edit the `DART/build_templates/mkmf.template` to point to the -correct locations of the includes and library files, recreate the `Makefile`s, -and try again. - - [](#runningSomething) - -### Checking the build — running something. - -The `DART/models/lorenz_63/work` directory is distributed with input files -ready to run a simple experiment: use 20 ensemble members to assimilate -observations "every 6 hours" for 50 days. Simply run the programs -*perfect_model_obs* and *filter* to generate the results to compare against -known results. Note that this section is not intended to provide any details of -why you are doing what you are doing - this is sort of a "black-box" test. See -[The Lorenz 63 model: what is it and why should we care?](#lorenz63) to see -more about what this "physically" means for this low-order model, and -[Data assimilation in DART using the Lorenz 63 model](#DAForLorenz63) for a -more in-depth view of this process. - -The Manhattan release uses netCDF files for the input/output file formats. -*ncgen* is a helpful tool to create netCDF files from an ASCII representation -and is part of any netCDF installation. The *lorenz_63* -`quickbuild.csh` script already runs this command, but it is repeated here for -clarity. Once the necessary netCDF input files are created from the ASCII -`.cdl` text files, run *perfect_model_obs* to generate the "true state" and *filter* to run the data assimilation: - -> ncgen -o perfect_input.nc perfect_input.cdl -> ncgen -o filter_input.nc filter_input.cdl -> ./perfect_model_obs -> ./filter - -There should now be the following output files: - -| | | -| :------ | :------ | -| **from executable "perfect_model_obs"** | | -| `perfect_output.nc` | The last timestep of the model state. | -| `true_state.nc` | The model trajectory - the **truth**. | -| `obs_seq.out` | The observations that were harvested as the true model was advanced (and which will be assimilated). | -| **from executable "filter"** | | -| `preassim.nc` | The ensemble of model states just before assimilation. This is the **prior**.
:dart: *NOTE*: this file is a time series of the prior model states at all times. | -| `filter_output.nc` | The ensemble of model states after assimilation. This is the **posterior**.
:dart: *NOTE*: this file has only the final time step of the posterior model state. | -| `analysis.nc` | The model trajectory of the posterior. | -| `obs_seq.final` | The observations and ensemble estimates of the 'observations'. | -| **from both** | | -| `dart_log.out` | The run-time log of the experiment. This grows with each execution and may safely be deleted at any time. | -| `dart_log.nml` | A record of the input settings of the experiment. This file may safely be deleted at any time. | - -Note that if you change the `input.nml` namelist values controlling -inflation (increasing the ensemble spread) and file output, several (perhaps -many) more files may be created. The section -[Data assimilation in DART using the Lorenz 63 model](#DAForLorenz63) below -describes how to make changes to the `input.nml` file and rerun the filter -experiment. - - [](#matlab) - -### Configuring the DART diagnostic tools for MATLAB® - -The Manhattan release of DART uses native MATLAB netCDF support and no -longer requires any third-party toolboxes or built-in MATLAB toolboxes. -To allow your environment to seamlessly use the DART MATLAB functions, -your MATLABPATH must be set such that you -have access to several DART directories. At the MATLAB prompt, type the following -(using the real path to your DART installation): - -> \>\> addpath('path_to_dart/diagnostics/matlab','-BEGIN') -> \>\> addpath('path_to_dart/docs/DART_LAB/matlab','-BEGIN') - -It is very convenient to put these lines in your *\~/matlab/startup.m* file so -they are executed every time MATLAB starts up. DART provides an example -`diagnostics/matlab/startup.m` that you can use, which is internally -documented through file comments. - - [](#verify) - -### Verifying the DART installation (requires MATLAB®) - -The Lorenz model is notoriously sensitive to very small changes; in fact, the -story of Lorenz discovering this sensitivity is a classic in the annals of the -study of chaos, which in turn was instrumental in the development of data -assimilation as a field of study. See -[The Lorenz 63 model: what is it and why should we care?](#Lorenz63) or -[What is data assimilation?](#WhatIsDA) for more information. - -This sensitivity is of practical interest for verifying these results. The -initial conditions files and observations sequences are provided in ASCII, -which is portable across systems, but there may be some machine-specific -round-off error in the conversion from ASCII to machine binary. As Lorenz 63 -is such a nonlinear model, extremely small differences in the initial -conditions may eventually result in noticeably different model trajectories. -Even different compiler flags may cause tiny differences that ultimately result -in large differences. Your results should start out looking VERY SIMILAR and -may diverge with time. - -The simplest way to determine if the installation is successful is to run some -of the functions available in `DART/diagnostics/matlab/`. Usually, we -launch MATLAB from the `DART/models/lorenz_63/work` directory and use the -MATLAB *addpath* command to make the `DART/matlab/` functions available for -execution in any working directory. - -In the case of this Lorenz model, we know the "true" (by definition) state of -the model that is consistent with the observations, which was generated by the -*perfect_model_obs* program as described in -[Checking the build — running something](#runningSomething). The -following MATLAB scripts compare the ensemble members with the truth and can -calculate the error in the assimilation: - - - - - - -
- -
-
-
-[unix prompt] cd DART/models/lorenz_63/work
-[unix prompt] matlab -nodesktop
-(lots of startup messages I'm skipping)
-
-    [matlab_prompt] addpath ../../../diagnostics/matlab
-    [matlab_prompt] plot_total_err
-    Input name of true model trajectory file;
-    (cr) for perfect_output.nc
-    perfect_output.nc
-    Input name of ensemble trajectory file;
-    (cr) for preassim.nc
-    preassim.nc
-    Comparing true_state.nc and
-              preassim.nc
-    [matlab_prompt] plot_ens_time_series
-    Input name of ensemble trajectory file;
-    (cr) for preassim.nc
-
-    Comparing true_state.nc and
-              preassim.nc
-    Using Variable state IDs 1  2  3
-
-    pinfo =
-
-      struct with fields:
-
-                     model: 'Lorenz_63'
-                   def_var: 'state'
-            num_state_vars: 1
-                num_copies: 20
-           num_ens_members: 20
-          ensemble_indices: [1 2 3 ... 18 19 20]
-             min_state_var: 1
-             max_state_var: 3
-            def_state_vars: [1 2 3]
-                     fname: 'preassim.nc'
-                truth_file: 'true_state.nc'
-                diagn_file: 'preassim.nc'
-                truth_time: [1 200]
-                diagn_time: [1 200]
-                      vars: {'state'}
-                      time: [200x1 double]
-        time_series_length: 200
-                       var: 'state'
-                  var_inds: [1 2 3]       
-
-
-		 -xxxx
-xxxx -
- -From the above *plot_ens_time_series* graphic, you can see the individual -green ensemble members becoming more constrained with less spread as time -evolves. If your figures look similar to these, you should feel confident that -everything is working as intended. Don't miss the opportunity to rotate the -"butterfly" plot for that classic chaos theory experience (perhaps while -saying, "life, uh, finds a way"). - -Congratulations! You have now successfully configured DART and are ready to -begin the next phase of your interaction with DART. You may wish to learn more -about: - -* [What is data assimilation?](#WhatIsDA) — a brief introduction to ensemble data assimilation. This section includes more information about the Lorenz 63 model and how to configure the `input.nml` file to play with DA experiments in DART using the Lorenz 63 model. -* [What is DART?](#WhatIsDART) — This section includes more information about DART and a basic flow chart of the overall DART workflow. -* [How do I run DART with my model?](#RunWithMyModel) -* [How do I add my observations to DART?](#RunWithMyObs) -* [How would I use DART for teaching students and/or myself?](#DartForEducation) -* [How can I contribute to DART?](#ContributeToDart) - -:dart: *REQUEST:* In the case that the above instructions had one or more -issues that either did not work for you as intended or were confusing, please -contact the DART software development team at dart @ ucar .edu . We value your -input to make getting started as smooth as possible for new DART users! - ------ - - [](#WhatIsDA) - -## What is data assimilation? - -\[[top](#top)\] - -As some users may be unfamiliar with data assimilation and/or appreciate a brief -refresher, in this section we provide a (very) basic introduction to the -subject. For additional information, see the section on -[How would I use DART for teaching students and/or myself?](#DartForEducation). - -Included in this section are the subsections: - -1. [Introduction to ensemble DA](#EnsDAIntro) - a brief introduction to the main concepts of DA -2. [The Lorenz 63 model: what is it and why should we care?](#Lorenz63) - more information about the Lorenz 63 model and its far-reaching consequences, and -3. [Data assimilation in DART using the Lorenz 63 model](#DAForLorenz63) - an expanded introduction to DA in DART that opens up the "black box" of the Lorenz 63 model - - [](#EnsDAIntro) - -### Introduction to ensemble DA - -Data assimilation is a powerful and widely used computational technique that -has many application areas throughout mathematics and science. At a very high -level, data assimilation refers to the process of merging prior -*forecasts* with new *observations*, creating a new analysis that is an -"optimal" blending of the two by taking into account their relative -uncertainties. - -The following animated graphic describes the data assimilation process at a -high level: - - - -Shown here are three ensemble members, each of which gives a different initial -prediction at the time *t**k*. Moving these predictions -forward in time to *t**k*+1 will give a new forecast distribution -called a *prior*. Suppose at this time there is also an observation, which will -have some uncertainty due to instrument noise, etc. Mapping each of the ensemble -members to the observations with a function *h* and applying **Bayes' rule** -will generate an update to the prior distribution, called here the *state -increment*. Adding the state increment to the ensemble members will give the -new *analysis* (also known as the *posterior*) at time *t**k*+1. -This process can then be repeated for each set of observations as many times as -necessary. - -Expanding on this somewhat, let's call the *i*th -ensemble member **x**i at the time step we are working on. In the -above graphic, there were three ensemble members, but in general there are -usually many more, typically in the range of 20-1000 depending on the -application. Each member **x**i can have -*n* components which together make up the **model state**. Each member contains -all the variables you want to find the best fit for at a particular time. These -variables are usually physically meaningful quantities; for example, this might -include the 3D values of water vapor, temperature, wind speed, etc. for an -atmospheric model. These values are expected to be advanced forward in time by -a model, which is why they are called the "model state." - -:dart: *NOTE*: we view the "model state" as the minimum amount of information -necessary to restart the model for a new forecast. - -At any particular time step there may be *m* **observations** available. These -observations are assumed to relate to the model state and provide "real -world" checks against the model forecast. A "forward operator," represented in -the above diagram by *h*, is a relationship that computes what an -observation is most likely to be given a model state. In other words, *h* maps -between **x**i and **y**j, giving the "expected -observation" of the *j*th observation given the *i*th -ensemble member. An observation may be of the same quantity as one found in the -model state at a particular location, in which case the *h* function mapping -them is trivial and the comparison is simple. The vector **y** may also contain -more complex derived functions of the state **x** (for example radar -observations of precipitation), in which case the *h* function that models this -mapping between **x** (in this example precipitation) and **y** (in this -example radar returns) may be an algorithm that is quite complicated. - -In practice, observations are never 100% reliable. The observations -themselves will have some uncertainty for example arising from instrument -noise. The instrument noise error variances are typically published -by the instrument manufacturer, and these observation errors are usually assumed -to be independent as true instrument "noise" should not be correlated in time -or space. Furthermore, since models have a finite resolution (i.e. they are "fuzzy"), -there is almost always an error that arises when comparing the model to the observations. -This is called the **representativeness error**. Put together, the potential "likelihood" -of the possible values of the observation forms the **observational error distribution** -in the above graphic. - -Finally, note that in real-world applications there are typically many fewer -observations than state variables, i.e. *m* is typically **much much less** than -*n*. In practice this means that the observations alone cannot be relied upon -to predict the model state; the ensemble approach with Bayes' rule is necessary. - -DART makes it easy to find the optimal solution to the above problem using an -ensemble filter algorithm (the most typically used algorithm is the -**Ensemble Adjustment Kalman Filter**; see -[Important capabilities of DART](#dartCapabilities) for more information). The -user specifies which state variables make up the **x** ensemble vectors, which -observations make up the **y** vector, and the observation error variances. The -ensemble of model states is assumed to be representative of the -uncertainty/spread in the model state. Finally, the user tells DART how to -advance the model from one forecast to the next. Once DART has this -information, it can proceed with optimally blending the observations and model -forecasts — in other words, performing data assimilation. - -The spread of the **ensemble** informs DART of the **uncertainty** in the model -state. This allows for as rich, complex, and meaningful relationships as the -data contained within the ensemble itself. By default, no implicit assumptions -about the relative uncertainties are required, as the data can speak for itself. -Areas of large uncertainty will naturally have large spread, as the ensemble -members will contain very different values at those locations, while areas of -low uncertainty will naturally have low spread due to the ensemble having -relatively similar values at those locations. Furthermore, relationships in -space and between variables can also be meaningfully derived. Of course this -means that the quality of the ensemble is crucial to the success of the DA -process, as uncertainty can only be accurately quantified if the ensemble is -representative of the "true" uncertainty inherent in the system. Due to the fact -that a relatively small number of ensemble members are typically used, -estimated correlations between two distant locations may become unreliable due -to sampling error. Thus, various techniques such as **covariance localization** -may be employed to improve the quality of estimated relationships and increase -skill in prediction. Furthermore, the ensemble spread may sometimes be deemed -"too small" or "too large" by various criteria, in which case a multiplicative -or additive **inflation** or **deflation**, respectively, may be applied. -In practice the ensemble method is usually far more accurate and -less error-prone than the main alternative of manually specifying uncertainty -by some manually-designed algorithm, and it is certainly less labor-intensive -to develop. - -This was a brief introduction to the important concepts of DA. For more -information, see -[How would I use DART for teaching students and/or myself?](#DartForEducation). - -In the next section, details of the Lorenz 63 model are provided to give a more -concrete illustration of the DA process. - ----- - - [](#Lorenz63) - -### The Lorenz 63 model: what is it and why should we care? - -In this section we present additional detail about the Lorenz 63 model. As we -previously treated this model as a black box, this section will provide -background on this highly relevant chaotic system for the purpose of deepening -understanding of DART and DA in general. - -In 1963, Edward Lorenz developed a simplified 3-variable model to investigate -atmospheric convection. By making several simplifications to the Boussinesq -approximation, the Lorenz model was derived for a single thin layer of fluid -uniformly heated from below and cooled from above. The [original paper](https://journals.ametsoc.org/doi/pdf/10.1175/1520-0469%281963%29020%3C0130%3ADNF%3E2.0.CO%3B2) -(Lorenz, E. N. 1963. Deterministic nonperiodic flow. *J. Atmos. -Sci.* **20**, 130-141.) has been cited over 20,000 times. -The relatively simple — yet nonlinear — system of ordinary -differential equations (ODEs) is: - -~~~ -X' = sigma*(Y-X) -Y' = -XZ + rX - Y -Z' = XY - bZ -~~~ - -where the left-hand sides of the equations are derivatives with respect to time. -Here, `X` is proportional to the rate of convection, `Y` is related to the -horizontal temperature variation, and `Z` is the vertical temperature -variation. There are three constant parameters: `sigma` relates to the Prandtl -number, `r` to the Rayleigh number, and `b` to the physical dimensions of the -layer. Note that `Y'` and `Z'` each have a nonlinear term (`-XZ` and `XY`, -respectively). - -The numerical investigation of the **chaos** arising from this system of ODEs -unexpectedly launched a revolution in our understanding of nature and lead to -numerous mathematical and scientific breakthroughs. While the chaotic nature of -certain systems such as the three-body problem had been investigated -previously, it was the electronic computer, which could compute thousands of -calculations per second, that allowed these ideas to be formalized. In -particular, Lorenz's model made it clear for the first time how an -infinitesimally small change in the initial conditions could end up having a -huge impact on the final results in certain situations. Lorenz discussed the -strange behavior of this model in -[The Essence of Chaos, University of Washington Press, 1995](https://uwapress.uw.edu/book/9780295975146/the-essence-of-chaos/): - -> > At one point I decided to repeat some of the computations in order to -examine what was happening in greater detail. I stopped the computer, -typed in a line of numbers that it had printed out a while earlier, and -set it running again. I went down the hall for a cup of coffee and -returned after about an hour, during which the computer had simulated -about two months of weather. The numbers being printed out were nothing -like the old ones. I immediately suspected a weak vacuum tube or some other -computer trouble, which was not uncommon, but before calling for service -I decided to see just where the mistake had occurred, knowing that this -could speed up the servicing process. Instead of a sudden break, I found -that the new values at first repeated the old ones, but soon afterward had -differed by one and then several units in the last decimal place. … -The numbers I had typed in were not the exact original numbers, but were -the rounded-off values that appeared in the original printout. The initial -round-off errors were the culprits; they were steadily amplifying until -they dominated the solution. In today’s terminology, there was chaos. - -Lorenz discovered that even in a model with just three variables, a very small -change in the initial conditions (in this case, the numbers he typed back into -the computer, which were very slightly different from the original numbers) -could cause the entire large-scale behavior to change. Lorenz's discovery has -many important practical implications: - -1. If tiny changes can grow to dominate a system, it is no longer possible to find the one set of "perfect" initial conditions and hope to allow the system to run forever with perfect forecasts. Instead, forecasting chaotic systems must be approached **statistically**. -2. There is a practical limit of predictability inherent in chaotic systems. In other words, the nonlinear dynamics of a chaotic model are inherently difficult to predict. Multiple evaluations (an **ensemble**) can be run with different plausible initial conditions to quantify this error growth. -3. In order to forecast chaotic systems effectively, periodic observations of the state are required to effectively guide the forecast and narrow the uncertainty. Since in real-world applications observations are almost always sparse compared to the number of state variables, merging observations and forecasts (i.e. **data assimilation**) is required to effectively forecast chaotic systems. - -While Lorenz 63 is a simple example of a chaotic system, there are many other -chaotic systems of real practical interest in areas such as weather prediction, -climate, oceanography, hydrology, ecology, biology, ... the list goes on and -on. In short, while the Lorenz model is a simple problem that can easily run -even on the most meager of computers today, it is representative of the same -problem of predictability that can be found throughout science. DART supports -the investigation of forecasting chaotic systems in *any* field where periodic -observations can be used to constrain the uncertainty using an ensemble. - - [](#DAForLorenz63) - -### Data assimilation in DART using the Lorenz 63 model - -In this section we open the "black box" of the Lorenz model that was previously -used in [Quick-start instructions for the impatient](#QuickStart) and -[More detailed DART setup instructions](#DetailedSetup). This section assumes -you have successfully run the Lorenz 63 model with the example observation -files that were distributed with the DART repository. In this section you will -learn in more detail how DART interacts with the Lorenz 63 model to perform data -assimilation. - -#### The input.nml namelist - -The `DART/models/lorenz_63/work/input.nml` file is the Lorenz model *namelist*, -which is a standard Fortran method for passing parameters from a text file -into a program without needing to recompile. There are many sections within -this file that drive the behavior of DART while using the Lorenz 63 model -for assimilation. Within `input.nml`, there is a section called *model_nml*, -which contains the model-specific parameters: - -~~~ -&model_nml - sigma = 10.0, - r = 28.0, - b = 2.6666666666667, - deltat = 0.01, - time_step_days = 0, - time_step_seconds = 3600, - solver = 'RK2' - / -~~~ - -Here, you can see the values for the parameters `sigma`, `r`, and `b` that were -discussed in the previous section. These are the original values Lorenz used -in the 1963 paper to create the classic butterfly attractor. - -#### The Lorenz 63 model code - -The Lorenz 63 model code, which is under -`DART/models/lorenz_63/model_mod.f90`, contains the lines: - -~~~ -subroutine comp_dt(x, dt) - -real(r8), intent( in) :: x(:) -real(r8), intent(out) :: dt(:) - -! compute the lorenz model dt from standard equations - -dt(1) = sigma * (x(2) - x(1)) -dt(2) = -x(1)*x(3) + r*x(1) - x(2) -dt(3) = x(1)*x(2) - b*x(3) - -end subroutine comp_dt -~~~ -which directly translates the above ODE into Fortran. - -Note that the routine `comp_dt` does not explicitly depend on the time -variable, only on the state variables (i.e. the Lorenz 63 model is time -invariant). - -:dart: *NOTE*: By default, the `model_mod.f90` follows the Lorenz 63 paper to -use the Runge-Kutta 2 scheme (otherwise known as RK2 or the midpoint scheme) to -advance the model. - -Since the Lorenz 63 model is time invariant, the RK2 code to advance the ODE -in time can be written as follows, again following the Lorenz 63 paper, for a -`fract` fraction of a time-step (typically equal to 1): - -~~~ -!------------------------------------------------------------------ -!> does single time step advance for lorenz convective 3 variable model -!> using two step rk time step - -subroutine adv_single(x, fract) - -real(r8), intent(inout) :: x(:) -real(r8), intent(in) :: fract - -real(r8) :: x1(3), x2(3), dx(3) - -call comp_dt(x, dx) ! compute the first intermediate step -x1 = x + fract * deltat * dx - -call comp_dt(x1, dx) ! compute the second intermediate step -x2 = x1 + fract * deltat * dx - -! new value for x is average of original value and second intermediate - -x = (x + x2) / 2.0_r8 - -end subroutine adv_single -~~~ - -Together, these two code blocks describe how the Lorenz 63 model is advanced -in time. You will see how DART uses this functionality shortly. - -#### The model time step and length of the data assimilation - -In the original Lorenz 63 paper, the model is run for 50 "days" using a -non-dimensional time-step of 0.01, which is reproduced in the namelist above. -This time-step was assumed equal to 3600 seconds, or one hour, in dimensional -time. This is also set in the namelist above. The Lorenz 63 model observation -file included with the DART repository uses observations of all three state -variables every six hours (so every six model steps) to conduct the -assimilation. - -If you were previously able to run the Matlab diagnostic scripts, you may -have noticed that the butterfly attractor for the included example does not -look as smooth as might be desired: - -Lorenz attractor
- -This is because the model output was only saved once every six "hours" at the -observation times. As an exercise, let's make a nicer-looking plot using the -computational power available today, which even on the most humble of computers -is many times greater than what Lorenz had in 1963. Let's change Lorenz's -classic experiment to the following: - -1. Make the non-dimensional timestep 0.001, a factor of 10 smaller, which will -correspond to a dimensional timestep of 360 seconds (6 minutes). This smaller -time-step will lead to a smoother model trajectory. -2. Keep the original ratio of time steps to observations included in the -DART repository of assimilating observations every six time steps, meaning we -now need observations every 36 minutes. - -Therefore, in order to conduct our new experiment, we will need to regenerate -the DART observation sequence files. - -To change the time-step, change the `input.nml` file in `DART/models/lorenz_63/work` to the following: - -~~~ -&model_nml - sigma = 10.0, - r = 28.0, - b = 2.6666666666667, - deltat = 0.001, - time_step_days = 0, - time_step_seconds = 360 - / -~~~ - -:dart: *NOTE*: the changes are to `deltat` and `time_step_seconds`. - -:dart: *NOTE*: we do not need to recompile the DART code as the purpose of -namelist files is to pass run-time parameters to a Fortran program without -recompilation. - -#### Updating the observation sequence - -Let's now regenerate the DART observation files with the updated timestep and -observation ratio. In a typical large-scale application, the user will provide -observations to DART in a standardized format called the *Observation Sequence* -file. Since there are no real observations of the Lorenz 63 system, we must create -our own synthetic observations - which may be done using *create_obs_sequence*, -*create_fixed_network_seq*, and *perfect_model_obs* programs; each of which we -will explain below. These helpful interactive programs are included with DART -to generate these observation sequence files for typical research or -education-oriented experiments. In such setups, observations (with noise added) -will be generated at regular intervals from a model "truth". This "truth" will only -be available to the experiment through the noisy observations but can later be used -for comparison purposes. The number of steps necessary for the ensemble members to -reach the true model state's "attractor" can be investigated and, for example, -compared between different DA methods. This is an example of an "OSSE" — see -[High-level DA workflows in DART](#dartWorkflow) for more information. - -The three programs used in this example to create an observation sequence -again are *create_obs_sequence*, *create_fixed_network_seq*, and *perfect_model_obs*. -*create_obs_sequence* creates a template for the observations, -*create_fixed_network_seq* repeats that template at multiple times, -and finally *perfect_model_obs* harvests the observation values. -These programs have many additional capabilities; if interested, see -the corresponding program's documentation. - -Let's now run the DART program *create_obs_sequence* to create the -observation template that we will later replicate in time: - -> \# Make sure you are in the DART/models/lorenz_63/work directory -> ./create_obs_sequence - -The program *create_obs_sequence* will ask for the number of observations. -Since we plan to have 3 observations at each time step (one for each of the -state variables), input **3**: - -~~~ -set_nml_output Echo NML values to log file only - --------------------------------------------------------- --------------- ASSIMILATE_THESE_OBS_TYPES -------------- - RAW_STATE_VARIABLE --------------------------------------------------------- --------------- EVALUATE_THESE_OBS_TYPES -------------- - none --------------------------------------------------------- ----------- USE_PRECOMPUTED_FO_OBS_TYPES -------------- - none --------------------------------------------------------- - -Input upper bound on number of observations in sequence -3 -~~~ - -For this experimental setup, we will not have any additional copies of the -data, nor will we have any quality control fields. So use **0** for both. - -~~~ -Input number of copies of data (0 for just a definition) -0 -Input number of quality control values per field (0 or greater) -0 -~~~ - -We now will setup each of the three observations. The program asks to enter --1 if there are no additional observations, so input anything else instead -(**1** below). Then enter **-1**, **-2**, and **-3** in sequence for the state -variable index (the observation here is just the values of the state variable). -Use **0 0** for the time (we will setup a regularly repeating observation -after we finish this), and **8** for the error variance for each observation. - -Finally, after inputting press enter to use the default output file -`set_def.out`. - -Input your values as follows: - -~~~ -input a -1 if there are no more obs -1 - Input -1 * state variable index for identity observations - OR input the name of the observation kind from table below: - OR input the integer index, BUT see documentation... - 1 RAW_STATE_VARIABLE --1 -input time in days and seconds (as integers) -0 0 -Input the error variance for this observation definition -8 -input a -1 if there are no more obs -1 - Input -1 * state variable index for identity observations - OR input the name of the observation kind from table below: - OR input the integer index, BUT see documentation... - 1 RAW_STATE_VARIABLE --2 -input time in days and seconds (as integers) -0 0 -Input the error variance for this observation definition -8 -input a -1 if there are no more obs -1 - Input -1 * state variable index for identity observations - OR input the name of the observation kind from table below: - OR input the integer index, BUT see documentation... - 1 RAW_STATE_VARIABLE --3 -input time in days and seconds (as integers) -0 0 -Input the error variance for this observation definition -8 -Input filename for sequence ( for set_def.out ) - -write_obs_seq opening formatted observation sequence file "set_def.out" -write_obs_seq closed observation sequence file "set_def.out" -create_obs_sequence Finished successfully. -~~~ - -#### Creating a regular sequence of observations - -We will now utilize another DART program that takes this `set_def.out` file as -input. The interactive program `create_fixed_network_seq` is a helper tool -that can be used to generate a DART observation sequence file made of a set of -regularly repeating observations. - -> \# Make sure you are in the DART/models/lorenz_63/work directory -> ./create_fixed_network_seq - -We want to use the default `set_def.out` file, so press return. We also want a -regularly repeating time sequence, so input **1**. - -~~~ -set_nml_output Echo NML values to log file only - --------------------------------------------------------- --------------- ASSIMILATE_THESE_OBS_TYPES -------------- - RAW_STATE_VARIABLE --------------------------------------------------------- --------------- EVALUATE_THESE_OBS_TYPES -------------- - none --------------------------------------------------------- ----------- USE_PRECOMPUTED_FO_OBS_TYPES -------------- - none --------------------------------------------------------- - -Input filename for network definition sequence ( for set_def.out ) - -To input a regularly repeating time sequence enter 1 -To enter an irregular list of times enter 2 -1 -~~~ - -We now will input the number of observations in the file. The purpose of this -exercise is to refine the time step used by Lorenz in 1963 by a factor of 10. -Since we want to keep the ratio of six model steps per observation and run for -50 days, we will need 2000 model observations (360 seconds × 6 × - 2000 = 50 days). - -:dart: *NOTE*: as we specified in `set_def.out`, there are 3 observations per -time step, so a total of 6000 observations will be generated. - -:dart: *NOTE*: the Lorenz 63 model dimensional time-step is related to -the observational time *only* through this mechanism. In other words, `deltat` -in the namelist could relate to virtually any dimensional time step -`time_step_seconds` if the observation times were not considered. However, DART -will automatically advance the model state to the observation times in order to -conduct the data assimilation at the appropriate time, then repeat this process -until no additional observations are available, thus indirectly linking -`deltat` to `time_step_seconds`. - -Enter **2000** for the number of observation times. The initial time will be -**0 0**, and the input period will be **0** days and **2160** seconds -(36 minutes). - -~~~ -Input number of observation times in sequence -2000 -Input initial time in sequence -input time in days and seconds (as integers) -0 0 -Input period of obs in sequence in days and seconds -0 2160 -~~~ - -The numbers 1 to 2000 will then be output by `create_fixed_network_seq`. Press -return to accept the default output name of `obs_seq.in`. The file suffix is -`.in` as this will be the input to the next program, *perfect_model_obs*. - -~~~ - 1 - 2 -... -1998 -1999 -2000 -What is output file name for sequence ( for obs_seq.in) - - write_obs_seq opening formatted observation sequence file "obs_seq.in" - write_obs_seq closed observation sequence file "obs_seq.in" - create_fixed_network_seq Finished successfully. -~~~ - -#### Running perfect_model_obs - -We are now ready to run *perfect_model_obs*, which will read in `obs_seq.in` -and generate the observations as well as create the "perfect" model trajectory. -"Perfect" here is a synonym for the known "true" state which is used to generate -the observations. Once noise is added (to represent observational uncertainty), -the output is written to `obs_seq.out`. - -> \# Make sure you are in the DART/models/lorenz_63/work directory -> ./perfect_model_obs - -The output should look like the following: - -~~~ -set_nml_output Echo NML values to log file only - initialize_mpi_utilities: Running single process - --------------------------------------------------------- --------------- ASSIMILATE_THESE_OBS_TYPES -------------- - RAW_STATE_VARIABLE --------------------------------------------------------- --------------- EVALUATE_THESE_OBS_TYPES -------------- - none --------------------------------------------------------- ----------- USE_PRECOMPUTED_FO_OBS_TYPES -------------- - none --------------------------------------------------------- - - quality_control_mod: Will reject obs with Data QC larger than 3 - quality_control_mod: No observation outlier threshold rejection will be done - perfect_main Model size = 3 - perfect_read_restart: reading input state from file - perfect_main total number of obs in sequence is 6000 - perfect_main number of qc values is 1 - - perfect_model_obs: Main evaluation loop, starting iteration 0 - move_ahead Next assimilation window starts at: day= 0 sec= 0 - move_ahead Next assimilation window ends at: day= 0 sec= 180 - perfect_model_obs: Model does not need to run; data already at required time - perfect_model_obs: Ready to evaluate up to 3 observations - - perfect_model_obs: Main evaluation loop, starting iteration 1 - move_ahead Next assimilation window starts at: day= 0 sec= 1981 - move_ahead Next assimilation window ends at: day= 0 sec= 2340 - perfect_model_obs: Ready to run model to advance data ahead in time - perfect_model_obs: Ready to evaluate up to 3 observations - -... - -perfect_model_obs: Main evaluation loop, starting iteration 1999 -move_ahead Next assimilation window starts at: day= 49 sec= 84061 -move_ahead Next assimilation window ends at: day= 49 sec= 84420 -perfect_model_obs: Ready to run model to advance data ahead in time -perfect_model_obs: Ready to evaluate up to 3 observations - -perfect_model_obs: Main evaluation loop, starting iteration 2000 -perfect_model_obs: No more obs to evaluate, exiting main loop -perfect_model_obs: End of main evaluation loop, starting cleanup -write_obs_seq opening formatted observation sequence file "obs_seq.out" -write_obs_seq closed observation sequence file "obs_seq.out" -~~~ - -You can now see the files `true_state.nc`, a netCDF file which has the perfect -model state at all 2000 observation times; `obs_seq.out`, an ASCII file which -contains the 6000 observations (2000 times with 3 observations each) of the -true model state with noise added in; and `perfect_output.nc`, a netCDF file -with the final true state that could be used to "restart" the experiment from -the final time (49.75 days in this case). - -We can now see the relationship between `obs_seq.in` and `obs_seq.out`: -`obs_seq.in` contains a "template" of the desired observation locations and -types, while `obs_seq.out` is a list of the actual observation values, in this -case generated by the *perfect_model_obs* program. - -:exclamation: *IMPORTANT*: `create_obs_seq` is used for this low-order model -because there are no real observations for Lorenz 63. For systems that have -real observations, DART provides a variety of *observation converters* -available to convert from native observation formats to the DART format. See -[Observation converters provided by DART](#obsConverters) for a list. - -#### Running the filter - -Now that `obs_seq.out` and `true_state.nc` have been prepared, DART can perform -the actual data assimilation. This will generate an ensemble of model states, -use the ensemble to estimate the prior distribution, compare to the "expected" -observation of each member, and update the model state according to Bayes' -rule. - -> \# Make sure you are in the DART/models/lorenz_63/work directory -> ./filter - -~~~ -set_nml_output Echo NML values to log file only -initialize_mpi_utilities: Running single process - --------------------------------------------------------- --------------- ASSIMILATE_THESE_OBS_TYPES -------------- - RAW_STATE_VARIABLE --------------------------------------------------------- --------------- EVALUATE_THESE_OBS_TYPES -------------- - none --------------------------------------------------------- ----------- USE_PRECOMPUTED_FO_OBS_TYPES -------------- - none --------------------------------------------------------- - -quality_control_mod: Will reject obs with Data QC larger than 3 -quality_control_mod: No observation outlier threshold rejection will be done -assim_tools_init: Selected filter type is Ensemble Adjustment Kalman Filter (EAKF) -assim_tools_init: The cutoff namelist value is 1000000.000000 -assim_tools_init: ... cutoff is the localization half-width parameter, -assim_tools_init: ... so the effective localization radius is 2000000.000000 -filter_main: running with an ensemble size of 20 -parse_stages_to_write: filter will write stage : preassim -parse_stages_to_write: filter will write stage : analysis -parse_stages_to_write: filter will write stage : output -set_member_file_metadata no file list given for stage "preassim" so using default names -set_member_file_metadata no file list given for stage "analysis" so using default names -Prior inflation: None -Posterior inflation: None -filter_main: Reading in initial condition/restart data for all ensemble members from file(s) - -filter: Main assimilation loop, starting iteration 0 -move_ahead Next assimilation window starts at: day= 0 sec= 0 -move_ahead Next assimilation window ends at: day= 0 sec= 180 -filter: Model does not need to run; data already at required time -filter: Ready to assimilate up to 3 observations -comp_cov_factor: Standard Gaspari Cohn localization selected -filter_assim: Processed 3 total observations - -filter: Main assimilation loop, starting iteration 1 -move_ahead Next assimilation window starts at: day= 0 sec= 21421 -move_ahead Next assimilation window ends at: day= 0 sec= 21780 -filter: Ready to run model to advance data ahead in time -filter: Ready to assimilate up to 3 observations -filter_assim: Processed 3 total observations -... - -filter: Main assimilation loop, starting iteration 199 -move_ahead Next assimilation window starts at: day= 49 sec= 64621 -move_ahead Next assimilation window ends at: day= 49 sec= 64980 -filter: Ready to run model to advance data ahead in time -filter: Ready to assimilate up to 3 observations -filter_assim: Processed 3 total observations - -filter: Main assimilation loop, starting iteration 200 -filter: No more obs to assimilate, exiting main loop -filter: End of main filter assimilation loop, starting cleanup -write_obs_seq opening formatted observation sequence file "obs_seq.final" -write_obs_seq closed observation sequence file "obs_seq.final" -~~~ - -Based on the default Lorenz 63 `input.nml` namelist for *filter* included in -the DART repository, the assimilation will have three stages: - -1. The *preassim* stage, where the ensemble is updated by advancing the -model. The file`preassim.nc`, which contains the pre-assimilation model -trajectories for all the ensemble members, will be written. -2. The *analysis* stage, where the data assimilation is conducted. The -post-assimilation model trajectories for all the ensemble members will be -written to `analysis.nc` -3. The *output* stage, which writes the file `obs_seq.final` containing the -actual observations as assimilated plus the ensemble forward-operator expected -values and any quality-control values. This stage also writes the -`filter_output.nc` file containing the ensemble state from the final cycle, -which could be used to restart the experiment. - -DART has now successfully assimilated our updated observations with a 6 minute -model time step and assimilation every 36 minutes. *:tada:* - -#### Verifying the nicer-looking results - -You can now run the verification scripts (as in the section -[Verify the results are correct](#verify)) in Matlab with the following -commands: - -> \>\> addpath ../../../diagnostics/matlab -> \>\> plot_ens_time_series - -Some additional commands to view the attractor from the ZY plane were used: - -> \>\> set(findall(gca, 'Type', 'Line'),'LineWidth',2); -> \>\> set(gca,'FontSize',18) -> \>\> xlabel('x') -> \>\> ylabel('y') -> \>\> zlabel('z') -> \>\> view([90 0]) - -We can now see the following smooth Lorenz 63 true state and ensemble mean -comparison with a 6 minute model time step and assimilation every 36 minutes: - -Lorenz attractor
- -As you can see, the ensemble mean in red matches the true state almost exactly, -although it took a number of assimilation cycles before the blue ensemble mean -was able to reach the red true state "attractor." - -You should now be able to play with the Lorenz 63 and/or other models in DART. -For more detailed information on the ins-and-outs of ensemble DA, see -[How would I use DART for teaching students and/or myself?](#DartForEducation). -For more concrete information regarding DART's algorithms and capabilities, see -the next section: [What is DART?](#WhatIsDART). To add your own model to DART, -see [How do I run DART with my model?](#RunWithMyModel). Finally, if you wish -to add your own observations to DART, see [How do I add my observations to DART?](#RunWithMyObs). - ----- - - [](#WhatIsDART) - -## What is DART? - -\[[top](#top)\] - -The Data Assimilation Research Testbed (DART) is an open-source community -facility that provides software tools for data assimilation research, -development, and education. Using DART's carefully engineered ensemble data -assimilation algorithms and diagnostic tools, atmospheric scientists, -oceanographers, hydrologists, chemists, and other geophysicists can construct -state-of-the-art data assimilation systems with unprecedented ease. - -In this section we will introduce DART in further detail. This includes: - -1. [Why should I use DART for my project?](#whyDart) -2. [A brief history of DART](#dartHistory) -3. [A high-level workflow of DA in DART](#dartWorkflow) -4. [DART's design philosophy](#dartDesign) -5. [Important capabilities of DART](#dartCapabilities) -6. [How to cite DART](#citeDart) - - [](#whyDart) - -### Why should I use DART for my project? - -A common pitfall for graduate students and professionals alike is to look at -the simplicity of data assimilation, in particular ensemble data assimilation, -and decide they can easily write their own DA system. Indeed, this is true. -After learning of the core algorithms, a talented programmer using their -favorite language could write a functional DA system in a manner of weeks -if not days. However, he or she will soon find that while the core of DA -systems are easy to write, the more "real" the system needs to be, the more -complex it will become. Writing a parallel DA system that can efficiently -utilize multiple cores with MPI is not straight-forward, and adding -covariance localization, observation operators, multiple models, and auxiliary -tools such as quality control and pre-processing will quickly dwarf the amount -of core DA code, not to mention the headaches involved in supporting multiple -computing environments, compilers, etc. - -DART employs a modular programming approach to apply an algorithm to move the -underlying models toward a state that is more consistent with information from -a set of observations. Models may be swapped in and out, as can different -DA algorithms. The method requires running multiple -instances of a model to generate an ensemble of states. A forward operator -appropriate for the type of observation being assimilated is applied to each of -the states to generate the model's estimate of the observation. - -DART remains the top choice for scientists, educators, and mathematicians -seeking mature and robust ensemble DA solutions without reinventing the wheel. -Here are some of the many benefits of using DART: - -1. DART is **freely available, open source, and released under the -[Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).** In short -this means that you are granted a copyright license stating you are free to -use, modify, and redistribute any derivative works derived from the DART system -provided that you maintain the license and copyright information. Of course, we -also ask that you credit DART in your publications, and kindly ask that you -contribute your modifications so that other users may benefit. See -[How should I cite DART?](#citeDart) and -[How can I contribute to DART?](#ContributeToDart) for more information. -2. DART is **fully parallel and carefully engineered** to run on systems -ranging from single-core research computers to the top performing multicore -supercomputers in the world. Writing scalable parallel code is arguably the -most difficult and time-consuming task in scientific computing today, but DART -has already carefully implemented and tested this project, and the code is -available for you to use out-of-the-box. For more information on how DART was -written (and continues to be developed), see [DART's design philosophy](#dartDesign). -3. DART contains **numerous tools that accelerate getting started** on both -research and "real-world" problems. Multiple rigorously tested inflation, -localization, perturbation, and other auxiliary data assimilation algorithms -are available for immediate use and testing. See [Important capabilities of DART](#dartCapabilities) for more information. -4. DART **makes adding a new model straightforward**. A new model only needs to -implement a list of (at most) 18 core functions or use the default behavior if -applicable to take advantage of DART's mature and robust DA algorithms. A -basic data assimilation system for a large model can be built in person-weeks, -and comprehensive systems have been built in a few months. See -[How do I run DART with my model?](#RunWithMyModel) for more information. -5. DART **makes it easy to add new observations** in order to test their -potential beneficial impact. Incorporating new observation types only requires -creating a forward operator that computes the expected value of an observation -given a model's state. See -[How do I add my observations to DART?](#RunWithMyObs) for more information. -6. DART **can be used to test new DA algorithms**. Many such algorithms have -been successfully implemented, tested, and published using DART. This is not -covered in this getting started guide as this is an "advanced user" -functionality, so for this purpose it is best to first get in touch with the -DART team at dart @ ucar.edu to make the process as smooth as possible. -7. Finally, and perhaps most importantly, DART **has world-class support** -available from the DART team at NCAR. A talented team of dedicated software -engineers and data assimilation scientists work together to continually improve -DART and support user needs. See the [About us](About_Us.md) for more -information about the DART team. - - [](#dartHistory) - -### A brief history of DART - -The DART project was initiated in August 2001, and in 2003, the Data -Assimilation Research Section (DAReS) was officially formed at NCAR. In -2004, the first officially supported version of DART was released. Consistent -version control history is available back to 2005, making DART an extremely -long-lived and well-supported software project. Since 2004, there have been -more than a dozen releases. The first release, *Easter*, began the trend of -naming the major releases after islands in alphabetical order in the -following sequence: - -| Release | Date | Brief description | -| :------- | :---------- | :---------- | -| Easter | 8 Mar 2004 | Initial release | -| Fiji | 29 Apr 2004 | Enhanced portability; support for CAM and WRF | -| Guam | 12 Aug 2004 | New observation modules | -| Pre-Hawaii | 20 Dec 2004 | New filtering algorithms | -| Hawaii | 28 Feb 2005 | New filtering algorithms | -| DA Workshop 2005 | 13 Jun 2005 | Tutorial, observation preprocessing | -| Pre-Iceland | 20 Oct 2005 | Huge expansion of real observation capability | -| Iceland | 23 Nov 2005 | Huge expansion of real observation capability, more | -| Post-Iceland | 20 Jun 2006 | Observation-space adaptive inflation, more | -| Pre-J | 02 Oct 2006 | Updated scalable filter algorithm | -| Jamaica | 12 Apr 2007 | Vertical localization, extensive MPI testing, more | -| Kodiak | 30 Jun 2011 | New obs types, new diagnostics, new utilities, much more | -| Lanai | 13 Dec 2013 | Support for many new models, chemistry/aerosol types, new diagnostics, new utilities, much more | -| Manhattan | 15 May 2017 | Native netCDF support, better scaling/performance, much more | - -In September 2009, DART was featured on the cover of the Bulletin of the -American Meteorological Society: - -BAMS Sept 2009 Cover
- -The September 2009 issue of BAMS can be found -[here](https://journals.ametsoc.org/toc/bams/90/9), while the DART article can -be found directly -[here](https://journals.ametsoc.org/doi/full/10.1175/2009BAMS2618.1). - -On the [Publications](Publications.md) page there are over 40 example -publications that use DART, although there are many additional publications -using DART not listed. The seminal BAMS paper has over 400 citations according -to [Google Scholar](http://scholar.google.com). The core algorithms used in -DART have also been cited many more times. For example, the core EAKF algorithm -([Anderson 2001](https://journals.ametsoc.org/doi/full/10.1175/1520-0493%282001%29129%3C2884%3AAEAKFF%3E2.0.CO%3B2)) -used in DART has over 1500 citations according to Google scholar. - - [](#dartWorkflow) - -### High-level DA workflows in DART - -In this section we present two high-level data assimilation workflows that show -the relevant DART programs with their inputs and outputs. These two workflows -represent two different types of DA experiments typically run. - -It is possible to run DART in **Observation System Simulation Experiment -(OSSE)** mode. In OSSE mode, a perfect "true" model trajectory is created, and -synthetic observations are generated from the "truth" with added noise. This -is useful to test the theoretical capability of DA algorithms, observations, -and/or models. In this document so far, we have conducted only OSSEs. - -It is also possible to run DART in a more realistic **Observation System -Experiment (OSE)** mode. In an OSE, there is no perfect model truth, which -is similar to real-world situations where the true values of the model state -will likely never be perfectly known. The observations (which again themselves -are noisy and imperfect) are the only way to get a look at the "truth" that is -estimated by the model state. In OSE mode, the user must provide observations -to DART, which are usually from real-world observation systems (which come with -all of their own idiosyncrasies and imperfections). DART can help generate -ensemble perturbations, or the user can specify their own. - -The filtering aspect is the same for both OSSE and OSE experiments, and many of -the same tools for data assimilation are available in OSSE and OSE modes. The -core difference, therefore, is the existence of the perfect model "truth." - -For a simple model such as Lorenz 63 investigated above, DART can typically -advance the model time explicitly through a Fortran function call, allowing the -filtering to compute all necessary time steps in sequence without exiting the -DART program. However, for larger models (or those that DART cannot communicate -with through Fortran), a shell-script may be necessary to run the model and -advance the time forward. For the largest models, the model state is typically -advanced in parallel over many computing nodes on a supercomputer. In this more -complex case, DART only considers one step at a time in order to combine the -observations and the prior ensemble to find the posterior analysis, which -will then be used to restart the model and continue the forecast. - -For efficiency reasons, data from models with large states may be written in -separate files for every ensemble member at every stage of the assimilation -process. Data from models with small states may be conveniently be written as -variables inside a single netCDF file. - -#### Simple model workflow with an OSSE - -The first example DA workflow is for a model that can be advanced by DART -with all ensemble members stored in a single file running an OSSE: - -OSSE workflow with all members in one file
- -As shown, the program *preprocess* takes the `input.nml` namelist file and -generates Fortran code for the observations. This code, along with the namelist, -is used by all subsequent programs. *create_obs_seq* is used to define a set -of observations in `set_def.out`, which can be replicated through the program -*create_fixed_network_seq* to create a `obs_seq.in` file. There are two inputs -to *perfect_model_obs*: the `obs_seq.in` file and `perfect_input.nc` (which here -is generated by `perfect_input.cdl` via *ncgen*). `obs_seq.in` provides -*perfect_model_obs* with the observation template (i.e. the location and type -of observations), while `perfect_input.nc` provides the initial state that -will be used to advance the model. On output, the "perfect" model state at -the final time, which can be used as a restart for running this procedure again, -will be written to `perfect_output.nc` (i.e. `perfect_output.nc` could be -renamed to `perfect_input.nc` to extend the OSSE), while the entire state -trajectory will be stored in `true_state.nc`. The noisy synthetic observations -and noise-free truth (for verification only) will be stored in `obs_seq.out`. The -observation values of `obs_seq.out` will be input to *filter* along with the -`filter_input.nc` (generated by `filter_input.cdl` via *ncgen*), which contains -the initial state for all the ensemble members. The output of *filter* is -`preassim.nc`, which contains the prior state for all the ensemble members -just before applying DA (so including prior inflation if it is being used); -`analysis.nc`, which contains the posterior state for all the ensemble members after assimilation (and including inflation if it is being used); -`filter_output.nc`, which is the final posterior that could be used to restart -the OSSE process; and `obs_seq.final`, which adds the forward-calculated -expected values *h*(**x**) for each observation. The `obs_seq.final` file can -be analyzed and binned by the *obs_diag* program, producing the file -`obs_diag_output.nc` which can be used for diagnostics. - -#### Complex model workflow with an OSE - -The second workflow is for a complex model with all ensemble members stored in -separate files running an OSE. In this case, DART will only operate on one -model output at a time. External programs will advance the model states, -generate the observations, and call DART again. The following diagram in shows -the high-level DART flow in this case: - -DART flow with netCDF files
- -Within a single time step, DART will use the *filter* program to run the -"Assimilate" portion of the above diagram and/or the "diagnostics" as follows: - -OSE workflow with separate files
- -The single time-step workflow for an OSE experiment within a single step is -slightly simpler than the OSSE equivalent as DART handles less of the process. -Like the OSSE case, the namelist and preprocessed observation source files are -input to all other DART programs. In the OSE case, however, the user must -provide an **obs converter** that will output a `obs_seq.out` file. There are -many DART utilities to make this process easier, but for the OSE case the -`obs_seq.out` file is ultimately the user's responsibility (to avoid -duplicating effort, see the list of existing observation types in -[Important capabilities of DART](#dartCapabilities)). Here, the option to -run with one file for each ensemble member is demonstrated. There are *k* -ensemble members used as input to *filter*, which also outputs *k* members for -the prior and posterior. The `obs_seq.final` and `obs_diag_output.nc` are used -in the same way as in the OSSE case. The names of the input files and output -files can be controlled by the user through the `filter_input_list.txt` and -`filter_output_list.txt` files, which can contain the user-specified list of -the ensemble input or output files, respectively. - -Another view of the stages of filter is shown in the following diagram: - -Filter stages
- -As shown here, an ensemble forecast is stored in `forecast.nc` , -to which prior inflation can be applied and stored in `preassim.nc`. Once -assimilation is applied, the output can be stored in `postassim.nc`, and -finally if posterior inflation is applied, the final analysis can be written in -`analysis.nc` . The model forecast will start from the analysis to advance the -model in order to start the cycle over again. - -:dart: *NOTE*: the "forecast" will be the same as the "preassim" -if prior inflation is not used, and the "postassim" will be the same -as the "analysis" if posterior inflation is not used. -The *stages_to_write* variable in the "&filter_nml" -section of the `input.nml` namelist controls which stages are output to file. -For a multi-file case, the potential *stages_to_write* are "input, forecast, -preassim, postassim, analysis, output" while for a single file the same stages -are available with the exception of "input." - -:dart: *NOTE*: in the above cycling diagram, there will actually be one file per -member, which is not shown here in order to simplify the process. - -:exclamation: *IMPORTANT*: the decision to store ensemble members as separate -files and whether to run an OSSE or OSE are independent. An OSSE can be run -with multiple files and an OSE can be run with all ensemble members stored in -a single file. - - [](#dartDesign) - -### DART's design philosophy - -In this section we cover DART's design philosophy. Understanding this philosophy -will make it easier to get started with DART, as you will quickly be able to -predict how and where to find a particular feature of DART. - -The main design goals of DART are to: - -1. Create a system that is **coherent** and **easy to understand**. DART is carefully engineered to have self-contained programs that each do one job and do it well. Likewise, DART *just does DA, and does it well.* -2. Release source code that is **as compatible as possible** with the widest possible number of systems. The code is written in Fortran 90, which is one of the lowest possible common denominators available on virtually all systems. See the section [Why Fortran?](#whyFortran) if this seems like a questionable decision to you in this modern world of Matlab, C++, Java, Python, Go, etc. -3. Strive to **limit library dependencies**. There is only one required dependency of DART: netCDF. Many modern systems have 10s or 100s of dependencies, each of which introduces complexity and the potential for bugs, lack of support, broken backwards compatibility, etc. If you've ever been frustrated struggling to debug relationships to packages you've never even heard of, you are likely to appreciate this DART design goal. Of course, there is nothing to stop you from using whatever dependencies you require, for example, to collect observations for the `obs_seq.out` in an OSE case, but DART by design will remain separate from that dependency for you and all other users. -4. Only **compile the code you need**. If you are only using a single model for your experiments, there is no reason to compile or even touch code for another model you never plan to use. Likewise, if you are not using a particular observation operator in your experiment, there is also no need to compile it or let it cause you headaches. DART recognizes this fact, and through the use of the *mkmf* utility and the *preprocess* program, only what you need will ever be compiled. -5. Use **explicit interfaces** to enforce contract programming. In practice this means that it is easy to add new models, observations operators, data assimilation algorithms, etc. as long as they can implement the required interface. This approach *allows all of the benefits of object-oriented programming without the added complexity for the end user.* -6. Provide results that are **reliable** and meaningful. The DART algorithms are carefully tested and maintained in order to be quickly published along with appropriate analysis. In a world of chaos, being able to quantify and shrink forecast uncertainty via data assimilation in a reliable way is a valuable tool for research and operations and everything in between. - -In short, DART is designed at each step to make it as easy as possible for -users to get up and running with their models, observations, and possibly even -data assimilation algorithm advances. - - [](#whyFortran) - -#### Why Fortran? - -Many users new to scientific computing such as graduate students raise their -eyebrows when they first hear that a program uses Fortran for active -development. Fortran is considered by many outside (and some inside) of the -scientific computing community to be a dinosaur, old and decrepit, and not -worthy of serious attention. However, this view is short-sighted. There is a -Chinese idiom 喜新厭舊, which means "to love the new and loathe the old," -indicating that just because something is old does not automatically make it -bad. - -While Fortran does have some outdated features that are far removed from the -mainstream of software engineering (such as implicit typing by first initial of -the variable), these can all be disabled, and the stylistic rules for -easy-to-read, modern Fortran are always followed by DART. On the other hand, -Fortran has many other attractive features that make it a top choice for modern -scientific computing. In particular, Fortran offers vectorization of matrices -that make it possible to operate on entire elements of an array at once or -perform linear algebra operations on multi-dimensional arrays. With or without -the use of the *colon operator* (:), Fortran multi-dimensional array support -makes mathematical algorithms easier to read than the equivalent code written -in many other languages. This highly intuitive Fortran syntax was adopted by -Matlab, NumPy, and other languages. Furthermore, for parallel programs using -distributed memory in *MPI*, Fortran remains a top choice along with C and C++ -when considering performance. Python code, for example, remains difficult to -parallelize via MPI, not to mention the difficulties in supporting -Python 2, Python 3, pip, anaconda, virtualenv, ... - -Altogether, for large mathematically-oriented programs that need to be -parallel, Fortran remains a top choice, especially considering the needs of -DART: - -1. DART does data assimilation, which is primarily mathematically-oriented operations on large data sets. -2. DART needs to be parallel with MPI to run on modern supercomputers. -3. Many users of DART are not software development professionals and appreciate straightforward and easily understandable code. -4. DART source distributions should be easy to compile and run reliably on many -different systems. In practice this means avoiding software features that might -not be supported on all compilers or systems. - -With these considerations in mind, the choice of Fortran for DART development -is clear. DART remains highly successful by keeping things simple and *not -fixing what is not broken* even if it isn't shiny and new. - - [](#dartCapabilities) - -### Important capabilities of DART - -In this section we discuss the capabilities of DART that may be of interest to -the user. This is a partial list of all of the functionality that is available -in DART, and additional capabilities and improvements are continually being -added. - -As mentioned above, DART allows for both OSSE and OSE systems of models large -and small. This allows users to test both theoretical limits of DA, models, and -observations with idealized experiments as well as to improve actual real-world -forecasts of chaotic systems with real observations. - -#### Models supported by DART - -A full list of models can be found [here](Models.md), but in brief the -models supported by DART include: - -| Model | Latest version | Model | Latest version | -| :------- | :---------- | :------- | :---------- | -| lorenz_63 | Manhattan | lorenz_84 | Manhattan | -| lorenz_96 | Manhattan | lorenz_96_2scale | Manhattan | -| lorenz_04 | Manhattan | simple_advection | Manhattan | -| bgrid_solo | Manhattan | WRF | Manhattan | -| MPAS | Manhattan | ATM | Manhattan | -| ROMS | Manhattan | CESM | Manhattan | -| CAM-FV | Manhattan | CAM-CHEM | Manhattan | -| WACCM | Manhattan | WACCM-X | Manhattan | -| CICE | Manhattan | CM1 | Manhattan | -| FESOM | Manhattan | NOAH-MP | Manhattan | -| WRF-Hydro | Manhattan | GCCOM | Lanai | -| LMDZ | Lanai | MITgcm_ocean | Lanai | -| NAAPS | Lanai | AM2 | Lanai | -| CAM-SE | Lanai | CLM | Lanai | -| COAMPS | Lanai | COSMO | Lanai | -| Dynamo | Lanai | GITM | Lanai | -| Ikeda | Lanai | JULES | Lanai | -| MPAS_ocean | Lanai | null_model | Lanai | -| openggcm | Lanai | PARFLOW | Lanai | -| sqg | Lanai | TIE-GCM | Lanai | -| WRF-CHEM | Lanai | ECHAM | Prior to Lanai| -| PBL_1d | Prior to Lanai| MITgcm_annulus | Prior to Lanai| -| forced_barot | Prior to Lanai| pe2lyr | Prior to Lanai| -| ROSE | Prior to Lanai| CABLE | Prior to Lanai| - -The models listed as "Prior to Lanai" will take some additional work to -integrate with a supported version of DART; please contact the dart @ ucar.edu -team for more information. The versions listed as "Lanai" will be ported to the -Manhattan version of DART depending on the needs of the user community as well -as the availablity of resources on the DART team. - - [](#obsConverters) - -#### Observation converters provided by DART - -Given a way to compute the expected observation value from the model -state, in theory any and all observations can be assimilated by DART through the -`obs_seq.out` file. In practice this means a user-defined observation converter -is required. DART provides many observation converters to make this process -easier for the user. Under the directory `DART/observations/obs_converters` -there are multiple subdirectories, each of which has at least one observation -converter. The list of these directories is as follows: - -| Observation | Directory | Format | -| :------- | :---------- | :------ | -| [Atmospheric Infrared Sounder](https://airs.jpl.nasa.gov/) satellite retrievals | AIRS | HDF-EOS | -| [Aviso](https://www.aviso.altimetry.fr/en/home.html): satellite derived sea surface height | Aviso | netCDF | -| Level 4 Flux Tower data from [AmeriFlux](http://ameriflux.lbl.gov) | Ameriflux | Comma-separated text | -| Level 2 soil moisture from [COSMOS](http://cosmos.hwr.arizona.edu/) | COSMOS | Fixed-width text | -| Doppler wind lidar | DWL | ASCII text | -| GPS retrievals of precipitable water | GPSPW | netCDF | -| GSI observation file | GSI2DART | Fortran binary | -| Global Temperature-Salinity Profile Program ([GTSPP](http://www.nodc.noaa.gov/GTSPP/index.html)) | GTSPP | netCDF | -| Meteorological Assimilation Data Ingest System ([MADIS](http://madis.noaa.gov/)) | MADIS | netCDF | -| [MIDAS](https://www.sciencedirect.com/science/article/pii/S0273117712001135) ionospheric obs | MIDAS | netCDF | -| [MODIS](https://modis.gsfc.nasa.gov/) satellite retrievals | MODIS | Comma-separated text | -| [NCEP PREPBUFR](https://www.emc.ncep.noaa.gov/mmb/data_processing/prepbufr.doc/document.htm) | NCEP/prep_bufr | PREPBUFR | -| NCEP ASCII observations | NCEP/ascii_to_obs | NCEP text files | -| [ROMS](https://www.myroms.org/) verification observations | ROMS | netCDF | -| Satellite winds from [SSEC](https://www.ssec.wisc.edu/data/) | SSEC | ASCII text | -| Sea surface temperature | SST | netCDF | -| Special Sensor Ultraviolet Spectrographic Imager ([SSUSI](https://ssusi.jhuapl.edu/)) retrievals | SSUSI | netCDF | -| World Ocean Database ([WOD](http://www.nodc.noaa.gov/OC5/WOD09/pr_wod09.html)) | WOD | World Ocean Database packed ASCII | -| [National Snow and Ice Data Center](http://nsidc.org/) sea ice obs | cice | Binary sea ice | -| VTEC [Madrigal](http://millstonehill.haystack.mit.edu/) upper atmospheric obs | gnd_gps_vtec | ASCII text | -| GPS obs from [COSMIC](http://www.cosmic.ucar.edu) | gps | netCDF | -| Oklahoma [Mesonet](http://www.mesonet.org/) MDF obs | ok_mesonet | Oklahoma Mesonet MDF files | -| [QuikSCAT](http://winds.jpl.nasa.gov/missions/quikscat/index.cfm) scatterometer winds | quikscat | HDF 4 | -| Radar reflectivity/radial velocity obs | Radar | WSR-88D (NEXRAD) | -| [MODIS Snowcover Fraction](https://modis.gsfc.nasa.gov/data/dataprod/mod10.php) obs | snow | General text | -| Text file (e.g. spreadsheet) obs | Text | General text | -| Total precipitable water from AQUA | tpw | HDF-EOS | -| Automated Tropical Cyclone Forecast ([ATCF](https://www.nrlmry.navy.mil/atcf_web/)) obs | Tropical Cyclones | Fixed width text | -| [LITTLE_R](http://www2.mmm.ucar.edu/mm5/On-Line-Tutorial/little_r/little_r.html) obs | var | little-r | -| [MM5](http://www2.mmm.ucar.edu/mm5/) 3D-VAR radar obs | var | MM5 3D-VAR 2.0 Radar data files | - -#### Data assimilation algorithms available in DART - -DART allows users to test the impact of using multiple different types of -algorithms for filtering, inflation/deflation, and covariance localization. - -DART offers numerous **filter algorithms**. These determine -how the posterior distribution is updated based on the observations and the -prior ensemble. The following table lists the filters supported in DART along with their type (set by *filter_kind* in `input.nml` under the "assim_tools_nml" section): - -| Filter # | Filter Name | References | -| :------- | :---------- | :---------- | -| 1 | EAKF (Ensemble Adjustment Kalman Filter) | **Anderson, J. L.**, 2001. An Ensemble Adjustment Kalman Filter for Data Assimilation. *Monthly Weather Review*, 129, 2884-2903. https://doi.org/10.1175/1520-0493%282001%29129%3C2884%3AAEAKFF%3E2.0.CO%3B2.

**Anderson, J. L.**, 2003. A local least squares framework for ensemble filtering. *Monthly Weather Review*, 131, 634-642. https://doi.org/10.1175/1520-0493%282003%29131%3C0634%3AALLSFF%3E2.0.CO%3B2

**Anderson, J., Collins, N.**, 2007: Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation. *Journal of Atmospheric and Oceanic Technology*, 24, 1452-1463. https://doi.org/10.1175/JTECH2049.1 | -| 2 | ENKF (Ensemble Kalman Filter) | **Evensen, G.**, 2003. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation. *Ocean Dynamics*. 53(4), 343–367. https://doi.org/10.1007%2Fs10236-003-0036-9 | -| 3 | Kernel filter | | -| 4 | Observation Space Particle filter | | -| 5 | Random draw from posterior | None.
:exclamation: *IMPORTANT*: (contact dart @ ucar.edu before using) | -| 6 | Deterministic draw from posterior with fixed kurtosis | None.
:exclamation: *IMPORTANT*: (contact dart @ ucar.edu before using) | -| 7 | Boxcar kernel filter | | -| 8 | Rank Histogram filter | **Anderson, J. L.,** 2010. A Non-Gaussian Ensemble Filter Update for Data Assimilation. *Monthly Weather Review*, 139, 4186-4198. https://doi.org/10.1175/2010MWR3253.1 | -| 9 | Particle filter | **Poterjoy, J.**, 2016. A localized particle filter for high-dimensional nonlinear systems. *Monthly Weather Review*, 144 59-76. https://doi.org/10.1175/MWR-D-15-0163.1 | - -DART also has several **inflation algorithms** available for both prior (the -first value in the namelist) and posterior (the second value in the namelist). -The following table lists the inflation "flavors" supported in DART along with -their type number (set by *inf_flavor* in `input.nml` under the "filter_nml" section): - -| Flavor # | Inflation flavor name | References | -| :------- | :---------- | :---------- | -| 0 | No inflation | n/a | -| 1 | (Not Supported) | n/a | -| 2 | Spatially-varying state-space (Gaussian) | **Anderson, J. L.**, 2009.
 Spatially and temporally varying adaptive covariance inflation for ensemble filters.
 *Tellus A*, 61, 72-83, https://doi.org/10.1111/j.1600-0870.2008.00361.x | -| 3 | Spatially-fixed state-space (Gaussian) | **Anderson, J. L.**, 2007. An adaptive covariance inflation error correction algorithm for ensemble filters.
 *Tellus A*, 59, 210-224, https://doi.org/10.1111/j.1600-0870.2006.00216.x | -| 4 | Relaxation to prior spread (posterior inflation only) | **Whitaker, J.S. and T.M. Hamill**, 2012. Evaluating Methods to Account for System Errors in Ensemble Data Assimilation. *Monthly Weather Review*, 140, 3078–3089, https://doi.org/10.1175/MWR-D-11-00276.1 | -| 5 | Enhanced spatially-varying state-space (inverse gamma) | **El Gharamti M.**, 2018.
 Enhanced Adaptive Inflation Algorithm for Ensemble Filters. *
Monthly Weather Review*, 2, 623-640, https://doi.org/10.1175/MWR-D-17-0187.1 | - -DART also offers the ability to correct for sampling errors. DART's -localization and sampling error correction algorithm is described in -> > **Anderson, J.L.**, 2012. Localization and Sampling Error Correction in -Ensemble Kalman Filter Data Assimilation. *Monthly Weather Review*, 140, -2359–2371. https://doi.org/10.1175/MWR-D-11-00013.1 - -This behavior can be turned on or off via the *sampling_error_correction* in `input.nml` under the "assim_tools_nml" section. The following covariance localization options are available (set by *select_localization* in -`input.nml` under the "cov_cutoff_nml" section): - -| Loc # | Localization type | References | -| :------- | :---------- | :---------- | -| 1 | Gaspari-Cohn eq. 4.10 | **Gaspari, G. and Cohn, S. E.**, 1999. Construction of correlation functions in two and three dimensions. *Quarterly Journal of the Royal Meteorological Society*, 125, 723-757. https://doi.org/10.1002/qj.49712555417 | -| 2 | Boxcar | None | -| 3 | Ramped boxcar | None | - -The following image depicts all three of these options: -localization - - [](#citeDart) - -### How to cite DART - -In order to cite DART, it is appropriate to cite the DART code itself, which -has a doi number, as: The Data Assimilation Research Testbed (Version X.Y.Z) [Software]. (2019). Boulder, Colorado: UCAR/NCAR/CISL/DAReS. http://doi.org/10.5065/D6WQ0202 - -:exclamation: *IMPORTANT*: Update the DART version and year as appropriate. - -The seminal reference for DART is: - -> > **Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn and A. Arellano**, 2009. The Data Assimilation Research Testbed: A Community Facility. *Bulletin of the American Meteorological Society*, 90, 1283-1296, https://doi.org/10.1175/2009BAMS2618.1 - -You should also consider citing the algorithm(s) you are using. The citations -for algorithms are available in -[Important capabilities of DART](#dartCapabilities). - ----- - - [](#RunWithMyModel) - -## How do I run DART with my model? - -\[[top](#top)\] - -In this section, we describe how to add a model to DART. Essentially, this -comes down to implementing a list of required subroutines or using the default -behaviors if appropriate. To cycle more complex models, additional scripting -may be required, especially if the model is run on a supercomputing queueing -system. Refer to your model's documentation for more information if -necessary. - -The following sections cover how to run DART with your model (simple or -complex): - -1. [Required `model_mod` routines](#requiredRoutines) -2. [Suggestions for a "simple" model](#simpleModel) -3. [Suggestions for a "complex" model](#complexModel) -4. [How to test your `model_mod` routines](#howToTestModelMod) - - [](#requiredRoutines) - -### Required `model_mod` routines - -There are 18 Fortran subroutines necessary to implement in -order to successfully integrate a model in DART. You will place these routines -in your `model_mod.f90` in a subdirectory with the name of your model in -`DART/models`. There is often a sensible default implementation that can be -used for each of these routines. For example, in the case of a model that -starts at a time of "0", for the required routine **init_time()** the following -code will use this default implementation: - -~~~ -use default_model_mod, only : init_time -~~~ -As in all Fortran programs, a comma-separated list of routines can be listed -after the colon. - -:exclamation: *IMPORTANT*: do not "use" the entire module without the -keyword "only" in order to avoid including the default behavior for all -subroutines contained in that module (in this example `default_model_mod`). - -The following table lists each of the 18 routines, their default modules -relative to `DART`, and the default behavior. If the default behavior is -not desired, see the section [How to add a "simple" model](#simpleModel) for a -model that DART can advance, or [How to add a "complex" model](#complexModel) -for a model that is advanced externally from DART. - -| Routine # / name | Purpose | Default module / directory | Default behavior | -| :---------- | :------------------------------ | :-------------------------------- | :------------------- | -| 1. **init_time()** | Set the *initial time* if not read from the restart file. | `default_model_mod` / `models/utilities` | Sets the initial time to 0 days, 0 seconds | -| 2. **init_conditions()** | For a "cold start" fill in an empty state vector with *initial conditions*. Many models cannot just make up values from thin air and thus choose to fail when this is requested. | `default_model_mod` / `models/utilities` | Sets the initial state to 0. To fail use `init_conditions => fail_init_conditions`. | -| 3. **get_model_size()** | Return the *number of items in the state vector*. | `default_model_mod` / `models/utilities` | Returns 1; i.e. there is only one item in the state. | -| 4. **static_init_model()** | *Initialize* DART with information about the model that will be used by the remaining `model_mod` routines. The procedure for doing this will depend on how complex the model is; see below for suggestions for implementation. | `default_model_mod` / `models/utilities` | Does nothing. | -| 5. **get_state_ meta_data()** | Takes an index into the state vector and returns the *location* corresponding to that value and optionally the *variable type*. See below for suggestions on implementation. | `default_model_mod` / `models/utilities` | Sets a missing location and the default variable type. | -| 6. **end_model()** | *Deallocate* any arrays allocated in **static_init_model()**. | `default_model_mod` / `models/utilities` | Does nothing. | -| 7. **adv_1step()** | If possible, *advance the model* state from one time to another. Complex models will be unable to implement this method and should fail. | `default_model_mod` / `models/utilities` | Call the error handler with the message "unable to advance model". | -| 8. **shortest_time_ between_assimilations()** | Return a namelist or a fixed value for the *minimum model advance time* between assimilations. Note that complex models will handle advancing the time externally. | `default_model_mod` / `models/utilities` | Returns a time period of 1 day. | -| 9. **model_interpolate()** | *Interpolate* a requested quantity to the given location to get an array of expected values for all ensemble members.
*NOTE*: this is often the most time consuming method to implement. | `default_model_mod` / `models/utilities` | Fail and set the expected observation to "missing." | -| 10. **nc_write_ model_atts()** | Add any *additional information* to the netCDF output diagnostic files. *NOTE*: the state will already be output by other routines, so this method should not create or write the state variables. | `default_model_mod` / `models/utilities` | Does nothing. | -| 11. **read_model_time()** | *Read* the model time from a state vector netCDF file. | `dart_time_io` / `assimilation_code/io/utilities` | Attempt to read the "time" variable from a state file in an intelligent way. | -| 12. **write_model_time()** | *Write* the model time to a state vector netCDF file. | `dart_time_io` / `assimilation_code/io/utilities` | Write the "time" variable from the file according to the DART calendar. | -| 13. **pert_model_copies()** | *Perturb* a state vector in order to create an ensemble. | `default_model_mod` / `models/utilities` | Add Gaussian noise with a specified amplitude to all parts of the state vector. | -| 14. **convert_ vertical_obs()** | Some 3D models have multiple vertical coordinates (e.g. pressure, height, or model level); this method *converts observations* between different vertical coordinate systems. | `location_mod/` `assimilation_code/` `location/XXX` | Do no conversion.
*NOTE*: the particular sub-directory of `location` to use is set in `path_names_` for each DART program. | -| 15. **convert_ vertical_state()** | Some 3D models have multiple vertical coordinates (e.g. pressure, height, or model level); this method *converts state* between different vertical coordinate systems. | `location_mod/` `assimilation_code/` `location/XXX` | Do no conversion.
*NOTE*: the particular sub-directory of `location` to use is set in `path_names_` for each DART program. | -| 16. **get_close_obs()** | Calculate *which observations are "close"* to a given location and, optionally, the distance. This is used for localization to reduce sampling error. | `location_mod/` `assimilation_code/` `location/XXX` | Uses the default behavior for determining distance.
*NOTE*: the particular sub-directory of `location` to use is set in `path_names_` for each DART program. | -| 17. **get_close_state()** | Calculate *which state points are "close"* to a given location and, optionally, the distance. This is used for localization to reduce sampling error. | `location_mod/` `assimilation_code/` `location/XXX` | Uses the default behavior for determining distance.
*NOTE*: the particular sub-directory of `location` to use is set in `path_names_` for each DART program. | -| 18. **nc_write_ model_vars()** | This method is not currently called, so just use the default routine for now. This method will be used in a future implementation. | `default_model_mod` / `models/utilities` | Does nothing. | - - [](#simpleModel) - -### Suggestions for a "simple" model - -A "simple" model is one where DART can advance the model through a function -call. As we saw above, Lorenz 63 falls into this category and can be used -as a reference. Here we provide some further advice on how to add this kind -of model to DART. - -The primary consideration with a simple model is how you will store the -state. If you have only a *single type of variable* in your state vector -(for example, the Lorenz 63 model), here are some hints on how to implement -your initialization and meta data routines: - -| Routine # / name | Suggested implementation | -| :---------- | :------------------------------ | -| 4. **static_init_ model()** | Your model_size will likely be set by namelist, so read it, allocate an array of that size, and precompute all the locations for each state vector item. Call **add_domain()** with the model size so DART knows how long the state vector is. | -| 5. **get_state_ meta_data()** | Return `QTY_STATE_VARIABLE` as the quantity, and return the location for that index through a look-up into the location array created during **static_init_ model()**. - -If you have *more than a single type of variable* in the state vector (for -example, "concentration", "wind", etc. as in the -`DART/models/simple_advection` model): - -| Routine # / name | Suggested implementation | -| :---------- | :------------------------------ | -| 4. **static_init_ model()** | Read from the namelist the number of fields to be used in the state vector. Use **add_domain()** to indicate which netCDF vars should be read. Read in any auxiliary data needed by interpolation code (for example, the grid topology). Cache the grid locations of the state variables as appropriate, and use **get_domain_size()** to compute the model_size. | -| 5. **get_state_ meta_data()** | Call **get_model_variable_indices()** and **get_state_kind()** to figure out the (*i*,*j*,*k*) indices and which variable this offset is. Use the (*i*,*j*,*k*) index to compute the grid location and return it along with the quantity. | - -Now, for either type of simple model, the following applies: - -| Routine # / name | Suggested implementation | -| :---------- | :------------------------------ | -| 6. **end_model()** | Deallocate any arrays allocated in **static_init_model()** | -| 7. **adv_1step()** | If possible, embed the code that computes **x**(*t*+1) = **F**(**x**(*t*)) or call a separate subroutine to advance the model state from one time to another. | -| 8. **shortest_time_ between_assimilations()** | Return a namelist or a fixed value for the minimum model advance time. | -| 9. **model_interpolate()** | Find the indices which enclose that location and interpolate to get an array of expected values. | -| 10. **nc_write_model_atts()** | Optionally add any desired attributes to the output diagnostic files. | - -The remaining routines can mostly use the defaults, except possibly for 11. **read_model_time()** and 12. **write_model_time()**, which may need to be customized -if using a model restart file that already stores time in a particular format. - -Note that there is often no need to convert vertical obs or states in a simple -model without vertical coordinate choices. - - [](#complexModel) - -### Suggestions for a "complex" model - -A "complex" model is typically a large geophysical model where the model must -be advanced outside of DART execution control. Here we provide some advice on -how to integrate this kind of model with DART. - -First of all, the 4. **static_init_model**, 5. **get_state_meta_data()** and -6. **end_model()** suggestions will match the multiple state variable in the -previous section as complex models will typically have multiple fields. - -An additional twist is that complex models may have different grid locations -for different variables, (i.e. grid staggering), but the above instructions -still apply. - -The 7. **adv_1step()** method for a complex model should fail, so the default -behavior is sufficient. - -The advice for the 8. **shortest_time_between_assimilations()** routine is -similar to the advice for a simple model: read the value from the namelist or -return a fixed time as appropriate. - -:dart: *NOTE*: since the model will not be advanced by DART, the value returned -here is irrelevant except for user information purposes. - -For the remaining routines, we give the following implementation suggestions: - -| Routine # / name | Suggested implementation | -| :---------- | :------------------------------ | -| 9. **model_ interpolate()** | Find the (*i*,*j*,*k*) indices which enclose that location, or search for the cell number. For some models you can compute (*i*,*j*) directly from a regular lat/lon grid, and in others you may have to search over a deformed grid. Any model code or utilities available for this purpose may prove very helpful as a starting point. In the end, you will use **get_state()** to retrieve an ensemble-sized array of values for each offset into the state vector, and then do interpolation to get an array of expected values. | -| 10. **nc_write_ model_atts()** | It is very helpful (but optional) to add grid information to assist in plotting your results. | -| 11. **read_model_ time()** | (see **write_model_time()** below) | -| 12. **write_model_ time()** | If the model time is stored in the netCDF files, supply routines that can read and write it in the correct format. The default routines will work if the model time matches what those routines expect: a time variable with an optional calendar variable. If no calendar is provided, the routine assumes fractional days. If the time variable is an array (i.e. more than one time step is stored in the file), read/write the last one. | -| 13. **pert_model_ copies()** | The default of adding Gaussian noise to all state variables may be undesirable. Complex models often have a method to perturb a state according to a particular formula or method. Otherwise, it may be necessary to perturb each variable with separate noise levels, only perturb certain variables, etc. | -| 14. **convert_ vertical_obs()** | (see **convert_vertical_state()** below)| -| 15. **convert_ vertical_state()** | Add code to convert between vertical coordinates (e.g. pressure, height, sigma levels, etc.) if appropriate. Code from the model or a model utility may be a very helpful starting point. | -| 16. **get_close_ obs()** | (see **get_close_state()** below) | -| 17. **get_close_ state()** | If you want to change the localization impact based on something other than the type or kind, put code here. You should test for vertical type and do the conversion on demand if it hasn't already been done. | - -As mentioned above, the most difficult routine to implement for a complex model -is typically 9. **model_interpolate()**. - - [](#howToTestModelMod) - -### How to test your `model_mod` routines - -The program `model_mod_check.f90` can be used to test the routines individually -before running them with *filter*. Add a `mkmf_model_mod_check` and -`path_names_model_mod_check` to your `DART/models/your_model/work` -subdirectory. You might find it helpful to consult another model matching your -model type (simple or complex). See the documentation for `model_mod_check` in -`DART/assimilation_code/programs/model_mod_check` for more information on -the tests available. - ----- - - [](#RunWithMyObs) - -## How do I add my observations to DART? - -\[[top](#top)\] - -First, you should understand that DART already supports a tremendous variety of -observations. To fully support an observation means that the observation can be -converted from its native format to the DART observation sequence format and that -the observation forward operator is already implemented. Keep in mind that -forward operators are not specific to any one model. - -The observation converters are in the *observations/obs_converter* directory -and you should look there for the documentation describing which converters -are available. - -The forward operators are functionally or logically grouped into Fortran -modules in the *observations/forward_operator* directory. DART employs a 'contractual' -style of programming in that the forward operator requests information from the -model, and if the model cannot provide it, the forward operator may request different -information in an attempt to collect the information needed to apply the operator. -If the model cannot provide any of the required information, the forward operator fails, -the DART QC for that observation is set to the appropriate value, and the program continues. - -1. [Background on DART observations](#obsBackground) -1. [Adding new observation *quantities*](#newQUANTITIES) -1. [Adding new observation *types*](#newTYPES) -1. ['Simple' observation support](#simpleObs) -1. [More complex observations](#complexObs) -1. [Example observation definition](#exampleObsDef) -1. [Observation sequence file](#obsSeqFile) - - [](#obsBackground) -### Background on DART observations - -The Iceland release of DART added a powerful and flexible mechanism to generate -the core observation operator code using the *preprocess* program. -The use of the *preprocess* program was covered above in -[Building the Lorenz_63 DART project](#building) and -[High-level DA workflows in DART](#dartWorkflow), so it is assumed you are -familiar with the basic workings of the *preprocess* program before continuing. - -To add your own observation support, you will need to create an *observation -definition*. The DART Fortran90 derived type *obs_def* provides an abstraction -of the definition of an observation. At a higher level, an *observation -sequence* (*obs_seq*) is composed of observation definitions associated with -observed values. The basic operations required to implement an observation -definition are an ability to compute a forward operator given the model state -vector, the ability to read/write the observation definition from/to a file, -and a capability to do a standard input driven interactive definition of the -observation definition. - -DART makes a further distinction between specific *observation types* and -generic *quantities*. Essentially, an *observation type* can be considered to be -instrument-specific and the *quantity* is something you learned about in physics. -As an example, 'RADIOSONDE_TEMPERATURE' is an *observation type* of the -fundamental *quantity* 'TEMPERATURE'. The role of the various obs_def input files -is to define the mapping between the types and quantities, and optionally to -provide type-specific processing routines. Specifying many observation types allows DART -to be able to evaluate some observations and assimilate others - even if the instruments -measure the same *quantity*. - -A single obs_def output module is created by the program *preprocess* from two -kinds of input files. First, a DEFAULT obs_def module (normally called -`DEFAULT_obs_def_mod.F90` and documented in the -`DART/observations/forward_operators` directory) is used as a template into -which the preprocessor incorporates information from zero or more special -obs_def modules (for example, `obs_def_1d_state_mod.f90` or -`obs_def_reanalysis_bufr_mod.f90`) as documented in the -`DART/observations/forward_operators` directory. If no special obs_def -files are included in the preprocessor namelist, a minimal `obs_def_mod.f90` is -created which can only support identity forward observation operators. - - [](#newQUANTITIES) -#### Adding a new observation *quantity* - -The `DART/assimilation_code/modules/observations/DEFAULT_obs_kind_mod.F90` file -must be edited to include a unique integer identifier for the quantity. -There are two, sometimes three places in the file that require modification. -The first is in the table of existing NAME-value pairs and the second is perhaps -halfway down the file in the section after: - -~~~ -! DART PREPROCESS OBS_QTY_INFO INSERTED HERE -~~~ - -If you add an observation quantity with an integer value greater than **max_defined_quantities**, -make sure you increase *max_defined_quantities*. - - [](#newTYPES) -#### Adding a new observation *type* - -The observation support modules are in the `DART/observations/forward_operators` directory. -If the observation type logically belongs in an existing observation module, it should be -fairly obvious how to add support for a new observation type. Feel free to use ideas from other -modules - look around! - -To add a new observation *type* which does not fit into any of the already-defined -obs_def files, a new file should be created in the -`DART/observations/forward_operators` directory. -These files are usually named according the the pattern `obs_def_X_mod.f90`, -where the *X* is either an instrument name, a data source, or a class of -observations. See the existing filenames in that directory for ideas. This new -obs_def filename must then be listed in the `input.nml` namelist for the model -in the *&preprocess_nml* section in the *input_files* namelist variable. This -variable is a string list type which can contain multiple obs_def filenames. -Running the preprocess program will then use the contents of the new obs_def -file to generate the needed output files for use in linking to the rest of the DART system. -*If the new observation type also requires a new observation quantity, there is -more work to be done.* Please see the list of available observation quantities in -`assimilation_code/modules/observations/DEFAULT_obs_kind_mod.f90` to determine -if an existing observation quantity is appropriate. - - [](#simpleObs) -### 'Simple' observation support - -If the new observation type can be directly interpolated by the *model_mod* -**model_interpolate()** interpolation routine (see [Required `model_mod` routines](#requiredRoutines) for more information) and has no additional -observation-specific code for reading, writing, or initializing the -observation, then the entire contents of the new file is: - -~~~ -! BEGIN DART PREPROCESS KIND LIST -! type, quantity, COMMON_CODE -! (repeat lines for each type) -! END DART PREPROCESS KIND LIST -~~~ - -DART will automatically generate all the interface code needed for these new -observation types through *preprocess*. For example, here is a real list of -observation types: - -~~~ -! BEGIN DART PREPROCESS KIND LIST -!VELOCITY, QTY_VELOCITY, COMMON_CODE -!TRACER_CONCENTRATION, QTY_TRACER_CONCENTRATION, COMMON_CODE -!TRACER_SOURCE, QTY_TRACER_SOURCE, COMMON_CODE -!MEAN_SOURCE, QTY_MEAN_SOURCE, COMMON_CODE -!OI_SEA_SURFACE_TEMPERATURE, QTY_TEMPERATURE, COMMON_CODE -! END DART PREPROCESS KIND LIST -~~~ - -The first column is the specific observation type and should be unique. The -second column is the generic quantity and must match the list of known -quantities in `obs_kind_mod.f90`. The first and second column often match, but -as shown in the last line this need not be the case. The third column must be -the keyword COMMON_CODE which tells the preprocess program to automatically -generate all necessary interface code for this type. COMMON_CODE also implicitly -means the "model_interpolate()" routine is used as the forward operator. - - - [](#complexObs) -### Obs definitions for observations needing special handling - -For observation types which have observation-specific routines, or must -interpolate using a combination of other generic quantities, or require -additional observation-specific data to compute, the following format is used: - -~~~ -! BEGIN DART PREPROCESS KIND LIST -! type, quantity -! (repeat lines for each type/quantity pair) -! END DART PREPROCESS KIND LIST -~~~ - -DART will need user-supplied interface code for each of the listed types. For -example, here is a real list without "COMMON_CODE" in the third column: - -~~~ -! BEGIN DART PREPROCESS KIND LIST -! DOPPLER_RADIAL_VELOCITY, QTY_VELOCITY -! RADAR_REFLECTIVITY, QTY_RADAR_REFLECTIVITY -! END DART PREPROCESS KIND LIST -~~~ - -:dart: *NOTE*: the difference between the simple obs_def case is the lack of -COMMON_CODE in the third column. - -:dart: *NOTE*: if interested, you can reference the `obs_def_wind_speed_mod.f90` -"get_expected_wind_speed()" function for an example of a non-COMMON_CODE -observation type. The operator is in the `DART/observations/forward_operators` -directory. - -In the non-COMMON_CODE case, DART needs additional information for how to process -the observation type. This will include code sections delimited by precisely formatted -comments, and possibly module code sections. This code will be combined by *preprocess* to -make a single observation definition file with only the desired observations. -There are six such locations within `obs_def_XXX.f90` where a new forward -operator should make changes to meet the interface of obs_def. - -:exclamation: *IMPORTANT:* as the code in these six comment sections will be -automatically combined with the code of other observation definitions, the -majority of the forward operator code should be defined inside a separate -module to keep variables and other private subroutines from colliding with -unrelated routines and variables in other forward operator files. In other -words, the code in the sections below should primarily call procedures from a -separate module you define. - -There are six sections of code inserted into the `DEFAULT_obs_def_mod` for -each of the obs_def modules that are requested. These are: - -#### 1. Use statements - -*preprocess* automatically collects and combines the *use* statements in all -observations definitions through the following comment block: - -~~~ -! BEGIN DART PREPROCESS USE OF SPECIAL OBS_DEF MODULE -! END DART PREPROCESS USE OF SPECIAL OBS_DEF MODULE -~~~ - -Any Fortran use statements for public subroutines or variables from other -modules should be placed between these lines, with comment characters in the -first column. - -For example, if the forward operator code includes a module with public -routines then a "use" statement like: - -~~~ - use obs_def_1d_state_mod, only : write_1d_integral, read_1d_integral, & - interactive_1d_integral, get_expected_1d_integral -~~~ - -needs to be added to your `obs_def_mod` so the listed subroutines are available -to be called. This would look like: - -~~~ -! BEGIN DART PREPROCESS USE OF SPECIAL OBS_DEF MODULE -! use obs_def_1d_state_mod, only : write_1d_integral, read_1d_integral, & -! interactive_1d_integral, get_expected_1d_integral -! END DART PREPROCESS USE OF SPECIAL OBS_DEF MODULE -~~~ - -#### 2. Code to compute the expected observation - -*preprocess* collects and combines the *obs processing* code for all -observations definitions through the following comment block: - -~~~ -! BEGIN DART PREPROCESS GET_EXPECTED_OBS_FROM_DEF -! END DART PREPROCESS GET_EXPECTED_OBS_FROM_DEF -~~~ - -These comments must enclose a case statement for each defined type that returns -the expected observation value based on the current values of the state vector. -The code must be in comments, with the comment character in the first column. - -The variables available to be passed to subroutines or used in this section of -code are: - -| Variable name | Intent | Description | -| :---------- | :----- | :---------- | -| state_handle | In | A handle to access the state vector (including all ensemble members) | -| ens_size | In | The number of ensemble members to operate on (between 1 and total ensemble size) | -| copy_indices | In | The indices the ensemble members (between 1 and total ensemble size) | -| location | In | The observation location | -| obs_type_ind | In | The index of the specific type of observation | -| obs_time | In | The time of the observation | -| error_var | In | The observation error variance | -| isprior | In | *true* for prior eval; *false* for posterior | - -The routine must fill in the values of these variables: - -| Variable name | Intent | Description | -| :---------- | :----- | :--------- | -| expected_obs | Out | The computed forward operator values (between 1 and ens_size) | -| istatus | Out | Return code: 0=ok, >0 is error, <0 reserved for system use | - -To call a model_mod interpolate routine directly, the argument list must match exactly: - -~~~ -interpolate(state, location, QTY_xxx, obs_val, istatus) -~~~ -with the *xxx* replaced with the correct quantity. - -This can be useful if the forward operator needs to retrieve values for fields -which are typically found in a model and then compute a derived value from them. - -#### 3. Code to read the observation metadata - -*preprocess* collects and combines the code to *read the observation metadata* -for all observations definitions through the following comment block: - -~~~ -! BEGIN DART PREPROCESS READ_OBS_DEF -! END DART PREPROCESS READ_OBS_DEF -~~~ - -These comments must enclose a case statement for each defined type that reads -any additional data associated with a single observation. If there is no -information beyond that for the basic obs_def type, the case statement must -still be provided, but the code can simply be "continue". The code must be in -comments, with the comment character in the first column. - -The variables available to be passed to subroutines or used in this section of -code are: - -| Variable name | Intent | Description | -| :---------- | :--------- | :--------- | -| ifile | In | The open unit number positioned ready to read | -| obs_def | Inout | The rest of the *obs_def* derived type for this obs | -| key | In | The index observation number in this sequence (read-only) | -| obs_val | In | The observation value, if needed | -| is_ascii | In | Logical to indicate how the file was opened, formatted or unformatted | - -The usual use of this routine is to read in additional metadata per observation -and to set the private key in the obs_def to indicate the metadata index to -use for this observation. Do not confuse the key in the obs_def with the key -argument to this routine; the latter is the global observation sequence number -for this observation. - -#### 4. Code to write the observation metadata - -*preprocess* collects and combines the code to *write the observation metadata* -for all observations definitions through the following comment block: - -~~~ -! BEGIN DART PREPROCESS WRITE_OBS_DEF -! END DART PREPROCESS WRITE_OBS_DEF -~~~ - -These comments must enclose a case statement for each defined type that writes -any additional data associated with a single observation. If there is no -information beyond that for the basic obs_def type, the case statement must -still be provided, but the code can simply be "continue." The code must be in -comments, with the comment character in the first column. - -The variables available to be passed to subroutines or used in this section of -code are: - -| Variable name | Intent | Description | -| :---------- | :----- | :--------- | -| ifile | In | The open unit number positioned ready to write | -| obs_def | In | The rest of the obs_def derived type for this obs | -| key | In | The index observation number in this sequence | -| is_ascii | In | A logical value to indicate how the file was opened, formatted or unformatted | - -The usual use of this routine is to write the additional metadata for this -observation based on the private key in the obs_def. Do not confuse this with -the key in the subroutine call which is the observation number relative to the -entire observation sequence file. - -#### 5. Code to interactively input the observation metadata - -*preprocess* collects and combines the code to *read input from the user for -the observation metadata* for all observations definitions through the -following comment block: - -~~~ -! BEGIN DART PREPROCESS INTERACTIVE_OBS_DEF -! END DART PREPROCESS INTERACTIVE_OBS_DEF -~~~ - -These comments must enclose a case statement for each defined type that prompts -the user for any additional data associated with a single observation. If there -is no information beyond that for the basic obs_def type, the case statement -must still be provided, but the code can simply be continue. The code must be -in comments, with the comment character in the first column. - -The variables available to be passed to subroutines or used in this section of -code are: - -| Variable name | Intent | Description | -| :---------- | :----- | :--------- | -| obs_def | Inout | The rest of the obs_def derived type for this obs. | -| key | In | The index observation number in this sequence. | - -The DART code will prompt for the rest of the obs_def values (location, type, -value, error) but any additional metadata needed by this observation type -should be prompted to, and read from, the console (e.g. write(\*,\*), and -read(\*,\*)). The code will generally set the obs_def%key value as part of -setting the metadata. - -#### 6. Code to preprocess the observations (optional) - -*preprocess* collects and combines the code to *preprocess the observation* -for all observations definitions through the following comment block: - -~~~ -! BEGIN DART PREPROCESS MODULE CODE -! END DART PREPROCESS MODULE CODE -~~~ - -If the code to process this observation requires module data and/or -subroutines, then these comments must surround the module definitions. -Unlike all the other sections, this comment pair is *optional,* and -if used, the code must **not** be in comments; it will be copied verbatim over -to the output file. - -:dart: *NOTE*: the "preprocess" here is for processing specific observations -and should not be confused with the *preprocess* program that generates the -observation Fortran code. - - [](#exampleObsDef) - -### Example observation definition - -This section contains an example observation definition file. As shown here, -it is possible to mix automatic code types and user-supplied code types in the -same list. Simply add the COMMON_CODE keyword on the lines which need no -special data or interfaces. For example, here is an extract from the 1d state -obs_def module, where the raw state variable needs only autogenerated code, but -the 1d integral has user-supplied processing code: - -~~~ -! BEGIN DART PREPROCESS KIND LIST -! RAW_STATE_VARIABLE, QTY_STATE_VARIABLE, COMMON_CODE -! RAW_STATE_1D_INTEGRAL, QTY_1D_INTEGRAL -! END DART PREPROCESS KIND LIST - - -! BEGIN DART PREPROCESS USE OF SPECIAL OBS_DEF MODULE -! use obs_def_1d_state_mod, only : write_1d_integral, read_1d_integral, & -! interactive_1d_integral, get_expected_1d_integral -! END DART PREPROCESS USE OF SPECIAL OBS_DEF MODULE - -! BEGIN DART PREPROCESS GET_EXPECTED_OBS_FROM_DEF -! case(RAW_STATE_1D_INTEGRAL) -! call get_expected_1d_integral(state, location, obs_def%key, obs_val, istatus) -! END DART PREPROCESS GET_EXPECTED_OBS_FROM_DEF - -! BEGIN DART PREPROCESS READ_OBS_DEF -! case(RAW_STATE_1D_INTEGRAL) -! call read_1d_integral(obs_def%key, ifile, fileformat) -! END DART PREPROCESS READ_OBS_DEF - -! BEGIN DART PREPROCESS WRITE_OBS_DEF -! case(RAW_STATE_1D_INTEGRAL) -! call write_1d_integral(obs_def%key, ifile, fileformat) -! END DART PREPROCESS WRITE_OBS_DEF - -! BEGIN DART PREPROCESS INTERACTIVE_OBS_DEF -! case(RAW_STATE_1D_INTEGRAL) -! call interactive_1d_integral(obs_def%key) -! END DART PREPROCESS INTERACTIVE_OBS_DEF - -! BEGIN DART PREPROCESS MODULE CODE -module obs_def_1d_state_mod - -use types_mod, only : r8 -use utilities_mod, only : register_module, error_handler, E_ERR, E_MSG -use location_mod, only : location_type, set_location, get_location -use assim_model_mod, only : interpolate -use cov_cutoff_mod, only : comp_cov_factor - -implicit none - -public :: write_1d_integral, read_1d_integral, interactive_1d_integral, & - get_expected_1d_integral - -... (module code here) - -end module obs_def_1d_state_mod -! END DART PREPROCESS MODULE CODE -~~~ - -See the `obs_def_1d_state_mod.f90` documentation for more details and examples -of each section. Also see `obs_def_wind_speed_mod.f90` "get_expected_wind_speed()" -for an example of a 3D geophysical forward operator. Both operators are in the -`DART/observations/forward_operators` directory. - - [](#obsSeqFile) - -### Observation sequence file creation - -DART uses an *observation sequence* file that contains the observations to be -assimilated. An *observation converter* will convert observations from the -native observation format into this DART-specific format. Ultimately, it is the -user's responsibility to convert the observations, but there are many helper -tools and utilities to make this process easier. - -:dart: *NOTE*: the observation sequence file can be written in either human- -readable ASCII or machine-readable binary. The binary representation will use -approximately two times less disk space, but the ASCII representation makes -manually inspecting the file contents much easier. For either representation, -the underlying data is the same. There is a setting in the DART `input.nml` -file under *obs_sequence_nml* called "write_binary_obs_sequence" that can be -used to write the format as either binary (".true.") or ASCII (".false"). -When the observation sequence file is read, the format is detected -automatically. - -For new obs converters, once you find what format the data is distributed in -you may be able to adapt one of the existing converters here for your own use. -Formats read by the existing converters include netCDF, HDF, LITTLE_R, text, -PREPBUFR, and many others. The section -[Observation converters provided by DART](#obsConverters) lists the converters -that are already available for DART and the formats of the files. The directory -of the observation converters is `DART/observations/obs_converters`. - -If you have looked and none of the existing converters are right for your data, -here are some suggestions for where to start creating a new converter. Create a -new subdirectory in the `obs_converters` directory. Copy with the recursive -option (`cp -r`) one of the existing converters and adapt to your needs. Our -suggestions for which converter to start from depends on the format of your -input observations to be converted. The following table lists our -recommendations: - -| Native format | Advice | -| :---------- | :--------- | -| netCDF | Start with the MADIS converters, and in particular try the `convert_madis_profiler.f90` file because it is the most straightforward. Another good option is `SST/oi_sst_to_obs.f90`. | -| Comma separated text | Start with the Ameriflux converter. | -| ASCII text | Start with the text converter. | -| HDF-EOS | Start with the AIRS converter. | -| BUFR or prepBUFR | Start with the NCEP converter. | -| Dense data, like Satellite swaths | Start with the tpw converter, which includes code that averages the raw data in space and time. | -| Ray-path integrated data | Start with the GPS converter, which includes code that traces a path and integrates values along the ray. | -| World Ocean Database packed ASCII | Start with the WOD converter. | - ----- - - [](#DartForEducation) - -## How would I use DART for teaching students and/or myself? - -\[[top](#top)\] - -There are two main methods of learning DART beyond this document: - -1. [The DART tutorial](#dartTutorial) -2. [DART_LAB](#dartLab) - - [](#dartTutorial) - -### The DART tutorial - -The [DART Tutorial](Tutorial.md) outlines a step-by-step approach to the -concepts of ensemble data assimilation. A set of 26 PDF files guides the user -through a mathematical introduction to Data Assimilation and Ensemble -Kalman Filter concepts. If you've been able to build the Lorenz 63 model, you -have correctly configured your `mkmf.template` and you are now able to run all -of the programs required by the tutorial. - - [](#dartLab) - -### DART_LAB - -[DART_LAB](../DART_LAB/DART_LAB.html) is a set of PDF presentation files and a -set of MATLAB® examples that comprise a fully self-contained introduction to -Data Assimilation and the Ensemble Kalman Filter concepts. These documents are -an excellent way to explore the ins-and-outs of DART and learn about ensemble data assimilation. - ----- - - [](#ContributeToDart) - -## How can I contribute to DART? - -\[[top](#top)\] - -In this section we describe how you can contribute your work to DART. As an -open-source project, we welcome and value your contributions for the benefit -of the community. You may want to get in touch with us at dart @ ucar.edu -before going too far down the development path and make sure you are not -duplicating efforts. - -DART development uses the public GitHub project available at -https://github.com/NCAR/DART. Before you get started on developing with us, -it's probably a good idea to be familiar with the -[GitHub workflow](https://guides.github.com/introduction/flow/). Essentially, -you should create a **fork** of the DART project, which is a publically visible -copy of the repository that you will manage. Create a **branch** for your -feature with an appropriate name for your project, and when you are finished -with your changes you can **commit** them to your fork. After testing locally -on your machine, you can push them to your fork. *At this point, everyone can -see the changes you made on your fork.* When you are ready to begin the -conversation about merging your work into the original project (called the DART -repository master), you can create a **pull request**, which will show your -changes. After reviewing and testing your changes, the pull request will be -addressed appropriately by the DART development team. - - [](#privateWorkflow) - -### What if I want my work to remain private until I publish? - -Some DART users want to work on a private branch until their work is ready -for public viewing. To accommodate users with these concerns, we describe here an -additional step to temporarily "hide" sensitive code that is intended to be -eventually contributed to DART after publication. - -The user interested in maintaining this privacy should create a public fork of -the DART repository as listed above and then create a **private** repository on -GitHub.com, following the steps listed here: -https://help.github.com/en/articles/create-a-repo. The name of this repo is -arbitrary. Now follow the steps at -https://help.github.com/en/articles/adding-a-remote to add the public fork as -a *remote* repository of the private repository. The remote name here could be -"public_fork" or "upstream." Follow the usual steps for pulling/pushing to/from -your private repository as in -https://git-scm.com/book/en/v2/Git-Basics-Working-with-Remotes - -The user can then conduct their development on the private repository, -adding additional team members as necessary. - -:dart: *NOTE*: only 3 collaborators are allowed on a free non-institutional -private repository. The DART team is happy to collaborate with you on your -private repository, but keep the 3 collaborator limit in mind if you are a free -GitHub.com user. - -\[[top](#top)\] diff --git a/guide/pages/Miscellany.md b/guide/pages/Miscellany.md deleted file mode 100644 index 21c3c58b46..0000000000 --- a/guide/pages/Miscellany.md +++ /dev/null @@ -1,499 +0,0 @@ ---- -title: Miscellany -layout: default ---- - - - -# Useful Software - -The following free open-source tools have proven to be very useful: - -1. [ncview](http://meteora.ucsd.edu/~pierce/ncview_home_page.html): a - great visual browser for netCDF files. -2. [Panoply](http://www.giss.nasa.gov/tools/panoply): another visual - browser for netCDF, HDF, and GRIB files with many options for map - projections and data slicing. -3. [the netCDF Operators (NCO)](http://nco.sourceforge.net/): tools to - perform operations on netCDF files like concatenating, differencing, - averaging, etc. -4. [An MPI environment](http://en.wikipedia.org/wiki/Message_Passing_Interface#Overview): - to run larger jobs in parallel. DART can be used without MPI, - especially the low order models where the memory use is small. The - larger models often require MPI so that filter can be run as a - parallel job, both for speed and memory size reasons. Common options - are [OpenMPI](http://www.open-mpi.org/) or - [MPICH](http://www.mpich.org/). See the DART MPI introduction in - [mpi_intro.html](dart_mpi.html). -5. [Observation Processing And Wind Synthesis (OPAWS)](http://code.google.com/p/opaws/): - OPAWS can process NCAR - Dorade (sweep) and NCAR EOL Foray (netcdf) radar data. It analyzes - (grids) data in either two-dimensions (on the conical surface of - each sweep) or three-dimensions (Cartesian). Analyses are output in - netcdf, Vis5d, and/or DART (Data Assimilation Research Testbed) - formats. -6. Some DART users have contributed scripts using the - [NCAR Command Language (NCL)](http://www.ncl.ucar.edu/Document/Manuals/Getting_Started/introduction.shtml) - for computation and plotting. - -The following licensed (commercial) tool has proven very useful: - -1. [MATLAB®](http://www.mathworks.com/products/matlab/): - An interactive and programming language for computation and visualization. - We supply our diagnostic and plotting routines as MATLAB® scripts. - -Free alternatives to MATLAB® (for which we unfortunately do not have the -resources to support, but would happily accept user contributions) include: -- [Octave](http://www.gnu.org/software/octave) -- [SciPy](http://www.scipy.org/) plus -- [matplotlib](http://matplotlib.org/) -- The [R](http://www.r-project.org/) programming language has - similiar functionality but a different enough syntax that the - diagnostic and plotting routines we supply which work with - MATLAB® are unlikely to be easy to port. - - -\[[top](#)\] - ------ - -# DART platforms/compilers/batch systems - -We work to keep the DART code highly portable. We avoid -compiler-specific constructs, require no system-specific functions, and -try as much as possible to be easy to build on new platforms. - -DART has been compiled and run on Apple laptops and workstations, Linux -clusters small and large, SGI Altix systems, IBM Power systems, IBM -Intel systems, Cray systems. - -DART has been compiled with compilers from Intel, PGI, Cray, GNU, IBM, -Pathscale. - -MPI versions of DART have run under batch systems including LSF, PBS, -Moab/Torque, and Sun Grid Engine. - - - -\[[top](#)\] - ------ - -## Platform-specific notes. - -Most of the platform-specific notes are in the appropriate -*mkmf.template.xxxx.yyyy* file. There are very few situations that -require making additional changes. - -### gfortran - -For some reason, the *gfortran* compiler does not require an *interface* -to the *system()* routine while all the other compilers we have tested -**do** need the interface. This makes it impossible to have a module -that is compiler-independent. The interface is needed in the -*null_mpi_utilities_mod.f90*, and/or *mpi_utilities_mod.f90*. The -problem surfaces at link time : - -~~~ -null_mpi_utilities_mod.o(.text+0x160): In function `__mpi_utilities_mod__shell_execute': -: undefined reference to `system_' -null_mpi_utilities_mod.o(.text+0x7c8): In function `__mpi_utilities_mod__destroy_pipe': -: undefined reference to `system_' -null_mpi_utilities_mod.o(.text+0xbb9): In function `__mpi_utilities_mod__make_pipe': -: undefined reference to `system_' -collect2: ld returned 1 exit status -make: *** [preprocess] Error 1 -~~~ - -There is a script to facilitate making the appropriate change to -```null_mpi_utilities_mod.f90``` and ```mpi_utilities_mod.f90```. Run the -shell script *DART/mpi_utilities/fixsystem* with no arguments to simply -'flip' the state of these files (i.e. if the system block is defined, it -will undefine the block by commenting it out; if the block is commented -out, it will define it by uncommenting the block). If you want to -hand-edit ```null_mpi_utilities_mod.f90``` and ```mpi_utilities_mod.f90```, -look for the comment block that starts ```\! BUILD TIP``` and follow the -directions in the comment block. - -### module mismatch errors - -Compilers create modules in their own particular manner ... a module -built by one compiler may not (will usually not) be useable by another -compiler. Sometimes it happens that the Fortran90 modules for the netCDF -interface compiled by compiler *A* is trying to be used by compiler *B*. -This generally results in an error message like: - -~~~ -Fatal Error: File 'netcdf.mod' opened at (1) is not a module file -make: *** [utilities_mod.o] Error 1 -~~~ - -The only solution here is to make sure the *mkmf.template* file is -referencing the appropriate netCDF installation. - -### *endian*-ness errors - -The *endian*-ness of the binary files is specific to the chipset, not -the compiler or the code (normally). There are some models that require -a specific *endian* binary file. Most compilers have some sort of -ability to read and/or write binary files of a specific (or non-native) -endianness by throwing some compile flags. It is generally an -'all-or-nothing' approach in that trying to micromanage which files are -opened with native endianness and which files are openened with the -non-native endianness is generally too time-consuming and fraught with -error to be of much use. If the compile flags exist and are known to us, -we try to include them in the comment section of the individual -*mkmf.template.xxxx.yyyy* file. - -With the Lanai and earlier versions of DART, endian problems were more common -and most often manifest themselves as 'time' errors in the DART -execution. The restart/initial conditions files have the valid time of -the ensuing model state as the first bit of information in the header, -and if these files are 'wrong'-endian, the encoded times are -nonsensical. Since DART now uses netCDF files, endian errors have been -greatly reduced and generally exist trying to ingest binary observation -sequence files or binary data from some other source. - -### MPI - -If you want to use MPI and are interested in testing something simple -before total immersion: try running the MPI test routines in the -*DART/doc/mpi* directory. This directory contains some small test -programs which use both MPI and the netCDF libraries. It may be simpler -to debug any build problems here, and if you need to submit a problem -report to your system admin people these single executables are much -simpler than the entire DART build tree. - - - -\[[top](#)\] - ------ - -# Frequently Asked Questions for DART - - - [General Info about DART](#General) - - [Installation Questions](#Install) - - [Questions about Using DART](#Using) - -## General Info about DART - - - -> What kind of data assimilation does DART do? - -There are two main techniques for doing data assimilation: variational -and ensemble methods. DART uses a variety of ensemble Kalman filter -techniques. - -> What parts of the DART source should I feel free to alter? - -We distribute the full source code for the system so you're free to edit -anything you please. However, the system was designed so that you should -be able to add code in a few specific places to add a new model, work -with new observation types, or change the assimilation algorithm. - -To add a new model you should be able to add a new -`DART/models/XXX/model_mod.f90` file to interface between your model and -DART. We expect that you should not have to alter any code in your model -to make it work with DART. - -To add new observation types you should be able to add a new -`DART/observations/forward_operators/obs_def_XXX_mod.f90` file. -If there is not already a converter for this observation type -you can add a converter in `DART/observations/obs_converters/XXX`. - -If you are doing data assimilation algorithm research you may be -altering some of the core DART routines in the -`DART/assimilation_code/modules/assimilation/assim_tools_mod.f90` or -`DART/assimilation_code/modules/assimilation/filter_mod.f90` -files. Please feel free to email DART support (dart at ucar.edu) for -help with how to do these modifications so they work with the parallel -version of DART correctly. - -If you add support for a new observation type, a new model, or filter -kind, we'd love for you to send a copy of it back to us for inclusion in -the DART -distribution. - -> What systems and compilers do you support? What other tools do I need? - -We run on almost any Linux-based system including laptops, clusters, and -supercomputers. This includes IBMs, Crays, SGIs, Macs. We discourage -trying to use Windows but it has been done using the CygWin package. - -We require a Fortran 90 compiler. Common ones in use are from GNU -(gfortran), Intel, PGI, PathScale, IBM, and g95. - -We need a compatible netCDF library, which means compiled with the same -compiler you build DART with, and built with the Fortran interfaces. - -You can run DART as a single program without any additional software. To -run in parallel on a cluster or other multicore platform you will need a -working MPI library and runtime system. If one doesn't come with your -system already OpenMPI is a good open-source option. - -Our diagnostic routines are MATLAB® scripts, which is a commercial -math/visualization package. Some users use IDL, NCL, or R but they have -to adapt our scripts themselves. - -### Installation Questions - - - -> How do I get started? - -Go to the extensive [DART web pages](../index.html) where there are -detailed instructions on checking the source out of our subversion -server, compiling, running the tutorials, and examples of other users' -applications of DART. - -If you really hate reading instructions you can try looking at the -README in the top level directory. But if you run into problems please -read the [full setup instructions](Getting_Started.md#installing) -before contacting us for help. We will start out suggesting you read -those web pages first anyway. - -> I'm trying to build with MPI and getting errors. - -The MPI compiler commands are usually scripts or programs which add -additional arguments and then call the standard Fortran compiler. If -there is more than one type of compiler on a system you must find the -version of MPI which was compiled to wrap around the compiler you are -using. - -In the `DART/developer_tests/mpi_utilities/tests` directory are some -small programs which can be used to test compiling and running with -MPI. - -If you are using version 1.10.0 of OpenMPI and getting compiler errors -about being unable to find a matching routine for calls to MPI_Get() -and/or MPI_Reduce(), please update to version 1.10.1 or later. There -were missing interfaces in the 1.10.0 release which are fixed in the -1.10.1 -release. - -> I'm getting errors related to netCDF when I try to build the executables. - -Any application that uses the netCDF data libraries must be compiled -with exactly the same compiler as the libraries were built with. On -systems which have either multiple compilers, or multiple versions of -the same compiler, there is the possibilty that the libraries don't -match the compiler you're using to compile DART. Options here are: - - - If there are multiple versions of the netCDF libraries installed, - find a method to select the right version; e.g. specify the exact - path to the include files and libraries in your mkmf.template file, - or load the right module if your system uses the 'module' command to - select software options. - - Change the version of the compiler you are using to build DART to - match the one used to build netCDF. - - Build your own version of the netCDF libraries with the compiler you - prefer to use. See [this web - page](http://www.unidata.ucar.edu/software/netcdf/docs/building_netcdf_fortran.html) - for help in building the libraries. DART requires only the basic - library with the netCDF 3 interfaces, but will work with netCDF 4 - versions. Building netCDF 4 does require additional libraries such - as HDF, libz, etc. - -If you believe you are using the right version of the compiler, then -check to see if the Fortran interfaces have been compiled into a single -library with the C code, or if there are two libraries, libnetcdf.a and -libnetcdff.a (note the 2 f's in the second library). The library lines -in your mkmf.template must reference either one or both libraries, -depending on what exists. This is a choice that is made by the person -who built the netCDF libraries and cannot be predicted -beforehand. - -> I'm getting errors about undefined symbol "_system_" when I try to compile. - -If you're running the Lanai release or code from the trunk later than -2013, the DART Makefiles should automatically call a script in the -`DART/assimilation_code/modules/utilities` directory named `fixsystem`. -This script tries to -alter the MPI source code in that directory to work with your compiler. -If you still get a compiler error look at this script and see if you -have to add a case for the name of your compiler. - -If you're running the Kodiak release or earlier, you have to run -`fixsystem` yourself before compiling. We distributed the code so it -would work without change for the `gfortran` compiler, but all other -compilers require that you run `fixsystem` before trying to -compile. - -> I have a netCDF library but I'm getting errors about unrecognized module format. - -The netCDF libraries need to be built by the same version of the same -compiler as you are building DART. If your system has more than one -compiler on it (e.g. intel ifort and gfortran) or multiple versions of -the same compiler (e.g. gfortran 4.1 and 4.5) you must have a version of -the netCDF libraries which was built with the same version of the same -compiler as you're using to build -DART. - -> I'm getting errors about `-lnetcdff` not found, or I'm getting errors about undefined symbols for things in the netCDF libraries. - -There are several important options when the netCDF libraries are built -that change what libraries you get and whether you have what you need. -The problems we run into most frequently are: - - - If the netCDF installation includes only the C library routines and - not the Fortran interfaces. We require both. - - The C routines are always in `-lnetcdf`, but the Fortran interfaces - can either be included in that single library or placed in a - separate `-lnetcdff` library (note 2 f's). - - If HDF support is included, additional libraries are required to - link an executable. Most of our `mkmf` template files have comments - about the usual list of required libraries that you need to include. - -Bottom line: What you need to set for the library list in your -`DART/build_templates/mkmf.template` file depends on how your netCDF was -built. - -> My model runs in single precision and I want to compile DART the same way. - -We recommend that you run an assimilation with Fortran 64-bit -reals (e.g. all real values are real\*8 or 'double precision'). However if your -model is compiled with 32-bit reals (real\*4 or 'single precision') there is -an option to build DART the same way. -Edit `DART/assimilation_code/modules/utilities/types_mod.f90` -and change the definition of `R8` to equal `R4` (comment out the -existing line and comment in the following line). Rebuild all DART -executables and it will run with single precision reals. We declare -every real variable inside DART with an explicit size, so we do not -recommend using compiler flags to try to change the default real -variable precision because it will not affect the DART code. - -### Questions about Using DART - - - -> I'm trying to run an MPI filter and I'm getting N copies of every message. - -Look in the log or in the standard output for the message: -'initialize_mpi_utilities: Running with N MPI processes.' Instead of -this message, if you see: `initialize_mpi_utilities: Running single -process` then you have NOT successfully compiled with MPI; you are -running N duplicate copies of a single-task program. Rerun the -quickbuild.csh script with the -mpi flag to force it to build filter -with mpif90 or whatever the mpi compiler wrapper is called on your -system. - -> How does DART interact with running my model? - -If you are running one of the "low-order" models (e.g. one of the Lorenz -models, the null model, the pe2lyr model, etc), the easiest way to run -is to let DART control advancing the model when necessary. You run the -"filter" executable and it runs both the assimilation and model advances -until all observations in the input observation sequence file have been -assimilated. See the "async" setting in the -[filter namelist documentation](../../assimilation_code/programs/filter/filter.html) -for more information. - -If you are running a large model with a complicated configuration and/or -run script, you will probably want to run the assimilation separately -from the model advances. To do this, you will need to script the -execution, and break up the observations into single timestep chunks per -file. The scripting will need to create filter input files from the -model files, link the current observation file to the input filename in -the namelist, copy or rename any inflation files from the previous -assimilation step, run filter, convert the filter output to model input -files, and then run the model. There are example scripts which do this -in the WRF shell_scripts directory, also the MPAS shell_scripts -directory. These scripts are both highly model-dependent as well as -computing system dependent. - -If you are running any of the CESM models (e.g. CAM, POP, CLM) then the -scripts to set up a CESM case with assimilation are provided in the DART -distribution. At run time, the run script provided by CESM is used. -After the model advance a DART script is called to do the assimilation. -The "multi-instance" capability of CESM is used to manage the multiple -copies of the components which are needed for assimilation, and to run -them all as part of a single job. - -> After assimilating, my model variables are out of range. - -One of the assumptions of the Kalman filter is that the model states and -the observation values have gaussian distributions. The assimilation can -work successfully even if this is not actually true but there are -certain cases where this leads to problems. - -If any of the model state values must remain bounded, for example values -which must remain positive, or must remain between 0 and 1, you may have -to add some additional code to ensure the posterior values obey these -constraints. It is not an indication of an error if after the -assimilation some values are outside the required range. - -Most users deal with this, successfully, by letting the assimilation -update the values as it will, and then during the step where the model -data is converted from DART format to the model native format, any -out-of-range values are changed at that time. For example, the WRF model -has a namelist item in the &model_nml namelist which can be set at -run-time to list which variables have minimum and/or maximum values and -the conversion code will enforce the given limits. - -Generally this works successfully, but if the observations or the model -are biased and the assimilation is continuously trying to move model -state out of range, the distribution can become seriously unbalanced. In -this case another solution, which requires more coding, might be to -convert the values to a log scale on import to DART, do the assimilation -with the log of the observation values, and then convert back to the -original scale at export time. This ensures the values stay positive, -which is common requirement for legal -values. - -> After assimilating, my job finished successfully but no values changed. - -This is a common problem, especially when adding a new observation type -or trying to assimilate with a new model. But it can happen at any time -and can be confusing about why nothing is changing. -See [the "Diagnostics" web page](Diagnostics.md#DidItWork) -for a list of common causes of the assimilation output state being -the same as the input state, and how to determine which one is responsible. - -> You have lots of namelists. How can I tell what to set? - -Each module in DART has an html web page which describes the namelists -in detail. Start with `DART/index.html` and follow the links to all the -other modules and namelists in the system. If you want help with setting -up an experiment the `DART/assimilation_code/modules/assimilation/filter.html` page has some -introductory advice for some of the more important namelist settings. - -> I'm not getting an error but I am getting MPI timeouts - -If your job is getting killed for no discernable reason but is usually -during computing prior or posterior forward operators, or during writing -the diagnostics file, the problem may be caused by the MPI timeout -limit. This usually happens only when the number of MPI tasks is much -larger than the number of ensemble members, and there are very slow -forward operator computations or very large states to write into the -diagnostics files. In the standard DART distribution only the first N -tasks (where N is the number of ensemble members) are doing work during -the forward operators, or only 1 task for writing diagnostic files. All -the other tasks will be waiting at an MPI barrier. If they wait there -long enough they reach the timeout threshold which assumes that at least -one or more other tasks have failed and so they exit. - -The solutions are either to set an environment variable that lengthens -the timeout threshold, run with fewer MPI tasks, or ask the DART team to -be a Beta user of a newer version of DART which does not have such large -time differentials between different MPI tasks. - -> filter is finishing but my job is hanging at exit - -If filter finishes running, including the final timestamp message to the -log file, but then the MPI job does not exit (the next line in the job -script is not reached), and you have set the MPI timeout to be large to -avoid the job being killed by MPI timeouts, then you have run into a bug -we also have seen. We believe this to be an MPI library bug which only -happens under a specific set of circumstances. We can reproduce it but -cannot find a solution. The apparent bug happens more frequently with -larger processor counts (usually larger than about 4000 MPI tasks), so -if you run into this situation try running with a smaller MPI task count -if possible, and not setting the MPI debug flags. We have seen this -happen on the NCAR supercomputer Yellowstone with both the MPICH2 and -PEMPI MPI libraries. - -\[[top](#)\] - ------ diff --git a/guide/pages/Models.md b/guide/pages/Models.md deleted file mode 100644 index b500d90d49..0000000000 --- a/guide/pages/Models.md +++ /dev/null @@ -1,1948 +0,0 @@ ---- -title: Documentation -layout: default ---- - -## Adding your efforts to DART. - -Please let us know if you have suggestions or code to contribute to -DART. We're a small group, but we are willing to listen and will make -every effort to incorporate improvements to the code. Email us at -. - -We are interested in updating model interfaces to be useful with the -most recent version of DART. If the model you are interested in is -not Manhattan-ready, and you would like it to be - we are here to help -and would like to participate. Please contact us. - -## Links to major sections of this document: -- [Downloadable datasets for DART.](#datasets) -- [Creating initial conditions for DART](#creating_ics) -- ['Perfect Model' or 'OSSE' experiments](#perfect_osse) -- [Adding a model to DART](#adding_a_model) - ------ - -# DART-supported models: - -There are two broad classes of models supported by DART. Some are -'low-order' models, generally single-threaded, subroutine-callable, and -idealized: there are no **real** observations of these systems. The -other class of models are 'high-order' models. There **are** real -observations of these systems. - -### Models that are ready to use with Manhattan: - [lorenz_63](#lorenz_63) -   [lorenz_84](#lorenz_84) - [9var](#NINEvar) -   [lorenz_96](#lorenz_96) - [lorenz_96_2scale](#lorenz_96_2scale) - [forced_lorenz_96](#forced_lorenz_96) -   [lorenz_04](#lorenz_04) - [simple_advection](#simple_advection) -   [bgrid_solo](#bgrid_solo) - [WRF](#wrf) -   [MPAS ATM](#mpas_atm) -   [ROMS](#ROMS) -   [CESM](#CESM) - [CAM-FV](#cam-fv) -   [CAM-SE](#cam-se) - [CAM-CHEM](#cam-chem) -   [WACCM](#WACCM)  - [WACCM-X](#WACCM-X) - [CICE](#CICE) -   [CLM](#CLM)  - [POP](#POP) - [CM1](#CM1) - [FESOM](#fesom) -   [NOAH-MP](#noah-mp) -   [WRF-Hydro](#wrf-hydro)  -   [WRF-CHEM](#wrf-chem) - [gitm](#gitm) -   [null](#null)  - [MITgcm_ocean](#MITgcm_ocean) - -### Models supported in Lanai (contact us if you want to update them): - [GCCOM](#GCCOM) -   [LMDZ](#LMDZ)  -   [NAAPS](#NAAPS) -   [AM2](#AM2) - [COAMPS](#COAMPS) -   [COSMO](#COSMO) -   [dynamo](#dynamo)  -   [ikeda](#ikeda) -   [jules](#jules)  - [mpas_ocean](#mpas_ocean) - [openggcm](#openggcm) -   [parflow](#parflow) -   [sqg](#sqg)  - [tiegcm](#tiegcm) - -### Models that were used a long time ago (these may not take that much work to revive): - [ECHAM](#ECHAM) -   [PBL_1d](#PBL_1d)  - [MITgcm_annulus](#MITgcm_annulus) -   [forced_barot](#forced_barot)  - [pe2lyr](#pe2lyr) - [ROSE](#rose)  -   [CABLE](#cable)  - - - - -\[[top](#)\] - ------ - -## The 'Manhattan-ready' models in DART. - - - - -### lorenz_63 - -This is the 3-variable model as described in: Lorenz, E. N. -1963. Deterministic nonperiodic flow. *J. Atmos. -Sci.* **20**, 130-141. -The system of equations is: - -~~~ -X' = -sigma*X + sigma*Y -Y' = -XZ + rX - Y -Z' = XY -bZ -~~~ - - - - -### lorenz_84 - -This model is based on:   Lorenz E. N., 1984: Irregularity: A -fundamental property of the atmosphere. *Tellus*,  **36A**, 98-110. -The system of equations is: - -~~~ -X' = -Y^2 - Z^2 - aX + aF -Y' = XY - bXZ - Y + G -Z' = bXY + XZ - Z -~~~ - -Where a, b, F, and G are the model parameters. - - - - -### 9var - -This model provides interesting off-attractor transients that behave -something like gravity waves. - - - - -### lorenz_96 - -This is the model we use to become familiar with new architectures, -i.e., it is the one we use 'first'. It can be called as a subroutine or -as a separate executable. We can test this model both single-threaded -and mpi-enabled. - -Quoting from the **Lorenz 1998** paper: - -> ... the authors introduce a model consisting of 40 ordinary -> differential equations, with the dependent variables representing -> values of some atmospheric quantity at 40 sites spaced equally about a -> latitude circle. The equations contain quadratic, linear, and constant -> terms representing advection, dissipation, and external forcing. -> Numerical integration indicates that small errors (differences between -> solutions) tend to double in about 2 days. Localized errors tend to -> spread eastward as they grow, encircling the globe after about 14 -> days. -> ... -> We have chosen a model with J variables, denoted by X1, -> ..., XJ; in most of our experiments we have let J = 40. The -> governing equations are: -> -> -> ~~~ -> dXj/dt = (Xj+1 - Xj-2)Xj-1 - Xj + F (1) -> ~~~ -> -> -> for *j* = 1, ..., J. To make Eq. (1) meaningful for all values of *j* -> we define X-1 = XJ-1, X0 = -> XJ, and XJ+1 = X1, so that the -> variables form a cyclic chain, and may be looked at as values of some -> unspecified scalar meteorological quantity, perhaps vorticity or -> temperature, at J equally spaced sites extending around a latitude -> circle. Nothing will simulate the atmosphere's latitudinal or vertical -> extent. - - - - -### lorenz_96_2scale - -This is the Lorenz 96 2-scale model, documented in Lorenz (1995). It -also has the option of the variant on the model from Smith (2001), which -is invoked by setting *local_y = .true.* in the namelist. The time -step, coupling, forcing, number of X variables, and the number of Ys per -X are all specified in the namelist. Defaults are chosen depending on -whether the Lorenz or Smith option is specified in the namelist. Lorenz -is the default model. Interface written by **Josh Hacker**. Thanks -Josh\! - - - - -### forced_lorenz_96 - -The forced_lorenz_96 model implements the standard L96 equations -except that the forcing term, F, is added to the state vector and is -assigned an independent value at each gridpoint. The result is a model -that is twice as big as the standard L96 model. The forcing can be -allowed to vary in time or can be held fixed so that the model looks -like the standard L96 but with a state vector that includes the constant -forcing term. An option is also included to add random noise to the -forcing terms as part of the time tendency computation which can help in -assimilation performance. If the random noise option is turned off (see -namelist) the time tendency of the forcing terms is 0. - - - - -### lorenz_04 - -The reference for these models is Lorenz, E.N., 2005: Designing chaotic -models. *J. Atmos. Sci.*, **62**, 1574-1587. -Model II is a single-scale model, similar to Lorenz 96, but with spatial -continuity in the waves. Model III is a two-scale model. It is -fudamentally different from the Lorenz 96 two-scale model because of the -spatial continuity and the fact that both scales are projected onto a -single variable of integration. The scale separation is achived by a -spatial filter and is therefore not perfect (i.e. there is leakage). The -slow scale in model III is model II, and thus model II is a deficient -form of model III. The basic equations are documented in Lorenz (2005) -and also in the model_mod.f90 code. The user is free to choose model II -or III with a Namelist variable. - - - - -### simple_advection - -This model is on a periodic one-dimensional domain. A wind field is -modeled using Burger's Equation with an upstream semi-lagrangian -differencing. This diffusive numerical scheme is stable and forcing is -provided by adding in random gaussian noise to each wind grid variable -independently at each timestep. An Eulerian option with -centered-in-space differencing is also provided. The Eulerian -differencing is both numerically unstable and subject to shock -formation. However, it can sometimes be made stable in assimilation mode -(see recent work by Majda and collaborators). - - - - -### bgrid_solo - -This is a dynamical core for B-grid dynamics using the Held-Suarez -forcing. The resolution is configurable, and the entire model can be run -as a subroutine. *Status:* supported. - - - - -### WRF - -The [Weather Research and Forecasting (WRF) -Model](http://www.wrf-model.org/) is a next-generation mesoscale -numerical weather prediction system designed to serve both operational -forecasting and atmospheric research needs. More people are using DART -with WRF than any other model. Note: The actual WRF code is not -distributed with DART. *Status:* supported. - - - - -### MPAS ATM - -[Model Prediction Across Scales - -atmosphere](https://mpas-dev.github.io/) *Status:* active - - - - -### ROMS - -[Regional Ocean Modelling System](https://www.myroms.org/) *Status:* active - - - - -### CESM - -There are several -[supported versions](http://www.cesm.ucar.edu/models/current.html) -of the Community Earth System Model (CESM) and its ancestors -([CCSM4.0](http://www.cesm.ucar.edu/models/ccsm4.0)). Contact us for -support for unreleased, developmental versions of CESM. Not all are -supported because each requires modification of some subroutines and -setup scripts in order to work with DART. The supported versions depend -to some degree on the CESM component(s) which will be used as the -assimilating model(s). See CAM-FV, POP, and CICE, below, and -[CESM DART guidelines](CESM_DART_guidelines.html). In -general, later versions of CESM can build the latest component models -plus any earlier versions of the component models. For example, CESM2.0 -can build CAM-FV version 6, 5, 4, ... while CESM1.2.1 can build CAM-FV -5, 4, ..., but not 6. Note: the source code for CESM component models is -not distributed with DART. - - - - - - - -### CAM-FV CAM-Chem WACCM WACCM-X - -See CESM, above. CAM-FV has been the "work horse" atmospheric climate -model for several generations of CESM releases. The Manhattan release of -DART provides interfaces to CESM1.5 and CESM2.0. The setup scripts for -those CESMs will currently build only the finite volume dynamical core -of CAM. This works for all of the variants of CAM-FV; CAM-Chem, WACCM, -WACCM-X. An interface between DART and the spectral element dy-core of -CAM is available in DART Classic and will be brought into the Manhattan -release when needed. -[(CAM5)](http://www.cesm.ucar.edu/models/cesm1.0/cam); Some SourceMods -and initial file ensembles for older and lower-resolution CAM-FVs are -available in -[DART/CAM datasets](http://www.image.ucar.edu/pub/DART/CAM/) *Status:* available -for community use. - - - - -### CAM-SE - -The Spectral Element core for CAM was supported under Lanai and the Manhattan -support for CAM-SE is ready for friendly testing. Please contact the DART -group for more details. - -The support for the CAM-SE core is on a separate branch: `jlaucar/DART:CAM_SE` - - - - -### CICE (pronounced 'sea ice') - -See CESM, above. The sea-ice component of -[CESM](http://www.cesm.ucar.edu/models/current.html) The interface of -[CESM-CICE](http://www.cesm.ucar.edu/models/cesm1.2/cice/) to DART is -through CESM1.5. **Cecilia Bitz** and **Yongfei Zhang** created the -interfaces for DART. *Status:* throroughly -beta-tested, full support awaiting the CESM2.0 release. - - - - -### CLM - -Assimilation with the [Community Land Model](http://www.cesm.ucar.edu/models/clm/) -is well supported and the system has been used for many research interests, from -biogeochemistry to snow, ice, soil moisture and more. DART/CLM has many research -branches and guidance for which branch is most appropriate is provided upon request. -There is support for CLM under the Lanai release and several development branches -that are consistent with the Manhattan release. The version distributed with the -Manhattan release is not as fully functional as the development branches. -Much of the original DART/CLM support was written by **Yongfei Zhang** while -she was at the University of Texas at Austin. - -Manhattan support for CLM is available upon request. - - - - -### POP - -See CESM, above. The Parallel Ocean Program -[(POP)](http://www.cesm.ucar.edu/models/cesm1.0/pop2/) was originally created by -the Los Alamos National Laboratory and has been modified to run in the [NCAR -Community Earth System Model -(CESM)](http://www.cesm.ucar.edu/models/current.html) framework. Additional -modifications are necessary for data assimilation and center around the need to -perform an adjustment upon restart to account for the fact that the input ocean -state has been modified by the assimilation. There are interfaces for CESM1.1.1 -and CESM1.2.1. *Status:* available for community use. - - - - -### CM1 - -Cloud Model 1 (CM1) version 18 (CM1r18) is a non-hydrostatic numerical model in -Cartesian 3D coordinates designed for the study of micro- to mesoscale -atmospheric phenomena in idealized to semi-idealized simulations. The CM1 model -was developed and is maintained by George Bryan at the National Center for -Atmospheric Research (NCAR) Mesoscale and Microscale Meteorology Laboratory -(MMM). The model code is freely available from the CM1 website: - and must be downloaded and compiled -outside of DART. -This model interface and scripting support were created by **Luke Madaus**. - - - - -### FESOM - -[FESOM](https://fesom.de/models/fesom14) is an unstructured mesh global -ocean model using finite element methods to solve the hydrostatic -primitive equations with the Boussinesq approximation. -The [FESOM model interface](../../models/FESOM/Readme.md), -scripting support and some diagnostic routines were -contributed by **Ali Aydoğdu**. -*Status:* available for community use. - - - - -### NOAH-MP - -NOAH-MultiParameterization land surface model [(NOAH-MP LSM)](https://ral.ucar.edu/solutions/products/noah-multiparameterization-land-surface-model-noah-mp-lsm) -Noah-MP is a land surface model (LSM) using multiple options -for key land-atmosphere interaction processes -[Niu et al., 2011](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2010JD015139) - - - - -### WRF-HYDRO - -The WRF-Hydro assimilation has been fully integrated. -DART support for WRF-Hydro is extensively used for -the channel-only configuration of WRF-Hydro. Originally, this was almost -entirely the work of **James McCreight** of NCAR's Research Applications -Laboratory (RAL). The DAReS team has been working with RAL to incorporate new -features such as localization restricted to watersheds, new inflation algorithms -and variable transformations that provide much better results when assimilating -non-gaussian quantities such as streamflow. - - - - -### WRF-CHEM - -Dr. Arthur Mizzi is the father of the WRF-Chem/DART project. -If you'd like to use WRF-Chem/DART, please [email Dr. Mizzi](mailto:mizzi@ucar.edu,dart@ucar.edu?subject=WRF-Chem/DART%20inquiry). - - - - -### GITM - -The Global Ionosphere Thermosphere Model -[(GITM)](https://ccmc.gsfc.nasa.gov/models/modelinfo.php?model=GITM) -is a 3-dimensional spherical code that models the Earth's thermosphere -and ionosphere system using a stretched grid in latitude and altitude. - - - - -### null_model - -This model provides very simple models for evaluating filtering -algorithms. It can provide simple linear growth around a fixed point, a -random draw from a Gaussian, or combinations of the two. - - - - -### MITgcm_ocean - -The [MIT ocean GCM](http://mitgcm.org/) version 'checkpoint59a' is the -foundation of the DART Lanai implementation. It was modified -by **Ibrahim Hoteit** (then of Scripps) to accomodate the interfaces -needed by DART. *Status:* supported for Lanai, Manhattan version has been -contributed and is being tested before being released. Please contact -the DART group to be a friendly beta tester. - - -\[[top](#)\] - ------ - - - -## Models that are supported by DART Lanai (or Classic) and could be ported to the Manhattan release if needed. Please contact us - we want to help! - - - -### General Curvilinear Coastal Ocean Model - GCCOM - -GCCOM is a three-dimensional, nonhydrostatic Large Eddy Simulation (LES), -rigid lid model that has the ability to run in a fully three-dimensional -general curvilinear coordinate system. Much of the work of supporting GCCOM in -DART was by **Mariangel Garcia** while she was at San Diego State University. -One article is -["Interfacing an ensemble Data Assimilation system with a 3D nonhydrostatic Coastal Ocean Model, an OSSE experiment"](https://ieeexplore.ieee.org/abstract/document/7760992) - -The GCCOM model support is on a separate branch: `classic_UCOAM_dev` - - - -### LMDZ - -The DART interfaces were prototyped by **Tarkeshwar Singh** of the -Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi. -From the LMDZ homepage: -> LMDZ is a general circulation model (or global climate model) developed -> since the 70s at the "Laboratoire de Météorologie Dynamique", which -> includes various variants for the Earth and other planets (Mars, -> Titan, Venus, Exoplanets). The 'Z' in LMDZ stands for "zoom" -> (and the 'LMD' is for 'Laboratoire de Météorologie Dynamique"). - - - -### NAAPS - - - -### AM2 - -The [FMS AM2](http://data1.gfdl.noaa.gov/~arl/pubrel/m/am2/doc/) model -is GFDL's atmosphere-only code using observed sea surface temperatures, -time-varying radiative forcings (including volcanos) and time-varying -land cover type. This version of AM2 (also called AM2.1) uses the -finite-volume dynamical core (Lin 2004). **Robert Pincus** (CIRES/NOAA ESRL -PSD1) and **Patrick Hoffman** (NOAA) wrote the DART interface and are -currently using the system for research. Note: the model code is not -distributed with DART. *Status:* supported - - - -### CABLE - -The Community Atmosphere Biosphere Land Exchange (CABLE) model is a -land surface model,used to calculate the fluxes of momentum, energy, -water and carbon between the land surface and the atmosphere and to -model the major biogeochemical cycles of the land ecosystem. The DART -interfaces for the standalone version of CABLE have preliminary support -and needs to be updated to be consistent with the Manhattan release. - - - - - -### coamps_nest - -The DART interface was originally written and supported by **Tim Whitcomb**. -The following model description is taken from the [COAMPS overview web -page:](http://www.nrlmry.navy.mil/coamps-web/web/view) - -> The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) has -> been developed by the Marine Meteorology Division (MMD) of the Naval -> Research Laboratory (NRL). The atmospheric components of COAMPS, -> described below, are used operationally by the U.S. Navy for -> short-term numerical weather prediction for various regions around the -> world. - -Note: the model code is not distributed with DART. *Status:* supported - - - -### COSMO - - - -### dynamo - -A Flux-Transport Dynamo model from **Mausumi Dikpati**. -The goal of this interface is to estimate the time variation of -velocities to match given spatio-temporal observation of magnetic fields. - - - -### ikeda - -**Converting the Ikeda model to be Manhattan-compliant would be a -relatively easy and quick learning experience for someone.** - -The Ikeda model is a 2D chaotic map useful for visualization data -assimilation updating directly in state space. There are three -parameters: a, b, and mu. The state is 2D, x = \[X Y\]. The equations -are: - -~~~ -X(i+1) = 1 + mu * ( X(i) * cos( t ) - Y(i) * sin( t ) ) -Y(i+1) = mu * ( X(i) * sin( t ) + Y(i) * cos( t ) ) -~~~ - -where - -~~~ -t = a - b / ( X(i)**2 + Y(i)**2 + 1 ) -~~~ - -Note the system is time-discrete already, meaning there is no delta_t. -The system stems from nonlinear optics -(Ikeda 1979, Optics Communications). -Interface written by **Greg Lawson**. Thanks Greg\! - - - -### JULES - - - -### MPAS Ocean - - - -### OpenGCCM - - - -### PARFLOW - - - -### SQG - - - -### TIEGCM - -The DART interfaces to the Thermosphere Ionosphere Electrodynamic General -Circulation Model [TIEGCM](http://www.hao.ucar.edu/modeling/tgcm/tie.php) -are fully supported in the Lanai release. -TIEGCM is a community model developed at the NCAR High Altitude Observatory and -is widely used by the space physics and aeronomy community. -DART/TIEGCM has been used to assimilate neutral mass density retrieved from -satellite-borne accelerometers and electon density obtained from ground-based -and space-based GNSS signals. TIEGCM2 is not yet supported, and the existing -interfaces need to be updated to work under the Manhattan release. - - -\[[top](#)\] - ------ - - - -## Models that were used a long time ago - - - -### ECHAM - -*Status:* orphaned. - - - -### PBL_1d - -The PBL model is a single column version of the WRF model. The -functionality for this has been folded into the regular WRF model_mod -interface so this version is no longer needed. See the WRF model_mod -namelist documentation for how to use the single-column features. -*Status:* orphaned, obsolete. - - - -### MITgcm_annulus - -The MITgcm annulus model as configured for this application within DART -is a non-hydrostatic, rigid lid, C-grid, primitive equation model -utilizing a cylindrical coordinate system. For detailed information -about the MITgcm, see http://mitgcm.org *Status:* orphaned. - - - -### FORCED_BAROT - -*Status:* orphaned. - - - -### pe2lyr - -This model is a 2-layer, isentropic, primitive equation model on a -sphere. *Status:* orphaned. - - - -### rose - -The rose model is for the stratosphere-mesosphere and was used by Tomoko -Matsuo (now at CU-Boulder) for research in the assimilation of -observations of the Mesosphere Lower-Thermosphere (MLT). Note: the model -code is not distributed with DART. *Status:* orphaned. - - - - - -\[[top](#)\] - ------ - -# Downloadable datasets for DART. - -The code distribution was getting cluttered with datasets, boundary -conditions, intial conditions, ... large files that were not necessarily -interesting to all people who downloaded the DART code. This is -compounded by the fact subversion makes a local (hidden) copy of the -original repository contents, so the penalty for being large is doubled. -It just made sense to make all the large files available on as -'as-needed' basis. - -To keep the size of the DART distribution down we have a separate -www-site to provide some observation sequences, initial conditions, and -general datasets. It is our intent to populate this site with some -'verification' results, i.e. assimilations that were known to be 'good' -and that should be fairly reproducible - appropriate to test the DART -installation. - -Please be patient as I make time to populate this directory. -(yes, 'make', all my 'found' time is taken ...) - -Observation sequences can be found at -[www.image.ucar.edu/pub/DART/Obs_sets](http://www.image.ucar.edu/pub/DART/Obs_sets) - - -Useful bits for CAM can be found at -[www.image.ucar.edu/pub/DART/CAM](http://www.image.ucar.edu/pub/DART/CAM). - -Useful bits for WRF can be found at -[www.image.ucar.edu/pub/DART/WRF](http://www.image.ucar.edu/pub/DART/WRF). - -Useful bits for MPAS_ocn can be found at -[www.image.ucar.edu/pub/DART/MPAS_OCN](http://www.image.ucar.edu/pub/DART/MPAS_OCN) - -Useful bits for CICE can be found at -[www.image.ucar.edu/pub/DART/CICE](http://www.image.ucar.edu/pub/DART/CICE) - -Verification experiments will be posted to -[www.image.ucar.edu/pub/DART/VerificationData](http://www.image.ucar.edu/pub/DART/VerificationData) -as soon as I can -get to it. These experiments will consist of initial conditions files -for testing different high-order models like CAM, WRF, POP ... The -low-order models are still distributed with verification data in their -*work* directories. - - - -\[[top](#)\] - ------ - -# Creating initial conditions for DART - -The idea is to generate an ensemble that has sufficient 'spread' to -cover the range of possible solutions. Insufficient spread can (and -usually will) lead to poor assimilations. Think 'filter divergence'. - -Generating an ensemble of initial conditions can be done in lots of -ways, only a couple of which will be discussed here. The first is to -generate a single initial condition and let DART perturb it with noise -of a nature you specify to generate as many ensemble members as you -like. The second is to take some existing collection of model states and -convert them to DART initial conditions files and then use the -[NCO operators](http://nco.sourceforge.net/) to set the proper date in the -files. The hard part is then coming up with the original collection of -model state(s). - -### Adding noise to a single model state - -This method works well for some models, and fails miserably for others. -As it stands, DART supplies a routine that can add gaussian noise to -every element of a state vector. This can cause some models to be -numerically unstable. You can supply your own -*model_mod:pert_model_copies()* if you want a more sophisticated -perturbation scheme. - -### Using a collection of model states. - -Simply collect the filenames of all the model netCDF files - one per -ensemble member - and specify them through the *input.nml*  -```&filter_nml:input_state_file_list = "restarts_in.txt"``` - -Frequently, the initial ensemble of restart files is some climatological -collection. For CAM experiments, we usually start with *N* different -'January 1' states ... from *N* different years. The timestamp in those -files can be ignored through namelist control. Experience has shown that -it takes less than a week of assimilating 4x/day to achieve a steady -ensemble spread. WRF has its own method of generating an initial -ensemble. For that, it is best to go to contact someone familiar with -WRF/DART. - - - -### Initial conditions for the low-order models. - -In general, there are 'restart files' for the low-order models that -already exist as ASCII sources for netCDF files. These files are usually -called ```work/filter_input.cdl``` and can be converted to netCDF files by -using the *ncgen -o* unix command. - -You can generate your own ensemble by adding noise to a single -```perfect_input.nc``` file and run *filter*. -The way to specify the input state file is to use the -*input_state_file_list* mechanism. Simply put the name of the file -into the file referenced by *input_state_file_list*. In this example, -```filter_input_list.txt``` would contain exactly one line - the string -"perfect_input.nc". You will also need an observation sequence file, -and you may want to explicitly state the start/stop times. - -~~~ -&filter_nml - ... - input_state_files = 'null', - input_state_file_list = 'filter_input_list.txt' - perturb_from_single_instance = .true. - ens_size = [whatever you want] - init_time_days = 0 - init_time_seconds = 0 - first_obs_days = 0 - first_obs_seconds = 0 - last_obs_days = 0 - last_obs_seconds = 0 - output_state_files = 'null', - output_state_file_list = 'filter_output_list.txt' - ... -~~~ - -In this example, the ensemble will be created with whatever file name -you put in ```filter_output_list.txt```. - - - - -\[[top](#)\] - ------ - -# 'Perfect Model' or 'OSSE' experiments. - -Once a model is compatible with the DART facility all of the -functionality of DART is available. This includes 'perfect model' -experiments (also called Observing System Simulation Experiments - -OSSEs). Essentially, the model is run forward from a known state and, at -predefined times, an observation forward operator is applied to the -model state to harvest synthetic observations. This model trajectory is -known as the 'true state'. The synthetic observations are then used in -an assimilation experiment. The assimilation performance can then be -evaluated precisely because the true state (of the model) is known. -Since the same forward operator is used to harvest the synthetic -observations as well as during the assimilation, the -'representativeness' error of the assimilation system is not an issue. - -The example described in this section uses low-order models, but the -logic and procedure is **exactly** the same for high-order models; the -complication is usually that researchers want more sophisticated -observation networks than those described here. All you have to do is -use the DART tools to create an observation sequence file (even a REAL -observation sequence file), and use that instead of creating one by hand -with *create_obs_sequence* and *create_fixed_network_sequence*. -*perfect_model_obs* will simply ignore the actual observation values -in this case and only use the observation metadata. Take care that the -observation error values in the file are appropriate for an OSSE - the -converters usually assume some sort of representativeness error in the -observation error specification. - -There are a set of MATLAB® functions to help explore the assimilation -performance in state-space as well as in observation-space. - - - -## Perfect Model Experiment Overview - -There are four fundamental steps to running an OSSE from within DART: - -1. [Create a blueprint](#obs_blueprint) of what, where, and when you - want observations. Essentially, define the metadata of the - observations without actually specifying the observation values. The - default filename for the blueprint is *obs_seq.in*. For simple - cases, this is just running - [create_obs_sequence](../../assimilation_code/programs/create_obs_sequence/create_obs_sequence.html) - and - [create_fixed_network_seq](../../assimilation_code/programs/create_fixed_network_seq/create_fixed_network_seq.html). - You can also use real observation sequences as long as you take care - to specify observation error variances that do not incorporate - representativeness error. -2. [Harvest the synthetic observations](#run_pmo) from the true model - state by running - [perfect_model_obs](../../assimilation_code/programs/perfect_model_obs/perfect_model_obs.html) - to advance the model from a known initial condition and apply the - forward observation operator based on the observation 'blueprint'. - The observation will have noise added to it based on a draw from a - random normal distribution with the variance specified in the - observation blueprint. The noise-free 'truth' and the noisy - 'observation' are recorded in the output observation sequence file. - The entire time-history of the true state of the model is recorded - in ```perfect_output.nc```. The default filename for the - 'observations' is ```obs_seq.out```. -3. [Assimilate the synthetic observations](#run_filter) with - [filter](../../assimilation_code/programs/filter/filter.html) in - the usual way. The prior/forecast states are preserved in - ```preassim.nc``` and the posterior/analysis states are preserved in - ```filter_output.nc``` . The default filename for the file with the - observations and (optionally) the ensemble estimates of the - observations is ```obs_seq.final``` . -4. [Check to make sure the assimilation was effective\!](#run_diagnostics) - Ensemble DA is not a black box\! - YOU must check to make sure you are making effective use of the - information in the observations\! - - - -### 1. Defining the observation metadata - the 'blueprint'. - -There are lots of ways to define an observation sequence that DART can -use as input for a perfect model experiment. If you have observations in -DART format already, you can simply use them. If you have observations -in one of the formats already supported by the DART converters -(check [DART/observations/obs_converters/README.md](obs_converters/README.md)), -convert it to a DART observation sequence. You may need to use the -[obs_sequence_tool](../../assimilation_code/programs/obs_sequence_tool/obs_sequence_tool.html) -to combine multiple observation sequence files into observation sequence -files for the perfect model experiment. Any existing observation values -and quality control information will be ignored by *perfect_model_obs*; -only the time and location information are used. In fact, any and all -existing observation and QC values will be removed. - -GENERAL COMMENT ABOUT THE INTERPLAY BETWEEN THE MODEL STOP/START -FREQUENCY AND THE IMPACT ON THE OBSERVATION FREQUENCY: There is usually -a very real difference between the dynamical timestep of the model and -when it is safe to stop and restart the model. The assimilation window -is (usually) required to be a multiple of the safe stop/start frequency. -For example, an atmospheric model may have a dynamical timestep of a few -seconds, but may be constrained such that it is only possible to -stop/restart every hour. In this case, the assimilation window is a -multiple of 3600 seconds. Trying to get observations at a finer -timescale is not possible, we only have access to the model state when -the model stops. - -If you do not have an input observation sequence, it is simple to create one. - -1. Run - [create_obs_sequence](../../assimilation_code/programs/create_obs_sequence/create_obs_sequence.html) - to generate the blueprint for the types of observations and - observation error variances for whatever locations are desired. -2. Run - [create_fixed_network_seq](../../assimilation_code/programs/create_fixed_network_seq/create_fixed_network_seq.html) - to define the temporal distribution of the desired observations. - -Both *create_obs_sequence* and *create_fixed_network_seq* -interactively prompt you for the information they require. This can be -quite tedious if you want a spatially dense set of observations. People -have been known to actually write programs to generate the input to -*create_obs_sequence* and simply pipe or redirect the information into -the program. There are several examples of these in the -*models/bgrid_solo* directory: ```column_rand.f90, -id_set_def_stdin.f90, ps_id_stdin.f90,``` and ```ps_rand_local.f90``` . -Be advised that some observation types have different input -requirements, so a 'one size fits all' program is a waste of time. - -NOTE: only the observation kinds in the ```input.nml  -&obs_kind_nml:assimilate_these_obs_types,evaluate_these_obs``` -will be available to the *create_obs_sequence* program. - -DEVELOPERS TIP: You can specify 'identity' observations as input to -*perfect_model_obs*. Identity observations are the model values AT the -exact gridcell location, there is no interpolation at all. Just a -straight table-lookup. This can be useful as you develop your model -interfaces; you can test many of the routines and scripts without having -a working *model_interpolate()*. - -More information about creating observation sequence files for OSSE's is -available in the -[Synthetic Observations section](Observations.md#obs_synthetic). - - - -### 2. Generating the true state and harvesting the observation values - *perfect_model_obs* - -[perfect_model_obs](../../assimilation_code/programs/perfect_model_obs/perfect_model_obs.html) -reads the blueprint and an initial state and applies the appropriate -forward observation operator for each and every observation in the -current 'assimilation window'. If necessary, the model is advanced until -the next set of observations is desired. When it has run out of -observations or reached the stop time defined by the namelist control, -the program stops and writes out restarts, diagnostics, observation -sequences, and a log file. This is fundamentally a single deterministic -forecast for 'as long as it takes' to harvest all the observations. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
default filenameformatcontents
perfect_input.ncnetCDFThe DART model state to start from. If the variables have a time dimension, The last timestep will be used as the starting point.
perfect_output.ncnetCDFThe DART model state at every assimilation timestep. This file has but one 'copy' - the truth. Dump the metadata and the time:
-ncdump -v time,MemberMetadata perfect_output.nc
obs_seq.outASCII or binary  
-DART-specific linked list		This file has the observations - the result of the forward -observation operator. This observation sequence file has two 'copies' -of the observation: the noisy 'copy' and the noise-free 'copy'. -The noisy copy is designated as the 'observation', the noise-free -copy is the truth. The observation-space diagnostic program -obs_diag has special options for using the true copy instead -of the observation copy. See the -obs_diag.html for details.
dart_log.outASCIIThe run-time output of perfect_model_obs .
- -Each model may define the assimilation window differently, but -conceptually, all the observations plus or minus half the assimilation -window are considered to be simultaneous and a single model state -provides the basis for all those observations. For example: if the -blueprint requires temperature observations every 30 seconds, the -initial model time is noon (12:00) and the assimilation window is 1 -hour; all the observations from 11:30 to 12:30 will use the same state -as input for the forward observation operator. The fact that you have a -blueprint for observations every 30 seconds means a lot of those -observations may have the same value (if they are in the same location). - -*perfect_model_obs* uses the *input.nml* for its control. A subset of -the namelists and variables of particular interest for -*perfect_model_obs* are summarized here. Each namelist is fully -described by the corresponding module document. - -~~~ -&perfect_model_obs_nml - ... - read_input_state_from_file = .true. # some models can start from preset ICs - single_file_in = .true # some models have nested domains ... - input_state_files = 'perfect_input.nc' # list of files ... for each domain - write_output_state_to_file = .true. - single_file_out = .true. - output_state_files = 'perfect_output.nc' # the time-evolution of the true state - async = 0 # totally depends on the model - adv_ens_command = './advance_ens.csh' # depends on the model - obs_seq_in_file_name = 'obs_seq.in' - obs_seq_out_file_name = 'obs_seq.out' - init_time_days = -1 # negative means use the time in ... - init_time_seconds = -1 # the 'restart_in_file_name' file - first_obs_days = -1 # negative means start at the first time in ... - first_obs_seconds = -1 # the 'obs_seq_in_file_name' file. - last_obs_days = -1 # negative means to stop with the last ... - last_obs_seconds = -1 # observation in the file. - / - -&obs_sequence_nml - write_binary_obs_sequence = .false. #.false. will create ASCII - easy to check. - / - -&obs_kind_nml - ... - assimilate_these_obs_types = 'RADIOSONDE_TEMPERATURE', - ... # list all the synthetic observation - ... # types you want - / - -&model_nml - ... - time_step_days = 0, # some models call this 'assimilation_period_days' - time_step_seconds = 3600 # some models call this 'assimilation_period_seconds' - # use what is appropriate for the model - / - -&utilities_nml - ... - termlevel = 1 # your choice - logfilename = 'dart_log.out' # your choice - / -~~~ - -Since *perfect_model_obs* generally requires advancing the model, and -the model may use MPI or require special ancillary files or forcing -files or ..., it is not possible to provide a single example that will -cover all possibilities. The subroutine-callable models (i.e. the -low-order models) can run *perfect_model_obs* very simply: - -> ./perfect_model_obs - - - -### 3. Performing the assimilation experiment - *filter* - -This step is done with the program -[filter](../../assimilation_code/programs/filter/filter.html), which -also uses ```input.nml``` for input and run-time control. A successful -assimilation will depend on many things: an approprite initial ensemble, -monitoring and perhaps correcting the ensemble spread, localization, -etc. It is simply not possible to design a one-size-fits-all system that -will work for all cases. It is **critically important** to analyze the -results of the assimilation and explore ways of making the assimilation -more effective. -The [DART tutorial](dart_tutorial.md) and the -[DART_LAB](dart_lab.md) exercises -are an invaluable resource to learn and understand how to determine the -effectiveness of, and improve upon, an assimilation experiment. The -concepts learned with the low-order models are directly applicable to -the most complicated models. - -**It is important to remember that if *filter* 'terminates normally', it -does not necessarily mean the assimilation was effective\!** - -The Manhattan release of DART allows for a very high degree of -customization when it comes to output. To stay focused on the concepts, -I will restrict the examples to models that have -```single_file_in=.true.```, ```single_file_out=.true.```, and -```stages_to_write='preassim','output'```. - -*filter* generally produces at least two state-space output diagnostic -files (```preassim.nc``` and ```filter_output.nc```) which contains values of -the ensemble mean, ensemble spread, perhaps the inflation values, and -(optionally) ensemble members for the duration of the experiment. -*filter* also creates an observation sequence file that contains the -input observation information as well as the prior and posterior -ensemble mean estimates of that observation, the prior and posterior -ensemble spread for that observation, and (optionally), the actual prior -and posterior ensemble estimates of that observation. Rather than -replicate the observation metadata for each of these, the single -metadata is shared for all these 'copies' of the observation. See -[An overview of the observation sequence](Observations.md#obs_seq_overview) -for more detail. -*filter* also produces a run-time log file that can greatly aid in -determining what went wrong if the program terminates abnormally. - -A very short description of some of the most important namelist -variables is presented here. Basically, I am only discussing the -settings necessary to get *filter* to run. I can guarantee these -settings WILL NOT generate the BEST assimilation. Again, see the module -documentation for a full description of each namelist. - -~~~ -&filter_nml <--- link to the full namelist description! - async = 0 - ens_size = 40 # something ≥ 20, please - num_output_state_members = 40 # of FULL DART model states to put in state-space output files - num_output_obs_members = 40 # of ensemble member estimates of observation to save - obs_sequence_in_name = 'obs_seq.out' # output from perfect_model_obs - obs_sequence_out_name = 'obs_seq.final' - init_time_days = -1 # the time in the restart file is correct - init_time_seconds = -1 - first_obs_days = -1 # same interpretation as with perfect_model_obs - first_obs_seconds = -1 - last_obs_days = -1 # same interpretation as with perfect_model_obs - last_obs_seconds = -1 - - single_file_in = .true. - input_state_file_list = 'filter_input_list.txt' file containing the list of input files - 1 per domain - stages_to_write = 'preassim', 'output' - single_file_out = .true. - output_state_file_list = 'filter_output_list.txt' file containing the list of (desired) output files - 1 per domain - write_all_stages_at_end = .false. - - inf_flavor = 0, 0 0 is 'do not inflate' - ... - / - - -&quality_control_nml - input_qc_threshold = 3.0, - outlier_threshold = 3.0 # Observation rejection criterion! - / - -&assim_tools_nml - filter_kind = 1 1 is EAKF, 2 is EnKF ... - cutoff = 0.2 this is your localization - units depend on type of 'location_mod' - / - -&obs_kind_nml - assimilate_these_obs_types = 'RAW_STATE_VARIABLE' Again, use a list ... appropriate for your model - / - -&model_nml - assimilation_perior_days = 0 the assimilation interval is up to you - assimilation_perior_seconds = 3600 - / -~~~ - -Once the namelist is set, execute *filter* to integrate the ensemble -forward with the final ensemble state written to the files in -```filter_output_list.txt```. For the low-order models and *bgrid_solo* -(i.e. the models that can be run with *single_file_in = .true.* and -```single_file_out = .true.```) the default filenames will be -```preassim.nc``` and ```filter_output.nc``` and will contain values -for 40 ensemble members once a day. - -~~~ -mpirun ./filter -OR- - -mpirun.lsf ./filter -OR- - -./filter -OR- - -however YOU run filter on your system! -~~~ - - - -### 4. ASSESS THE PERFORMANCE! - -All the concepts of spread, rmse, rank histograms that were taught in -the DART tutorial and in DART_LAB should be applied now. Try the -techniques described in the -[Did my experiment work?](Diagnostics.md#DidItWork) section. -The 'big three' state-space diagnostics are repeated here because -they are so important. -The first two require the ```perfect_output.nc```. - -| | | -| --- | --- | -| *plot_bins.m* | plots the rank histograms for a set of state variables. This requires you to have all or most of the ensemble members available in the ```preassim.nc``` or ```filter_output.nc``` files. | -| *plot_total_err.m* | plots the evolution of the error (un-normalized) and ensemble spread of all state variables. | -| *plot_ens_mean_time_series.m* | plots the evolution of a set of state variables - just the ensemble mean (and Truth, if available). *plot_ens_time_series.m* is actually a better choice if you can afford to write all/most of the ensemble members to the ```preassim.nc``` and ```filter_output.nc``` files. | - -### DON'T FORGET ABOUT THE OBSERVATION-SPACE DIAGNOSTICS\! - - - - -\[[top](#)\] - ------ - -# Adding a model to DART - Overview - - - - [](#own_model_requirements) - -## Requirements: if you have your own model - -If you want to run your own model -all you need is an executable and some scripts to interface with DART - we have -templates and examples. If your model can be called as a subroutine, *life is good*. -Again - we have -templates, examples, and a [../../models/README.md](models/README.md) -describing the required interfaces. - -Starting with the Jamaica release, there is an option to compile with -the MPI (Message Passing Interface) libraries in order to run the -assimilation step in parallel on hardware with multiple CPUs. Note that -this is optional; MPI is not required. If you do want to run in -parallel, then we also require a working MPI library and appropriate -cluster or SMP hardware. See the -[MPI intro](dart_mpi.html) -for more information on running with the MPI option. - -One of the beauties of ensemble data assimilation is that even if -(particularly if) your model is single-threaded, you can still run -efficiently on parallel machines by dealing out each ensemble member (a -unique instance of the model) to a separate processor. If your model -cannot run single-threaded, fear not, DART can do that too, and simply -runs each ensemble member one after another using all the processors for -each instance of the model. - - - -DART is designed to work with many models *without* modifications to the -DART routines **or** the model source code. DART can 'wrap around' your -model in two ways. One can be used if your model can be called as a -subroutine, the other is for models that are separate executables. -Either way, there are some steps that are common to both paths. - -Please be aware that several of the high-order models (CAM and WRF, in -particular) have been used for years and their scripts have incorporated -failsafe procedures and restart capabilities that have proven to be -useful but make the scripts complex - more complex than need be for the -initial attempts. Truly, some of the complexity is no longer required -for available platforms. Then again, we're not running one instance of a -highly complicated computer model, we're running N of them. - -*NEW* The DART Manhattan release provides native netCDF read/write -support. Consequently, there is no need for translation routines that we -have traditionally been calling *model_to_dart* or *dart_to_model*. -If, however, your model does not use netCDF for I/O, these programs must -be written. We have a lot of experience writing these converters - you -should not be afraid to ask for advice or for code to start from. - -*NEW* Manhattan provides a program to help test the required interfaces: -[assimilation_code/programs/model_mod_check/model_mod_check.f90](../../assimilation_code/programs/model_mod_check/model_mod_check.html). -Many models start with this and modify it to suit their needs. Be aware -that some of the model-specific *model_mod_check.f90* programs use -deprecated features. Focus on the ones for Manhattan-compliant -models. - -## The basic steps to include your model in DART - each of these topics has its own section farther down. - -1. Copy the *models/template* directory and files to your own DART - model directory. -2. Modify the *model_mod.f90* file to return specifics about your - model. This module MUST contain all the required interfaces (no - surprise) but it can also contain more interfaces as is convenient. - The required interfaces calling syntax (argument list) should not be - modified in any way. -3. *If your model cannot be called as a subroutine:* Modify - *shell_scripts/advance_model.csh* to collect all the input files - needed to advance the model into a clean, temporary directory, - convert the state vector file into input to your model, run your - model, and convert your model output to the expected format for - another assimilation by DART. DART will write out a control file - that contains some information that must be passed to - *advance_model.csh*: for example, the number of ensemble members, - the input and output filenames for each ensemble member, etc. - 1. Prepare a directory (or multiple directories) with the contents - needed to advance your model. - 2. Modify the input to your model communicating the run-time - settings necessary to integrate your model from one time to - another *arbitrary* time in the future. - 3. Convert (if necessary) your input file to netCDF. - 4. Run the model (you may need to watch the MPI syntax) - 5. Convert (if necessary) the model output to a netCDF file DART - can use for the next assimilation. -4. *If a single instance of your model needs to advance using all the - MPI tasks*, there is one more script that needs to work - - *shell_scripts/run_filter.csh*. This script must do quite a lot. - Find some examples in the *models/\*/shell_scripts* directories. -5. \[optional step\] Modify the MATLAB® routines to know about the - specifics of the netCDF files produces by your model (sensible - defaults, for the most part.) -6. *Test*. Generally, it is a good strategy to use DART to create a - synthetic observation sequence with *ONE* observation location - and - *ONE* observation type - for several assimilation periods. With - that, it is possible to run *perfect_model_obs* and then *filter* - without having to debug too much stuff at once. A separate document - will address how to test your model with DART. - -## Programming style - -\#1 Don't shoot the messenger. We have a lot of experience trying to -write portable/reproducible code and offer these suggestions. All of -these suggestions are for the standalone DART components. We are not -asking you to rewrite your model. If your model is a separate -executable, leaving it untouched is fine. **Writing portable code for -the DART components will allow us to include your model in the nightly -builds and reduces the risk of us making changes that adversely affect -the integration with your model.** There are some routines that have to -play with the core DART routines, these are the ones we are asking you -to write using these few simple guidelines. - - - Use explicit typing, do not use or rely on the 'autopromote' flag on - your compiler. - - Use the *intent()* attribute. - - Use the *use, xxx_mod, only : bob, sally* statements for routines - from other modules. This really helps us track down things and - ensures you're using what you think you're using. - - Use Fortran namelists for I/O if possible. - - Check out the existing parameters/routines in - *assimilation_code/modules/utilities/types_mod.f90*, - *assimilation_code/modules/utilities/utilities_mod.f90*, and - *assimilation_code/modules/utilities/time_manager_mod.f90*. You - are free to use these and are encouraged to do so. No point - reinventing the wheel and these routines have been tested - extensively. - -Hopefully, you have no idea how difficult it is to build each model with -'unique' compile options on N different platforms. Fortran90 provides a -nice mechanism to specify the type of variable, please do not use -vendor-specific extensions. (To globally autopromote 32bit reals to -64bit reals, for example. That is a horrible thing to do, since vendors -are not consistent about what happens to explicitly-typed variables. -Trust me. They lie. It also defeats the generic procedure interfaces -that are designed to use a single interface as a front-end to multiple -'type-specific' routines.) Compilers do abide by the standard, however, -so DART code looks like: - -~~~ - character(len=8) :: crdate - integer, dimension(8) :: values - ... - real(r4) :: a,b - real(r8) :: bob - integer :: istatus, itype - ... - real(r8), intent(in) :: x(:) - type(location_type), intent(in) :: location - integer, intent(in) :: itype - integer, intent(out) :: istatus - real(r8), intent(out) :: obs_val -~~~ - -depending on the use. The *r4* and *r8* types are explicitly defined in -*assimilation_code/modules/utilities/types_mod.f90* to accurately -represent what we have come to expect from 32bit and 64bit floating -point real variables, respectively. If you like, you can redefine *r8* -to be the same as *r4* to shrink your memory requirement. The people who -run with WRF frequently do this. Do not redefine the *digits12* -parameter, that one must provide 64bit precision, and is used in -precious few places. - ------ - - - -## Adding a model to DART - Specifics - -If your model is a separate executable, it would be wise to look at the -heavily commented template script -[models/template/shell_scripts/advance_model.csh](https://github.com/ncar/dart/models/template/shell_scripts/advance_model.csh) -and then a few higher-order models to see how they do it. -Become familiar with [DART's use of MPI](dart_mpi.html), the -[options for parallelism](filter_async_modes.html), and -the *filter* namelist parameter -[*async*](../../assimilation_code/programs/filter/filter.html#Namelist). - - - -### 1. Copying the template directory - -A little explanation/motivation is warranted. If the model uses the -standard layout, it is much easier to include the model in the nightly -builds and testing. For this reason alone, please try to use the -recommended directory layout. Simply looking at the *DART/models* -directory should give you a pretty good idea of how things should be -laid out. Copy the *template* directory and its contents. -The point of copying this directory is to get a ```model_mod.f90``` that works -as-is and you can modify/debug the routines one at a time. - -The destination directory (your model directory) should be in the -*DART/models* directory to keep life simple. Moving them around will -cause problems for the ```work/mkmf_xxxxx``` configuration files. Each -model directory should have a *work* and *shell_scripts* directories, -and may have a *matlab* directory, a *src* directory, or anything else -you may find convenient. - -Now, you must change all the ```work/path_names_xxxxx``` file contents to -reflect the location of your ```model_mod.f90```. - - - -### 2. model_mod.f90 - -We have templates, examples, and a document describing the required -interfaces in the DART code tree - -[DART/models/template/model_mod.html](Manhattan/models/template/model_mod.html). -Every(?) user-visible DART program/module is intended to have a matching -piece of documentation that is distributed along with the code. The DART -code tree always has the most current documentation. - -Check out *time_manager_mod.f90* and *utilities_mod.f90* for -general-purpose routines ... - -Use Fortran namelists for I/O if possible. - -Modify the *model_mod.f90* file to return specifics about your model. -This module MUST contain all the required interfaces (no surprise) but -it can also contain many more interfaces as is convenient. This module -should be written with the understanding that print statements and error -terminations will be executed by multiple processors/tasks. To restrict -print statements to be written once (by the master task), it is -necessary to preface the print as in this example: -~~~ -if (do_output()) write(*,*)'model_mod:namelist cal_NML',startDate_1,startDate_2 -~~~ - - - -#### Required Interfaces in model_mod.f90 - -No matter the complexity of the model, the DART software requires a few -interface routines in a model-specific Fortran90 module *model_mod.f90* -file. The *models/template/model_mod.f90* file has extended comment -blocks at the heads of each of these routines that go into much more -detail for what is to be provided. **You cannot change the types or -number of required arguments to any of the required interface -routines.** You can add optional arguments, but you cannot go back -throught the DART tree to change the gazillion calls to the mandatory -routines. It is absolutely appropriate to look at existing models to get -ideas about how to implement the interfaces. Finding a model -implementation that is functionally close to yours always helps. - -The table of the mandatory interfaces and expected programming -degree-of-difficulty -is: - -| subroutine callable | separate executable | routine | description | -| ------------------- | ------------------- | ------- | ----------- | -| easy | easy | [get_model_size](Manhattan/models/template/model_mod.html#get_model_size) | This function returns the size of all the model variables (prognostic or diagnosed or ...) that are packed into the 1D DART state vector. That is, it returns the length of the DART state vector as a single scalar integer. | -| depends | trivial | [adv_1step](Manhattan/models/template/model_mod.html#adv_1step) | For subroutine-callable models, this routine is the one to actually advance the model 1 timestep (see *models/bgrid_solo/model_mod.f90* for an example). For non-subroutine-callable models, this is a NULL interface. Easy. | -| depends | depends | [get_state_meta_data](Manhattan/models/template/model_mod.html#get_state_meta_data) | This routine takes as input an integer into the DART state vector and returns the associated location and (optionally) variable type from *obs_kind/obs_kind_mod.f90*. (See *models/\*/model_mod.f90* for examples.) Since DART uses netCDF and is responsible for the storage order, this is generally pretty easy. | -| easy | easy | [shortest_time_between_assimilations](Manhattan/models/template/model_mod.html#shortest_time_between_assimilations) | This routine returns the smallest increment in time (in seconds) that the model is capable of advancing the state in a given implementation. For example, the dynamical timestep of a model is 20 minutes, but there are reasons you don't want to (or cannot) restart at this interval and would like to restart AT MOST every 6 hours. For this case, *shortest_time_between_assimilations* should return *21600*, i.e. 6\*60\*60. This is also interpreted as the nominal **assimilation period**. This interface is required for all applications. | -| easy | easy | [end_model](Manhattan/models/template/model_mod.html#end_model) | Performs any shutdown and cleanup needed. Good form would dictate that you should deallocate any storage allocated when you instantiated the model (from *static_init_model*, for example). | -| depends | depends | [static_init_model](Manhattan/models/template/model_mod.html#static_init_model) | Called to do one-time initialization of the model. This generally includes setting the grid information, calendar, etc. | -| trivial | trivial | [init_time](Manhattan/models/template/model_mod.html#init_time) | Returns a time that is somehow appropriate for starting up a long integration of the model **IFF** the *&perfect_model_obs_nml* namelist parameter *read_input_state_from_file = .false.* If this option is not to be used in *perfect_model_obs*, this can be a routine that simply throws a fatal error. | -| easy | easy | [init_conditions](Manhattan/models/template/model_mod.html#init_conditions) | Companion interface to *init_time*. Returns a model state vector that is somehow appropriate for starting up a long integration of the model. Only needed **IFF** the *&perfect_model_obs_nml* namelist parameter *read_input_state_from_file = .false.* | -| trivial - difficult | trivial - difficult | [nc_write_model_atts](Manhattan/models/template/model_mod.html#nc_write_model_atts) | This routine is used to write the model-specific attributes to netCDF files created by DART. If you are simply updating existing (template) netCDF files, this routine is very easy. The subroutine in the *models/template/model_mod.f90* WILL WORK for new models but does not know anything about prognostic variables or geometry or ... Still, it is enough to get started without doing anything. More meaningful coordinate variables etc. are needed to supplant the default template. This can be as complicated as you like - see existing models for examples. | -| trivial - medium | trivial - medium | [nc_write_model_vars](Manhattan/models/template/model_mod.html#nc_write_model_vars) | This routine is currently unused but anticipated for future enhancements. The default routine in *default_model_mod* should be referenced. | -| trivial | trivial | [get_close_obs](Manhattan/models/template/model_mod.html#get_close_obs) | This is the routine that takes a single observation location and a list of other observation locations, returns the indices of all observation locations close to the single observation along with the number and the distances for the close ones. This is generally a 'pass-through' routine to a routine of the same name in the location module. | -| trivial | trivial | [get_close_state](Manhattan/models/template/model_mod.html#get_close_state) | This is the routine that takes a single observation location and a list of state locations, returns the indices of all the state locations close to the observation as well as the number and the distances for the close ones. This is generally a 'pass-through' routine to a routine of the same name in the location module. | -| depends | hard | [model_interpolate](Manhattan/models/template/model_mod.html#model_interpolate) | This is one of the more difficult routines. Given a DART state vector, a location, and a desired generic 'quantity' (like QTY_SURFACE_PRESSURE, QTY_TEMPERATURE, QTY_SPECIFIC_HUMIDITY, QTY_PRESSURE, ... ); return the desired scalar quantity and set the return status accordingly. This is what enables the model to use observation-specific 'forward operators' that are part of the common DART code. | -| depends | trivial - difficult | [convert_vertical_obs](Manhattan/models/template/model_mod.html#convert_vertical_obs) | If needed, the difficulty lies in the complexity of the model vertical coordinate system. | -| depends | trivial - difficult | [convert_vertical_state](Manhattan/models/template/model_mod.html#convert_vertical_state) | If needed, the difficulty lies in the complexity of the model vertical coordinate system. | -| depends | trivial - difficult | [pert_model_copies](Manhattan/models/template/model_mod.html#pert_model_copies) | This routine is used to generate initial ensembles. This may be a NULL interface if you can tolerate the default perturbation strategy of adding noise to every state element **or** if you generate your own ensembles outside the DART framework. There are other ways of generating ensembles ... climatological distributions, bred singular vectors, voodoo ... | -| easy | easy | [read_model_time](Manhattan/models/template/model_mod.html#read_model_time) | This routine simply stores a copy of the ensemble mean of the state vector within the *model_mod*. The ensemble mean may be needed for some calculations (like converting model sigma levels to the units of the observation - pressure levels, for example). | -| easy | easy | [write_model_time](Manhattan/models/template/model_mod.html#read_model_time) | This routine simply stores a copy of the ensemble mean of the state vector within the *model_mod*. The ensemble mean may be needed for some calculations (like converting model sigma levels to the units of the observation - pressure levels, for example). | - -### If your model is subroutine-callable - you're done\! - - - -## Adding support for a "simple" model - -### overview - -each routine includes usual things it often has to do for subroutine-callable -models which can manufacture an initial condition state vector. -[model_mod_check.f90](../../assimilation_code/programs/model_mod_check/model_mod_check.html) -can be used to test these routines individually before you run it with *filter*. -start with all defaults from other modules and add, in order the following -routines: - -1. init_time() - -2. init_conditions() -for a "cold start" fill in an empty state vector with initial conditions and set the initial time. if the state vector is all 0s and the time is 0, you can use the default routines. - -3. get_model_size() -return number of items in the state vector - -**if you have only a single type of variable in your state vector, use the next two:** - -4. static_init_model() -often your model_size is set by namelist. allocate an array of that size and precompute all the locations for each state vector item. call add_domain() with the model size so dart knows how long the state vector is. - -5. get_state_meta_data() -return QTY_STATE_VARIABLE as the quantity if present, and return the location for that index by looking it up in a location array. - -**if you have more than a single type of variable in the state vector:** - -4. static_init_model() -read namelist to see how many fields are going to be read in for the state -vector. use add_domain() to indicate which netcdf vars should be read. read in -any auxiliary data needed by interpolation code (eg. topology). read template -file to set grid locations. use get_domain_size() to compute model_size. - -5. get_state_meta_data() -call get_model_variable_indices() and get_state_kind() to figure -out the i,j,k indices and which variable this offset is. use the i,j,k -to compute the grid location and return it along with the quantity. - -**now continue** - -6. end_model() -deallocate any arrays allocated in static_init_model() - -**at this point you can assimilate identity obs at the model time** - -7. adv_1step() -if possible, embed the code that computes x(t+1) = F(x(t)). or call a -separate subroutine to advance the model state from one time to another. - -8. shortest_time_between_assimilations() -return a namelist or a fixed value for the minimum model advance time. - -**at this point you can assimilate a time series of identity obs** - -9. model_interpolate() -where the bulk of the work often is. this routine gets passed the location and quantity of the observation. find the indices which enclose that location and interpolate to get an array of expected values. - -**at this point you can assimilate obs at locations other than state vector points.** - -10. nc_write_model_atts() -add attributes to the output diagnostic files. - -**anything below here generally can use the default routines in other modules:** - -11. read_model_time() - -12. write_model_time() -generally can use the system defaults, unless you have a model restart file that already stores time in a particular format. - -13. pert_model_copies() -the default is to add gaussian noise to the entire model state. if you -want to only perturb a single variable, or perturb it with different -noise ranges you can add code here. used to generate an ensemble from a -single model state for *filter*. - -14. convert_vertical_obs() - -15. convert_vertical_state() -unused in models without vertical coordinate choices - -16. get_close_obs() - -17. get_close_state() -often unused unless you want to modify the localization behavior - -18. nc_write_model_vars() -not currently called, leave it using the default routine. here for -possible future implementation. - - - -## Adding support for a "complex" model - -### overview - -Each routine includes usual things it often has to do for a large -geophysical model. this is different from the low order models. -[model_mod_check.f90](../../assimilation_code/programs/model_mod_check/model_mod_check.html) -can be used to test these routines individually before you run it with -*filter*. start with all defaults from other modules and add, in order -the following routines: - -1. static_init_model() -read namelist to see how many fields are going to be read in for the -state vector. use add_domain() to indicate which netcdf vars should be -read. read in any auxiliary data needed by interpolation code (eg. -topology). read template file to set grid locations. use -get_domain_size() to compute model_size. - -2. end_model() -deallocate any arrays allocated in static_init_model() - -3. get_model_size() -return model_size computed in static_init_model() - -4. shortest_time_between_assimilations() -return a namelist or a fixed value for the minimum model advance time. - -5. read_model_time() - -6. write_model_time() -if the time is stored in the netcdf files, supply routines that can read -and write it in the correct format. we have default routines that work -if it matches what those routines expect: a time variable with an -optional calendar variable. if none, it's fractional days. if the time -variable is an array, read/write the last one. - -7. get_state_meta_data() -call get_model_variable_indices() and get_state_kind() to figure -out the i,j,k indices and which variable this offset is. use the i,j,k -to compute the grid location and return it along with the quantity. - -**now you can assim identity obs** - -8. model_interpolate() -where the bulk of the work will be. get the location and quantity of the -observation. find the i,j,k indices which enclose that location, or -search for the cell number. can compute i,j in a regular lat/lon grid, -have to search in a deformed grid. if multiple vertical options, -different ensemble members may result in more than a single level. use -get_state() to get the ensemble-sized array of values for each offset -into the state vector, and do interpolation to get an array of expected -values. - -**other obs** - -9. nc_write_model_atts() -can leave for later. eventually add grid info to the diag files for -plotting. - -10. convert_vertical_obs() - -11. convert_vertical_state() -if this model has a choice of multiple vertical coordinates (e.g. -pressure, height, etc) add code here to convert between the possible -verticals. - -12. get_close_obs() - -13. get_close_state() -if you want to change the impact based on something other than the type -or kind, put code here. should test for vertical and do the conversion -on demand if it hasn't already been done. - -14. pert_model_copies() -the default is to add gaussian noise to the entire model state. if you -want to only perturb a single variable, or perturb it with different -noise ranges you can add code here. used to generate an ensemble from a -single model state for *filter*. - -15. init_time() - -16. init_conditions() - -17. adv_1step() -generally not used in large geophysical models, but if you can generate -a single model state without reading in a file, supply code in -init_conditions. if you can advance the model via a subroutine, add the -code to adv_1step. - -18. nc_write_model_vars() -not currently called, leave it using the default routine. here for -possible future implementation. - - - -## Cycling Models - -### Overview - -For simple models which can be advanced by a subroutine call, the -*filter* program is driven by the input observation sequence. It -assimilates all observations in the current assimilation window and then -advances the model state until the window includes the next available -observation. When it runs out of observations, *filter* exits. - -For complex models which are themselves an MPI program or have -complicated scripting to run the model here are some simplified -considerations for scripting an experiment. A "cycling script" would -need to: - -1. \[if needed\] Run the ensemble of models forward to the time of the - first observation. -2. The input observation sequence file should be created (or trimmed) - to only include observations in the current window. -3. Run *filter* to assimilate all observations in the current window. -4. Save a copy of the output files and diagnostic files. Often a - timestamp is used as part of the filename or subdirectory name to - make it unique. -5. Run the ensemble of models forward in time. -6. Run *filter* again. -7. Repeat until all observations have been assimilated. - -### In More Detail - -The *filter* program requires an ensemble of model output files in -NetCDF file format as input. If the model does not use NetCDF a -translation step from the model native format to NetCDF is needed. The -files are often named with the ensemble number as part of the name and -also with a timestamp as part of the filename or part of a subdirectory -name which contains all the files for that timestep. Symbolic links can -be used to link a common simpler name to a file with a timestamp in the -filename or directory name. - -The *filter* program also requires an input observation sequence file. -Often these are named with a timestamp to indicate the central time of -the observations, e.g. *obs_seq.2010-10-04.00:00:00* and then a common -name (e.g. *obs_seq.out*) is used with a symbolic link to indicate the -right file for input. - -If adaptive inflation is being used the *filter* program also requires -inflation input files. Again, timestamps in the names with a common -symbolic link name are often used here. - -The *filter* program runs. - -The output of the *filter* program include updated model files using one -of three different workflows: - -1. The *filter* program directly overwrites the input files. - - Advantages: uses the least amount of disk usage and minimizes - file copying. - - Disadvantages: if something crashes the files can be left in an - indeterminate state making restarting more complicated. -2. The script copies the input files to the output names, and the - *filter* program updates the existing files. - - Advantages: The *filter* program can easily be restarted in case - of problems because the original input files are unchanged. The - output files are immediately available to be used as input to - the model. - - Disadvantages: uses more disk space. -3. The *filter* program creates new output files from scratch. - - Advantages: The output files are smaller since they only contain - the state vector and no other grid or auxiliary information. The - *filter* program can easily be restarted in case of problems. - - Disadvantages: generally requires a post-processing step to - insert the updated state information into full model restart - files. - -The script should also save the *obs_seq.final* diagnostic file, -possibly with a timestamp in the filename or subdirectory name, and the -updated inflation files in the case where adaptive inflation is used. - -The script can run the ensemble of models forward in time in many ways. -A few of the ways we're aware of are: - -1. If a queuing system is available, the ensemble of models can be - submitted either as independent jobs or using the batch system's job - array syntax. They run as soon as resources are available. The - disadvantage is it can be complicated to know when all the jobs have - finished successfully. -2. On smaller clusters the ensemble members can be advanced one after - the other in a loop. There is no question about when the last member - has been advanced and it requires no more resources than running a - single copy of the model. The disadvantage is this is the slowest - wall-clock way to advance the ensemble. - - - -### 3. advance_model.csh - -### The Big Picture for models advanced as separate executables. - -The normal sequence of events is that DART reads in its own restart file -(do not worry about where this comes from right now) and eventually -determines it needs to advance the model. DART needs to be able to take -its internal representation of each model state vector, the valid time -of that state, and the amount of time to advance the state - and -communicate that to the model. When the model has advanced the state to -the requested time, the output must be ingested by DART and the cycle -begins again. DART is entirely responsible for reading the observations -and there are several programs for creating and manipulating the -observation sequence files. - -There are a couple of ways to exploit parallel architectures with DART, -and these have an immediate bearing on the design of the script(s) that -control how the model instances (each model *copy*) are advanced. -Perhaps the conceptually simplest method is when each model instance is -advanced by a single processor element. DART calls this *async = 2*. It -is generally efficient to relate the ensemble size to the number of -processors being used. - -The alternative is to advance every model instance one after another -using all available processors for each instance of the model. DART -calls this *async = 4*, and requires an additional script. For -portability reasons, DART uses the same processor set for both the -assimilation and the model advances. For example, if you advance the -model with 96 processors, all 96 processors will be employed to -assimilate. If your model requires 2000 processors, all 2000 will be -employed for the assimilation. Some people exploit the queueing systems -on their large machines to allow for the explicit customization of how -many tasks are used for each model advance and for an assimilation. - -*advance_model.csh* is invoked in one of two ways: 1) if *async = 2* -then *filter* uses a *system()* call, or 2) if *async = 4* then -*run_filter.csh* makes the call. Either way there are three arguments. - -1. the process number of the caller - could be the master task ID - (zero) or (especially if *async = 2*) a process id that gets related - to the copy. When multiple copies are being advanced simultaneously, - each of the advances happens in its own run-time directory. -2. the number of state copies belonging to that process -3. the name of the (ASCII) filter_control_file for that process. The - filter_control file contains the following information (one per - line): the ensemble member, the name of the input file (containing - the DART state vector), and the name of the output file from the - model containing the new DART state vector. For example, - -
- 1 - assim_model_state_ic.0001 - assim_model_state_ud.0001 - 2 - assim_model_state_ic.0002 - assim_model_state_ud.0002 - ... -
- -#### async = 2 ... advancing many copies at the same time - -Modify *shell_scripts/advance_model.csh* to: - -1. Collect all the input files needed to advance the model into a - clean, temporary directory. -2. Create a routine or set of routines to modify the input to your - model communicating the run-time settings necessary to integrate - your model from one time to another *arbitrary* time in the future. - These routines are called in the *advance_model.csh* script. Every - model is controlled differently, so writing detailed descriptions - here is pointless. -3. Determine how many tasks you have, and how many ensemble members you - have. Determine how many 'batches' of ensemble members must be done - to advance all of them. With 20 tasks and 80 ensemble members, you - will need to loop 4 times, (for example) clean the temporary directory, - and -4. Loop over the following steps - each loop advances one ensemble - member: - 1. If necessary, convert the DART (posterior) file into input for - your model, After DART has assimilated the observations and - created new (posterior) states, it may be necessary to - post-process these states to impose model-specific limitations. - There is nothing in the ensemble filter methodology that - restricts posteriors to be physically meaningful (soil moistures - could be slightly negative, for example), or that related - quantities in the state have been conserved. Since each model - handles these situations differently, it is up to the user to - write any post-processing routines that may be necessary to - check for viable input to the model. Frequently this is done by - a program called *dart_to_model.f90*. If you need to do this, - you will also need to create/modify a *mkmf_dart_to_model* - and *path_names_dart_to_model* specific to your model. - 2. run your model, and - 3. if necessary, convert your model output (the prior) to netCDF or - simply rename or link to the appropriate filename for another - assimilation by DART. - -During this initial phase, it may be useful to _leave_ the temporary -directory intact until you verify everything is as it should be. - -#### async = 4 ... advancing each copy one at a time - -In addition to modifying *shell_scripts/advance_model.csh* as -described above, you must also modify *shell_scripts/run_filter.csh* -in the following way: THIS PART NEEDS TO BE FILLED IN - - - -### 4. Testing Strategies - under construction - -Generally testing when you add a new model to DART includes: -Checking the converter programs. -Checking the model advance control. -Starting with one observation in a known location, with a known value -and error specification. -Performing a 'perfect model' experiment for a few timesteps. -Looking at what the assimilation did with: - -> work % ncdiff filter_output.nc preassim.nc Innov.nc -> work % ncview Innov.nc - - - -### 5. Adding MATLAB® support for your own model - under construction. - -Only needed for state-space diagnostics. -Define a structure with required elements. -Examples exist in the *diagnostics/matlab/private* directory. - - - ------ - -## Examples - under construction - -1. observation location/value plots -2. [a brief explanation of 'localization'](../../assimilation_code/modules/assimilation/assim_tools_mod.html) -3. namelist settings for damped adaptive spatially-varying group filter - - - ------ - -## Namelists - -Many DART programs have namelists to specify run-time control. Some -programs use one or more modules - each module may have its own -namelist. As a consequence, we find it convenient to have one file -(called ```input.nml```) specifying all the namelists. - - -An example namelist for each program is automatically built when the -makefile is generated by *mkmf_xxxxx*. The example namelist is named -```input.nml.xxxxx_default``` where *xxxxx* is the name of the program. The -example namelists have default values, which may not be appropriate for -your use. The default ```input.nml``` in each *work* directory generally has -better values. As usual, the documentation for each module is the best -place to get information about the namelist settings. - -\[[top](#)\] - ------ diff --git a/guide/pages/Observations.md b/guide/pages/Observations.md deleted file mode 100644 index 100e01479b..0000000000 --- a/guide/pages/Observations.md +++ /dev/null @@ -1,881 +0,0 @@ ---- -title: Observations -layout: default ---- - -# DART Observation support and processing - -## Links to major sections of this document: -- [An overview of the DART 'preprocess' program](#preprocess) -- [An overview of the observation sequence](#obs_seq_overview) - - [Creating observations and sequences](#obs_creation) - - [synthetic observations](#obs_seq_osse) - - [real observations](#obs_real) -- [Working with observation sequences](#obs_seq_manip) -- [The difference between observation TYPE and QUANTITY](#adding_types) - -~~The DART distribution includes a full set of documentation. Once you -download DART, you may view the documentation offline by opening the -*index.html* file in the top-level DART directory. If you want to -explore the documentation page without downloading DART, you may -[view the documentation for the Manhattan release](Manhattan/docs/index.html).~~ - - - ---- - -## An overview of the DART 'preprocess' program - -~~First and foremost, check out -[preprocess.html](../../assimilation_code/programs/preprocess/preprocess.html) -for detailed information.~~ - -**The *preprocess* program actually *builds* the source code that supports -observations.** That source code is then used by all -the remaining modules. It is **imperative** to actually **run** -*preprocess* before building any executables. This is how the same code -can assimilate synthetic 'observations' for the Lorenz_63 model and -real radar reflectivities for WRF without needing to specify a set of -radar operators for the Lorenz_63 model\! - -*preprocess* combines multiple 'obs_def' modules into one -`obs_def_mod.f90` that is then used by the rest of DART. Additionally, -a new `obs_kind_mod.f90` is built that will provide support for -associating the specific observation **TYPES** with corresponding -(generic) observation **QUANTITIES**. More on that later. The list of -source codes is contained in the *&preprocess_nml* namelist and they -ultimately determine what observations and operators are supported. If -you want to add another 'obs_def' module, you **must** rerun -*preprocess* and recompile the rest of your project. *preprocess* is -designed to abort if the files it is supposed to build already exist. -For this reason, it is necessary to remove a couple files (if they -exist) before you run the preprocessor. It is just a good habit to -develop and is done automatically if you -use the DART *quickbuild.csh* script. - -~~~ -\rm -f ../../../observations/forward_operators/obs_def_mod.f90 -\rm -f ../../../observations/forward_operators/obs_kind_mod.f90 -./preprocess -ls -l ../../../observations/forward_operators/obs_def_mod.f90 -ls -l ../../../observations/forward_operators/obs_kind_mod.f90 -~~~ - -For example, with a namelist that looks like: - -~~~ -&preprocess_nml - input_obs_kind_mod_file = '../../../assimilation_code/modules/observations/DEFAULT_obs_kind_mod.F90' - output_obs_kind_mod_file = '../../../assimilation_code/modules/observations/obs_kind_mod.f90' - input_obs_def_mod_file = '../../../observations/forward_operators/DEFAULT_obs_def_mod.F90' - input_files = '../../../observations/forward_operators/obs_def_gps_mod.f90', - '../../../observations/forward_operators/obs_def_QuikSCAT_mod.f90', - '../../../observations/forward_operators/obs_def_GWD_mod.f90', - '../../../observations/forward_operators/obs_def_altimeter_mod.f90', - '../../../observations/forward_operators/obs_def_reanalysis_bufr_mod.f90' - output_obs_def_mod_file = '../../../observations/forward_operators/obs_def_mod.f90' - / -~~~ - -*preprocess* will combine `DEFAULT_obs_def_mod.F90, obs_def_gps_mod.f90, -obs_def_QuikSCAT_mod.f90, obs_def_GWD_mod.f90, obs_def_altimeter_mod.f90`, -and `obs_def_reanalysis_bufr_mod.f90`, into `obs_def_mod.f90` - -which can be used by the rest of the project. - -### Building and Running 'preprocess' - -*preprocess* is an executable, so it should come as no surprise that it -must be built in the normal DART fashion. The -`DART/build_templates/mkmf.template` must be correct for your -environment, and the *input.nml* must have your desired preprocess_nml -set correctly. Given that ... - -~~~ -csh mkmf_preprocess -make -./preprocess -~~~ - -will build and run *preprocess*. - -The first command generates an appropriate `Makefile` and the -`input.nml.preprocess_default` file. The second command results in the -compilation of a series of Fortran90 modules which ultimately produces -an executable file: *preprocess*. The third command actually runs -preprocess - which builds the new `obs_kind_mod.f90` and -`obs_def_mod.f90` source code files. The rest of DART may now be built. - -### The rationale for 'preprocess' - -**IMPORTANT**: Since each 'observation quantity' may require different -amounts of metadata to be read or written; any routine to read or write -an observation sequence **must** be compiled with support for those -particular observations. This is the **whole point** of the -'preprocess' process\! The supported observations are listed in the -`input.nml&obs_kind_nml` block. - -For example, radar observations need extra metadata to specify the -location of the radar in addition to the location of the observation, -radiosondes only require the observation location. GPS occultations need -the locations of the two satellites so the forward operator can -integrate along the raypath, cosmic ray soil moisture sensors (yes, they -exist) have forward operators that require site-specific calibration -parameters that are not part of the model and must be included in the -observation metadata. That sort of thing. - -For this reason, we strongly recommend that you use the DART routines -to read and process DART observation sequence files. - - - ---- - -## An overview of the observation sequence - -Observation sequences are complicated, there's just no better way to -describe it. Trying to automatically accomodate a myriad of observation -file formats, structure, and metadata is simply not an easy task. For -this reason, DART has its own format for observations and a set of -programs to convert observations from their original formats to DART's -format. There are definitely some things to know ... - -An `obs_seq.in` file actually contains no observation quantities. -It may be best thought of as a **perfectly**-laid-out notebook - just -waiting for an observer to fill in the actual observation quantities. -All the rows and columns are ready, labelled, and repeated for every -observation time and platform. This file is generally the start of a -"perfect model" experiment. Essentially, one instance of the model is -run through *perfect_model_obs* - which applies the appropriate -forward operators to the model state and 'writes them down' in our -notebook. The completed notebook is renamed `obs_seq.out`. - -An `obs_seq.out` file contains a linked list of observations - -potentially (and usually) observations from different platforms and of -different quantities - each with their own error characteristics and -metadata. These files arise from running *perfect_model_obs* **OR** -from any number of converter programs. The creation of observation -sequences from real observations is not automatic and an email to the -DART team asking for advice for your specific types of observations is -perfectly within reason. - -There is something called an `obs_seq.final` file - which contains -everything in the `obs_seq.out` file as well as a few additional -'copies' of the observation. Remember, DART is an ensemble algorithm. -Each ensemble member must compute its own estimate of the observation -for the algorithm. The `obs_seq.final` file *may* contain each of these -estimates (namelist controlled). Minimally, the mean and spread of the -ensemble estimates is recorded in the `obs_seq.final` file. The best -method of determining the performance of your 'real world' experiment is -to compare in *observation-space* since we can never know the model -state that perfectly represents the real world. - -**IMPORTANT**: Since each 'observation type' may require different -amounts of metadata to be read or written; any routine to read or write -an observation sequence **must** be compiled with support for those -particular observations. The supported observations are listed in the -`input.nml&obs_kind_nml` block. This is the **whole point** of the -'preprocess' process ... - - - - - - - - - - - - -
observation sequence file structureobs_seq.outobs_seq.final
There are extensible parts of the observation sequence -file; for example, the number of observation kinds contained in the -file, whether the locations have 1 or more components, how -many quality control values are available for each -observation, where those quality control values come from, -how many 'copies' of each observation there -are ... et cetera. The images to the right are links to -full-size images. **They are from entirely separate -experiments. They are just meant to show the flexibility -of the file format.**The structure of an obs_seq.out fileThe structure of an obs_seq.final file
- - - - ---- - -## Creating observations and sequences. - -**It is strongly encouraged that you use a single observation to test a -new model implementation.** - -Experience has shown that starting 'simple' is the fastest way to good -results. Starting with a **single** observation will exercise a -sufficient portion of the procedure and provide insight into where to -spend more effort. Starting with a single *synthetic* observation will -allow you to focus on the more interesting parts of the DART scheme -without getting bogged down in the world of observation data formats. - - - -### Creating a synthetic observation sequence. - -There are several steps to create an observation sequence file, which -follows directly from the modular nature of the DART programming -philosophy. - -1. Decide what observations you want to investigate and edit the - `input.nml&obs_kind_nml` block. -2. Build and run *preprocess* to create code that supports the - observations you want. -3. Build and run *create_obs_sequence* to define the specifics about - the observation you want. -4. Build and run *create_fixed_network_sequence* to replicate those - specifics through time. -5. Build and run *perfect_model_obs* to create an observation - consistent with the model state and specified error distribution at - the requested times and locations. - - - - -#### Example: generating observations for the Lorenz '63 model. - -1\) There are no 'real' observations for the Lorenz '63 model, so the -appropriate namelist settings are: - -~~~ -&obs_kind_nml - assimilate_these_obs_types = 'RAW_STATE_VARIABLE' / - -&preprocess_nml - input_obs_def_mod_file = '../../../observations/forward_operators/DEFAULT_obs_def_mod.F90' - output_obs_def_mod_file = '../../../observations/forward_operators/obs_def_mod.f90' - input_obs_kind_mod_file = '../../../assimilation_code/modules/observations/DEFAULT_obs_kind_mod.F90' - output_obs_kind_mod_file = '../../../assimilation_code/modules/observations/obs_kind_mod.f90' - input_files = '../../../observations/forward_operators/obs_def_1d_state_mod.f90' - / -~~~ - -2\) Run *preprocess* in the normal fashion. - -3\) *create_obs_sequence* creates an *observation set definition* -(typically named `set_def.out`), the time-independent part of an -observation sequence. It may help to think of it as trying to define -what sorts of observations will be taken at one 'reading' ... you walk -out to the box and take temperature, humidity, and wind observations all -at the same time and place, for example. You can think of it as one page -in an observer's notebook, and only contains the *location, type,* and -*observational error characteristics* (normally just the diagonal -observational error variance) for a related set of observations. There -are no actual observation values, nor are there any times associated -with the definition. The program is interactive and queries the user for -the information it needs. Begin by creating a minimal observation set -definition in which each of the 3 state variables of L63 is directly -observed with an observational error variance of 1.0 for each -observation. To do this, use the following input sequence (the text -including and after \# is a comment and does not need to be entered): - -The following is a screenshot (much of the verbose logging has been left -off for clarity), the user input looks *like this*. - -~~~ - [unixprompt]$ ./create_obs_sequence - Starting program create_obs_sequence - Initializing the utilities module. - Trying to log to unit 10 - Trying to open file dart_log.out - - -------------------------------------- - Starting ... at YYYY MM DD HH MM SS = - 2017 3 28 10 15 30 - Program create_obs_sequence - -------------------------------------- - - set_nml_output Echo NML values to log file only - Trying to open namelist log dart_log.nml - ------------------------------------------------------ - - - -------------- ASSIMILATE_THESE_OBS_TYPES -------------- - RAW_STATE_VARIABLE - -------------- EVALUATE_THESE_OBS_TYPES -------------- - ------------------------------------------------------ - - ---------- USE_PRECOMPUTED_FO_OBS_TYPES -------------- - ------------------------------------------------------ - - Input upper bound on number of observations in sequence - 4 - Input number of copies of data (0 for just a definition) - 0 - Input number of quality control values per field (0 or greater) - 0 - input a -1 if there are no more obs - 0 - Input -1 * state variable index for identity observations - OR input the name of the observation type from table below: - OR input the integer index, BUT see documentation... - 1 RAW_STATE_VARIABLE - -1 - input time in days and seconds - 0 0 - Input error variance for this observation definition - 1.0 - input a -1 if there are no more obs - 0 - - { this gets repeated ... until you tell it to stop ... } - - input a -1 if there are no more obs - -1 - Input filename for sequence ( set_def.out usually works well) - set_def.out - write_obs_seq opening formatted file set_def.out - write_obs_seq closed file set_def.out -~~~ - -Rest assured that if you requested to assimilate more realistic -observation types, you will be queried for appropriate information by -*create_obs_sequence*. Below is a table that explains all of the input -you should need to supply for observations of the L63 model state. - -~~~ -4 # upper bound on num of observations in sequence -0 # number of copies of data (0 for just a definition) -0 # number of quality control values per field (0 or greater) -0 # -1 to exit/end observation definitions - --1 # observe state variable 1 -0   0 # time -- days, seconds -1.0 # observational variance -0 # -1 to exit/end observation definitions - --2 # observe state variable 2 -0   0 # time -- days, seconds -1.0 # observational variance -0 # -1 to exit/end observation definitions - --3 # observe state variable 3 -0   0 # time -- days, seconds -1.0 # observational variance --1 # -1 to exit/end observation definitions - -set_def.out # Output file name -~~~ - - -4\) *create_fixed_network_sequence* takes the observation set -definition and repeats it in time, essentially making multiple pages in -our notebook. Again, the program is interactive and queries the user for -information. You should be able to simply follow the prompts. The table -below represents the input needed for the L63 -example: - -~~~ -set_def.out # Input observation set definition file -1 # Regular spaced observation interval in time -1000 # 1000 observation times -0, 43200 # First observation after 12 hours (0 days, 12 * 3600 seconds) -0, 43200 # Observations every 12 hours -obs_seq.in # Output file for observation sequence definition -~~~ - -5\) *perfect_model_obs* advances the model from the state defined by -the initial conditions file specified in the `input.nml` and 'applies -the forward operator' to harvest observations to fill in the observation -sequence specified in `obs_seq.in`. The observation sequence finally -has values for the observations and is saved in a file generally named -*obs_seq.out*. *perfect_model_obs* is namelist-driven, as opposed to -the previous two (whose input is a lot harder to specify in a namelist). -Take a look at (and modify if you like) the - `input.nml&perfect_model_obs_nml` section of the namelist. - -The End. Not only should you have an observation sequence file (usually -`obs_seq.out`) , you also have a file containing the exact evolution of -the model consistent with those observations - the true state: -`perfect_output.nc`. - - - ---- - -## Real Observations - Converting to a DART-compatible format. - -[OVERVIEW](#Overview) / -[DATA SOURCES](#DataSources) / -[DECISIONS](#Decisions) / -[PROGRAMS](#Programs) / -[FUTURE PLANS](#FuturePlans) - -Real observations come in a mind-boggling diversity of formats. We have -converters for many formats in the `DART/observations/obs_converters` -directory. The documentation for that directory is listed in the -[README.md](../../observations/obs_converters/README.md). - -The converters are designed to work on one input file format and create -(or add to) an output observation sequence. It may be desirable to -post-process multiple observation sequence files with the -[obs_sequence_tool](../../assimilation_code/programs/obs_sequence_tool/obs_sequence_tool.html) -... to select for timeframe, geographic region, etc. - -Many of the formats require their own libraries (like HDF), and require -intimate knowledge of the data format to extract the portions required -for the [DART observation sequence file](#obs_seq_overview). Please -feel free to -browse the converters and their companion documentation. Feel free to -donate converters for formats we don't already support\! We like that -kind of stuff. - -The DART framework enforces a clean separation between observations and -the models used for assimilation. The same observations can be used in -any model which understands how to generate a value for the requested -type of observation from the models' state-space values (i.e. the -forward observation operator must exist - DART provides many for the -most common state variables). - -In many cases, the original datasets are in a standard scientific format -like netCDF, HDF, or BUFR, and library routines for those formats can be -used to read in the original observation data. The DART software -distribution includes Fortran subroutines and functions to help create a -sequence of observations in memory, and then a call to the DART -observation sequence write routine will create an entire *obs_seq* file -in the correct format. - -In many cases, a single, self-contained program can convert directly -from the observation location, time, value, and error into the DART -format. In other cases, especially those linking with a complicated -external library (e.g. BUFR), there is a two-step process with two -programs and an ASCII intermediate file. We are currently leaning -towards single-step conversions but either approach can be used for new -programs. - -The DART system comes with several types of location modules for -computing distances appropriately. The two most commonly used are for -data in a 1D system and for data in a 3D spherical coordinate system. -All the programs in the `DART/observations` directory assume the -`assimilation_code/location/threed_sphere/location_mod.f90` 3D sphere -location module is being used. - -With the myriad of observation file formats, HDF, Grib, BUFR, netCDF, -... we simply have not had the time nor need to support all of them. The -converters are a work in progress. There are currently about 10 other -observation sources and types which we are in the process of collecting -information and conversion programs for and which will eventually be -added to this directory. In the meantime, if you have converters for -data or interest in something that is not in the repository, please -email the DART group. Your best bet is to contact our group at -*dart@ucar.edu* with a specific request and we can steer you to the most -similar process. - -### Overview - -Real-world observations of earth-system data come from a variety of -sources, including radiosondes, satellites, ships, aircraft, weather -stations, etc. The files in this `observations` directory can be used to -convert data from a variety of native formats into a common DART -*observation sequence* format. - -Synthetic observations are those not based on an actual instrument -reading of a system, but instead are fabricated to have a known value, -or have values computed by running a model, possibly with a fixed amount -of simulated noise added. These observations can be used for testing, -determining the sensitivity of the model to assimilation, and for -designing new observation systems. The DART system includes several ways -to create synthetic observations. See the [Programs](#Programs) section -below for more details. - -The DART framework enforces a clean separation between observations and -the models they are assimilated into. The same observations can be used -in any model which understands how to generate a value for the requested -type of observation from its state space values. - -In many cases a single, self-contained program can convert directly from -the observation location, time, value, and error into the DART format. -In other cases, especially those linking with a complicated external -library (e.g. BUFR), there is a two-step process with two programs and -an ASCII intermediate file. We are currently leaning towards single-step -conversions but either approach can be used for new programs. - -Frequently the original datasets are in a standard scientific format -like netCDF, HDF, or BUFR, and library routines for those formats can be -used to read in the original observation data. - -The DART software distribution includes Fortran subroutines and -functions to help create a sequence of observations in memory, and then -a call to the DART observation sequence write routine will create an -entire *obs_seq* file in the correct format. - -The DART system comes with several types of location modules for -computing distances appropriately. Two of the ones most commonly used -are for data in a 1D system and for data in a 3D spherical coordinate -system. All the programs here assume the -`location/threed_sphere/location_mod.f90` 3D sphere location module is -being used. - -There are currently some additional observation sources and types which -we are in the process of collecting information and conversion programs -for and which will eventually be added to this directory. In the -meantime, if you have converters for data or interest in something that -is not in the repository, -please [email the DART group](mailto:dart@ucar.edu). - - - ------ - -### DATA SOURCES AND FORMATS - -See the various subdirectories here, which generally include information -on where the example data was obtained and in what format it is -distributed. Most data is available for download off the web. The Data -Support Section (DSS) at NCAR has large data repositories, the MADIS -data center distributes observations in netCDF format, GTS real-time -weather data is available from various sources. For new converters, if -you can find what format the data is distributed in you may be able to -adapt one of the existing converters here for your own use. Formats read -by the existing converters include netCDF, HDF, little-r, text, -Prepbufr, amongst others. - -See the [Programs](#Programs) section below for a list of the current -converter programs. - -If you have looked and none of the existing converters are right for -your data, here are some suggestions for where to start creating a new -converter. Create a new subdirectory in the *observations* directory. -Copy with the recursive option (*cp -r*) one of the existing converters -and adapt to your needs. Our suggestions for which converter to start -from depends on the format of your input observations to be converted. -If your input data format is: - -| format | advice | -| :----- | :----- | -| netCDF | Start with the *MADIS* converters, and in particular try the `convert_madis_profiler.f90` file because it is the most straightforward. Another good option is `SST/oi_sst_to_obs.f90`. | -| Comma separated text | Start with the *Ameriflux* converter. | -| Generic text | Start with the *text* converter. | -| HDF-EOS5 | Start with the *AIRS* converter. | -| BUFR or prepBUFR | Start with the *NCEP* converter. | -| Dense data, like Satellite swaths | Start with the *tpw* converter, which includes code that averages the raw data in space and time. | -| Ray-path integrated data | Start with the *GPS* converter, which includes code that traces a path and integrates values along the ray. | -| World Ocean Database packed ASCII | Start with the *WOD* converter. | - - - - - - - ------ - -### DECISIONS YOU MIGHT NEED TO MAKE - -#### Time - -Time enters into the assimilation system in 3 places: the timestamp of -the state vector data (the current model time when this data was -produced), the time of each observation, and the minimum time period the -model should be called to advance (the assimilation window size). The -internal timestepping of the model is unrelated to any of these times -and is outside the scope of the assimilation system. - -The basic time type in DART is a pair of integers; one for the day -number and one for the number of seconds. Generally the low order -models, which aren't direct geophysical models, use time directly as a -sequence of days starting at 0 and incrementing in any appropriate -number of seconds or days. The observations assimilated into these -systems do not need to use a calendar. - -Observations of a real-world system usually are distributed with a -year/month/day, hour/min/seconds timestamp. There are routines in DART -to convert back and forth between the (day-number/seconds) format and a -variety of (year/month/day) calendars. See -[the time manager documentation](../../assimilation_code/modules/utilities/time_manager_mod.html#time_type) -for more details on how DART stores time information and the types of -available calendars. Some climate models which do long runs (100s or -1000s of years) use a modified calendar for simplicity in computation, -e.g. months which always have 30 days, or no leap years. When trying to -assimilate real observations into these models there may be calendar -issues to solve. - -The smallest resolvable unit of time in DART is a second. To model a -system which operates on sub-second time scales the time can be scaled -up by some factor. As long as the observation time, the state data time, -and the minimum model advance time are expressed in the same scaled time -units, there is no problem. - -#### Error Variances - -Observations must specify an associated expected error variance. Each individual -observation stores its own error variance value, so it can be a constant value -for all observations of that type or it can vary by location, by height, -by magnitude of the observed value, etc. This value is the expected -instrument error variance plus the representativeness error variance of the model. -The model error variance includes deficiencies in the equations representing the -processes of the system as well as errors introduced by representing a -continuous system as a series of discrete points. While the instrument -error and the representativeness error could be specified separately, -they each have the same impact on the assimilation and can be difficult -to determine with any real accuracy. For simplicity, in DART (and most -current assimilation software) they are combined and specified as a -single value, which we frequently call the 'observation error'. Keep in -mind we really mean 'observation error variance'. - -The instrument error is generally supplied by the instrument maker. -Sadly, it is frequently surprisingly difficult to find these values. For -the representativeness error, a set of artificial observations could be -generated with the -[perfect_model_obs](../../assimilation_code/programs/perfect_model_obs/perfect_model_obs.html) -program and an assimilation experiment could be run to generate an -estimate of the error in the model. In practice however most people make -an educated guess on the values of the error and then start with a -larger than expected value and decrease it based on the results of -running some test assimilations. For these tests the namelist for the -[outlier threshold](../../assimilation_code/programs/filter/filter.html#Namelist) -should be disabled by setting it to -1 (the default value is 3). This -value controls whether the observation is rejected because the observed -value is too far from the ensemble mean. - -If the diagnostics show that the difference between the mean of the -forward operators and the observed value is consistently smaller than -the specified observation error, then the error is probably too large. A -error that is too large reduces the impact of an observation on the state. If -the specified observation error is too small it is likely the -observation will be rejected when the outlier threshold is enabled, and -the observation will not be assimilated. It is important to look at the -output observation sequence files after an assimilation to see how many -observations were assimilated or rejected, and also at the RMSE -([root mean squared error](http://www.wikipedia.org/wiki/RMSE)) versus the -total spread. DART includes Matlab diagnostic routines to create these -types of plots. The observation RMSE and total spread should be roughly -commensurate. The total spread includes contributions from both the -ensemble variance and the observational error variance, so it can be -adjusted by changing the error values on the incoming observations. -There are other ways to adjust the ensemble spread, including -[inflation](../../assimilation_code/programs/filter/filter.html#Inflation), -so the observation error is not the only factor to consider. - -One last recommendation: if possible, the Prior forward operator values -should be compared against the observations after several assimilation -cycles. If you plot results using the Posterior values it is always -possible for the assimilation to overfit the observations and look good -on the diagnostic plots. But the actual test is to then advance the -model and look at how the forecast of the state compares to the -observations. - -#### Observation Types - -All observations have to have a specific 'type'. There are namelist -controls to turn on and off the assimilation of observations at run-time -by type, or to only evaluate the forward operator for an observation but -have no impact on the state. Several of the diagnostics also group -observations by type to give aggregate statistics after an assimilation. -Generally types are based on both the observing platform or instrument -as well as the 'kind' of observation, e.g. RADIOSONDE_TEMPERATURE, -ARGO_SALINITY, etc. Each type is associated with a single underlying -generic 'kind', which controls what forward operator code is called -inside the model, e.g. QTY_TEMPERATURE, QTY_DENSITY, etc. - -See the [observations/forward_operators/obs_def_mod.html](https://www.image.ucar.edu/DAReS/DART/Manhattan/observations/forward_operators/obs_def_mod.html) -for more details on -how to use and add new DART types. The DART `obs_kind_mod.f90` defines a -list of already defined observation types, and users can either use -existing observation types in 'obs_def_xxx_mod.f90' files, or define -their own. Be aware that `obs_kind_mod.f90` is autogenerated by *preprocess*, -so until you configure and run *preprocess*, `obs_kind_mod.f90` will not exist. - -#### Observation Locations - -The two most common choices for specifying the location of an -observation are the -[threed_sphere](../../assimilation_code/location/threed_sphere/location_mod.html) -and the [oned](../../assimilation_code/location/oned/location_mod.html) -locations. For observations of a real-world system, the 3D Sphere is -generally the best choice. For low-order, 1D models, the 1D locations -are the most commonly used. The observation locations need to match the -type of locations used in the model in that you cannot read observations -on a unit circle (1D) when using models that require 3D Sphere locations. - -The choice of the vertical coordinate system may also be important. -For the 3D Sphere, the vertical coordinate system choices are: - -| string | integer value | meaning | -| :----- | :------------ | :------ | -| VERTISUNDEF | -2 | has no specific vertical location (undefined) | -| VERTISSURFACE | -1 | surface value (value is surface elevation in m) | -| VERTISLEVEL | 1 | by model level | -| VERTISPRESSURE | 2 | by pressure (in pascals) | -| VERTISHEIGHT | 3 | by height (in meters) | -| VERTISSCALEHEIGHT | 4 | by scale height (unitless) | - -The choice of the vertical coordinate system may have ramifications for vertical -localization, depending on your model's ability to convert from one coordinate -system to another. `VERTISUNDEF` is typically used for column-integrated quantities. -`VERTISLEVEL` only makes sense for synthetic observations. - - - -\[[top](#)\] - ------ - -### PROGRAMS - -The **DART/observations/obs_converters** directory contains a variety of -converter programs to read various external formats and convert the -observations into the format required by DART. - -Each directory has at least one converter: - - - [AIRS](AIRS/README.html) - - [Aviso+/CMEMS](AVISO/AVISO.html) - - [Ameriflux](Ameriflux/level4_to_obs.html) - - [COSMOS](COSMOS/COSMOS_to_obs.html) - - [DWL](DWL/dwl_to_obs.html) - - [GOES](GOES/README.md) - - [GPSPW](GPSPW/README) - - [GSI2DART](GSI2DART/README) - - [GTSPP](GTSPP/GTSPP.html) - - [MADIS](MADIS/MADIS.html) - - [MIDAS](MIDAS/MIDAS_to_obs.html) - - [MODIS](MODIS/MOD15A2_to_obs.htm) - - [NCEP (prepbufr -\> ascii)](NCEP/prep_bufr/prep_bufr.html) - - [NCEP (ascii -\> obs_seq)](NCEP/ascii_to_obs/create_real_obs.html) - - [ROMS](ROMS/ROMS.htm) - - [SSEC](SSEC/SSEC.html) - - [SST](SST/SST.html) - - [SSUSI](SSUSI/convert_f16_edr_dsk.html) - - [WOD](WOD/WOD.html) - - [cice](cice/cice_to_obs.html) - - [gnd_gps_vtec](gnd_gps_vtec/README) - - [GPS](gps/gps.html) - - [ok_mesonet](ok_mesonet/ok_mesonet.html) - - [QuikSCAT](quikscat/QuikSCAT.html) - - [Radar](radar/radar.html) - - [snow](snow/snow_to_obs.html) - - [Text](text/text_to_obs.html) - - [tpw](tpw/tpw.html) - - [Tropical Cyclones](tropical_cyclone/tc_to_obs.html) - - [Var (little-r)](var/littler_tf_dart.html) - - [Var (radar)](var/rad_3dvar_to_dart.html) - -There are also a couple utilities of note: - - - [even_sphere](even_sphere/README) - a utility for generating - evenly-spaced observation locations that can then be used in a - perfect model experiment. - - [obs_error](obs_error/README) - modules that specify observation - errors based on what is used by ECMWF and NCEP - -In addition the following external program produces DART observation -sequence files: - - - [Observation Processing And Wind Synthesis (OPAWS)](http://code.google.com/p/opaws/): - OPAWS can process NCAR Dorade (sweep) and NCAR EOL Foray (netCDF) - radar data. It analyzes (grids) data in either two-dimensions - (on the conical surface of each sweep) or three-dimensions (Cartesian). - Analyses are output in netCDF, Vis5d, and/or DART - (Data Assimilation Research Testbed) formats. - -For generating synthetic observations, see the -[create_obs_sequence](../../assimilation_code/programs/create_obs_sequence/create_obs_sequence.html) -program documentation. You can also generate observation files based on -text input. See the [text_to_obs](text/text_to_obs.html) program -documentation. Or for simulating a large complex observing system, you -can use the DART library routines in a Fortran program to compute the -observation information and have the DART routines write the output -file. - -See the -[perfect_model](../../assimilation_code/programs/perfect_model_obs/perfect_model_obs.html) -program documentation on how to run a model with a set of observations -that have only locations, types, and times, and have the forward -operators compute the observation values. - - - ------ - -### FUTURE PLANS - -Contact the [DART development group](mailto:dart@ucar.edu) if you have -observations in a different format that you want to convert. We can give -you advice and pointers on how to approach writing the code. - - - -\[[top](#)\] - ---- - -# Working with observation sequences. - -First and foremost, check out the -[obs_sequence_tool.html](../../assimilation_code/programs/obs_sequence_tool/obs_sequence_tool.html) -document for detailed information and examples. - -*obs_sequence_tool* is the primary tool for manipulating observation -sequence files. Observations sequence files are linked lists of -observations organized by time. That is to say, the observations may -appear in any order in the file, but traversing the linked list will -result in observations ordered by time. *obs_sequence_tool* can be -used to combine observation sequences, convert from ASCII to binary or -vice-versa, extract a subset of observations, etc. - -For testing, it is terribly useful to extract a small number of -observations (like ONE) from an existing observation sequence file. - - - -\[[top](#)\] - ---- - -# The difference between observation TYPE and QUANTITY. - -Broadly speaking, observation TYPES are specific instances of a generic -observation QUANTITY. The distinction is useful for several reasons, not -the least of which is to evaluate observation platforms. Zonal wind -observations from QuikSCAT vs. radiosondes, for example. They are both -observations of zonal winds (what we call QTY_U_WIND_COMPONENT), but -they are different observation TYPES; QKSWND_U_WIND_COMPONENT, and -RADIOSONDE_U_WIND_COMPONENT, respectively. The forward observation -operators are implemented based on observation QUANTITY. When requested, -the model generates a QTY_U_WIND_COMPONENT, it doesn't need to know -that it will be compared to a QuikSCAT value or a radiosonde value. - -**However**, it is usually scientifically very interesting to be able to -compare the assimilations one TYPE of observation vs. another. One -observation sequence file can have lots of types of observations; DART -has the capability to assimilate (or evaluate) any combination of -observation types without getting bogged down in dataset management. The -same observation sequence can be used for experiments that -include/exclude certain observation types - ensuring that you are -performing the experiment you THINK you are performing -... - -# Adding support for a new observation TYPE. - -[DART/observations/forward_operators/obs_def_mod.html](../../observations/forward_operators/obs_def_mod.html) -is the source for detailed information. - ---- diff --git a/guide/pages/Presentations.md b/guide/pages/Presentations.md deleted file mode 100644 index d0a4e29aaf..0000000000 --- a/guide/pages/Presentations.md +++ /dev/null @@ -1,1359 +0,0 @@ - -# (Some) Presentations featuring DART: The Data Assimilation Research Testbed - ----- -## 2020 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Moha Gharamti** Nov 2020 : - Presented to the UCAR Executive Committee of the Board of Trustees
- Improving Earth System Model Predictions using Data Assimilation: Research and Applications - [\[presentation - pdf\]](http://www.image.ucar.edu/pub/DART/2020/2020_MEG_ExecComm.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Brett Raczka** Aug 2020 : - Presented at the - [2020 ESA Annual Meeting](https://eco.confex.com/eco/2020/meetingapp.cgi/Home/0) - held virtually.
- Towards a complex terrain Carbon Monitoring System (CMS-Mountains): Development and testing in the western U.S. - [\[abstract\]](https://eco.confex.com/eco/2020/meetingapp.cgi/Paper/87176) - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2020/Raczka_ESAposter_2020.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2020 : - Presented at the - [Earth System Prediction Working Group](http://www.cesm.ucar.edu/events/workshops/2020/#presentations-espwg) session of the - [2020 CESM Workshop](http://www.cesm.ucar.edu/events/workshops/2020/) in - in Boulder, CO, USA
- A New CAM6+DART 80-Member Ensemble Reanalysis for 2011-2019: - Initial Conditions for CAM, CLM and CICE Forecasts and Forcing for POP, CLM and CICE - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2020/espwg_CAM6_reanalysis.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2020 : - Presented at the - [AMS Centennial Meeting 2020](https://annual.ametsoc.org/2020/) in Boston, MA, USA
- Nonlinear, Non-Gaussian Extensions for Ensemble Filter Data Assimilation. - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2020/2020_AMS_JLA_nonlinear_da.pptx) - [\[pdf\]]( http://www.image.ucar.edu/pub/DART/2020/2020_AMS_JLA_nonlinear_da.pdf)
- Results from an Ensemble Reanalysis with the Community Earth System Model 2.1. - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2020/2020_AMS_JLA_CESM_rean_final.pptx) - [\[pdf\]]( http://www.image.ucar.edu/pub/DART/2020/2020_AMS_JLA_CESM_rean_final.pdf)
- The Data Assimilation Research Testbed: Nonlinear Algorithms and Novel Applications - for Community Ensemble Data Assimilation - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2020/2019_AGU_2020_AMS_DART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Moha El Gharamti** Jan 2020 : - Presented at the - [AMS Centennial Meeting 2020](https://annual.ametsoc.org/2020/) in Boston, MA, USA
- A new adaptive hybrid ensemble Kalman filter and optimal interpolation. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2020/2020_AMS_MEG_hybrid_oi.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jan 2020 : - Presented at the - [AMS Centennial Meeting 2020](https://annual.ametsoc.org/2020/) in Boston, MA, USA
- Hydro-DART: Ensemble Streamflow Assimilation with WRF-Hydro and the Data Assimilation Research Testbed. - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2020/2020_AMS_TJH_wrfhydro.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2020/2020_AMS_TJH_wrfhydro.pdf) - ----- -## 2019 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Dec 2019 : - Presented at the - [AGU Fall Meeting 2019](http://fallmeeting.agu.org/2019/) in San Francisco, CA, USA
- Nonlinear, Non-Gaussian Extensions for Ensemble Filter Data Assimilation - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2019/2019_AGU_JLA_nonlinear_da.pptx) - [\[pdf\]]( http://www.image.ucar.edu/pub/DART/2019/2019_AGU_JLA_nonlinear_da.pdf)
- Results from an Ensemble Reanalysis with the Community Earth System Model 2.1 - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2019/2019_AGU_JLA_CESM_rean_final.pptx) - [\[pdf\]]( http://www.image.ucar.edu/pub/DART/2019/2019_AGU_JLA_CESM_rean_final.pdf)
- The Data Assimilation Research Testbed: Nonlinear Algorithms and Novel Applications - for Community Ensemble Data Assimilation - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2019/2019_AGU_2020_AMS_DART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Nov 2019 : - Presented at the CISL Seminar Series in Boulder, CO, USA
- CISL's Data Assimilation Research Section: Accelerating NCAR Science with Ensemble Data Assimilation - [\[youtube\]](https://www.youtube.com/watch?v=rxPGKFxJAkE) - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2019/2019_CISL_JLA_seminar.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2019/2019_CISL_JLA_seminar.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2019 : - Presented at the - [AMS Annual Meeting 2019](https://annual.ametsoc.org/2019/) in Phoenix, AZ, USA
- Nonlinear, Nongaussian Ensemble Data Assimilation with Rank - Regression and a Rank Histogram Filter - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2019/2019_AMS_nonlinear_JLA.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2019/2019_AMS_nonlinear_JLA.pdf) - ----- -## 2018 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Dec 2018 : - Presented at the - [AGU Fall Meeting 2018](http://fallmeeting.agu.org/2018/) in Washington DC, USA
- Hydro-DART: Ensemble Streamflow Assimilations with WRF-Hydro and the - Data Assimilation Research Testbed. - [\[talk - pdf\]](http://www.image.ucar.edu/pub/DART/2018/2018_AGU_H23A_08_HydroDART_TJH.pdf)
- The Data Assimilation Research Testbed (DART): Ensemble Data - Assimilation for NCAR Community Earth System Models - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2018/20189_AGUAMS_DART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** May 2018 : - Presented at the - [Workshop: Atmosphere, Oceans, and Computational - Infrastructure](http://workshop.caltech.edu/fesm/aoc_infrastructure/index.html) - at JPL/Caltech, California
- DA and Short-term Prediction Motivate the Need for Careful Software - Development In Earth System Models: A Case Study - [\[pptx\]](anderson_da_model_devel.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2018/2018_Caltech_JLA_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Apr 2018 : - Presented at the [Terrestrial Systems Research: Monitoring, - Prediction and High Performance - Computing](http://conference.tr32.de/) in Bonn Germany
- Ensemble Data Assimilation with the Community Land Model and the US - National Water Model - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2018/2018_CLM_HydroDART_TJH.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Mar 2018 : - Presented at the [6th International Symposium on Data - Assimilation](https://isda2018.wavestoweather.de/) in Munich, - Germany
- Exploiting Nonlinear Relations between Observations and State - Variables in Ensemble Filters - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2018/ISDA_2018_nonlinear_talk_JLA.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2018/ISDA_2018_nonlinear_talk_JLA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2018 : - Presented at the [AMS Annual Meeting - 2018](https://annual.ametsoc.org/2018/) in Austin, TX, USA
- Exploiting Nonlinear Relations between Observations and State - Variables in Ensemble Filters - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2018/2018_AMS_nonlinear_JLA.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2018/2018_AMS_nonlinear_JLA.pdf)
- Using the Data Assimilation Research Testbed for Climate System - Applications - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2018/2018_AMS_esm_da_JLA.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2018/2018_AMS_esm_da_JLA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Moha El Gharamti** Jan 2018 : - Presented at the [AMS Annual Meeting - 2018](https://annual.ametsoc.org/2018/) in Austin, TX, USA
- Adaptive Prior Inflation for Ensemble Filters: Application to a - Large-Scale Atmospheric Model - [\[talk - pdf\]](http://www.image.ucar.edu/pub/DART/2018/MEG_AMS_2017_talk.pdf)
- Ensemble Data Assimilation for Very Large Atmosphere, Ocean and - Coupled Models with the Data Assimilation Research Testbed - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2018/2018_AMS_DART_MEG.pdf) - ----- -## 2017 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Dec 2017 : - Presented at the [AGU Fall Meeting - 2017](http://fallmeeting.agu.org/2017/) in New Orleans, LA, USA
- Empowering Geoscience with Improved Data Assimilation Using the Data - Assimilation Research Testbed "Manhattan" Release - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2017/2017_AGU_DART_KDR.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Moha El Gharamti** Jun 2017 : - Presented at the [12th International EnKF - Workshop](http://www.iris.no/enkf/previous-workshops/2017-solstrand-hotel-bad-os) - in Os, Norway
- Enhanced Adaptive Inflation Algorithm for Ensemble Filters - [\[talk - pdf\]](http://www.image.ucar.edu/pub/DART/2017/MEG_Os_2017_enkf_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Moha El Gharamti** Feb 2017 : - Presented at the [ASLO 2017 Ocean Sciences Meeting: Mountains to the - Sea](https://www.sgmeet.com/aslo/honolulu2017/) in Honolulu, Hawaii
- Biological Reanalysis with Online Parameter Estimation in the North - Atlantic and the Arctic - [\[talk - pptx\]](http://www.image.ucar.edu/pub/DART/2017/MEG_ASLO_2017_talk.pptx) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** 28 Aug 2017 - 1 Sep 2017 : - Presented at the Nanjing DA tutorial in Nanjing, China
- Introduction to Data Assimilation - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_da_intro.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_da_intro.pdf)
- Practical Implementations of the Ensemble Kalman Filter - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_eakf.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_eakf.pdf)
- Parallel Implementations of Ensemble Kalman Filters for Huge - Geophysical Models - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_scalable_ekf.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_scalable_ekf.pdf)
- Methods for Computing Localization of Observation Impacts in - Ensemble Kalman Filters - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_localization.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_localization.pdf)
- Assimilating Observations with Spatially and Temporally Correlated - Errors in a Global Atmospheric Model - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_correlated_error.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_Nanjing_2017_correlated_error.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** 21 Aug 2017 : - Presented at [The 7th International Workshop on Catchment - Hydrological Modeling and Data - Assimilation](http://cahmda.westgis.ac.cn/) in Xi'an, China
- Ensemble Data Assimilation for Observations with Spatially and - Temporally Correlated Errors - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_CAHMDA_VII_correlated_error.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_CAHMDA_VII_correlated_error.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** 18 Aug 2017 : - Presented at the Training day for [The 7th International Workshop on - Catchment Hydrological Modeling and Data - Assimilation](http://cahmda.westgis.ac.cn/) in Xi'an, China
- Basic Concept of Land Surface Data Assimilation - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_CAHMDA_VII_da_intro.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_CAHMDA_VII_da_intro.pdf)
- Practical Implementations of the Ensemble Kalman Filter (with a - focus on Land Surface Data Assimilation) - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2017/JLA_CAHMDA_VII_eakf.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2017/JLA_CAHMDA_VII_eakf.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2017 : - Presented at the [CESM 2017 - Workshop](http://www.cesm.ucar.edu/events/workshops/ws.2017/) in - Boulder, CO, USA
- Ensemble Data Assimilation for Very Large Atmosphere, Ocean and - Coupled Models with the Data Assimilation Research Testbed - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2017/2017_CESM_DART_KDR.pdf)
- It has the same title as the AMS 2017 poster, but has somewhat - different content. - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jan 2017 : - Presented at the [AMS Annual Meeting - 2017](https://annual.ametsoc.org/2017/) in Seattle, WA, USA
- Ensemble Data Assimilation for Very Large Atmosphere, Ocean and - Coupled Models with the Data Assimilation Research Testbed - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2017/2017_AMS_DART_KDR.pdf) - ----- -## 2016 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Dec 2016 : - Presented at the [AGU Fall Meeting - 2016](http://fallmeeting.agu.org/2016/) in San Francisco CA, USA
- DART: A Community Facility Providing State-of-the-Art, Efficient - Ensemble Data Assimilation for Large (Coupled) Geophysical Models - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2016/2016_AGU_DART_TJH.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Apr 2016 : - Presented at the [EGU General Assembly 2016](http://www.egu2016.eu/) - in Vienna, Austria
- Assimilating Observations with Spatially and Temporally Correlated - Errors in a Global Atmospheric Model - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2016/2016_jla_EGU_correlated_error.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2016/2016_jla_EGU_correlated_error.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Apr 2016 : - Presented at the [SIAM Conference on Parallel Processing in - Scientific Computing](http://www.siam.org/meetings/pp16) in Paris, France
- Parallel Implementations of Ensemble Kalman Filters for Huge Geophysical Models - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2016/2016_jla_siam_scalable.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2016/2016_jla_siam_scalable.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2016 : - Presented at the [AMS Annual Meeting - 2016](https://annual.ametsoc.org/2016/) in New Orleans LA, USA
- Assimilating Observations with Correlated Errors in an ensemble - Kalman filter - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jan 2016 : - Presented at the [AMS Annual Meeting - 2016](https://annual.ametsoc.org/2016/) in New Orleans LA, USA
- DART: New Research Using Ensemble Data Assimilation in Geophysical - Models - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2015/20156_AGUAMS_DART.pdf) - ----- -## 2015 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Dec 2015 : - Presented at the [AGU Fall Meeting - 2015](http://fallmeeting.agu.org/2015/) in San Francisco CA, USA
- DART: New Research Using Ensemble Data Assimilation in Geophysical - Models - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2015/20156_AGUAMS_DART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Oct 2015 : - Presented at the State University of New York at Albany in Albany, - New York, USA
- Building State-of-the-Art Forecast Systems with the Ensemble Kalman - Filter - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2015/JLA_Albany_2015_SotA.pptx) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2015/JLA_Albany_2015_SotA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2015 : - Presented at the [20th Annual CESM - Workshop](https://www2.cesm.ucar.edu/events/ws-2015) in Breckenridge - CO, USA
- DART Data Assimilation with CESM Models - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2015/DART_CESM_2015.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** May 2015 : - Presented at the [STATMOS Summer School in Data - Assimilation](http://www.stat.osu.edu/~oksana/summer-school-in-data-assimilation-2015.html) - in Boulder CO, USA
- Getting to know the Data Assimilation Research Testbed - DART - [\[presentation - pdf\]](http://www.image.ucar.edu/pub/DART/2015/DART_STATMOS_TJH_2015.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Feb 2015 : - Presented at the [CESM Atmospheric Modeling Working - Group](https://www2.cesm.ucar.edu/working-groups/amwg/meetings/20150217) - in Boulder CO, USA
- DART Initial Conditions for a Refined Grid CAM-SE Forecast of - Hurricane Katrina - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2015/2015_AMWG_CAM-SE.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jan 2015 : - Presented at the [AMS Annual Meeting - 2015](https://annual.ametsoc.org/2015/) in Phoenix AZ, USA
- DART: Tools and Support for Ensemble Data Assimilation Research, - Operations, and Education (same title as last year, different - content!) - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2014/2014_AGU_DART_A23C-3251.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jan 2015 : - Presented as part of the Colloquium Series in the Computational - Science Deparment of San Diego State University in San Diego CA, - USA
- The keys to (successful) ensemble data assimilation. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2015/DART_SDSU_TJH_2015.pdf) - ----- -## 2014 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Dec 2014 : - Presented at the [AGU Fall Meeting - 2014](http://fallmeeting.agu.org/2014/) in San Francisco CA, USA
- DART: Tools and Support for Ensemble Data Assimilation Research, - Operations, and Education (same title as last year, different - content!) - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2014/2014_AGU_DART_A23C-3251.pdf)
- Open-Source Data Assimilation for Land Models and Multiscale - Observations. - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2014/2014_AGU_DART_LAND_IN41A-3647.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson, Tim Hoar** Oct 2014 : - Presented at the [International Cooperative for Aerosol Prediction - Meeting](http://icap.atmos.und.edu/) in Boulder CO, USA
- Verification of Ensemble Forecasts
- Anderson, part I: - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2014/ICAP_2014_JLA.pdf)
- Hoar, part II: - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2014/ICAP_2014_TJH.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Sep 2014 : - Presented at the [Catchment-based Hydrological Model Data - Assimlation (CAHMDA VI) and Hydrologic Ensemble Prediction - Experiment (HEPEX-DAFOH III) Joint - Workshop](http://www.jsg.utexas.edu/ciess/cahmda-vi-hepex-dafoh-iii/) - in Austin TX, USA
- CAHMDA/DAFOH Ensemble Data Assimilation Tutorial - [\[agenda - pdf\]](http://www.image.ucar.edu/pub/DART/2014/CAHMDA_VI_agenda.pdf)  - [\[Introduction - pdf\]](http://www.image.ucar.edu/pub/DART/2014/CAHMDA_VI_Intro.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Nancy Collins** June 2014 : - Presented to the ocean modeling research section of - [CMCC](http://www.cmcc.it/divisions/oda) in Lecce, Italy
- Scalable Computing Challenges in Ensemble Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2014/2014_CMCC_NSC.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jun 2014 : - Presented at the [19th Annual CESM - Workshop](https://www2.cesm.ucar.edu/events/ws-2014) in Breckenridge - CO, USA
- CESM, CLM, Observations, and Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2014/DART_CLM_CESM2014_TJH.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Feb 2014 : - Presented at the [CESM Atmospheric Modeling Working - Group](https://www2.cesm.ucar.edu/working-groups/amwg/meetings/20140210) - in Boulder CO, USA
- Comparing CAM-FV and CAM-SE in a 'perfect model' data assimilation - environment - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/CESM_2013_Homme_DA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Helen Kershaw** Feb 2014 : - Presented at the [SIAM Conference of Parallel Processing for - Scientific Computing 2014](http://www.siam.org/meetings/pp14/) in - Portland, OR, USA
- Parallelization Challenges for Ensemble Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2015/2015_SIAM_HK.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jan 2014 : - Presented at the [AMS Annual Meeting - 2014](https://annual.ametsoc.org/2014/) in Atlanta GA, USA
- DART: Tools and Support for Ensemble Data Assimilation Research, - Operations, and Education - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2013/20134_AGUAMS_DART.pdf)
- Ensemble data assimilation for soil-vegetation-atmosphere systems. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2014/2014_AMS_TJH_235997.pdf) - ----- -## 2013 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Dec 2013 : - Presented at the [AGU Fall Meeting - 2013](http://fallmeeting.agu.org/2013/) in San Francisco CA, USA
- DART: Tools and Support for Ensemble Data Assimilation Research, - Operations, and Education - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2013/20134_AGUAMS_DART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Alicia Karspeck** Oct 2013 : - Presented at the [Sixth WMO Symposium on Data - Assimilation](http://www.ncep.noaa.gov/events/2013/wmo6da) in - College Park MD, USA
- An ensemble approach for the estimation of observational error - variance
- With application to the nominal 1-degree POP2 ocean model. - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2013/2013_WMO_Alicia.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Oct 2013 : - Presented at the [Sixth WMO Symposium on Data - Assimilation](http://www.ncep.noaa.gov/events/2013/wmo6da) in - College Park MD, USA
- Achieving Superior Tropical Cyclone Intensity Forecasts by Improving - the Assimilation of High-Resolution Satellite Data into Mesoscale - Prediction Models - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2013/2013_WMO_HuiLiu.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Oct 2013 : - Presented at the [Sixth WMO Symposium on Data - Assimilation](http://www.ncep.noaa.gov/events/2013/wmo6da) in - College Park MD, USA
- Ensemble Data Assimilation for Soil-Vegetation-Atmosphere Systems - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2013/2013_WMO_clm_TJH.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Nancy Collins** Aug 2013 : - Presented at the [FRCRC - Symposium](http://www.uwyo.edu/uw/news/2013/08/uw-to-host-front-range-hpc-symposium-aug.-13-15.html) - in Laramie WY, USA
- Scalable Computing Challenges in Ensemble Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/Collins_FRCRC.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Nancy Collins** Jun 2013 : - Presented at the [18th Annual CESM - Workshop](https://www2.cesm.ucar.edu/events/ws-2013) in Breckenridge - CO, USA
- CESM/DART: An ensemble data assimilation system for fully coupled - models. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/Collins-SEWG-2013.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jun 2013 : - Presented at the [18th Annual CESM - Workshop](https://www2.cesm.ucar.edu/events/ws-2013) in Breckenridge - CO, USA
- Data Assimilation for CLM:a comprehensive overview in 12 minutes! - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/DART_CLM_CESM2013.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Ally M. Toure** Apr 2013 : - Presented at the [MODIS Science Team - Meeting](http://modis.gsfc.nasa.gov/sci_team/meetings/201304) in - Silver Spring MD, USA
- Multi-Sensor Snow Data Assimilation: Part1: Assimilation of the - MODIS Snow Cover Fraction the Community Land Model (CLM04) - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2013/MODIS_Team_meeting_TOURE_al_201304_v9.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Mar 2013 : - Presented at the [TR32 International - Symposium](http://tr32meeting.uni-koeln.de/) "Patterns in - Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling & Data - Assimilation" in Bonn, Germany
- The keys of ensemble data assimilation for - soil-vegetation-atmosphere systems. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/DART_LSM_TR32_2013_TJH.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Mar 2013 : - Presented at the [Institute for Mathematics and its Applications - (IMA) Workshop on Stochastic Modeling of the Oceans and - Atmosphere,](http://www.ima.umn.edu/2012-2013/W3.11-15.13) in - Minneapolis MN, USA
- Ensemble Data Assimilation for Climate System Component Models - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/2013_IMA_ensemble_filter_JLA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Feb 2013 : - Presented at the [CESM Atmospheric Modeling Working - Group](https://www2.cesm.ucar.edu/working-groups/amwg/meetings/20130211) - in Boulder CO, USA
- Data Assimilation with SE-CAM and DART - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/CESM_2013_Homme_DA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jan 2013 : - Presented at the [93rd American Meteorological Society Annual - Meeting](http://annual.ametsoc.org/2013/) in Austin TX, USA
- New Developments in the Data Assimilation Research Testbed - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2013/2013_AMS_NewDART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2013 : - Presented at the [2013 AMS Special Symposium on Advancing Weather - and Climate Forecasts: Innovative Techniques and - Applications](http://annual.ametsoc.org/2013/index.cfm/programs-and-events/conferences-and-symposia/special-symposium-on-advancing-weather-and-climate-forecasts-innovative-techniques-and-applications/) - in Austin TX, USA
- The Data Assimilation Research Testbed: A Community Facility for - Ensemble Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2013/2013_AGU_testbed_JLA.pdf) - ----- -## 2012 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Dec 2012 : - Presented at the **NSF EarthCube Workshop: Shaping the Development - of EarthCube to Enable Advances in Data Assimilation and Ensemble - Prediction** in Boulder CO, USA
- The Data Assimilation Research Testbed: A Community Facility for - Ensemble Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/2012_NSF_earth3_JLA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Dec 2012 : - Presented at the [Third COSMOS - Workshop](http://cosmos.hwr.arizona.edu/workshop3/workshop3.html) in - Tucson AZ, USA
- DART and Land Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/DART_LSM_COSMOS_2012.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Rafael Rosolem** Dec 2012 : - Presented at the [AGU Fall - Meeting](http://fallmeeting.agu.org/2012/) in San Francisco CA, USA
- Data Assimilation of Cosmic-ray Derived Soil Moisture - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/Rosolem_AGU_H23G-04_20121204.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **[Alexey Morozov](http://www-personal.umich.edu/~morozova/MySite/Publications.html)** Dec 2012 : - Presented at the [AGU Fall - Meeting](http://fallmeeting.agu.org/2012/) in San Francisco CA, USA
- Data Assimilation and Parameter Estimation for the Global - Ionosphere-Thermosphere Model using the Ensemble Adjustment Kalman - Filter - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2012/AlexeyMorozovAGU2012.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Oct 2012 : - Presented at the [Sixth FORMOSAT-3/COSMIC Data Users' - Workshop](http://www.cosmic.ucar.edu/oct2012workshop/index.html) in - Boulder, Colorado
- Improvements to WRF/DART Ensemble Assimilation System for RO Data - Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/COSMIC2012liu.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jul 2012 : - Presented at the [5th International Workshop on Catchment - Hydrological Modelling and Data assimilation - (CAHMDA-V)](http://cahmda.itc.nl//) in Enschede, Netherlands
- Data Assimilation with DART - [\[ppt\]](http://www.image.ucar.edu/pub/DART/2012/CAHMDA-V_Tutorial.ppt) - [\[pptx\]](http://www.image.ucar.edu/pub/DART/2012/DARTCLM_CAHMDA_2012.pptx) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jun 2012 : - Presented at the **17th Annual CESM Workshop** in Breckenridge, CO, USA
- Data Assimilation with CLM & DART - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/DARTCLM_CESM_2012.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Nancy Collins** Jun 2012 : - Presented at the **17th Annual CESM Workshop** in Breckenridge, CO, USA
- Status of using DART with multi-instance CESM - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/SEWG_CESM_2012.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2012 : - Presented at the **17th Annual CESM Workshop** in Breckenridge, CO, USA
- Data Assimilation with Multi-Instance CESM Components and DART - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2012/KDR_poster_CESM_2012.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Mar 2012 : - Presented at Cornell University in Ithaca, NY, USA
- An Introduction to Ensemble Data Assimilation and the Data - Assimilation Research Testbed (DART) - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/ensemble_DA_and_DART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Andrew Fox** Mar 2012 : - Presented at the [2012 Land Model Working - Group](http://www.cesm.ucar.edu/working_groups/Land/) in Boulder, CO, USA
- Ensemble-based Data Assimilation for the Community Land Model - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/LMWG_fox_022812.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Feb 2012 : - Presented at the [2012 Ocean Sciences - Meeting](http://www.sgmeet.com/osm2012/) in Salt Lake City, UT, USA
- Ensemble Data Assimilation and Uncertainty Quantification - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/jla_ocean_sciences_2012.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Alicia Karspeck** Feb 2012 : - Presented at the [2012 Ocean Sciences - Meeting](http://www.sgmeet.com/osm2012/) in Salt Lake City, UT, USA
- POPDART: An Ensemble Data Assimilation System for the Ocean - Component of CESM - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2012/OS2012_Karspeck_poster.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jan 2012 : - Presented at the [2012 AMS Annual - Meeting](http://annual.ametsoc.org/2012/) in New Orleans, LA, USA
- Challenges for Robust Ocean Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/dart_agu2012_200472_pop.pdf)
- Land Surface Data Assimilation: DART and CLM - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2012/dart_agu2012_200094_clm.pdf)
- New Developments in the Data Assimilation Research Testbed - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2011/2011_AGU_newDART.pdf) - ----- -## 2011 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Andrew Fox** Dec 2011 : - Presented at the [2011 AGU Fall - Meeting](http://www.agu.org/meetings/) in San Francisco, CA, USA
- A Model-Data Fusion Approach to Integrate National Ecological - Observatory Network Observations into an Earth System Model - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2011/2011_AGU_DARTCLM_Yongfei.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Yongfei Zhang** (UT Austin) Dec 2011 : - Presented at the [2011 AGU Fall - Meeting](http://www.agu.org/meetings/) in San Francisco, CA, USA
- Snow Data Assimilation: DART and CLM - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2011/2011_AGU_DARTCLM_Yongfei.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Dec 2011 : - Presented at the [2011 AGU Fall - Meeting](http://www.agu.org/meetings/) in San Francisco, CA, USA
- A Non-Gaussian Ensemble Filter for Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/2011_AGU_nongauss_da_JLA.pdf)
- Challenges in Developing Coupled Earth System Model Data - Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/2011_AGU_dart_coupled_esm_da.pdf)
- New Developments in the Data Assimilation Research Testbed - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2011/2011_AGU_newDART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Alicia Karspeck** Oct 2011 : - Presented at the [(WCRP) World Climate Research Programme Open - Sciences Conference 2011](http://conference2011.wcrp-climate.org/) - in Denver, CO, USA
- Deep transient circulation resulting from the initialization of an - ocean ensemble assimilation system - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2011/WCRP2011_Karspeck.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Sep 2011 : - Presented at the [Earth System Prediction Capability - Workshop](http://www7320.nrlssc.navy.mil/ESPC_DA/) at the University - of Maryland, MD, USA
- The Data Assimilation Research Testbed: A Community Ensemble DA - Facility - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/JLA_ESPC_da.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Sep 2011 : - Presented at the [SAMSI UQ - Workshop](http://www.samsi.info/workshop/2011-12-uq-program-methodology-opening-workshop-and-tutorials) - in Research Triangle Park, NC, USA
- Ensemble Data Assimilation and Uncertainty Quantification - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/ensemble_da_uq.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson, Nancy Collins** Jun 2011 : - Presented at the **16th Annual CESM Workshop** in Breckenridge, CO, USA
- A Wishlist of CESM Capabilities for Support of Ensemble Data - Assimilation with DART - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/JLA_CESM_breck.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2011 : - Presented at the **16th Annual CESM Workshop** in Breckenridge, CO, USA
- Loosely Coupled Atmosphere-Ocean Data Assimilation: CAM-DART-POP - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_CLIVAR_BWI_tjh.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** May 2011 : - Presented at the [International Cooperative for Aerosol Prediction - (ICAP/AEROCAST) Ensemble Forecasts and Data - Assimilation](http://bobcat.aero.und.edu/jzhang/ICAP) in Boulder, CO, USA
- Introduction to Ensemble Kalman Filters and the Data Assimilation - Research Testbed - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/jla_icap.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2011 : - Presented at the [91st Annual Meeting of the - AMS](http://www.ametsoc.org/meet/annual) in Seattle, WA, USA
- Localization and Correlation in Ensemble Kalman Filters. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/JLA_sec_seattle.pdf)
- A Loosely Coupled Ocean-Atmosphere Ensemble Assimilation System. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2011/AMS_2011_CAMDARTPOP_JLA.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **McLay, J., J. Hansen, J. Hacker, - D. Hodyss, T. Hoar, N. Collins, T. Whitcomb, A. Reinecke, J. Anderson** 2011 : - Presented at the 15th Symposium on Integrated Observing and - Assimilation Systems for the Atmosphere, Oceans, and Land Surface - (IOAS-AOLS), 91st AMS Annual Meeting, Seattle, WA, USA
- NOGAPS DART: A comparison of EnKF-based and local ET-based ensemble - perturbations. - -![](http://www.image.ucar.edu/images/pin4.gif) **Reinecke P.A., J. Doyle,** 2011 : - Presented at the 91st AMS Annual Meeting, Seattle, WA, USA
- Ensemble error growth and the mesoscale predictability of - cyclogenesis in the lee of the European Alps. - -![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A.,** 2011 : - Presented at the High Impact Weather Working Group Workshop, Norman, OK, USA
- GOES-R ABI proxy radiance dataset generation and data assimilation - activities at CIMSS. - -![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A., D. C. Hartung, - D. D. Turner, R. A. Petersen, W. F. Feltz, and E. Janzon,** 2011 : - Presented at the 15th Conference on Integrated Observing and - Assimilation Systems for Atmosphere, Oceans, and Land Surface, - Seattle, WA, USA
- Assimilation of surface-based profiler observations during an - observation system simulation experiment. Part 1: Analysis Impact. - -![](http://www.image.ucar.edu/images/pin4.gif) **Hartung, D. C., J. A. Otkin, - R. A Petersen, D. D. Turner, and W. F. Feltz,** 2011 : - Presented at the 15th Conference on Integrated Observing and - Assimilation Systems for Atmosphere, Oceans, and Land Surface, - Seattle, WA, USA
- Assimilation of surface-based profiler observations during an - observations system simulation experiment. Part 2: Forecast Assessment. - ----- -## 2010 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Dec 2010 : - Presented at the [2010 AGU Fall - Meeting](http://www.agu.org/meetings/fm10/) in San Francisco, CA, USA
- The Use of the Data Assimilation Research Testbed for Initializing - and Evaluating IPCC Decadal Forecasts. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_AGU_decadal_forecasts_KDR.pdf)
- The Data Assimilation Research Testbed: New Algorithms and Applications - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_AGU_DARTupdate_kdr.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **C. Lee, M. I. Richardson, - W. G. Lawson, J. L. Anderson, N. Collins, T. Hoar, M. Mischna, and A. D. Toigo,** Dec 2010 : - Presented at the [2010 AGU Fall - Meeting](http://www.agu.org/meetings/fm10/) in San Francisco, CA, - USA
- Preliminary Results from Ensemble Data assimilation of the Martian - Atmosphere using TES radiances Abstract P53E-1563. - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_AGU_CLee_MarsPoster.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Dec 2010 : - Presented at the *JCSDA/HFIP Workshop on satellite data assimilation - for hurricane forecasting* in Miami, FL, USA
- Impact of Assimilating GPS RO refractivity on Tropical Cyclone - Position and Intensity Analyses and Forecasts - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_Hui_Miami.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Dec 2010 : - Presented at the *JCSDA/HFIP Workshop on satellite data assimilation - for hurricane forecasting* in Miami, FL, USA
- Future Directions in Ensemble DA for Hurricane Prediction - Applications - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_JLA_Miami.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Nov 2010 : - Presented at the *NASA Sounder Workshop and Science Team Meeting* in - Greenbelt, MD, USA
- Improving forecasts of tropical cyclone intensity and track using - AIRS water vapor observations with an ensemble data assimilation - system. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_Hui_NASA_SWSTM.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Nov 2010 : - Presented at [CLIVAR: Evaluation of Reanalyses – Developing an - Integrated Earth System Analysis (IESA) - Capability](http://www.usclivar.org/Reanalysis2010.php) in - Baltimore, MD, USA
- Loosely Coupled Atmosphere-Ocean Data Assimilation: CAM-DART-POP - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_CLIVAR_BWI_tjh.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Wicker, L. J., E. R. Mansell, - D. C. Dowell and D. T. Dawson,** Oct. 2010 : - Preprints, [25th Conf. on Severe Local - Storms](http://www.ametsoc.org/meet/fainst/201025sls.html), Denver - CO, Amer. Meteor. Soc., Paper 12B.5.
- High-resolution storm-scale numerical weather prediction using EnKF - for the 8 May 2003 Moore Oklahoma tornadic supercell. - -![](http://www.image.ucar.edu/images/pin4.gif) **Dowell, D., G. Romine, and C. Snyder,** Oct. 2010 : - Preprints, [25th Conf. on Severe Local - Storms](http://www.ametsoc.org/meet/fainst/201025sls.html), Denver, - Colorado, Amer. Meteor. Soc., Paper 9.5.
- Ensemble storm-scale data assimilation and prediction for severe - convective storms. - -![](http://www.image.ucar.edu/images/pin4.gif) **C. Lee, M. I. Richardson, W. G. Lawson, - and J. L. Anderson**, Oct 2010 : - Presented a the American Astronomical Society, DPS meeting \#42, - \#30.09; Pasadena, CA, USA Bulletin of the American Astronomical - Society, Vol. 42, p.1029
- Ensemble Data Assimilation of the Martian Atmosphere Using - Temperature and Radiance Data from the Thermal Emission - Spectrometer - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Sep 2010 : - Presented at [CLIVAR Working Group on Ocean Model - Development](http://www.clivar.org/organization/wgomd/decadal/decadal.php) - in Boulder, CO, USA
- Weakly Coupled Atmosphere-Ocean Data Assimilation: CAM-DART-POP - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_CLIVAR_WGOMD_tjh.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tomoko Matsuo, E. A. Araujo-Pradere, - and J. L. Anderson** June 2010 : - Presented at the International Beacon Satellite Symposium, - Barcelona, Spain
- Ensemble Kalman Filtering for Assimilation of GPS-based Ionospheric - Observations. - -![](http://www.image.ucar.edu/images/pin4.gif) **Turner, D. D., J. A. Otkin, - D. C. Hartung, R. Peterson, W. F. Feltz, and E. Janzon** June 2010 : - 10th Workshop on Infrared Remote Sensing Applications, Quebec City, - QB, Canada
- Impact of assimilating retreivals of temperature and humidity - profiles from a ground-based network on a winter storm forecast. - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2010 : - Presented at the *CESM User Group meeting 2010*, Breckenridge, CO, USA
- New Ocean, Atmosphere, Land, and Sea Ice Reanalyses from CESM1 (CAM5) - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2010/CESM_2010_AMWG_KDR.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Jun 2010 : - Presented at the *CESM User Group meeting 2010*, Breckenridge, CO, USA
- POP-DART; The Leading Edge of Coupled Ocean-Atmosphere Reanalyses - [\["poster" - pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_CESM_Breck_TJH.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** May 2010 : - Presented at [The 5th International Workshop on Ensemble Kalman Filter for Model - Updating](http://www.iris.no/enkf/previous-workshops/2010-clarion-hotel-admiral-bergen), - Bergen, Norway
- Reducing the Impact of Sampling Errors in Ensemble Filters. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_Bergen_JLA_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** May 2010 : - Presented at the [29th Conference on Hurricanes and Tropical - Meteorology](http://www.ametsoc.org/meet/fainst/201029hurricanes.html) - in Tucson, AZ, USA
- Impact of Assimilating Environmental Satellite Observations on - Tropical Storm Position and Intensity Analyses and Forecasts - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_Hui_AMS_Tucson.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Apr 2010 : - Presented at [The 4th EnKF Workshop](http://hfip.psu.edu/EDA2010) in - Rensselaerville, NY, USA
- Localization and Correlation in Ensemble Kalman Filters. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2010/2010_NY_JLA_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A., and W. Lewis** Apr 2010 : - Presented at The 4th Ensemble Data Assimilation Workshop, - Renssellaerville, NY, USA
- Assimilation of simulated ABI infrared brightness temperatures using - an ensemble Kalman filter. - -![](http://www.image.ucar.edu/images/pin4.gif) **T. Hoar, Jeffrey Anderson, - N. Collins, and B. Nadiga** Jan 2010 : - Presented at the [14th Symposium on Integrated Observing and - Assimilation Systems for the Atmosphere, Oceans, and Land Surface - (IOAS-AOLS)](http://ams.confex.com/ams/90annual/techprogram/programexpanded_576.htm), - 90th Annual AMS Meeting, Atlanta, GA USA
- POP-DART: An ensemble assimilation system for the parallel ocean - program. [\[abstract & link to - presentation\]](http://ams.confex.com/ams/90annual/techprogram/paper_162468.htm) - -![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A., and W. E. Lewis** Jan 2010 : - Presented at the 14th Symposium on Integrated Observing and - Assimilation Systems for the Atmosphere, Oceans, and Land Surface, - Atlanta, GA. Assimilation of simulated infrared brightness - temperatures as part of an OSSE employing the Ensemble Kalman Filter. - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeffrey Anderson, T. Hoar, - K. Raeder, N. Collins, H. Liu, G. S. Romine, and A. F. Arellano** Jan 2010 : - Presented at the [14th Symposium on Integrated Observing and - Assimilation Systems for the Atmosphere, Oceans, and Land Surface - (IOAS-AOLS)](http://ams.confex.com/ams/90annual/techprogram/programexpanded_576.htm), - 90th Annual AMS Meeting, Atlanta, GA USA
- DART: A community facility for ensemble data assimilation. - [\[abstract\]](http://ams.confex.com/ams/90annual/techprogram/paper_162479.htm) - -![](http://www.image.ucar.edu/images/pin4.gif) **Petersen, R.A., J. A. Otkin, - D. Hartung, D. Turner, W. F. Feltz and E. Janzon** 2010 : - Presented at the NOAA/NASA/FAA Aviation Requirements Workshop, Boulder, CO, USA
- A \`Nationwide Network of Networks': Investigating the impacts of a potential - ground-based profiling system using an Observing System Simulation Experiment. - -![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A.** 2010 : - Presented at the 17th Conference on Satellite Meteorology and - Oceanography, Annapolis, MD, USA
- The relative impact of clear and cloudy sky infrared brightness - temperatures within an ensemble data assimilation system. - -![](http://www.image.ucar.edu/images/pin4.gif) **Turner, D., J. A. Otkin, - D. Hartung, R. Peterson, W. Feltz, and E. Janzon** 2010 : - Presented at the EU COST 0702: Integrated Ground-based Observations - of Essential Variables for Climate and Operational Meteorology. Paris, France
- Impact of assimilating retrievals of temperature and humidity - profiles from a ground-based network on a winter storm forecast. - -![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A., and W. Lewis** 2010 : - Presented at the 11th Annual WRF User's Workshop, Boulder, CO, USA
- Assimilation of simulated ABI infrared brightness temperatures using - an ensemble Kalman filter data assimilation system. - -![](http://www.image.ucar.edu/images/pin4.gif) **Turner, D. D., J. A. Otkin, - D. C. Hartung, R. Peterson, W. F. Feltz, and E. Janzon** 2010 : - Presented at the 10th Workshop on Infrared Remote Sensing - Applications, Quebec City, QB, Canada
- Impact of assimilating retreivals of temperature and humidity - profiles from a ground-based network on a winter storm forecast. - -![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A., and W. E. Lewis** 2010 : - Presented at the 14th Symposium on Integrated Observing and - Assimilation Systems for the Atmosphere, Oceans, and Land Surface, - Atlanta, GA, USA
- Assimilation of simulated infrared brightness temperatures as part - of an OSSE employing the Ensemble Kalman Filter. - ----- -## 2009 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Dec 2009 : - Presented at the [2009 AGU Fall - Meeting](http://www.agu.org/meetings/fm09/), San Francisco, CA, USA
- DART: A Facility for Ensemble Data Assimilation - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_AGU_AMS_kdr.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Dec 2009 : - Presented at the Ocean Modeling Working Group meetings, Boulder, CO, USA
- Ensemble Data Assimilation for POP. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_OMWG_JLA_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Oct 2009 : - Presented at the COSMIC Meetings, Boulder, CO, USA
- Initialization of the Environment of Tropical Cyclogenesis by - Assimilating RO Data using an Ensemble Filter. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_COSMIC_HUI_ernesto.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Oct 2009 : - Presented at the COSMIC Meetings, Boulder, CO, USA
- Impacts of COSMIC GPS Observations on a Climate Reanalysis. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_COSMIC_JLA_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Sep 2009 : - Presented at the [Computing in Atmospheric Sciences - Workshop](http://www.cisl.ucar.edu/dir/CAS2K9), Annecy, France
- Ensemble Data Assimilation for Large Atmospheric Models - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_CAS2K9_JLA_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Sep 2009 : - Presented as a seminar at the ECMWF, Reading, UK
- Parallel Implementation of Sequential Ensemble Kalman Filters - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_ECMWF_JLA_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Robert Pincus** Sep 2009 : - Presented at the [ECMWF Seminar 2009 Diagnosis of Forecasting and - Data Assimilation - Systems](http://www.ecmwf.int/newsevents/meetings/annual_seminar/2009/index.html), - Reading, UK
- Assimilating clouds in perfect global models
- (or trying to - a work in slow progress). - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_ECMWF_Pincus.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2009 : - Presented at the CCSM User Group meeting 2009, Breckenridge, CO, USA
- New Diagnostics of CAM from DART - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_CCSM_kdr.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Nedjeljka Zagar** April 2009 : - Presentations at EGU, Vienna, Austria.
- Diagnosis of analysis and forecast ensembles by using normal modes - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_EGU_zagar_poster2.pdf)
- Uncertainties of estimates of intertio-gravity energy in the - atmosphere - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2009/2009_EGU_zagar_poster1.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Dorita Rostkier-Edelstein** Feb 2009 : - Presented at the Institute of Earth Sciences, the Hebrew University - of Jersusalem, Israel.
- Probabilistic estimation of the state of the planetary boundary - layer using surface observations, a column model and an ensemble filter. - ----- -## 2008 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Emanuele Di Lorenzo** Dec 2008 : - Presentation at AGU, San Francisco, California.
- Theory and Applications of Variational, Sequential and Bayesian - Hierarchical Methods in Data Assimilation - [\[abstract\]](http://www.image.ucar.edu/pub/DART/2008/2008_AGU_edl_abstract.txt) - -![](http://www.image.ucar.edu/images/pin4.gif) **Ibrahim Hoteit** Dec 2008 : - Presentation at AGU, San Francisco, California.
- A MITgcm/DART Ocean Analysis and Prediction System with Application - to the Gulf of Mexico - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_AGU_hoteit_poster.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Dec 2008 : - Presentation and poster at AGU, San Francisco, California.
- Diagnosis of Noise in the FV core using DART. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/KDR_CCSM08_DART.pdf)
- A General Purpose Data Assimilation Facility: DART. - [\[poster\]](http://www.image.ucar.edu/pub/DART/2008/2008_AGU_kdr_poster.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Dorita Rostkier-Edelstein** Dec 2008 : - Presented at the Department of Geophysics and Planetary Sciences, - Tel-Aviv University, Tel-Aviv, Israel
- Probabilistic Nowcasting of PBL Profiles: A Factor Separation Study. - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Dec 2008 : - Presented at the 2008 CAOS Workshop: *Real Time Filtering and - Prediction for Turbulent Signals in Weather and Climate*, New York - City, New York, USA
- Nonlinear, Non-Gaussian Ensemble Filters. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_CAOS_jla_nonlin_nongauss.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Nov 2008 : - Presented at the NCAR Foothills Laboratory, Boulder, CO, USA
- Nonlinear, Non-Gaussian Ensemble Filters for Data Assimilation. - -![](http://www.image.ucar.edu/images/pin4.gif) **Greg Lawson** Nov 2008 : - Presented at the Third International Workshop on The Mars - Atmosphere: Modeling and Observations. Williamsburg, VA, USA
- Ensemble-based Data Assimilation with Mapping Datasets of the - Martian Atmosphere. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_WGLawson_Williamsburg_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Dorita Rostkier-Edelstein** Nov 2008 : - Presented at the Jacob Blaustein Institutes for Desert Research, - Ben-Gurion University of the Negev, Sede Boqer Campus, Israel
- Probabilistic estimation of the state of the planetary boundary - using surface observations, a column model and an ensemble filter. - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Oct 2008 : - Presented at The University of Utah, Salt Lake City, Utah, USA
- Predicting Hurricanes and Improving Climate Models using Ensemble - Data Assimilation. - [\[ppt\]](http://www.image.ucar.edu/pub/DART/2008/JLA_2008_Utah.ppt) - -![](http://www.image.ucar.edu/images/pin4.gif) **Bill Kuo** Oct 2008 : - Presented at the FORMOSAT-3/COSMIC Workshop 2008, Taipei, Taiwan
- The Impact of FORMOSAT-3/COSMIC GPS RO Data on Typhoon Prediction. - -![](http://www.image.ucar.edu/images/pin4.gif) **Jennifer Kay** Oct 2008 : - Presented at NCAR, Boulder, CO, USA
- Evaluating the atmospheric forcing on recent Arctic sea ice loss. - -![](http://www.image.ucar.edu/images/pin4.gif) **Dorita Rostkier-Edelstein** Sep 2008 : - Presented at the Meeting of the Israel Meteorological Society, - Bet-Dagan, Israel
- Ensemble filter data assimilation: Application to probabilistic - nowcasting of boundary layer profiles. - -![](http://www.image.ucar.edu/images/pin4.gif) **Alicia Karspeck** Aug 2008 : - Presented at the Joint Statistical Meetings 2008, Denver, CO, USA
- El Niño state estimation using the ensemble Kalman filter - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Aug 2008 : - Presented at the Joint Statistical Meetings 2008, Denver, CO, USA
- Application of Radio Occultation Data in Analyses and Forecasts of - Tropical Cyclones Using an Ensemble Assimilation System. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_JSM_liu_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Aug 2008 : - Presented at the Joint Statistical Meetings 2008, Denver, CO, USA
- NCAR's Data Assimilation Research Testbed: DART. 'Plug and Play' - Ensemble Data Assimilation. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_JSM_tjh_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeffrey Anderson** Aug 2008 : - Presented at the Joint Statistical Meetings 2008, Denver, CO, USA
- Using Small Ensembles in High Dimensions: Hierarchical Bayesian - Approaches to Adaptive Ensemble Filters. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_JSM_jla_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeffrey Anderson** July 2008 : - Presented at the University of Maryland, MD, USA
- A non-gaussian ensemble filter for nonlinear models and discrete - structures. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_UMD_jla_rank_hist_filter.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Nedjeljka Zagar** Jun 2008 : - Presented at the CCSM User Group meeting 2008, Breckenridge, CO, USA
- Diagnosis of model biases by using DART - [\[poster - pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008_CCSM_ZAGAR_poster.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** Jun 2008 : - Presented at the CCSM User Group meeting 2008, Breckenridge, CO, USA
- Diagnosis of Noise in the FV core using DART. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/KDR_CCSM08_DART.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Apr 2008 : - Presented at the *Hurricanes* session of the 2008 AMS, Orlando, FL, USA
- The Impact of Radio Occultation Data on Analysis of the Subtropical - Anticyclone over Western Pacific Ocean - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/2008Apr_AMS_liu.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Apr 2008 : - Presented at the *Workshop on Kalman Filtering*, 4-9 April 2008, - Marble Falls, TX, USA
- Adaptive Inflation for Ensemble Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/jla_texas_enkf_talk.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Matsuo, T., J. L. Anderson, - D. R. Marsh, and A. K. Smith** 2008 : - Presented to the EGU General Assembly 2008, Vienna, Austria
- Ensemble Kalman filtering for assimilation of mesosphere and lower - thermosphere observations. - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Feb 2008 : - Presented at the *Atmosphere Model Working Group* (AMWG), 14 - February 2008, Boulder, CO, USA
- Using the DART-CAM Ensemble Data Assimilation System for Climate - Model Development - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/jla_amwg_08.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2008 : - Presented at the *88th AMS Annual Meeting*, 20--24 January 2008, New - Orleans, Louisiana, USA
- The Relation Between Ensemble Size and Expected Error in Ensemble - Filter Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2008/Anderson_AMS1_Jan08.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Josh Hacker** Jan 2008 : - Presented at the *12th Conference on Integrated Observing and - Assimilation Systems for Atmosphere, Oceans, and Land Surface - (IOAS-AOLS)*, 20--24 January 2008, New Orleans, Louisiana, USA
- PBL State estimates with surface observations, a column model, and - an ensemble filter: Probabilistic evaluation under various mesoscale - regimes. [\[extended - abstract\]](http://www.image.ucar.edu/pub/DART/2008/AMS2008_paper8.6_Hacker.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Jan 2008 : - Presented at the *88th AMS Annual Meeting*, 20--24 January 2008, New - Orleans, Louisiana, USA
- Improving Forecasts of Tropical Cyclones with Innovative GPSRO - Satellite Observations [\[pdf of - poster\]](http://www.image.ucar.edu/pub/DART/2008/2008Jan_AMS_liu_poster.pdf)
- Improving Analyses and Forecasts of the Asian Monsoon with GPS Radio - Occultation Refractivity Observations [\[pdf of - talk\]](http://www.image.ucar.edu/pub/DART/2008/2008Jan_AMS_liu_talk.pdf) - ----- -## 2007 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **W. G. Lawson, M. I. Richardson, - D. J. McCleese, J. T. Schofield, O. Aharonson, S. B. Calcutt, P. G. J. Irwin, - D. M. Kass, C. B. Leovy, S.R. Lewis, D. A. Paige, P. L. Read, F. W. Taylor, - and R. W. Zurek**, 2007 : - Presented at the Seventh International Conference on Mars, Pasadena, California, USA
- Adapting State of the Art Data Assimilation Approaches for Use with - the Mars Climate Sounder and the PlanetWRF Martian GCM. LPI - Contribution No. 1353, p 3321. - -![](http://www.image.ucar.edu/images/pin4.gif) **Nancy Collins** Nov 2007 : - Presented at Supercomputing 2007, Reno, NV, USA
- DART overview. [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007/2007_SC07_nsc.pdf) - [\[animation1 (.mov)\]](http://www.image.ucar.edu/pub/DART/2007/ENKF_machine_6fps.mov) - [\[animation2 (.mov)\]](http://www.image.ucar.edu/pub/DART/2007/L96basicDA.mov) - [\[animation3 (.mov)\]](http://www.image.ucar.edu/pub/DART/2007/L96_growth.mov) - -![](http://www.image.ucar.edu/images/pin4.gif) **W. G. Lawson, M. I. Richardson, - D. J. McCleese, J. L. Anderson, Y. Chen, and C. Snyder** 2007 : - Eos Trans. AGU, 88(52), Fall Meeting Suppl.,
- Ensemble-Based Data Assimilation with a Martian GCM Abstract P11A-0251. - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Nov 2007 : - Talk presented at the NCAR/Univ of Wyoming Retreat, Steamboat Springs, CO, USA
- Data Assimilation Research Section - Overview - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007/2007_UWY_DART_overview_tjh.pdf) - [\[animation1 (.mov)\]](http://www.image.ucar.edu/pub/DART/2007/ENKF_machine_6fps.mov) - [\[animation2 (.mpg)\]](http://www.image.ucar.edu/pub/DART/2007/global_atmosphere1.mpg) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Aug 2007 : - Presented at SPIE, San Diego, CA, USA
- The Impacts of Radio Occultation Data on analyses and prediction of - the Monsoon and hurricanes using WRF ensemble forecast system. - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Jul 2007 : - Presented at the IUGG, Data Assimilation session, Perugia, Italy.
- Assimilation of GPS radio occultation refractivity with WRF using a - non-local observation operator. - [\[poster-pdf\]](http://www.image.ucar.edu/pub/DART/2007_IUGG_hui.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jun 2007 : - Presented at the CCSM Summer Workshop, Breckenridge, CO, USA
- Data Assimilation for CAM - Research & Development. - [\[poster-pdf\]](http://www.image.ucar.edu/pub/DART/2007_CCSM_jla.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **David Dowell** Jun 2007 : - Presented at the WRF User's Workshop, Boulder, CO, USA
- Mesoscale WRF Surface-Data Assimilation: Spring 2007 Experiments at - the National Severe Storms Laboratory - [\[pdf\]](http://www.image.ucar.edu/pub/DART/Dowell_13jun2007_WRF_Workshop.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jun 2007 : - Presented at the 22nd Conference on Weather Analysis and - Forecasting/18th Conference on Numerical Weather Prediction, Park - City, UT, USA
- DART: A Community Tool for Ensemble Data Assimilation Research and Development. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_WAFNWP_jla_dart_overview.pdf),
- An Introduction to Ensemble Kalman Filtering. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_WAFNWP_jla_tutorial.pdf), - and
- A Boxcar Kernel Filter for Assimilation of Discrete Structures (and Other Stuff). - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_WAFNWP_jla_boxcar_filter.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Josh Hacker** Jun 2007 : - Presented at the 22nd Conference on Weather Analysis and - Forecasting/18th Conference on Numerical Weather Prediction, Park - City, UT, USA
- PBL state estimation with surface observations, a column model and - an ensemble filter. - -![](http://www.image.ucar.edu/images/pin4.gif) **Nancy Collins** May 2007 : - Presented at the LCI International Conference on High-Performance - Clustered Computing, South Lake Tahoe, CA, USA
- Parallel Implementation of Ensemble Filter Algorithms for Data - Assimilation. - [\[poster-pdf\]](http://www.image.ucar.edu/pub/DART/2007_LCI_nsc.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Kevin Raeder** May 2007 : - Presentation and poster at the AGU Spring meeting 2007, Acapulco, Mexico.
- Adaptive Spatially-varying Variance Inflation in an Ensemble Filter. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_AGU_kdr_talk.pdf)
- A General Purpose Data Assimilation Facility: DART. - [\[poster\]](http://www.image.ucar.edu/pub/DART/2007_AGU_kdr_poster.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Josh Hacker** Apr 2007 : - Presented to the EGU General Assembly 2007, Vienna, Austria
- State estimation and predictability in the planetary boundary layer. - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Apr 2007 : - Presented at MSRI Symposium on Climate Change, Berkeley, CA, USA.
- Using Observations to Estimate Climate Model Parameters - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_MSRI_jla.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Mar 2007 : - Presented at NCAR, Boulder, CO, USA.
- The Data Assimilation Research Testbed: Powerful, Adaptive, and Fast - Assimilation for Geophysical Applications. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_ARG_jla.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Ave Arellano** Jan 2007 : - Presented to the AMS, San Antonio, TX, USA.
- Constraining Tropospheric CO using Ensemble-based Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_AMS_ave.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2007 : - Talk presented to the AMS, San Antonio, TX, USA.
- Capabilities and Limitations of Ensemble Data Assimilation - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_AMS_jla_space_weather.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Jeff Anderson** Jan 2007 : - Talk presented to the Atmospheric Modeling Working Group (AMWG), - Boulder, CO, USA.
- Using the DART-CAM Ensemble Data Assimilation System for Climate - Model Development. - [\[pdf\]](http://www.image.ucar.edu/pub/DART/2007_AMWG_jla.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **D. Rostkier-Edelstein** Jan 2007 : - Department seminar to The Department of Geophysics and Planetary - Sciences, Tel-Aviv University, Israel
- PBL State Estimation with Surface Observations, a Column Model and - an Ensemble Filter. - ----- -## 2006 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **W. G. Lawson, M. I. Richardson, - D. J. McCleese, J. T. Schofield, O. Aharonson, S. B. Calcutt, P. G. J. Irwin, - D. M. Kass, C. B. Leovy, S.R. Lewis, D. A. Paige, P. L. Read, F. W. Taylor, - and R. W. Zurek** 2006 : - Presented at the [2006 AGU Fall - Meeting](http://www.agu.org/meetings/fm06/) in San Francisco, CA, USA
- Data Assimilation With the Mars Climate Sounder abstract P23B-0061 - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Dec 2006 : - Presented at the China National Climate Center, Beijing, China
- Application of GPS radio occultation data in improving regional - analyses of water vapor and temperature over tropical oceans. - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Dec 2006 : - Talk presented at the AGU Fall meeting, San Francisco, CA, USA
- Influence of COSMIC Satellite data in improving analyses over - tropical oceans - -![](http://www.image.ucar.edu/images/pin4.gif) **Alexis Zubrow** Oct 2006 : - Presented at the 5th Annual CMAS Conference, Chapel Hill, NC, USA
- EAKF-CMAQ: Development and initial evaluation of an Ensemble - Adjustment Kalman Filter based data assimilation for CO. [\[Extended - Abstract\]](http://www.cmascenter.org/conference/2006/abstracts/zubrow_session7.pdf) - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Oct 2006 : - Talk presented at the First FORMSAT-3/COSMIC User Data Workshop, - Boulder, CO, USA
- Application of GPS radio occultation refractivity in improving - tropical analyses of tempeature and water vapor. - -![](http://www.image.ucar.edu/images/pin4.gif) **D. Rostkier-Edelstein** Oct 2006 : - Talk presented to the Environmental Sciences Division of the Israel - Institute for Biological Research, Israel
- PBL State Estimation with Surface Observations, a Column Model and - an Ensemble Filter. - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Aug 2006 : - Talk presented at the IEEE International Geoscience and Remote - Sensing Symposium, Denver, CO, USA
- Assimilations of Global Positioning System radio occultation - observations with an Ensemble filter in Atmospheric Prediction Models. - -![](http://www.image.ucar.edu/images/pin4.gif) **D. Rostkier-Edelstein** Aug 2006 : - Talk presented at the 10th Annual George Mason University Conference - on Atmospheric Transport and Dispersion Modeling, George Mason - University, Fairfax, VA, USA
- Estimates of boundary layer profiles by means of ensemble-filter - assimilation of near surface observations in a parameterized PBL - - Application to transport and dispersion. - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Jun 2006 : - Presented at the 7th WRF User Workshop, Boulder, CO, USA
- Exploring application of GPS RO data in improving analyses of T and - Q in radiosonde sparse regions using WRF ensemble data assimilation system. - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Apr 2006 : - Talk presented at the 2nd Ensemble Data Assimilation Workshop, - Austin, TX, USA
- Exploring application of Radio Occultation measurements in Improving - Analyses in traditional data sparse regions with WRF/DART ensemble - data assimilation system. - -![](http://www.image.ucar.edu/images/pin4.gif) **Josh Hacker** Apr 2006 : - Presented at the 2nd Ensemble Data Assimilation Workshop, Austin, TX, USA
- Retrieving PBL profiles with ensemble-filter assimilation: - Experiments with real observations, uncovering model behavior, and - finding parameter distributions. - ----- -## 2005 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Hui Liu** Dec 2005 : - Preliminary results from assimilation of GPS radio occultation data - in WRF using an ensemble filter. - [\[ppt\]](http://www.image.ucar.edu/pub/COSMIC_05Dec19_post.ppt) - -![](http://www.image.ucar.edu/images/pin4.gif) **Alain Caya** 2005 : - Evaluation of an Ensemble Kalman Filter for WRF against a 3D-Var - Assimilation System. - [\[pdf\]](http://www.image.ucar.edu/pub/WRF_EnsembleFilter.pdf) - ----- -## 2004 ----- - -![](http://www.image.ucar.edu/images/pin4.gif) **Tim Hoar** Oct 2004 : - Presented at the "Workshop on Ensemble Methods" at the UK Met Office in Exeter.
- An Operational-Quality Ensemble Assimilation System for NCAR's CAM - Climate Model. - diff --git a/guide/pages/Publications.md b/guide/pages/Publications.md deleted file mode 100644 index e22a8cb97c..0000000000 --- a/guide/pages/Publications.md +++ /dev/null @@ -1,1040 +0,0 @@ - -# Publications using DART - - **This list gets updated as frequently as possible, however some more - recent publications may also be available on the UCAR/NCAR online - database [https://opensky.ucar.edu](https://opensky.ucar.edu/).** - Simply use it and search for "ensemble data assimilation" (for - example). Many, if not most, are related to DART. The following list - also contains some publications from our collaborators. If you would - like to list your publication that uses DART, please let us know! - (dart@ucar.edu) - -### To cite DART: - -Cite DART using the following text: - -> The Data Assimilation Research Testbed (Version X.Y.Z) [Software]. (2019). Boulder, Colorado: UCAR/NCAR/CISL/DAReS. [http://doi.org/10.5065/D6WQ0202](http://doi.org/10.5065/D6WQ0202) - -Update the DART version and year as appropriate. - -The seminal reference is: - -**Anderson, J. L., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn -and A. Arellano**, 2009 -The Data Assimilation Research Testbed: A Community Facility. -*Bulletin of the American Meteorological Society*, **90**, 1283-1296, -[doi:10.1175/2009BAMS2618.1](http://dx.doi.org/10.1175/2009BAMS2618.1) - ---- -## 2020 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Castruccio, F. S., A. Karspeck, G. - Danabasoglu, J. Hendricks, T. J. Hoar, N. S. Collins, J. L. Anderson**, 2020 - An EnOI‐based Data Assimilation System with DART for a High‐Resolution - Version of the CESM2 Ocean Component. - *Journal of Advances in Modeling Earth Systems*, **12**, - [doi:10.1029/2020MS002176](https://doi.org/10.1029/2020MS002176) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Moore, A., J. Zavala-Garay, - H. G. Arango, C. A. Edwards, J. L. Anderson, T. J. Hoar**, 2020 - Regional and basin scale applications of ensemble adjustment Kalman filter - and 4D-Var ocean data assimilation systems. - *Progress in Oceanography*, **189**, 102450, - [doi:0.1016/j.pocean.2020.102450](https://doi.org/10.1016/j.pocean.2020.102450) - ---- -## 2019 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Gopalakrishnan, G., I. Hoteit, - B. Cornuelle, and D. Rudnick**, 2019 - Comparison of 4DVAR and EnKF states estimates and forecasts in the Gulf of - Mexico. - *Quarterly Journal of the Royal Meteorological Society*, **145**, 1354-1376, - [doi:10.1002/qj.3493](https://doi.org/10.1002/qj.3493) - ---- -## 2018 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Aydoğdu, A., Hoar, T. J., - Vukicevic, T., Anderson, J. L., Pinardi, N., Karspeck, A., Hendricks, J., - Collins, N., Macchia, F., and Özsoy, E.**, 2018 - OSSE for a sustainable marine observing network in the Sea of Marmara. - *Nonlinear Processes in Geophysics*, **25**, 537-551, - [doi.org/10.5194/npg-25-537-2018](https://doi.org/10.5194/npg-25-537-2018) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **El Gharamti M.**, 2018 - Enhanced Adaptive Inflation Algorithm for Ensemble Filters. - *Monthly Weather Review*, **2**, 623-640, - [doi:10.1175/MWR-D-17-0187.1](http://dx.doi.org/doi:10.1175/MWR-D-17-0187.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Fox, A. M., Hoar, T. J., - Anderson, J. L., Arellano, A. F., Smith, W. K., Litvak, M. E., et al.**, 2018 - Evaluation of a data assimilation system for land surface models using CLM4.5. - *Journal of Advances in Modeling Earth Systems*, **10**, 2471–2494, - [doi.org/10.1029/2018MS001362](https://doi.org/10.1029/2018MS001362) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Karspeck, A. R., G. Danabasoglu, - J. L. Anderson, S. Karol, N. Collins, M. Vertenstein, K. Raeder, T. Hoar, - R. Neale, J. Edwards and A. Craig**, 2018 - A global coupled ensemble data assimilation system using the Community - Earth System Model and the Data Assimilation Research Testbed. - *Quarterly Journal of the Royal Meteorological Society*, - [doi:10.1002/qj.3308](http://dx.doi.org/doi:10.1002/qj.3308) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Toye, H., S. Kortas, P. Zhan - and I. Hoteit**, 2018 - A fault-tolerant HPC scheduler extension for large and operational ensemble - data assimilation: Application to the Red Sea. - *Journal of Computational Science*, **27**, 46-56, - [doi:10.1016/j.jocs.2018.04.018](http://dx.doi.org/doi:10.1016/j.jocs.2018.04.018) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zhang, Y. -F., C. M. Bitz, - J. L. Anderson, N. Collins, J. Hendricks, T. Hoar, K. Raeder and F. Massonnet**, 2018 - Insights on Sea Ice Data Assimilation from Perfect Model Observing System - Simulation Experiments. - *Journal of Climate*, - [doi:10.1175/JCLI-D-17-0904.1](http://dx.doi.org/doi:10.1175/JCLI-D-17-0904.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Pedatella, N. M., H.‐L. Liu, - D. R. Marsh, K. Raeder, J. L. Anderson, J. L. Chau, L. P. Goncharenko - and T. A. Siddiqui**, 2018 - Analysis and Hindcast Experiments of the 2009 Sudden Stratospheric - Warming in WACCMX+ DART. - *Journal of Geophysical Research: Space Physics*, **123**, 3131-3153, - [doi:10.1002/2017JA025107](http://dx.doi.org/doi:10.1002/2017JA025107) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Clark, A. J., I. L. Jirak, - S. R. Dembek, G. J. Creager, F. Kong, K. W. Thomas, K. H. Knopfmeier, B. T. Gallo, - C. J. Melick, M. Xue, and K. A. Brewster**, 2018 - The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous - Weather Testbed Spring Forecasting Experiment. - *Bulletin of the American Meteorological Society*, - [doi:10.1175/WAF-D-16-0178.1](http://dx.doi.org/doi:10.1175/WAF-D-16-0178.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Pan, S., J. Gao, D. J. Stensrud, - X. Wang and T. A. Jones**, 2018 - Assimilation of Radar Radial Velocity and Reflectivity, - Satellite Cloud Water Path, and Total Precipitable Water for - Convective-Scale NWP in OSSEs. - *Journal of Atmospheric and Oceanic Technology*, **35**, 67-89, - [doi:10.1175/JTECH-D-17-0081.1](http://dx.doi.org/doi:10.1175/JTECH-D-17-0081.1) - ---- -## 2017 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Gaubert, B., H. M. Worden, - A. F. J. Arellano, L. K. Emmons, S. Tilmes, J. Barré, S. Martinez Alonso, - F. Vitt, J. L. Anderson, F. Alkemade, S. Houweling, D. P. Edwards**, 2017 - Chemical feedback from decreasing carbon monoxide emissions. - *Geophysical Research Letters*, **44**, 9985-9995, - [doi:10.1002/2017GL074987](http://dx.doi.org/doi:10.1002/2017GL074987) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Lee, J. A., J. P. Hacker, - L. Delle Monache, B. Kosović, A. Clifton, F. Vandenberghe and J. S. Rodrigo**, 2017 - Improving wind predictions in the marine atmospheric boundary layer through - parameter estimation in a single-column model. - *Monthly Weather Review*, **145**, 5-24, - [doi:10.1175/MWR-D-16-0063.1](http://dx.doi.org/doi:10.1175/MWR-D-16-0063.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Gu, S. -Y., H. Liu, - N. M. Pedatella, X. Dou, and Y. Liu**, 2017 - On the wave number 2 eastward propagating quasi 2 day wave at middle and high latitudes. - *Journal of Geophysical Research: Space Physics*, **122**, 4489-4499, - [doi:10.1002/2016JA023353](http://dx.doi.org/doi:10.1002/2016JA023353) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Hodyss, D., J. L. Anderson, - N. Collins, W. F. Campbell and P. A. Reinecke**, 2017 - Quadratic polynomial regression using serial observation processing: - Implementation within DART. - *Monthly Weather Review*, **145**, 4467-4479, - [doi:10.1175/MWR-D-17-0089.1](http://dx.doi.org/doi:10.1175/MWR-D-17-0089.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Kwon, Y., Z. -L. Yang, T. J. Hoar - and A. M. Toure**, 2017 - Improving the radiance assimilation performance in estimating snow water - storage across snow and land-cover types in North America. - *Journal of Hydrometeorology*, **18**, 651-668, - [doi:10.1175/JHM-D-16-0102.1](http://dx.doi.org/doi:10.1175/JHM-D-16-0102.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Keclik, A. M., C. Evans, - P. J. Roebber and G. S. Romine**, 2017 - The influence of assimilated upstream, preconvective dropsonde observations - on ensemble forecasts of convection initiation during the Mesoscale - Predictability Experiment. - *Monthly Weather Review*, **145**, 4747-4770, - [doi:10.1175/MWR-D-17-0159.1](http://dx.doi.org/doi:10.1175/MWR-D-17-0159.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Liu, X., A. P. Mizzi, - J. L. Anderson, I. Y. Fung and R. C. Cohen**, 2017 - Assimilation of satellite NO2 observations at high spatial resolution using OSSEs. - *Atmospheric Chemistry and Physics*, **17**, 7067-7081, - [doi:10.5194/acp-17-7067-2017](http://dx.doi.org/doi:10.5194/acp-17-7067-2017) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Ha, S., C. Snyder, W. C. Skamarock, - J. Anderson and N. Collins**, 2017 - Ensemble Kalman filter data assimilation for the Model for - Prediction Across Scales (MPAS). - *Monthly Weather Review*, **145**, 4673-4692, - [doi:10.1175/MWR-D-17-0145.1](http://dx.doi.org/doi:10.1175/MWR-D-17-0145.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Rubin, J. I., J. S. Reid, - J. A. Hansen, J. L. Anderson, B. N. Holben, P. Xian, D. L. Westphal and J. Zhang**, 2017 - Assimilation of AERONET and MODIS AOT observations using variational - and ensemble data assimilation methods and its impact on aerosol forecasting skill. - *Journal of Geophysical Research: Atmospheres*, **122**, 4967-4992, - [doi:10.1002/2016JD026067](http://dx.doi.org/doi:10.1002/2016JD026067) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Chen, C. ‐H., C. Lin, Wei. ‐H. Chen, - and T. Matsuo**, 2017 - Modeling the ionospheric prereversal enhancement by using coupled - thermosphere‐ionosphere data assimilation. - *Geophysical Research Letters*, **44**, 1652-1659, - [doi:10.1002/2016GL071812](http://dx.doi.org/doi:10.1002/2016GL071812) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Poterjoy, J., R. A. Sobash - and J. L. Anderson**, 2017 - Convective-scale data assimilation for the weather research and forecasting - model using the local particle filter. - *Monthly Weather Review*, **145**, 1897-1918, - [doi:10.1175/MWR-D-16-0298.1](http://dx.doi.org/doi:10.1175/MWR-D-16-0298.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Chen, C. ‐H., C. Lin, Wei. ‐H. Chen, - T. Matsuo and W. H. Chen**, 2017 - The impact of FORMOSAT-5/AIP observations on the ionospheric space weather. - *Terrestrial, Atmospheric and Oceanic Sciences*, **28**, 129-137, - [doi:10.3319/TAO.2016.09.30.01(EOF5)](http://dx.doi.org/doi:10.3319/TAO.2016.09.30.01(EOF5)) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Velden, C., W. E. Lewis, W. Bresky, - D. Stettner, J. Daniels and S. Wanzong**, 2017 - Assimilation of high-resolution satellite-derived atmospheric motion vectors: - Impact on HWRF forecasts of tropical cyclone track and intensity. - *Monthly Weather Review*, **145**, 1107-1125, - [doi:10.1175/MWR-D-16-0229.1](http://dx.doi.org/doi:10.1175/MWR-D-16-0229.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Kerr, C. A., D. J. Stensrud - and X. Wang**, 2017 - Verification of convection-allowing model ensemble analyses of near-storm - environments using MPEX upsonde observations. - *Monthly Weather Review*, **145**, 857-875, - [doi:10.1175/MWR-D-16-0287.1](http://dx.doi.org/doi:10.1175/MWR-D-16-0287.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Madaus, L. E. and C. F. Mass**, 2017 - Evaluating Smartphone Pressure Observations for Mesoscale Analyses and Forecasts. - *Monthly Weather Review*, **32**, 511-531, - [doi:10.1175/WAF-D-16-0135.1](http://dx.doi.org/doi:10.1175/WAF-D-16-0135.1) - ---- -## 2016 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Gaubert, B., A. F. Arellano Jr., - J. Barré, H. M. Worden, L. K. Emmons, S. Tilmes, R. R. Buchholz, F. Vitt, - K. Raeder, N. Collins, J. L. Anderson, C. Wiedinmyer, S. Martinez Alonso, - D. P. Edwards, M. O. Andreae, J. W. Hannigan, C. Petri, K. Strong, N. Jones** , 2016 - Toward a chemical reanalysis in a coupled chemistry-climate model: - An evaluation of MOPITT CO assimilation and its impact on tropospheric composition. - *Journal of Geophysical Research: Atmospheres*, **121**, 7310-7343, - [doi:10.1002/2016JD024863](http://dx.doi.org/doi:10.1002/2016JD024863) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Jewtoukoff, V., R. Plougonven, - A. Hertzog, C. Snyder and G. Romine**, 2016 - On the prediction of stratospheric balloon trajectories: - Improving winds with mesoscale simulations. - *Journal of Atmospheric and Oceanic Technology*, **33**, 1629-1647, - [doi:10.1175/JTECH-D-15-0110.1](http://dx.doi.org/doi:10.1175/JTECH-D-15-0110.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Gu, S. -Y., H. -L. Liu, - N. M. Pedatella, X. Dou and Z. Shu**, 2016 - The quasi-2 day wave activities during 2007 boreal summer period as - revealed by Whole Atmosphere Community Climate Model. - *Journal of Geophysical Research: Space Physics*, **121**, 2743-2754, - [doi:10.1002/2015JA022225](http://dx.doi.org/doi:10.1002/2015JA022225) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Pedatella, N., J. Oberheide, - E. K. Sutton, H. Liu, J. L. Anderson and K. D. Raeder**, 2016 - Short-term nonmigrating tide variability in the mesosphere, thermosphere, - and ionosphere. - *Journal of Geophysical Research: Space Physics*, **121**, 3621-3633, - [doi:10.1002/2016JA022528](http://dx.doi.org/doi:10.1002/2016JA022528) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zhao, L., Z. -L. Yang - and T. J. Hoar**, 2016 - Global soil moisture estimation by assimilating AMSR-E brightness - temperatures in a coupled CLM4-RTM-DART system. - *Journal of Hydrometeorology*, **17**, 2431-2454, - [doi:10.1175/JHM-D-15-0218.1](http://dx.doi.org/doi:10.1175/JHM-D-15-0218.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Dikpati, M., D. Mitra - and J. L. Anderson**, 2016 - Role of response time of a Babcock-Leighton solar dynamo model in - meridional flow-speed reconstruction by EnKF data assimilation. - *Advances in Space Research*, **58**, 1589-1595, - [doi:10.1016/j.asr.2016.08.004](http://dx.doi.org/doi:10.1016/j.asr.2016.08.004) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Mizzi, A. P., A. F. Arellano, - D. P. Edwards, J. L. Anderson and G. Pfister**, 2016 - Assimilating compact phase space retrievals of atmospheric composition - with WRF-Chem/DART: a regional chemical transport/ensemble Kalman filter - data assimilation system. - *Geoscientific Model Development*, **9**, 965-978, - [doi:10.5194/gmd-9-965-2016](http://dx.doi.org/doi:10.5194/gmd-9-965-2016) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Dikpati M., J. L. Anderson - and D. Mitra**, 2016 - Data assimilation in a solar dynamo model using ensemble Kalman filters: - Sensitivity and robustness in reconstruction of meridional flow speed. - *The Astrophysical Journal*, **828**, 91, - [doi:10.3847/0004-637X/828/2/91](http://dx.doi.org/doi:10.3847/0004-637X/828/2/91) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Penny, A. B., J. P. Hacker, - P. A. Harr**, 2016 - Analysis of tropical storm formation based on ensemble data assimilation - and high-resolution numerical simulations of a nondeveloping disturbance. - *Monthly Weather Review*, **144**, 3631-3649, - [doi:10.1175/MWR-D-16-0100.1](http://dx.doi.org/doi:10.1175/MWR-D-16-0100.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Garcia, M., T. Hoar, M. Thomas, - B. Bailey and J. Castillo**, 2016 - Interfacing an ensemble Data Assimilation system with a 3D nonhydrostatic - Coastal Ocean Model, an OSSE experiment. - *OCEANS MTS/IEEE Monterey*, pp. 1-11, - [doi:10.1109/OCEANS.2016.7760992](http://dx.doi.org/doi:10.1109/OCEANS.2016.7760992) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Chartier, A. T., T. Matsuo, - J. L. Anderson, N. S. Collins, T. J. Hoar, G. Lu, C. N. Mitchell, A. J. Coster, - L. J. Paxton, G. S. Bust**, 2016 - Ionospheric data assimilation and forecasting during storms. - *Journal of Geophysical Research: Space Physics*, **121**, 764-778, - [doi:10.1002/2014JA020799](http://dx.doi.org/doi:10.1002/2014JA020799) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Rubin, J. I., J. S. Reid, - J. A. Hansen, J. L. Anderson, N. S. Collins, T. J. Hoar, T. Hogan, P. Lynch, - J. McLay, Carolyn A. Reynolds, W. R. Sessions, D. L. Westphal and J. Zhang**, 2016 - Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) - and its application of the Data Assimilation Research Testbed (DART) - in support of aerosol forecasting. - *Atmospheric Chemistry and Physics*, **15**, 28069-28132, - [doi:10.5194/acpd-15-28069-2015](http://dx.doi.org/doi:10.5194/acpd-15-28069-2015) - ---- -## 2015 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Barré, J., B. Gaubert, - A. F. J. Arellano, H. M. Worden, D. P. Edwards, M. Deeter, J. L. Anderson, - K. D. Raeder, N. S. Collins, S. Tilmes, G. Francis, C. Clerbaux, L. Emmons, - G. Pfister, P.-F. Coheur and D. Hurtmans**, 2015 - Assessing the impacts of assimilating IASI and MOPITT CO retrievals using - CESM-CAM-chem and DART. - *Journal of Geophysical Research: Atmospheres*, **120**, no. 19, - [doi:10.1002/2015JD023467](http://dx.doi.org/doi:10.1002/2015JD023467) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Bernardet, L., V. Tallapragada, - S. Bao, S. Trahan, Y. Kwon, Q. Liu, M. Tong, M. Biswas, T. Brown, D. Stark, - L. Carson, R. Yablonsky, E. Uhlhorn, S. Gopalakrishnan, X. Zhang, T. Marchok, - Y. H. Kuo and R. Gall**, 2015 - Community support and transition of research to operations for the - Hurricane Weather Research and Forecasting Model. - *Bulletin of the American Meteorological Society*, **96**, 953-960, - [doi:10.1175/BAMS-D-13-00093.1](http://dx.doi.org/doi:10.1175/BAMS-D-13-00093.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Ha, S.-Y., J. Berner - and C. M. Snyder**, 2015 - A comparison of model error representations in mesoscale ensemble data assimilation. - *Monthly Weather Review*, **143**, 3893-3911, - [doi:10.1175/MWR-D-14-00395.1](http://dx.doi.org/doi:10.1175/MWR-D-14-00395.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Singh, T., R. Mitta, H.C. Upadhyaya**, - 2015 - Ensemble Adjustment Kalman Filter Data Assimilation for a Global Atmospheric Model. - *International Conference on Dynamic Data-Driven Environmental Systems Science*, - 284-298, [doi:10.1007/978-3-319-25138-7_26](http://dx.doi.org/doi:10.1007/978-3-319-25138-7_26) - - ---- -## 2014 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Wu, T.-C., H. Liu, S. J. Majumdar, - C. S. Velden, J. L. Anderson**, 2014 - Influence of assimilating satellite-derived atmospheric motion vector observations - on numerical analyses and forecasts of tropical cyclone track and intensity. - *Monthly Weather Review*, **142**, 49-71, - [doi:10.1175/MWR-D-13-00023.1](http://dx.doi.org/doi:10.1175/MWR-D-13-00023.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Lei, L. and J. L. Anderson**, 2014 - Impacts of frequent assimilation of surface pressure observations on - atmospheric analyses. - *Monthly Weather Review*, **142** 4477-4483, - [doi:10.1175/MWR-D-14-00097.1](http://dx.doi.org/doi:10.1175/MWR-D-14-00097.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Dikpati, M., J. L. Anderson - and D. Mitra**, 2014 - Ensemble Kalman filter data assimilation in a Babcock-Leighton solar dynamo - model: An observation system simulation experiment for reconstructing - meridional flow speed. - *Geophysical Research Letters*, **41** 5361-5369, - [doi:10.3847/0004-637X/828/2/91](http://dx.doi.org/doi:10.3847/0004-637X/828/2/91) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Ha, S.-Y. and C. Snyder**, 2014 - Influence of surface observations in mesoscale data assimilation using an - ensemble Kalman filter. - *Monthly Weather Review*, **142** 1489-1508, - [doi:10.1175/MWR-D-13-00108.1](http://dx.doi.org/doi:10.1175/MWR-D-13-00108.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Pedatella, N., K. D. Raeder, - J. L. Anderson and H. Liu**, 2014 - Ensemble data assimilation in the Whole Atmosphere Community Climate Model. - *Journal of Geophysical Research: Atmospheres*, **119** 9793-9809, - [doi:10.1002/2014JD021776](http://dx.doi.org/doi:10.1002/2014JD021776) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zhang, Y.-F., T. J. Hoar, - Z.-L. Yang, J. L. Anderson, A. M. Toure and M. Rodell**, 2014 - Assimilation of MODIS snow cover through the Data Assimilation Research Testbed - and the Community Land Model version 4. - *Journal of Geophysical Research: Atmospheres*, **142** 1489-1508, - [doi:10.1002/2013JD021329](http://dx.doi.org/doi:10.1002/2013JD021329) - - ---- -## 2013 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Hoteit, I., T. J. Hoar, - G. Gopalakrishnan, N. Collins, J. L. Anderson, B. Cornuelle, A. Kohl - and P. Heimbach**, 2013 - A MITgcm/DART ensemble analysis and prediction system with application - to the Gulf of Mexico. - *Dynamics of Atmospheres and Oceans*, **63**, 1-23, - [doi:10.1016/j.dynatmoce.2013.03.002](http://dx.doi.org/doi:10.1016/j.dynatmoce.2013.03.002) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Hacker, J. and W. Angevine**, 2013 - Ensemble data assimilation to characterize surface-layer errors in - numerical weather prediction models. - *Monthly Weather Review*, **141**, 1804-1821, - [doi:10.1175/MWR-D-12-00280.1](http://dx.doi.org/doi:10.1175/MWR-D-12-00280.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Rostkier-Edelstein, D. - and J. Hacker**, 2013 - Impact of flow-dependence, column covariance, and forecast-model type on - surface-observations assimilation for probabilistic PBL-profiles nowcasts. - *Weather Forecasting*, **28**, 29-54, - [doi:10.1175/WAF-D-12-00043.1](http://dx.doi.org/doi:10.1175/WAF-D-12-00043.1) - - ![](http://www.image.ucar.edu/images/pin4.gif) **Karspeck, A., S. Yeager, - G. Danabasoglu, T. J. Hoar, N. S. Collins, K. D. Raeder, J. L. Anderson, - J. Tribbia**, 2013 - An ensemble adjustment Kalman filter for the CCSM4 ocean component. - *Journal Of Climate*, **26**, 7392-7413, - [doi:10.1175/JCLI-D-12-00402.1](https://doi.org/10.1175/JCLI-D-12-00402.1) - - ![](http://www.image.ucar.edu/images/pin4.gif) **Morozov, A. V., A. J. Ridley, - D. S. Bernstein, N. Collins, T. J. Hoar and J. L. Anderson**, 2013 - Data assimilation and driver estimation for the Global Ionosphere-Thermosphere Model - using the Ensemble Adjustment Kalman Filter. - *Journal of Atmospheric and Solar-Terrestrial Physics*, **104**, 126-136, - [doi:10.1016/j.jastp.2013.08.016](http://dx.doi.org/doi:10.1016/j.jastp.2013.08.016) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Morozov, A. V., A. J. Ridley, - D. S. Bernstein, N. Collins, T. J. Hoar and J. L. Anderson**, 2013 - Development and verification of a new wind speed forecasting system - using an ensemble Kalman filter data assimilation technique in a fully - coupled hydrologic and atmospheric model. - *Journal of Advances in Modeling Earth Systems*, **5**, 785-800, - [doi:10.1002/jame.20051](http://dx.doi.org/doi:10.1002/jame.20051) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Romine, G. S., C. S. Schwartz, - C. Snyder, J. L. Anderson and M. L. Weisman**, 2013 - Model bias in a continuously cycled assimilation system and its influence - on convection-permitting forecasts. - *Monthly Weather Review*, **141**, 1263-1284, - [doi:10.1175/MWR-D-12-00112.1](http://dx.doi.org/doi:10.1175/MWR-D-12-00112.1) - ---- -## 2012 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2012 - Localization and Sampling Error Correction in Ensemble Kalman Filter Data Assimilation. - *Monthly Weather Review*, **140**, 2359–2371, - [doi.org/10.1175/MWR-D-11-00013.1](https://doi.org/10.1175/MWR-D-11-00013.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Raeder, K, J. L., Anderson, - N. Collins, T. J. Hoar, J. E. Kay, P. H., Lauritzen and R. Pincus**, 2012 - DART/CAM: An Ensemble Data Assimilation for CESM Atmospheric Models. - *Journal of Climate*, **25**, 6304-6317, - [doi:10.1175/JCLI-D-11-00395.1](http://dx.doi.org/doi:10.1175/JCLI-D-11-00395.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Dikpati, M. and J. L. Anderson**, 2012 - Evaluating potential for data assimilation in a flux-transport dynamo model - by assessing sensitivity and response to meridional flow variation. - *The Astrophysical Journal*, **756** 20 pp. 20 pp., - [doi:10.1088/0004-637X/756/1/20](http://dx.doi.org/doi:10.1088/0004-637X/756/1/20) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Lee, I-Te, T. Matsuo, A. D. Richmond, - J. Y. Liu, Wenbin Wang, C. H. Lin, J. L. Anderson and M. Q. Chen**, 2012 - Assimilation of FORMOSAT‐3/COSMIC electron density profiles into a coupled - thermosphere/ionosphere model using ensemble Kalman filtering. - *Journal of Geophysical Research: Space Physics*, **117** A10, - [doi:10.1029/2012JA017700](http://dx.doi.org/doi:10.1029/2012JA017700) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Lauritzen, P. H., A. A. Mirin, - J. Truesdale, K. Raeder, J. L. Anderson, J. Bacmeister and R. B. Neale**, 2012 - Implementation of new diffusion/filtering operators in the CAM-FV dynamical core. - The International Journal of High Performance Computing Applications, **26** 63-73, - [doi:10.1177/1094342011410088](http://dx.doi.org/doi:10.1177/1094342011410088) - - ---- -## 2011 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Pendergrass, A. G., G. J. Hakim, - D. S. Battisti and G. Roe**, 2011 - Coupled air-mixed-layer temperature predictability for climate reconstruction. - *Journal of Climate*, **25**, 459-472, - [doi:10.1175/2011JCLI4094.1](http://dx.doi.org/doi:10.1175/2011JCLI4094.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Matsuo, T. - and E. A. Araujo-Pradere**, 2011 - Role of thermosphere-ionosphere coupling in a global ionospheric specification. - *Radio Science*, **46**, RS0D23, 7pp., - [doi:10.1029/2010RS004576](http://dx.doi.org/doi:10.1029/2010RS004576) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zagar, N., J. Tribbia, - J. L. Anderson and K. Raeder**, 2011 - Balance of the Background-Error Variances in the Ensemble Assimilation System DART/CAM. - *Monthly Weather Review*, **139**, 2061-2079, - [doi:10.1175/2011MWR3477.1](http://dx.doi.org/doi:10.1175/2011MWR3477.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **C. Lee, W. G. Lawson, - M. I. Richardson, J. L. Anderson, N. Collins, T. Hoar and M. Mischna**, 2011 - Demonstration of ensemble data assimilation for Mars using DART, MarsWRF, - and radiance observations from MGS TES. - *Journal of Geophysical Research*, **116**, E11011, 17 pp., - [doi:10.1029/2011JE003815](http://dx.doi.org/doi:10.1029/2011JE003815) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A.**, 2011 - Assessing the impact of the covariance localization radius when assimilating - infrared brightness temperature observations using an ensemble Kalman filter. - *Monthly Weather Review*, **140**, 543-561, - [doi:10.1175/MWR-D-11-00084.1](http://dx.doi.org/doi:10.1175/MWR-D-11-00084.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A., D. C. Hartung, - D. D. Turner, R. Peterson, W. F. Feltz and E. Janzon**, 2011 - Assimilation of surface-based boundary layer profiler observations during - a cool season weather event using an Observation System Simulation Experiment. - Part 1: Analysis impact. - *Monthly Weather Review*, **139**, 2309-2326, - [doi:10.1175/2011MWR3622.1](http://dx.doi.org/doi:10.1175/2011MWR3622.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J. A., D. C. Hartung, - D. D. Turner, R. Peterson, W. F. Feltz and Erik Janzon**, 2011 - Assimilation of surface-based boundary layer profiler observations during - a cool season weather event using an Observation System Simulation Experiment. - Part 2: Forecast assessment. - *Monthly Weather Review*, **139**, 2327-2346, - [doi:10.1175/2011MWR3623.1](http://dx.doi.org/doi:10.1175/2011MWR3623.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Kay, J., K. Raeder, A. Gettelman - and J. L. Anderson**, 2011 - The boundary layer response to recent Arctic sea ice loss and implications - for high-latitude climate feedbacks. - *Journal of Climate*, **24**, 428-447, - [doi:10.1175/2010JCLI3651.1](http://dx.doi.org/doi:10.1175/2010JCLI3651.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Lauritzen, P.H., A. Mirin, - J. E. Truesdale, K. Raeder, J. L. Anderson, J. Bacmeister and R. B. Neale**, 2011 - Implementation of new diffusion/filtering operators in the CAM-FV dynamical core. - *International Journal of High Performance Computing Applications*, **26**, 63-73, - [doi:10.1177/1094342011410088](http://dx.doi.org/doi:10.1177/1094342011410088) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Pincus, R., P. Hofmann, J. Robert, - J. L. Anderson, K. Raeder, N. Collins and J. S. Whitaker**, 2011 - Can Fully Accounting for Clouds in Data Assimilation Improve Short-Term - Forecasts by Global Models? - *Monthly Weather Review*, **139**, 3, pp. 946-957, - [doi:10.1175/2010MWR3412.1](http://dx.doi.org/doi:10.1175/2010MWR3412.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Dorita Rostkier-Edelstein - and Joshua P. Hacker**, 2011 - Experience and Conclusions from the Factor Separation Method: - Ensemble Data Assimilation and Forecasting Applications - The Factor Separation Method in the Atmosphere- Applications and Future Prospects, - Pinhas Alpert and Tatiana Sholokhman, Cambridge University Press, ISBN: 9780521191739 - - ---- -## 2010 ---- - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2010 - A Non-Gaussian Ensemble Filter Update for Data Assimilation. - *Monthly Weather Review*, **138** pp. 4186-4198, - [doi:10.1175/2010MWR3253.1](http://dx.doi.org/doi:10.1175/2010MWR3253.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Arellano, A. F., P. G. Hess, - D. P. Edwards and D. Baumgardner**, 2010 - Constraints on black carbon aerosol distribution from Measurement Of Pollution - In The Troposphere (MOPITT) CO - *Geophysical Research Letters*, **37** L17801, - [doi:10.1029/2010GL044416](http://dx.doi.org/doi:10.1029/2010GL044416) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Aksoy, A., D. C. Dowell and C. Snyder**, 2010 - A multicase comparative assessment of the ensemble Kalman filter for - assimilation of radar observations. Part II: Short-range ensemble forecasts. - *Monthly Weather Review*, **138**, pp 1273-1292, - [doi:10.1175/2009MWR3086.1](http://dx.doi.org/doi:10.1175/2009MWR3086.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **C. Lee, M. I. Richardson, W. G. Lawson - and J. L. Anderson**, 2010 - Ensemble Data Assimilation of the Martian Atmosphere Using Temperature - and Radiance Data from the Thermal Emission Spectrometer - *American Astronomical Society DPS meeting #42*, #30.09 - Bulletin of the American Astronomical Society, Vol. 42, p.1029 - - - ![](http://www.image.ucar.edu/images/pin4.gif) **C. Lee, M. I. Richardson, - W. G. Lawson, J. L. Anderson, N. Collins, T. Hoar, M. Mischna - and A. D. Toigo**, 2010 - Initial Results from Ensemble Data Assimilation of Radiances and Retrieved - Temperatures from TES and MCS in a Martian GCM. - *2010 AGU Fall Meeting*, San Francisco, Abstract P53E-1563. - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Davis, C., W. Wang, J. Dudhia - and R. Torn**, 2010 - Does increased horizontal resolution improve hurricane wind forecasts? - *Weather and Forecasting*, **25**, 1826-1841, - [doi:10.1175/2010WAF2222423.1](http://dx.doi.org/doi:10.1175/2010WAF2222423.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Davis, C., W. Wang, S. Cavallo, - J. Done, J. Dudhia, S. Fredrick, J. Michalakes, G. Caldwell, T. Engel and R. Torn**, 2010 - High-resolution hurricane forecasts. - *Computing in Science and Engineering*, **13**, 22-30, - [doi:10.1109/MCSE.2010.74](http://dx.doi.org/doi:10.1109/MCSE.2010.74) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Dowell, D., G. Romine and C. Snyder**, 2010 - Ensemble storm-scale data assimilation and prediction for severe convective storms. - *25th Severe Local Storms Conference*  Denver, Colorado, Amer. Meteor. Soc., paper 9.5. - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Otkin, J.A.**, 2010 - Clear and cloudy-sky infrared brightness temperature assimilation - using an ensemble Kalman filter. - *Journal of Geophysical Research*, **115**,**D19207**, 14pp, - [doi:10.1029/2009JD013759](http://dx.doi.org/10.1029/2009JD013759) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Torn, R. D.**, 2010 - Ensemble-based Sensitivity Analysis applied to African Easterly Waves. - *Weather and Forecasting*, **25**, 61-78, - [doi:10.1029/2008JD011375](http://dx.doi.org/doi:10.1175/2009WAF2222255.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Torn, R. D.**, 2010 - Performance of a Mesoscale Ensemble Kalman Filter (EnKF) - During the NOAA High-Resolution Hurricane Test. - *Monthly Weather Review*, **138**, 4375-4392, - [doi:10.1175/2010MWR3361.1](http://dx.doi.org/doi:10.1175/2010MWR3361.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Rostkier-Edelstein, D. - and J. Hacker**, 2010 - The roles of surface-observation ensemble assimilation and model complexity - for nowcasting of PBL profiles: A factor separation analysis. - *Weather and Forecasting*, **25**, 1670-1690, - [doi:10.1175/2010WAF2222435.1](http://dx.doi.org/doi:10.1175/2010WAF2222435.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zagar, N., J. Tribbia, - J. L. Anderson, K. Raeder and D. T. Kleist**, 2010 - Diagnosis of systematic analysis increments by using normal modes. - *Quarterly Journal of the Royal Meteorological Society*, **136**, 61-76, - [doi:10.1002/qj.533](http://dx.doi.org/doi:10.1002/qj.533) - - -## 2009 - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L., T. Hoar, - K. Raeder, H. Liu, N. Collins, R. Torn and A. Arellano**, 2009 - The Data Assimilation Research Testbed: A Community Facility. - *Bulletin of the American Meteorological Society*, **90**, 1283-1296, - [doi:10.1175/2009BAMS2618.1](http://dx.doi.org/10.1175/2009BAMS2618.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2009 - Spatially and temporally varying adaptive covariance inflation for ensemble filters. - *Tellus A*, **61**, 72-83, - [doi:10.1111/j.1600-0870.2008.00361.x](http://dx.doi.org/doi:10.1111/j.1600-0870.2008.00361.x) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2009 - Ensemble Kalman filters for large geophysical applications. - *IEEE Control Systems Magazine*, **29**, 66-82, - [doi:10.1109/MCS.2009.932222](http://dx.doi.org/doi:10.1109/MCS.2009.932222) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Aksoy, A., D. C. Dowell - and C. Snyder**, 2009 - A Multicase Comparative Assessment of the Ensemble Kalman Filter - for Assimilation of Radar Observations. Part I: Storm-Scale Analyses. - *Monthly Weather Review*, **137**, 1805-1824, - [doi:10.1175/2008MWR2691.1](http://dx.doi.org/doi:10.1175/2008MWR2691.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Edwards, D. P., A. F. Arellano - and M. N. Deeter**, 2009 - A satellite observation system simulation experiment for carbon monoxide - in the lowermost troposphere, - *J. Geophys. Res.* **114**, D14304, - [doi:10.1029/2008JD011375](http://dx.doi.org/doi:10.1029/2008JD011375) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Hamill, T. M., J. S. Whitaker, - J. L. Anderson and C. M. Snyder**, 2009 - Comments on "Sigma-Point Kalman filter data assimilation methods for strongly - nonlinear systems". - *Journal of the Atmospheric Sciences*, **66**, 3498-3500, - [doi:10.1175/2009JAS3245.1](http://dx.doi.org/doi:10.1175/2009JAS3245.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Rostkier-Edelstein, D. - and J. P. Hacker**, 2009 - Probabilistic nowcasting of PBL profiles with surface observations - and an ensemble filter. - *The 23rd Conference on Weather Analysis and Forecasting/19th - Conference on Numerical Weather Prediction* , Omaha, NE, USA  - [extended abstract](http://ams.confex.com/ams/23WAF19NWP/techprogram/paper_153200.htm) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Thomas, S. J., J. P. Hacker, - and J. L. Anderson**, 2011 - A robust formulation of the ensemble Kalman filter. - *Quarterly Journal of the Royal Meteorological Society*, **135**, 507-521, - [doi:10.1002/qj.372](http://dx.doi.org/doi:10.1002/qj.372) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zagar, N., J. Tribbia, - J. L. Anderson, K. Raeder**, 2009 - Uncertainties of estimates of inertia-gravity energy in the atmosphere. - Part I: Intercomparison of four analysis systems - *Monthly Weather Review*, **137**, 3837-3857, - [doi:10.1175/2009MWR2815.1](http://dx.doi.org/doi:10.1175/2009MWR2815.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zagar, N., J. Tribbia, - J. L. Anderson and K. Raeder**, 2009 - Uncertainties of estimates of inertia-gravity energy in the atmosphere. - Part II: Large-scale equatorial waves. - *Monthly Weather Review*, **137**, 3858-3873, - [doi:10.1175/2009MWR2816.1](http://dx.doi.org/doi:10.1175/2009MWR2816.1) - - ---- -## 2008 ---- - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anthes, R. A., P. A. Bernhardt, - Y. Chen, L. Cucurull, K. F. Dymond, D. Ector, S. Healy, S.-P. Ho, D. C. Hunt, - Y.-H. Kuo, H. Liu, K. Manning, C. McCormick, T. K. Meehan, W. J. Randel, - C. Rocken, W. Schreiner, S. V. Sokolovskiy, S. Syndergaard, D. C. Thompson, - K. E. Trenberth, T.-K. Wee, N. L. Yen and Z. Zeng**, - The COSMIC/FORMOSAT-3 Mission: Early Results, - *Bulletin of the American Meteorological Society*, **89** No. 3, 313-333, - [doi:10.1175/BAMS-89-3-313](http://dx.doi.org/doi:10.1175/BAMS-89-3-313) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Snyder, C., T. Bengtsson, - P. Bickel and J. L. Anderson**, 2008 - Obstacles to high-dimensional particle filtering. - *Monthly Weather Review*, **136**, 4629-4640, - [doi:10.1175/2008MWR2529.1](http://dx.doi.org/doi:10.1175/2008MWR2529.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zubrow, A., L. Chen - and V. R. Kotamarthi**, 2008 - EAKF-CMAQ: Introduction and evaluation of a data assimilation for CMAQ - based on the ensemble adjustment Kalman filter, - *J. Geophys. Res.*, **113**, D09302, - [doi:10.1029/2007JD009267](http://dx.doi.org/doi:10.1029/2007JD009267) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Liu H., J. L. Anderson, - Y.-H. Kuo, C. Snyder and A. Caya**, 2008 - Evaluation of a non-local observation operator in assimilation of - CHAMP radio occultation refractivity with WRF. - *Monthly Weather Review*, **136** No.1, 242-256, - [doi:10.1175/2007MWR2042.1](http://dx.doi.org/doi:10.1175/2007MWR2042.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Khare, S. P., J. L. Anderson, - T. J. Hoar and D. W. Nychka**, 2008 - An investigation into the application of an ensemble Kalman smoother to - high-dimensional geophysical systems. - *Tellus Series A-dynamic Meteorology and Oceanography*, **60**, 97-112, - [doi:10.1111/j.1600-0870.2007.00281.x](http://dx.doi.org/doi:10.1111/j.1600-0870.2007.00281.x) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Torn, R. D. and G. J. Hakim**, 2008 - Ensemble-based sensitivity analysis. - *Monthly Weather Review*, **136**, 663-677, - [doi:10.1175/2007MWR2132.1](http://dx.doi.org/doi:10.1175/2007MWR2132.1) - - ---- -## 2007 ---- - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2007 - An adaptive covariance inflation error correction algorithm for ensemble filters. - *Tellus A*, **59**, 210-224, - [doi:10.1111/j.1600-0870.2006.00216.x](http://dx.doi.org/doi:10.1111/j.1600-0870.2006.00216.x) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2007 - Exploring the need for localization in ensemble data assimilation using - a hierarchical ensemble filter. - *Physica D*, **230**, 99-111, - [doi:10.1016/j.physd.2006.02.011](http://dx.doi.org/doi:10.1016/j.physd.2006.02.011) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. and N. Collins**, 2007 - Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation. - *Journal of Atmospheric and Oceanic Technology*, **24**, 1452-1463, - [doi:10.1175/JTECH2049.1](http://dx.doi.org/doi:10.1175/JTECH2049.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Arellano, A. F., K. Raeder, - J. L. Anderson, P. Hess, L. K. Emmons, D. P. Edwards, G. G. Pfister, - T. L. Campos and G. W. Sachse**: 2007 - Evaluating model performance of an ensemble-based chemical data assimilation - system during INTEX-B field mission, - *Atmos. Chem. Phys.*, **7**, 5695-5710, - [doi:10.5194/acp-7-5695-2007](http://dx.doi.org/doi:10.5194/acp-7-5695-2007) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Arellano, A. F., P. Hess, - D. Edwards, J. L. Anderson, K. Raeder, L. K. Emmons, G. G. Pfister, - T. L. Campos, G. Diskin, J. Jimenez and R. Subramanian**, 2007 - Chemical Data Assimilation of MOPITT CO and MODIS AOD Retrievals - in the Community Atmosphere Model. - *Eos. Trans. AGU*, **88(52)**, Fall Meet. Suppl., Abstract A14D-03. - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Edwards, D., A. Arellano - and M. Deeter**, 2007 - Defining Requirements for Future Satellite Air Quality Chemistry Observations. - *Eos. Trans. AGU*, **88(52)**, Fall Meet. Suppl., Abstract A54C-01. - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Lawson, W. G., M. I. Richardson, - D. J. McCleese, J. T. Schofield, O. Aharonson, S. B. Calcutt, P. G. J. Irwin, - D. M. Kass, C. B. Leovy, S.R. Lewis, D. A. Paige, P. L. Read, F. W. Taylor - and R. W. Zurek**, 2007 - Adapting State of the Art Data Assimilation Approaches for Use with the - Mars Climate Sounder and the PlanetWRF Martian GCM. - Seventh International Conference on Mars, Pasadena, Californa, - LPI Contribution No. 1353, p 3321. - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Lawson, W. G., M. I. Richardson, - D. J. McCleese, J. L. Anderson, Y. Chen and C. Snyder**, 2007 - Ensemble-Based Data Assimilation with a Martian GCM - *Eos Trans. AGU*, 88(52), Fall Meeting Suppl., Abstract P11A-0251. - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Hacker, J. P., J. L. Anderson - and M. Pagowski**, 2007 - Improved vertical covariance estimates for ensemble filter assimilation - of near-surface observations. - *Monthly Weather Review*, **135**, 1021-1036, - [doi:10.1175/MWR3333.1](http://dx.doi.org/DOI:10.1175/MWR3333.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Hacker, J. P. - and D. Rostkier-Edelstein**, 2007 - PBL state estimation with surface observations, a column model, and an ensemble filter. - *Monthly Weather Review*, **135**, Issue 8, 2958-2972, - [doi:10.1175/MWR3443.1](http://dx.doi.org/doi:10.1175/MWR3443.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Karspeck, A. and J. L. Anderson**, 2007 - Experimental implementation of an ensemble adjustment filter - for an intermediate ENSO model. - *Journal of Climate*, **20** 4638-4658, - [doi:10.1175/JCLI4245.1](http://dx.doi.org/DOI:10.1175/JCLI4245.1) - - ----- -## 2006 ----- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Aksoy, A., F. Zhang - and J. W. Nielsen-Gammon**, 2006 - Ensemble-based simultaneous state and parameter estimation with MM5. - *Geophysical Research Letters*, **33**, L12801 - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Aksoy, A., F. Zhang - and J. W. Nielsen-Gammon**, 2006 - Ensemble-based simultaneous state and parameter estimation in a - two-dimensional sea breeze model. - *Monthly Weather Review*, **134**, 2951-2970, - [doi:10.1175/MWR3224.1](http://dx.doi.org/DOI:10.1175/MWR3224.1) - - - ![](http://www.image.ucar.edu/images/pin4.gif) **Chen, Y. and C. Snyder**, 2006 - Assimilation of hurricane position with an ensemble Kalman Filter. - *Geophysical Research Abstracts*, **8**, 09910 - - - ![](http://www.image.ucar.edu/images/pin4.gif) **D. Rostkier-Edelstein, J. Hacker - and M. Pagowski**, 2006 - Estimates of boundary layer profiles by means of ensemble-filter - assimilation of near surface observations in a parameterized PBL. - *Proceedings of the 17th Symposium on Boundary Layers and Turbulence* - AMS, San Diego, 16-22 May 2006. - - ![](http://www.image.ucar.edu/images/pin4.gif) **W. G. Lawson, M. I. Richardson, - D. J. McCleese, J. T. Schofield, O. Aharonson, S. B. Calcutt, P. G. J. Irwin, - D. M. Kass, C. B. Leovy, S.R. Lewis, D. A. Paige, P. L. Read, F. W. Taylor - and R. W. Zurek** 2006 - Data Assimilation With the Mars Climate Sounder - *2006 Fall AGU Meeting*, abstract P23B-0061 - - ![](http://www.image.ucar.edu/images/pin4.gif) **J. Hacker, M. Pagowski - and D. Rostkier-Edelstein**, 2006 - Parameter estimation in land-surface model using atmospheric data - assimilation: finding distributions for use in an ensemble prediction system. - *Proceedings of the 17th Symposium on Boundary Layers and Turbulence*, - AMS, San Diego, 16-22 May 2006. - ---- -## 2005 ---- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L., B. Wyman, - S. Zhang and T. Hoar**, 2005 - Assimilation of surface pressure observations using an ensemble - filter in an idealized global atmospheric prediction system. - *Journal of the Atmospheric Sciences*, **62**, 2925-2938, - [doi:10.1175/JAS3510.1](http://dx.doi.org/DOI:10.1175/JAS3510.1) - - ----- -## BD ... Before DART ----- - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zhang, F., C. Snyder and J. Sun**, 2004 - Impacts of initial estimate and observation availability on - convective-scale data assimilation with an ensemble Kalman filter. - *Monthly Weather Review*, **132**, 1238-1253. - - ![](http://www.image.ucar.edu/images/pin4.gif) **Bengtsson, T., C. Snyder - and D. Nychka**, 2003 - A nonlinear filter that extends to high dimensional systems. - *Journal of Geophysical Research-Atmosphere*, **108**, 1-10 - - ![](http://www.image.ucar.edu/images/pin4.gif) **Tribbia, J. J. and D. P. Baumhefner**, 2004 - Scale interactions and atmospheric predictablility: An updated perspective. - *Monthly Weather Review*, **132**, 703-713. - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2003 - A local least squares framework for ensemble filtering. - *Monthly Weather Review*, **131**, 634-642, - [doi:10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2](http://dx.doi.org/10.1175/1520-0493(2003)131%3C0634%3AALLSFF%3E2.0.CO%3B2) - - ![](http://www.image.ucar.edu/images/pin4.gif) **Tippett, M. K., J. L. Anderson, - C. H. Bishop, T. M. Hamill and J. S. Whitaker**, 2003 - Ensemble square root filters. - *Monthly Weather Review*, **131**, 1485-1490. - - ![](http://www.image.ucar.edu/images/pin4.gif) **Zhang, S. and J. L. Anderson**, 2003 - Impact of spatially and temporally varying estimates of error - covariance on assimilation in a simple atmospheric model. - *Tellus*, **55A**, 126-147. - - ![](http://www.image.ucar.edu/images/pin4.gif) **Snyder, C. and F. Zhang**, 2003 - Assimilation of simulated Doppler radar observations with an ensemble Kalman filter - *Monthly Weather Review*, **131**, 1663-16776. - - ![](http://www.image.ucar.edu/images/pin4.gif) **Anderson, J. L.**, 2001 - An Ensemble Adjustment Kalman Filter for Data Assimilation. - *Monthly Weather Review*, **129**, 2884-2903, - [doi:10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2](http://dx.doi.org/10.1175/1520-0493(2001)129%3C2884%3AAEAKFF%3E2.0.CO%3B2) - ----- diff --git a/guide/pages/Research.md b/guide/pages/Research.md deleted file mode 100644 index 218aa50cb5..0000000000 --- a/guide/pages/Research.md +++ /dev/null @@ -1,373 +0,0 @@ ---- -title: "Research" -layout: default -menu: [main] ---- - -## **R**esearch - -DART research is broadly categorized along three avenues: one is the -research toward data assimilation algorithmic and computational -efficiency, another is toward implementing and exploiting the -information in observations with one model or another, and another is to -learn more about the behavior of an individual model - perhaps by -looking at systematic features of the increments. - -### If you would like to add your research highlight using DART, please let us know\! - - - ------ - -### Research Projects involving DART - - - [Model Performance](#modelperformance) - - [WACCM](#waccm) - - [MPAS ATM](#mpasatm) - - [OpenGGCM](#space) - - [Chemical Transport](#chemistry) - - [\[novel\] observations like GPS RO](#gpsro) - - [Sensitivity Analyses](#sensitivity) - - [Carbon Monoxide](#cmaqco) - - [Data Assimilation on Mars](#mars) - - [Inertio-Gravity waves](#gravity) - - [Boundary Layer projects](#pbl1d) - - [Lorenz '96](#loworder) - - \[\[Damped\] Adaptive\] Inflation Algorithms - - Radar Reflectivity - - Ocean Modeling - - Tropical Cyclones - - - ------ - -### Model Performance - - ---- - - - - - - -
DART/CAM spaghetti plotEnsemble Data assimilation can provide qualitative and quantitative uncertainty for quantities of interest to weather forecasters. [link to more information]
-
-Kevin Raeder, raeder@ucar.edu
- - ------ - -### Model for Prediction Across Scales (MPAS) - - ---- - - - - - - -

- Spaghetti diagram of the ensemble analysis in terms of geopotential height at 500 mb valid at 12Z 5 Dec 2008. -		 -Data assimilation for MPAS is available as an ensemble Kalman filter (EnKF) -implemented through (Data Assimilation Research Testbed)[http://dart.ucar.edu] - -This work is done in a collaborative effort between the -[Mesoscale and Microscale Meteorology (MMM)](http://www.mmm.ucar.edu) -Division and the DART development team. DART support for both MPAS-Atmosphere -and MPAS-Ocean are available as part of the standard DART package. - -In collaboration with NOAA's Earth System Research Laboratory, -related efforts are also underway to explore ensemble data assimilation for MPAS-Atmosphere with the -Gridpoint Statistical Interpolation (GSI) scheme that is operational at -the National Centers for Environmental Prediction. -
- - ---- - - - - - -
- -Grid structure used in the MPAS/DART interface -The MPAS/DART interface is -built on MPAS's unstructured centroidal Voronoi mesh, -and does not regrid to latitude-longitude grids. In MPAS, the finite-volume -approach based on a C-grid staggering retains prognostic equations for mass -at the center of finite-volume cells and for the normal component of velocity -(u) at the faces (or edges in 2D) of the cells. -The normal component of velocity (u) at cell edges is then used to -reconstruct zonal and meridional winds at cell centers (V) - using radial basis functions (RBFs). -To avoid the singularity issue on the poles, the cartesian coordinate is employed. - -The forward operators on the unstructured grid mesh are constructed as follows. -The dual of the Voronoi mesh, or the triangular mesh (shown as dashed lines in the -figure), is used to search the closest cell center to an arbitrary (or observation) -point, then find three cell centers of the triangle enclosing the desired point. -Mass fields are then horizontally interpolated from the cell centers to the observation -location using a barycentric (e.g., area-weighted) interpolation. -
- -While the observed wind quantities are zonal and meridional winds, the normal -component of velocity (u) is the only prognostic wind variable in MPAS, -we thus implement a couple of different ways of assimilating wind observations. -The options determine which wind variables are used in the forward operator to -compute expected observation values; how the horizontal interpolation is computed -in that forward operator; and how the assimilation increments are applied to update -the wind quantities in the state vector of the analysis. - -Preliminary results based on real data assimilation experiments indicate that -performance is better when the zonal and meridional winds are used as input to the -forward operator that uses Barycentric interpolation and the prognostic u -wind is incrementally updated. However, there remain scientific questions about -how best to handle the wind fields under different situations. For the details -on the wind data strategy, refer to the -documentation in DART. - -For any questions or future collaboration, please contact either -Soyoung Ha (syha@ucar.edu) or the DART team (dart@ucar.edu) - - - ------ - -### Whole Atmosphere Community Climate Model (WACCM) - - ---- - - - - - - -
WACCM graphic  
This is an excerpt from Applying the science and technology of data assimilation by Brian Bevirt 07/11/2017 as part of a CISL News series describing the many ways CISL improves modeling beyond providing supercomputing systems and facilities.
-These plots show measured and modeled zonal mean temperatures between 70N and 90N during the January 2009 sudden warming of the stratosphere. The bottom plot shows the observed temperatures (in degrees Kelvin, see legend at right), the center plot shows how this state of the atmosphere was simulated by the specified-dynamics version of the WACCM model, and the top plot shows WACCM's improved result after using DART to assimilate middle atmosphere observations.
-
-The key point in this figure is that WACCM+DART captures both the stratosphere warming and mesosphere cooling that are seen in the observations. Also seen in the specified-dynamics version of WACCM, the elevated stratopause that forms at high altitudes around day 30 descends too fast compared to the observations. The elevated stratopause is maintained at a high altitude in the WACCM+DART simulation. This has implications for the descent of species from the mesosphere into the stratosphere. Accurate representation of the mesosphere dynamics is important for the ionosphere variability during sudden stratosphere warming events. (Figure courtesy of Nick Pedatella, HAO)
-
-[link to more information]
-
-Nick Pedatella, nickp@ucar.edu Hanli Liu, liuh@ucar.edu Jing Liu, jingliu@ucar.edu
- - - ------ - -### Open Geospace General Circulation Model (thermosphere/ionosphere/magnetosphere) - - ---- - - - - - - -
OpenGGCM graphicThe primary goal of this project is to combine the OpenGGCM (Open Geospace General Circulation Model) with the NCAR Data Assimilation Research Testbed (DART), which implements an Ensemble Kalman Filter (EnKF) and will enable the sequential assimilation of ionosphere, thermosphere, and magetosphere data.
-
-We will also optimize model parameters by including them into the state vector. This will improve model accuracy even when no data are assimilated. [link to more information]
-
-Jimmy Raeder, J.Raeder@unh.edu
- - - ------ - -### Chemical Data Assimilation - - ---- - - - - - - -
CAM-Chem/DART CO ColumnWe are currently applying an ensemble-based chemical data assimilation system, consisting of regional to global chemical transport models (CAM-Chem, WRF-Chem) in conjunction with DART, for a joint assimilation of meteorological observations and satellite-derived CO measurements from MOPITT and aerosol optical depth (AOD) measurements from MODIS. The chemical data assimilation system has been recently used for near-real time chemical forecasting (see https://espo.nasa.gov/arctas/) to support flight planning during the NASA Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS). [link to more information]
-
-Ave Arellano, arellano@ucar.edu
- - - ------ - -### GPS RO Observations and Tropical Cyclone Forecasting - - ---- - - - - - - -
GPS RO geometry schematicProfiles of atmospheric quantities deduced from GPS Radio Occultation data are available in otherwise data-sparse regions and provide information used to forecast the behavior of tropical cyclones. The COSMIC/FORMOSAT-3 mission has been providing about 2000 data profiles per day since September 2007. [link to more information]
-
-Hui Liu, hliu@ucar.edu
- - - ------ - -### Sensitivity Analyses - - ---- - - - - - - -
DART/WRF sensitivity plotForecast sensitivity analysis provides an objective means of evaluating how initial condition errors affect a forecast and where to gather additional observations to reduce forecast errors. Most sensitivity studies use the adjoint of a linearized forecast model to determine the gradient of a forecast metric with respect to the initial conditions. Adjoints suffer from a number of difficulties including coding, linearity assumptions, and moist processes. Ensemble-based sensitivity analysis provides an attractive alternative to adjoint-based methods because it combines data assimilation and sensitivity analysis in a consistent manner. This image illustrates the effect of zonal winds aloft on the position of Hurricane Katrina. [link to more information]
-
-Ryan Torn, torn@atmos.albany.edu
- - - ------ - -### Assimilation of CO - - ---- - - - - - - -
CMAQ CO assimilation resultThis project describes an integrated approach to modeling atmospheric chemistry with trace gas data assimilation. Specifically, we ran CMAQ from within DART to assimilate both synthetic and real observations of CO for the period of June 2001. [link to more information]
-
-Alexis Zubrow, azubrow@unc.edu
- - - ------ - -### Assimilation on MARS - - ---- - - - - - - -
DART/MARS_Lawson graphicThe planetary atmospheres group at Caltech has produced a global and planetary version of NCAR's WRF (Weather Research and Forecasting) Model. We are using DART to attempt data assimilation within the Mars atmosphere using the Mars version of WRF, MarsWRF, as our GCM. [link to more information]
-
-Greg Lawson, wglawson@gps.caltech.edu
- - - ------ - -### Inertio-Gravity waves - - ---- - - - - - - -
DART/CAM normal modesThis project aims at quantifying the impact of various motion types in analysis and forecast fields by using normal modes. The DART/CAM is the main analysis system used in the project. The first question addressed is about how large part of the atmospheric energy is associated with the inertio-gravity motions, an important part of the global circulation primarily because of their role in the tropical system. [link to more information]
-
-Nedjeljka Zagar, nedjeljka.zagar@fmf.uni-lj.si
- - - ------ - -### Planetary Boundary Layer - - ---- - - - - - - -
DART/CAM spaghetti plotA long-term goal of this work is to find an efficient system for probabilistic planetary boundary layer (PBL) nowcasting that can be employed wherever surface observations are present. One approach showing promise is the use of a single column model (SCM) and ensemble filter data assimilation techniques. [link to more information]
-
-Dorita Rostkier-Edelstein, rostkier@ucar.edu
-Josh Hacker, hacker@ucar.edu
- - - ------ - -### The Lorenz '96 model - -Lorenz, E. N., and K. A. Emanuel, 1998: -Optimal sites for supplementary weather observations: Simulations with a small model. -*J. Atmos. Sci.*, **55**, 399-414. -[10.1175/1520-0469(1998)055\<0399:OSFSWO\>2.0.CO;2](https://doi.org/10.1175/1520-0469(1998)055%3C0399:OSFSWO%3E2.0.CO;2) - - ----- - - - - - - - -
bad rank histogramThe Lorenz '96 model is one of our favorite models. In our implementation, it is a 40-variable model that can be used to test inflation algorithms, the effects of localization schemes, the integrity of the DART installation itself, the state-space diagnostic routines; it is extensively used in the tutorial, and can even be run as a standalone executable to test the MPI support on a machine. [link to more information]
-
- -Jeff Anderson, jla@ucar.edu, and
-Tim Hoar, thoar@ucar.edu		good rank histogram
- ------ diff --git a/guide/pages/Tutorial.md b/guide/pages/Tutorial.md deleted file mode 100644 index 71256f7b3f..0000000000 --- a/guide/pages/Tutorial.md +++ /dev/null @@ -1,46 +0,0 @@ ---- -title: Dart Tutorial -layout: default ---- - -# DART Tutorial - -The DART Tutorial is intended to aid in the understanding of ensemble -data assimilation theory and consists of step-by-step concepts and -companion exercises with DART. The diagnostics in the tutorial use -Matlab®. To configure your environment to use Matlab and the -DART diagnostics, see the -[Configuring Matlab® for netCDF & DART](Getting_Starting.md#matlab) -section of the **Getting_Started** document. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Section 1 Filtering For a One Variable System
Section 2 The DART Directory Tree
Section 3 DART Runtime Control and Documentation
Section 4 How should observations of a state variable impact an unobserved state variable? Multivariate assimilation.
Section 5 Comprehensive Filtering Theory: Non-Identity Observations and the Joint Phase Space
Section 6 Other Updates for An Observed Variable
Section 7 Some Additional Low-Order Models
Section 8 Dealing with Sampling Error
Section 9 More on Dealing with Error; Inflation
Section 10Regression and Nonlinear Effects
Section 11Creating DART Executables
Section 12Adaptive Inflation
Section 13Hierarchical Group Filters and Localization
Section 14Observation Quality Control
Section 15DART Experiments: Control and Design
Section 16Diagnostic Output
Section 17Creating Observation Sequences
Section 18Lost in Phase Space: The Challenge of Not Knowing the Truth
Section 19DART-Compliant Models and Making Models Compliant: Coming Soon
Section 20Model Parameter Estimation
Section 21Observation Types and Observing System Design
Section 22Parallel Algorithm Implementation: Coming Soon
Section 23Location Module Design
Section 24Fixed Lag Ensemble Kalman Smoother (not available yet)
Section 25A Simple 1D Advection Model: Tracer Data Assimilation
- ------ diff --git a/guide/pages/WRF_DART_guidelines.md b/guide/pages/WRF_DART_guidelines.md deleted file mode 100644 index 803cbc8f4b..0000000000 --- a/guide/pages/WRF_DART_guidelines.md +++ /dev/null @@ -1,70 +0,0 @@ ---- -title: WRF and DART -layout: default ---- - -### The WRF Weather Model and DART - - - -> How does DART interact with running WRF? - -Most users with large WRF domains run a single cycle of filter to do -assimilation, and then advance each ensemble member of WRF from a -script, possibly submitting them in a batch to the job queues. - -For smaller WRF runs, if WRF can be compiled without MPI (the 'serial' -configuration) then filter can cycle inside the same program, advancing -multiple ensemble members in parallel. See the WRF documentation pages -for more -details. - -> I have completed running *filter* and I have the *filter_restart.\#\#\#\#* files. -> Can you refer me to the utility to convert them back to a set of *wrfinput_d01* files? - - - -If you are using the *advance_model.csh* script that is distributed -with DART, it will take care of converting the filter output files back -to the WRF input files for the next model advance. - -If you are setting up a free run or doing something different than what -the basic script supports, read on to see what must be done. - -When you finish running DART it will have created a set of -*sssss.\#\#\#\#* restart files, where the *sssss* part of the filename -comes from the setting of *&filter_nml :: restart_out_file_name* -(and is frequently *filter_restart*). The *.\#\#\#\#* is a 4 digit -number appended by filter based on the ensemble number. These files -contain the WRF state vector data that was used in the assimilation, -which is usually a subset of all the fields in a *wrfinput_d01* file. - -*dart_to_wrf* is the standard utility to insert the DART state -information into a WRF input file, e.g. *wrfinput_d01*. For multiple -WRF domains, a single run of the converter program will update the -*_d02*, *_d03*, ..., files at the same time as the *_d01* file. - -In the *input.nml* file, set the following: - -~~~ -&dart_to_wrf_nml - model_advance_file = .false. - dart_restart_name = 'filter_restart.####', -/ -~~~ - -where '\#\#\#\#' is the ensemble member number. There is no option to -alter the input/output WRF filename. Run *dart_to_wrf*. Remember to -preserve each *wrfinput_d01* file or you will simply keep overwriting -the information in the same output file. Repeat for each ensemble member -and you will be ready to run WRF to make ensemble forecasts. - -If filter is advancing the WRF model, and you want to spawn forecasts -from intermediate assimilation steps: -Use the *assim_model_state_ic.\#\#\#\#* files instead of the -*filter_restart.\#\#\#\#* files, and set the *model_advance_file* -namelist item to be *.true.* . - -\[[top](#)\] - ------ diff --git a/guide/pages/api.md b/guide/pages/api.md deleted file mode 100644 index 86be09ddb0..0000000000 --- a/guide/pages/api.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: API documentation -layout: default ---- - -## List of ford documentation versions - -{% assign sorted = site.api | sort: 'title' | reverse %} - - - - - {% for api in sorted %} - - - - {% endfor %} -
API Documents
{{ api.title }}
diff --git a/guide/pages/dart_lab.md b/guide/pages/dart_lab.md deleted file mode 100644 index a12d28e07c..0000000000 --- a/guide/pages/dart_lab.md +++ /dev/null @@ -1,60 +0,0 @@ ---- -title: DART_LAB -layout: default ---- - -# DART_LAB Tutorial - -## Overview -DART_LAB is a MATLAB®-based tutorial to demonstrate the principles of -ensemble data assimilation. -DART_LAB consists of PDF tutorial materials and MATLAB® exercises. -See below for links to the PDF files and a list of the -corresponding MATLAB scripts. -The DART_LAB tutorial begins at a more introductory level than the materials in -the tutorial directory, and includes hands-on exercises at several points. - - ------ - - - - - - - - - -
Section 1The basics in 1D.
Section 2How should observations of a state variable impact an unobserved state variable? Multivariate assimilation.
Section 3Sampling error and localization.
Section 4The Ensemble Kalman Filter (Perturbed Observations).
Section 5Adaptive Inflation.
- - - ------ - -## MATLAB® Hands-On Exercises - -In the `matlab` subdirectory are a set of MATLAB scripts and GUI -(graphical user interface) programs which are exercises that go with the -tutorial. Each is interactive with settings that can be changed and -rerun to explore various options. A valid -[MATLAB](http://www.mathworks.com/products/matlab/) -license is needed to run these scripts. - -The exercises use the following functions: - -| function | description | -| --- | :--- | -| `gaussian_product` | graphical representation of the product of two gaussians | -| `oned_ensemble` | explore the details of ensemble data assimilation for a scalar | -| `oned_model` | simple ensemble data assimilation example | -| `oned_model_inf` | simple ensemble data assimilation example *with inflation* | -| `run_lorenz_63` | ensemble DA with the 3-variable Lorenz '63 dynamical model - the "butterfly" model | -| `run_lorenz_96` | ensemble DA with the 40-variable Lorenz '96 dynamical model | -| `run_lorenz_96_inf` | ensemble DA with the 40-variable Lorenz '96 dynamical model *with inflation* | -| `twod_ensemble` | demonstrates the impact of observations on unobserved state variables | - -To run these, cd into the `DART_LAB/matlab` directory, start matlab, and -type the names at the prompt. - ------ diff --git a/guide/pages/dart_mpi.md b/guide/pages/dart_mpi.md deleted file mode 100644 index 96e2d861c1..0000000000 --- a/guide/pages/dart_mpi.md +++ /dev/null @@ -1,304 +0,0 @@ ---- -title: Dart MPI -layout: default ---- - -# DART parallelism - -This document has two sections, the first is DART's use of MPI for the -core DART routines, and the second section -['Filter "async" Mode Diagrams'](#async0) -describes the options for advancing models that may or may not -have their own parallel considerations. - -## Introduction - -The latest release of the DART system includes an MPI option. MPI stands -for 'Message Passing Interface', and is both a library and run-time -system that enables multiple copies of a single program to run in -parallel, exchange data, and combine to solve a problem more quickly. -The latest release of DART does **NOT** require MPI to run; the default -build scripts do not need nor use MPI in any way. However, for larger -models with large state vectors and large numbers of observations, the -data assimilation step will run much faster in parallel, which requires -MPI to be installed and used. However, if multiple ensembles of your -model fit comfortably (in time and memory space) on a single processor, -you need read no further about MPI. - -MPI is an open-source standard; there are many implementations of it. If -you have a large single-vendor system it probably comes with an MPI -library by default. For a Linux cluster there are generally more -variations in what might be installed; most systems use a version of MPI -called MPICH. In smaller clusters or dual-processor workstations a -version of MPI called either LAM-MPI or OpenMPI might be installed, or -can be downloaded and installed by the end user. (Note that OpenMP is a -different parallel system; OpenMPI is a recent effort with a confusingly -similar name.) - -An "MPI program" makes calls to an MPI library, and needs to be compiled -with MPI include files and libraries. Generally the MPI installation -includes a shell script called *mpif90* which adds the flags and -libraries appropriate for each type of fortran compiler. So compiling an -MPI program usually means simply changing the fortran compiler name to -the MPI script name. - -These MPI scripts are built during the MPI install process and are -specific to a particular compiler; if your system has multiple fortran -compilers installed then either there will be multiple MPI scripts -built, one for each compiler type, or there will be an environment -variable or flag to the MPI script to select which compiler to invoke. -See your system documentation or find an example of a successful MPI -program compile command and copy it. - - - -## DART use of MPI - -To run in parallel, only the DART *filter* program (and possibly the -companion *wakeup_filter* program) need be compiled with the MPI -scripts. ~~All other DART executables should be compiled with a standard -F90 compiler and are not MPI enabled.~~ (And note again that *filter* can -still be built as a single executable like previous releases of DART; -using MPI and running in parallel is simply an additional option.) To -build a parallel version of the *filter* program, the *mkmf_filter* -command needs to be called with the '-mpi' option to generate a `Makefile` -which compiles with the MPI scripts instead of the basic Fortran compiler. - -See the *quickbuild.csh* script in each `$DART/models/*/work` directory -for the commands that need to be edited to enable the MPI utilities. You -will also need to edit the `$DART/build_templates/mkmf.template` file to call the -proper version of the MPI compile script if it does not have the default -name, is not in a standard location on the system, or needs additional -options set to select between multiple Fortran compilers. - -MPI programs generally need to be started with a shell script called -*mpirun* or *mpiexec*, but they also interact with any batch control -system that might be installed on the cluster or parallel system. -Parallel systems with multiple users generally run some sort of batch -system (e.g. LSF, PBS, SLURM, POE, LoadLeveler, etc). You submit a job request -to this system and it schedules which nodes are assigned to which jobs. -Unfortunately the details of this vary widely from system to system; -consult your local web pages or knowledgeable system admin for help -here. Generally the run scripts supplied with DART have generic sections -to deal with LSF, PBS, no batch system at all, and sequential execution, -but the details (e.g. the specific queue names, accounting charge codes) -will almost certainly have to be adjusted. - -The data assimilation process involves running multiple copies -(ensembles) of a user model, with an assimilation computation -interspersed between calls to the model. There are many possible -execution combinations, including: - - - Compiling the assimilation program *filter* with the model, - resulting in a single executable. This can be either a sequential or - parallel program. - - Compiling *filter* separately from the model, and having 2 separate - executables. Either or both can be sequential or parallel. - -The choice of how to combine the *filter* program and the model has 2 -parts: building the executables and then running them. At build time, -the choice of using MPI or not must be made. At execution time, the -setting of the `async` namelist value in the `filter_nml` section -controls how *filter* interacts with the model. - -Choices include: - - - **async = 0** - The model and *filter* programs are compiled into a single executable, - and when the model needs to advance, *filter* calls a - subroutine. See [the async 0 diagram](#async0) which - illustrates this option. - - **async = 2** - The model is compiled into a sequential (single task) program. If - *filter* is running in parallel, each filter task will execute the - model independently to advance the group of ensembles. See - [the async 2 diagram](#async2) which illustrates this option. - - **async = 4** - The model is compiled into an MPI program (parallel) and only - filter task 0 tells the startup script when it is time to advance - the model. Each ensemble is advanced one by one, with the model - using all the processors to run in parallel. See - [the async 4 diagram](#async4) which illustrates this option. - - **async ignored** (sometimes referred to as 'async 5', but not an actual namelist setting) - This is the way most large models run now. There is a separate - script, outside of *filter*, which runs the N copies of the model to - do the advance. Then *filter* is run, as an MPI program, and it only - assimilates for a single time and then exits. The external script - manages the file motion between steps, and calls both the models and - *filter* in turn. - -This release of DART has the restriction that if the model and the -*filter* program are both compiled with MPI and are run in 'async=4' -mode, that they both run on the same number of processors; e.g. if -*filter* is run on 16 processors, the model must be started on 16 -processors as well. Alternatively, if the user model is compiled as a -single executable (async=2), *filter* can run in parallel on any number -of processors and each model advance can be executed independently -without the model having to know about MPI or parallelism. - -Compiling and running an MPI application can be substantially more -complicated than running a single executable. There are a suite of small -test programs to help diagnose any problems encountered in trying to run -the new version of DART. Look in -[developer_tests/mpi_utilities/tests/README](../../developer_tests/mpi_utilities/tests/README) -for instructions and a set of tests to narrow down any difficulties. - -### Performance issues and timing results - -Getting good performance from a parallel program is frequently -difficult. Here are a few of reasons why: - - - Amdahl's law - You can look up the actual formula for this "law" in the Wikipedia, - but the gist is that the amount of serial code in your program - limits how much faster your program runs on a parallel machine, and - at some point (often much sooner than you'd expect) you stop getting - any speedup when adding more processors. - - Surface area to volume ratio - Many scientific problems involve breaking up a large grid or array - of data and distributing the smaller chunks across the multiple - processors. Each processor computes values for the data on the - interior of the chunk they are given, but frequently the data along - the edges of each chunk must be communicated to the processors which - hold the neighboring chunks of the grid. As you increase the number - of processors (and keep the problem size the same) the chunk size - becomes smaller. As this happens, the 'surface area' around the - edges decreases slower than the 'volume' inside that one processor - can compute independently of other processors. At some point the - communication overhead of exchanging edge data limits your speedup. - - Hardware architecture system balance - Raw CPU speeds have increased faster than memory access times, which - have increased faster than access to secondary storage (e.g. I/O to - disk). Computations which need to read input data and write result - files typically create I/O bottlenecks. There are machines with - parallel filesystems, but many programs are written to have a single - processor read in the data and broadcast it to all the other - processors, and collect the data on a single node before writing. As - the number of processors increases the amount of time spent waiting - for I/O and communication to and from the I/O node increases. There - are also capacity issues; for example the amount of memory available - on the I/O node to hold the entire dataset can be insufficient. - - NUMA memory - Many machines today have multiple levels of memory: on-chip private - cache, on-chip shared cache, local shared memory, and remote shared - memory. The approach is referred as Non-Uniform Memory Access (NUMA) - because each level of memory has different access times. While in - general having faster memory improves performance, it also makes the - performance very difficult to predict since it depends not just on - the algorithms in the code, but is very strongly a function of - working-set size and memory access patterns. Beyond shared memory - there is distributed memory, meaning multiple CPUs are closely - connected but cannot directly address the other memory. The - communication time between nodes then depends on a hardware switch - or network card, which is much slower than local access to memory. - The performance results can be heavily influenced in this case by - problem size and amount of communication between processes. - -Parallel performance can be measured and expressed in several different -ways. A few of the relevant definitions are: - - - Speedup - Generally defined as the wall-clock time for a single processor - divided by the wall-clock time for N processors. - - Efficiency - The speedup number divided by N, which for perfect scalability will - remain at 1.0 as N increases. - - Strong scaling - The problem size is held constant and the number of processors is - increased. - - Weak scaling - The problem size grows as the number of processors increases so the - amount of work per processor is held constant. - -We measured the strong scaling efficiency of the DART 'filter' program -on a variety of platforms and problem sizes. The scaling looks very good -up to the numbers of processors available to us to test on. It is -assumed that for MPP (Massively-Parallel Processing) machines with -10,000s of processors that some algorithmic changes will be required. -These are described in **Anderson & Collins** -["Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation"](https://doi.org/10.1175/JTECH2049.1) 2007. - -### User considerations for their own configurations - -Many parallel machines today are a hybrid of shared and distributed -memory processors; meaning that some small number (e.g. 2-32) of CPUs -share some amount of physical memory and can transfer data quickly -between them, while communicating data to other CPUs involves slower -communication across either some kind of hardware switch or fabric, or a -network communication card like high speed ethernet. - -Running as many tasks per node as CPUs per shared-memory node is in -general good, unless the total amount of virtual memory used by the -program exceeds the physical memory. Factors to consider here include -whether each task is limited by the operating system to 1/Nth of the -physical memory, or whether one task is free to consume more than its -share. If the node starts paging memory to disk, performance takes a -huge nosedive. - -Some models have large memory footprints, and it may be necessary to run -in MPI mode not necessarily because the computation is faster in -parallel, but because the dataset size is larger than the physical -memory on a node and must be divided and spread across multiple nodes to -avoid paging to disk. - - - ---- - -## Filter async mode diagrams - -### Options for parallelism both in DART and in the model advances: - -### Simplest case, async=0: -[](../images/async0.gif) - -This is a single MPI executable, with each call to the model being -simply a subroutine call from each MPI task. - - - ---- - -### Parallel advance, async=2: -[](../images/async2a.gif) - -The *filter* executable is one MPI program, and the model is a single, -sequential executable. Each MPI task uses the unix "system()" call to -invoke a shell script (*advance_model.csh*) which runs the models as -independent programs. - -Other views of how the async=2 option is structured; these may be more -or less helpful. - -#### Parallel advance, async=2: -[](../images/async2_v1.gif) - -#### Parallel advance, async=2, second version: -[](../images/async2_v2.gif) - -#### Parallel model advance, async=2, - -showing how data is communicated -between filter and the model thru intermediate files. -IC are 'initial condition' files, UD are 'updated' files. -[](../images/async2_wfiles.gif) - - - ---- - -### Parallel model advance, async=4: -[](../images/async4.gif) - -The *filter* executable is one MPI program, and the *model* is also an MPI -program. The filter executable communicates with the *runme_filter* shell -script, which sequentially invokes *mpirun* to advance each of the model -runs, one per ensemble member, still using *advance_model.csh*. - - -Parallel model advance, async=4, showing how data is communicated -between *filter* and the model thru intermediate files. -IC are 'initial condition' files, UD are 'updated' files. -[](../images/async4_wfiles.gif) - ---- diff --git a/guide/pages/random_seq_mod.md b/guide/pages/random_seq_mod.md deleted file mode 100644 index ee226f0202..0000000000 --- a/guide/pages/random_seq_mod.md +++ /dev/null @@ -1,594 +0,0 @@ -[](#TOP) - -MODULE random\_seq\_mod -======================= - - ------------------------------------------------------------------------ ------------------------------------------------------------------- - ![DART project logo](../../../docs/images/Dartboard7.png){height="70"} Jump to [DART Documentation Main Index](../../../docs/index.html) - ------------------------------------------------------------------------ ------------------------------------------------------------------- - -[INTERFACES](#Interface) / [NAMELIST](#Namelist) / [FILES](#FilesUsed) / -[REFERENCES](#References) / [ERRORS](#Errors) / [PLANS](#FuturePlans) / -[PRIVATE COMPONENTS](#PrivateComponents) / [TERMS OF USE](#Legalese) - -Overview --------- - -Provides access to any number of reproducible random sequences. Can -sample from uniform, gaussian, two-dimensional gaussian, gamma, inverse -gamma, and exponential distributions. - -The current random sequence generator is a Fortran version of the [GNU -Library](http://www.gnu.org/software/gsl/) implementation of the -[Mersenne Twister](http://en.wikipedia.org/wiki/Mersenne_twister) -algorithm. The original code is in the C language and the conversion to -Fortran was done by the DART team. - -There are test programs in the *developer\_tests/random\_seq* directory -which show examples of calling these routines. Build and run these tests -in the *test* subdirectory. - -Use within Dart -------------- - -One of the design goals of the DART software when random numbers are used, is that the generator should be seeded so running the same program again on the same input data produces the same results. This allows differences in results to be attributed to changes in the input data or settings, not changes in the random number sequence. - -There are several strategies for selecting a seed depending on how the random sequence of values will be used. - -Constant value: - -Setting a constant seed guarantees the results will be reproducible and is the simplest to code. This should be used when the program results will be compared only to other values generated by the same run of this program, not between different runs of the program. For example, a constant should not be used if the results of a run will be compared to runs on other ensemble members or runs with data from other model times. - -Ensemble member number: - -If the ensemble member number is available it is often a good choice for the seed value, to guarantee that different members will use different random sequences. For our MPI-enabled programs, this strategy should be used when a single task handles all the work for any single ensemble member. If the work for an ensemble member is spread across multiple tasks, see below for 'both ensemble member and task number'. - -Task number: - -If the code using random numbers is part of a parallel program, then usually each task needs to set a different seed to generate different sequences of values in each task. If the task number is used as the seed the results will be reproducible as long as the number of tasks in the job is held constant. There are several places in the DART code where it does not have access to the model time or the ensemble number at the place where the seed must be set. In this case, the best it can do is to use the task number or a value based on the task number, and document that to get entirely reproducible results the user must keep the task count constant. The DART routine *mpi_utilities_mod::my_task_id()* is available to return the current task number. Note that task numbers follow the MPI standard of starting with 0. - -Both ensemble member and task number: - -If the ensemble number and the task number are available, they can be combined to generate different random sequences. This is needed, for example, when a task only has part of an ensemble member's state and needs to perturb it with random values. Some care must be taken when combining the numbers to ensure that the combination is unique. For example, one formula used is of the form: (100000 * task_number) + ensemble_member_number. This creates unique numbers for any case using less than 100,000 ensemble members (a fairly safe guess). - -Model/State timestamp: - -If you are running the same program but with different input data and the random sequence should be different for each invocation of the program, there is a time manager routine, *time_manager_mod::generate_seed()* that generates a reproducible seed given a dart *time_type*. For example, this strategy is used in the *perfect_model_obs* program to initialize the random sequence of draws from a gaussian distribution to add noise to synthetic observations. If a constant seed was used the same noise values would be added to the observations each time the program was run, even if the input model data was different, potentially generating duplicate series of noise values for each run. So this program uses the timestamp of the input model data to set the seed, resulting in reproducible random sequences that are different for a series of invocations. - - -\[[top](#)\] - - ------------------------------------------------------------------------- -[](#OtherModulesUsed) -OTHER MODULES USED ------------------- - - types_mod - utilities_mod - - -\[[top](#)\] - - ------------------------------------------------------------------------- -[](#Interface) -PUBLIC INTERFACES ------------------ - - -------------------------------- --------------------------------------------------------- - *use random\_seq\_mod, only :* [random\_seq\_type](#random_seq_type) -   [init\_random\_seq](#init_random_seq) -   [random\_uniform](#random_uniform) -   [random\_gaussian](#random_gaussian) -   [several\_random\_gaussians](#several_random_gaussians) -   [twod\_gaussians](#twod_gaussians) -   [random\_gamma](#random_gamma) -   [random\_inverse\_gamma](#random_inverse_gamma) -   [random\_exponential](#random_exponential) - -------------------------------- --------------------------------------------------------- - -A note about documentation style. Optional arguments are enclosed in -brackets *\[like this\]*. - -[](#random_seq_type) - -
- - type random_seq_type - private - - integer :: mti - integer(i8) :: mt(624) - real(r8) :: lastg - logical :: gset - - end type random_seq_type - -
- -
- -This type is used to uniquely identify a sequence. Keeps the state -history of the linear congruential number generator. In this -implementation it is based on the Mersenne Twister from the GNU -Scientific Library. - -
- - -[](#init_random_seq) - -
- - call init_random_seq(r [ , seed] ) - - type(random_seq_type), intent(inout) :: r - integer, optional, intent(in) :: seed - -
- -
- -Initializes a random sequence for use. This must be called before any -random numbers can be generated from this sequence. Any number of -independent, reproducible random sequences can be generated by having -multiple instances of a random\_seq\_type. A specified integer seed, -optional, can produce a specific 'random' sequence. - - | arg | description | - |--------|-------------------------------------------| - | *r* | A random sequence type to be initialized.| - | *seed* | A seed for a random sequence. | - - -
- - -[](#random_uniform) - -
- - var = random_uniform(r) - - real(r8) :: random_uniform - type(random_seq_type), intent(inout) :: r - -
- -
- -Returns a random draw from a uniform distribution on interval \[0,1\]. - - ------------------- --------------------------------------------------- - *random\_uniform* A random draw from a Uniform\[0,1\] distribution. - *r* An initialized random sequence type. - ------------------- --------------------------------------------------- - -
- -\ -[](#random_gaussian)\ - -
- -*var = random\_gaussian(r, mean, standard\_deviation)* - real(r8) :: random_gaussian - type(random_seq_type), intent(inout) :: r - real(r8), intent(in) :: mean - real(r8), intent(in) :: standard_deviation - -
- -
- -Returns a random draw from a Gaussian distribution with the specified -mean and standard deviation. - -See [this Wikipedia -page](https://en.wikipedia.org/wiki/Normal_distribution) for more -explanation about this function. - - ----------------------- --------------------------------------------- - *random\_gaussian* A random draw from a gaussian distribution. - *r* An initialized random sequence type. - *mean* Mean of the gaussian. - *standard\_deviation* Standard deviation of the gaussian. - ----------------------- --------------------------------------------- - -
- -\ -[](#several_random_gaussians)\ - -
- -*call several\_random\_gaussians(r, mean, standard\_deviation, n, rnum)* - type(random_seq_type), intent(inout) :: r - real(r8), intent(in) :: mean - real(r8), intent(in) :: standard_deviation - integer, intent(in) :: n - real(r8), dimension(:), intent(out) :: rnum - -
- -
- -Returns *n* random samples from a gaussian distribution with the -specified mean and standard deviation. Array *rnum* must be at least -size *n*. - - ----------------------- -------------------------------------- - *r* An initialized random sequence type. - *mean* Mean of the Gaussian to be sampled. - *standard\_deviation* Standard deviation of the Gaussian. - *n* Number of samples to return - *rnum* The random samples of the Gaussian. - ----------------------- -------------------------------------- - -
- -\ -[](#twod_gaussians)\ - -
- -*call twod\_gaussians(r, mean, cov, rnum)* - type(random_seq_type), intent(inout) :: r - real(r8), dimension(2), intent(in) :: mean - real(r8), dimension(2,2), intent(in) :: cov - real(r8), dimension(2), intent(out) :: rnum - -
- -
- -Returns a random draw from a 2D gaussian distribution with the specified -mean and covariance. - -The algorithm used is from Knuth, exercise 13, section 3.4.1. See [this -Wikipedia -page](https://en.wikipedia.org/wiki/Multivariate_normal_distribution) -for more explanation about this function. - - -------- -------------------------------------- - *r* An initialized random sequence type. - *mean* Mean of 2D gaussian distribution. - *cov* Covariance of 2D gaussian. - *rnum* Returned random draw from gaussian. - -------- -------------------------------------- - -
- -\ -[](#random_gamma)\ - -
- -*var = random\_gamma(r, rshape, rscale)* - real(r8) :: random_gamma - type(random_seq_type), intent(inout) :: r - real(r8), intent(in) :: rshape - real(r8), intent(in) :: rscale - -
- -
- -Returns a random draw from a Gamma distribution with specified *rshape* -and *rscale*. Both must be positive. - -Note that there are three different parameterizations in common use: - -1. With shape parameter κ (kappa) and scale parameter θ (theta). -2. With shape parameter α (alpha) and rate parameter β (beta). Alpha is - the same as kappa, and beta is an inverse scale parameter so β = - 1/θ. -3. With shape parameter κ (kappa) and mean parameter μ (mu). μ = κ/β, - so β = κ/μ. - -This form uses the first parameterization, shape (κ) and scale (θ). The -distribution mean is κθ and the variance is κ(θ²). -This routine is based on the Gamma(a,b) generator from the GNU -Scientific library. See [this Wikipedia -page](https://en.wikipedia.org/wiki/Gamma_distribution) for more -explanation of the various parameterizations of this function. - - ----------------- ---------------------------------------------------------------------------------------------------------------- - *random\_gamma* A random draw from a gamma distribution. - *r* An initialized random sequence type. - *rshape* Shape parameter. Often written as either alpha or kappa. - *rscale* Scale parameter. Often written as theta. If you have a rate parameter (often beta) pass in (1/rate) for scale. - ----------------- ---------------------------------------------------------------------------------------------------------------- - -
- -\ -[](#random_inverse_gamma)\ - -
- -*var = random\_inverse\_gamma(r, rshape, rscale)* - real(r8) :: random_inverse_gamma - type(random_seq_type), intent(inout) :: r - real(r8), intent(in) :: rshape - real(r8), intent(in) :: rscale - -
- -
- -Returns a random draw from an inverse Gamma distribution with the -specified *shape* and *scale*. Both must be positive. If you have 'rate' -instead of 'scale' pass in (1/rate) for scale. - -See [this Wikipedia -page](https://en.wikipedia.org/wiki/Inverse-gamma_distribution) for more -explanation about this function. - - -------------------------- ---------------------------------------------------------------------------------------------------------------- - *random\_inverse\_gamma* A random draw from an inverse gamma distribution. - *r* An initialized random sequence type. - *rshape* Shape parameter. Often written as either alpha or kappa. - *rscale* Scale parameter. Often written as theta. If you have a rate parameter (often beta) pass in (1/rate) for scale. - -------------------------- ---------------------------------------------------------------------------------------------------------------- - -
- -\ -[](#random_exponential)\ - -
- -*var = random\_exponential(r, rate)* - real(r8) :: random_exponential - type(random_seq_type), intent(inout) :: r - real(r8), intent(in) :: rate - -
- -
- -Returns a random draw from an exponential distribution with the -specified *rate*. If you have a scale parameter (which is the same as -the mean, the standard deviation, and the survival parameter), specify -(1/scale) for rate. - -See [this Wikipedia -page](https://en.wikipedia.org/wiki/Exponential_distribution) for more -explanation about this function. - - ----------------------- ---------------------------------------------------------------------------------------------------- - *random\_exponential* A random draw from an exponential distribution. - *r* An initialized random sequence type. - *rate* Rate parameter. Often written as lambda. If you have a scale parameter pass in (1/scale) for rate. - ----------------------- ---------------------------------------------------------------------------------------------------- - -
- -\ -[](#Namelist) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -NAMELIST --------- - -This module has no namelist input. - -[](#FilesUsed) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -FILES ------ - -- NONE - -[](#References) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -REFERENCES ----------- - -1. Knuth, Vol 2. -2. [GNU Scientific Library Reference - Manual](http://www.gnu.org/software/gsl/manual/html_node/Random-Number-Generation.html) - -[](#Errors) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -ERROR CODES and CONDITIONS --------------------------- - -
- -Routine -Message -Comment -  -  -  - -
- -KNOWN BUGS ----------- - -none at this time - -[](#FuturePlans) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -FUTURE PLANS ------------- - -none at this time - -[](#PrivateComponents) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -PRIVATE COMPONENTS ------------------- - - --- -------------------------- -   [init\_ran](#init_ran) -   [ran\_unif](#ran_unif) -   [ran\_gauss](#ran_gauss) -   [ran\_gamma](#ran_gamma) - --- -------------------------- - -[](#init_ran)\ - -
- -*call init\_ran(s, seed)* - type(random_seq_type), intent(out) :: s - integer, intent(in) :: seed - -
- -
- -Initializes a random sequence with an integer. Any sequence initialized -with the same integer will produce the same sequence of pseudo-random -numbers. - - -------- ---------------------------------------- - *s* A random sequence to be initialized - *seed* An integer seed to start the sequence. - -------- ---------------------------------------- - -
- -\ -[](#ran_unif)\ - -
- -*var = ran\_unif(s)* - real(r8) :: ran_unif - type(random_seq_type), intent(inout) :: s - -
- -
- -Generate the next uniform \[0, 1\] random number in the sequence. - - ------------- --------------------------------------------------------- - *ran\_unif* Next uniformly distributed \[0, 1\] number in sequence. - *s* A random sequence. - ------------- --------------------------------------------------------- - -
- -\ -[](#ran_gauss)\ - -
- -*var = ran\_gauss(s)* - real(r8) :: ran_gauss - type(random_seq_type), intent(inout) :: s - -
- -
- -Generates a random draw from a standard gaussian. - - -------------- ----------------------------------------- - *ran\_gauss* A random draw from a standard gaussian. - *s* A random sequence. - -------------- ----------------------------------------- - -
- -\ -[](#ran_gamma)\ - -
- -*var = ran\_gamma(r, rshape, rscale)* - real(r8) :: ran_gamma - type(random_seq_type), intent(inout) :: r - real(r8), intent(in) :: rshape - real(r8), intent(in) :: rscale - -
- -
- -Generates a random draw from a Gamma distribution. See notes in the -[random\_gamma()](#random_gamma) section about (alpha,beta) vs -(kappa,theta) vs (kappa,mu) parameterizations. This is transcribed from -C code in the GNU Scientific library and keeps the (shape,scale) -interface. - - -------------- ------------------------------------------------------------- - *ran\_gamma* A random draw from a Gamma distribution. - *r* A random sequence. - *rshape* Shape parameter. - *rscale* Scale parameter. (This is the inverse of a rate parameter.) - -------------- ------------------------------------------------------------- - -
- -\ -[](#Legalese) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -Terms of Use ------------- - -DART software - Copyright UCAR. This open source software is provided by -UCAR, "as is", without charge, subject to all terms of use at - diff --git a/guide/pages/release.md b/guide/pages/release.md deleted file mode 100644 index e5a7311c66..0000000000 --- a/guide/pages/release.md +++ /dev/null @@ -1,599 +0,0 @@ ---- -title: Documentation -layout: default ---- - -# DART Manhattan Release Notes - -## Dart Overview - -The Data Assimilation Research Testbed (DART) is designed to facilitate -the combination of assimilation algorithms, models, and real (or -synthetic) observations to allow increased understanding of all three. -The DART programs are highly portable, having been compiled with many -Fortran 90 compilers and run on linux compute-servers, linux clusters, -OSX laptops/desktops, SGI Altix clusters, supercomputers running AIX, -and more. Read the -[Customizations](Getting_Started.html#customizations) -section for help in building on new platforms. - -DART employs a modular programming approach to apply an Ensemble Kalman -Filter which adjusts model values toward a state that is more consistent -with information from a set of observations. Models may be swapped in -and out, as can different algorithms in the Ensemble Kalman Filter. The -method requires running multiple instances of a model to generate an -ensemble of states. A forward operator appropriate for the type of -observation being assimilated is applied to each of the states to -generate the model's estimate of the observation. Comparing these -estimates and their uncertainty to the observation and its uncertainty -ultimately results in the adjustments to the model states. See the -[DART_LAB](dart_lab.html) demos or read more -[in the DART tutorial](Tutorial.html). - -DART diagnostic output can be written that contains the model state -before and after the adjustment, along with the ensemble mean and -standard deviation, and prior or posterior inflation values if inflation -is enabled. There is also a text file, `obs_seq.final`, with the model -estimates of the observations. There is a suite of MATLAB® functions -that facilitate exploration of the results, but the netCDF files are -inherently portable and contain all the necessary metadata to interpret -the contents with other analysis programs such as NCL, R, etc. - -To get started running with Lorenz 63 model refer to the -[Lorenz 63 section](Getting_Started.html#Lorenz63) of the *Getting Started* document. - - - -\[[top](#)\] - ------ - -## About the Manhattan layout change ... - -If you have been updating from the rma\_trunk branch of the DART -subversion repository you will notice that the code tree has been -simplified to be more intuitive for users. The new top level directory -structure looks like : - - - *README* - - *COPYRIGHT* - - *assimilation\_code* - - *build\_templates* - - *diagnostics* - - *docs* - - *models* - - *observations* - -We suggest that current users checkout a fresh version of Manhattan in a -new location. To see which files need to be moved, run 'svn status' on -your original checked out version. Anything with an M or ? in the first -column needs to be moved to the new location in the new tree. Please -[contact DART](mailto:dart@ucar.edu) if you have any issues migrating -your existing code to the new tree structure. - -There is a list of non-backwards compatible changes -([see below](#Nonbackward)), and a list of new options and functions. - -The Manhattan release will continue to be updated for the next few -months as we continue to add features. Checking out the Manhattan -release branch and running 'svn update' from time to time is the -recommended way to update your DART tree. - - - -\[[top](#)\] - ------ - -## Non-backwards Compatible Changes - -Unlike previous releases of DART, this version contains more -non-backwards compatible changes than usual. Please examine the -following list carefully. We do suggest you check out the Manhattan -release into a new location and migrate any local changes from previous -versions as a second step. - -Changes in the Manhattan release (15 May 2015) which are *not* backwards -compatible with the Lanai release (13 Dec 2013): - -1. We no longer require model data to be converted to DART format - restart files. We directly read and write netCDF format only. To - specify the input and output files for filter, there are new - namelist items in the &filter_nml namelist: - `input_state_file_list` and `output_state_file_list` . - -2. The information formerly in `Prior_Diag.nc` and - `Posterior_Diag.nc` has been moved. If you are reading and writing - ensemble members from different files, the state information, the - ensemble mean and standard deviation, and the inflation mean and - standard deviation will all be read and written to separate files: - - - *\[stage\]_member\_\#\#\#\#.nc* - - *\[stage\]_mean.nc* - - *\[stage\]_sd.nc* - - *\[stage\]_priorinf_{mean,sd}.nc* (if prior inflation is turned on) - - *\[stage\]_postinf_{mean,sd}.nc* (if posterior inflation is turned on) - - If you are reading and writing ensemble members from a single file, - all this information will now be in a single netCDF file but will be - stored in different variables inside that file: - - - *\[var\].nc* - - *\[var\]_mean.nc* - - *\[var\]_sd.nc* - - *\[var\]_priorinf_{mean,sd}.nc* (if prior inflation is turned on) - - *\[var\]_postinf_{mean,sd}.nc* (if posterior inflation is turned on) - - We also now have options for writing files at six stages of the - assimilation cycle: `'input', 'forecast', 'preassim', 'postassim', 'analysis', 'output'`. - This is set in the \&filter_nml namelist with ```stages_to_write```. - -3. New `model_mod.f90` required routines: - - - *vert_convert()* - - *query_vert_localization_coord()* - - *pert_model_copies()* - - *read_model_time()* - - *write_model_time()* - - There are default version of these available to use if you have no - special requirements. - -4. Several of the `model_mod.f90` argument lists have changed - - - *model_interpolate()* now takes in the 'state_handle' as an - argument rather than a state vector array. It also return an - array of 'expected_obs' and 'istatus' for each of the ensemble - members - - *get_state_meta_data()* also requires the 'state_handle' as - an argument rather than a state vector array. - - *nc_write_model_atts()* has an additional argument - `model_mod_writes_state_variables`. If `TRUE` then the model_mod - is expected to write out the state variables, if `FALSE` DART will - write out the state variable (this is the prefered method for - adding new models, it requires less code from the model developer) - -5. There are several namelist changes mainly in the &filter_nml and - &perfect_model_mod which are outlined in detail in - [Manhattan_diffs_from_Lanai](Manhattan_diffs_from_Lanai.html) - -6. All modules have been moved to *DART/assimilation_code/modules/* - directory. And similarly all of the programs have moved to - *DART/assimilation_code/programs/* - -7. The location modules which were stored in *locations* have moved to - *DART/assimilation_code/location* directory - -8. The observation converters which were stored in *observations* have - moved to *DART/observations/obs_converters* directory - -9. The forward operators have moved from - *obs_def/obs_def_\*_mod.f90* to - *observations/forward_operators* - -10. The tutorial files have moved to *DART/docs/tutorial* directory - -11. The *fill_inflation_restart* program now creates - inflation files in netCDF format. You can still create inflation files - using *ncap2*. Here is an example using version 4.4.2 or - later of the NCO tools: - - ``` - ncap2 -s "T=1.0;U=1.0;V=1.0" wrfinput_d01 prior_inf.nc' - ncap2 -s "T=0.6;U=0.6;V=0.6" wrfinput_d01 prior_sd.nc' - ``` - -12. The default flags in the mkmf_template.XXX files have been updated - to be more consistent with current compiler versions. - -13. If you enable the sampling error correction option, the required - data is now read from a single netCDF file which supports multiple - ensemble sizes. A program is provided to compute additional ensemble - sizes if they are not in the default file. - -14. Our use of TYPES and KINDS has been very confusing in the past. In - Manhattan we have tried to make it clearer which things in DART are - generic quantities (QTY) - temperature, pressure, etc - and which - things are specific types of observations - Radiosonde_temperature, - Argo_salinity etc. - - Below is a mapping between old and new routine names here for - reference. We have made these changes to all files distributed with - DART. If you have lots of code developed outside of the subversion - repository, please contact [DART](mailto:dart@ucar.edu) for a sed - script to help automate the changes. - - Public routines, existing name on left, replacement on right: - - ~~~ - assimilate_this_obs_kind() => assimilate_this_type_of_obs(type_index) - evaluate_this_obs_kind() => evaluate_this_type_of_obs(type_index) - use_ext_prior_this_obs_kind() => use_ext_prior_this_type_of_obs(type_index) - - get_num_obs_kinds() => get_num_types_of_obs() - get_num_raw_obs_kinds() => get_num_quantities() - - get_obs_kind_index() => get_index_for_type_of_obs(type_name) - get_obs_kind_name() => get_name_for_type_of_obs(type_index) - - get_raw_obs_kind_index() => get_index_for_quantity(qty_name) - get_raw_obs_kind_name() => get_name_for_quantity(qty_index) - - get_obs_kind_var_type() => get_quantity_for_type_of_obs(type_index) - - get_obs_kind() => get_obs_def_type_of_obs(obs_def) - set_obs_def_kind() => set_obs_def_type_of_obs(obs_def) - - get_kind_from_menu() => get_type_of_obs_from_menu() - - read_obs_kind() => read_type_of_obs_table(file_unit, file_format) - write_obs_kind() => write_type_of_obs_table(file_unit, file_format) - - maps obs_seq nums to specific type nums, only used in read_obs_seq: - map_def_index() => map_type_of_obs_table() - - removed this. apparently unused, and simply calls get_obs_kind_name(): - get_obs_name() - - apparently unused anywhere, removed: - add_wind_names() - do_obs_form_pair() - ~~~ - - Public integer parameter constants and routine formal argument - names, old on left, new on right: - - ~~~ - KIND_ => QTY_ - kind => quantity - - TYPE_ => TYPE_ - type => type_of_obs - - integer parameters: - max_obs_generic => max_defined_quantities (not currently public, stays private) - max_obs_kinds => max_defined_types_of_obs - ~~~ - -15. For smaller models we support single file input and output. These - files contain all of the member information, mean, standard - deviation and inflation values for all of the state variables. This - can be run with cycling and all time steps will be appended to the - file. - - For *perfect_model_obs* we provide a `perfect_input.cdl` file - which contains a single ensemble member which will be considered the - 'truth' and observations will be generated based on those values. - The output will contain all of the cycling timesteps all of the - state variables. - - For *filter* we provide a `filter_input.cdl` file which contains - all of the state member variables and potentially inflation mean and - standard deviation values. The output will contain all of the - cycling timesteps all of the state variables. Additionally you have - the option to write out different stages during the assimilation in - the \&filter_nml 'stages_to_write' mentioned above. - - To generate a netCDF file from a .cdl file run: - - ``` - ncgen -o perfect_input.nc perfect_input.cdl - ncgen -o filter_input.nc filter_input.cdl - ``` - - - -\[[top](#)\] - ------ - -## New Features - - - DART now reads and writes netCDF files for the model state - information. If your model uses netCDF file format, you no longer - need *model_to_dart* or *dart_to_model* to translate to a DART - format file. If your model does not use netCDF, you can adapt your - *model_to_dart* and *dart_to_model* executables to read and write a - netCDF file for DART to use. - - The read/write code is part of the core DART routines so no code is - needed in the model_mod model-specific module. There is a new - routine [add_domain()](state_structure.html) that a - model_mod::static_init_model() can user to define which netCDF - variables should be part of the model state, and what DART quantity - (formerly kind) they correspond to. - - DART no longer limits the size of a model state to the size of a - single MPI task's memory. The state is read in variable by variable - and distributed across all MPI tasks, so the memory use is much - smaller than previous versions of DART. One-sided MPI communication - is used during the computation of forward operator values to get - required parts of the state from other tasks. - - Many of the DART namelists have been simplified, and some items have - moved to a more specific namelist. - - Observation sequence files can include externally computed forward - operator values which can be used in the assimilation instead of - calling a forward operator inside DART. - - The DART directory structure has been reorganized to make it easier - to identify the various software tools, modules, documentation and - tutorials supplied with the system. - - The MATLAB® diagnostic routines have been updated to not require the - MEXNC toolbox. These routines use the built-in netCDF support that - comes with MATLAB®. - - There is a new Particle Filter type. Please contact us if you are - interested in using it. - - DART can now take subsets of observation types and restrict them - from impacting certain quantities in the state during the - assimilation. A tool to simplify constructing the table of - interactions is provided (obs_impact_tool). - - State Structure - - Contains information about dimensions and size of variables in - your state. There is a number of accessor functions to get - variable information such as `get_variable_size()`. See the - [state_structure.html](state_structure.html) for more details. - - The POP model_mod now can interpolate Sea Surface Anomaly - observations. - - - -\[[top](#)\] - ------ - -## Supported Models - -Currently we support the models listed below. There are several new -models that have been added that are not on the Lanai Release including -CM1, CICE, and ROMS. Any previously supported models not on this list -are still supported in DART -[classic](http://www.image.ucar.edu/DAReS/DART/classic/index.html) - - - **9var** - - DART interface documentation for the - [9var](models/9var/model_mod.html) model. - - **bgrid_solo** - - DART interface documentation for the - [bgrid solo](models/bgrid_solo/model_mod.html) model. - - **CAM-FV, CAM-CHEM, WACCM, WACCM-X** - - DART interface documentation for the - [CAM finite volume](models/cam-fv/model_mod.html) family of global atmospheric models. - - Documentation for the - [CAM model](http://www.cesm.ucar.edu/models/atm-cam/). - - **CICE (NEW)** - - DART interface documentation for the - [CICE](models/cice/model_mod.html) model. - - Documentation for the - [CICE model](http://www.cesm.ucar.edu/models/ccsm4.0/cice/). - - **CM1 (NEW)** - - DART interface documentation for the - [CM1 cloud-resolving model](models/cm1/model_mod.html). - - Documentation for the - [CM1 model](http://www2.mmm.ucar.edu/people/bryan/cm1/). - - **forced_lorenz_96** - - DART interface documentation for the - [forced lorenz_96](models/forced_lorenz_96/model_mod.html) model. - - **lorenz_63** - - DART interface documentation for the - [lorenz_96](models/lorenz_63/model_mod.html) model. - - **lorenz_84** - - DART interface documentation for the - [lorenz_84](models/lorenz_84/model_mod.html) model. - - **lorenz_96** - - DART interface documentation for the - [lorenz_96](models/lorenz_96/model_mod.html) model. - - **lorenz_04** - - DART interface documentation for the - [lorenz_04](models/lorenz_04/model_mod.html) model. - - **MPAS_ATM** (netCDF overwrite not supported for update_u_from_reconstruct = .true. ) - - DART interface documentation for the - [MPAS atmosphere](models/mpas_atm/model_mod.html) model. - - Documentation for the - [MPAS model](https://mpas-dev.github.io/atmosphere/atmosphere.html). - - **POP** - - DART interface documentation for the - [POP](models/POP/model_mod.html) global ocean model. - - Documentation for the - [POP model](http://www.cesm.ucar.edu/models/ccsm2.0/pop/). - - **ROMS (NEW)** - - DART interface documentation for the - [ROMS](models/ROMS/model_mod.html) regional ocean model. - - Documentation for the [ROMS model](https://www.myroms.org/). - - **simple_advection** - - DART interface documentation for the - [simple advection](models/simple_advection/model_mod.html) model. - - **WRF** - - DART interface documentation for the - [WRF](models/wrf/model_mod.html) regional forecast model. - - Documentation for the [WRF model](http://www.wrf-model.org/index.php). - - **NOAH-MP** - - Documentation for [(NOAH-MP LSM)](https://ral.ucar.edu/solutions/products/noah-multiparameterization-land-surface-model-noah-mp-lsm) - - **FESOM** - - DART interface documentation for the - [FESOM model interface](models/FESOM/Readme.md) - - **GITM** - - Documentation for the The Global Ionosphere Thermosphere Model - [(GITM)](https://ccmc.gsfc.nasa.gov/models/modelinfo.php?model=GITM) - - -The *DART/models/template* directory contains sample files for adding a -new model. See the -[Adding a Model](Models.html#adding_a_model) -section of the DART web pages for more help on adding a new model. - - - -\[[top](#)\] - ------ - -## Changed Models - - - WRF - - Allow advanced microphysics schemes (needed interpolation for 7 - new kinds) - - Interpolation in the vertical is now done in log(p) instead of - linear pressure space. log(p) is the default, but a compile-time - variable can restore the linear interpolation. - - Added support in the namelist to avoid writing updated fields - back into the wrf netCDF files. The fields are still updated - during the assimilation but the updated data is not written back - to the wrfinput file during the dart_to_wrf step. - - Fixed an obscure bug in the vertical convert routine of the wrf - model_mod that would occasionally fail to convert an obs. This - would make tiny differences in the output as the number of mpi - tasks change. No quantitative differences in the results but - they were not bitwise compatible before and they are again now. - - CAM - - DART/CAM now runs under the CESM framework, so all options - available with the framework can be used. - - Support for the SE core (HOMME) has been developed but is NOT - part of this release. Please contact the [DART Development - Group](mailto:dart@ucar.edu) if you have an interest in this - configuration of CAM. - - Simple Advection Model - - Fixed a bug where the random number generator was being used - before being called with an initial seed. - - - -\[[top](#)\] - ------ - -## New Observation Types/Forward Operators - - - Many new observation types related to land and atmospheric chemistry - have been added. See the - [obs_kind_mod.f90](../../assimilation_code/modules/observations/DEFAULT_obs_kind_mod.F90) - for a list of the generic quantities now available. - - New forward operator for Sea Ice (cice) ice thickness observations. - See the - [obs_def_cice_mod.f90](../../observations/forward_operators/obs_def_cice_mod.f90) - file for details. - - New forward operator for Carbon Monoxide (CO) Nadir observations. - See the - [obs_def_CO_Nadir_mod.f90](../../observations/forward_operators/obs_def_CO_Nadir_mod.f90) - file for details. - - New forward operator for Total Cloud Water in a column observations. - See the - [obs_def_cwp_mod.f90](../../observations/forward_operators/obs_def_cwp_mod.f90) - file for details. - - - -\[[top](#)\] - ------ - -## New Observation Types/Sources - - - AVISO - Added an observation converter for Sea Surface Height Anomaly - observations. Documentation in - [convert_aviso.f90](../../observations/obs_converters/AVISO/convert_aviso.f90) - (source). - - cice - Added an obs_sequence converter for Sea Ice observations. - Documentation in - [cice_to_obs.html](../../observations/obs_converters/cice/cice_to_obs.html). - - GPSPW - Added an obs_sequence converter for GPS precipitable water - observations. Documentation in - [convert_gpspw.f90](../../observations/obs_converters/GPSPW/convert_gpspw.f90) - (source). - - MODIS - Added an obs_sequence converter for MODIS FPAR (Fraction of - Photosynthetically Active Radiation) and LAI (Leaf Area Index) - obseverations. Documentation in - [MOD15A2_to_obs.html](../../observations/obs_converters/MODIS/MOD15A2_to_obs.html). - - ok_mesonet - Added an obs_sequence converter for the Oklahoma Mesonet - observations. Documentation in - [ok_mesonet.html](../../observations/obs_converters/ok_mesonet/ok_mesonet.html). - - ROMS - Added an obs_sequence converter for ROMS ocean data. This converter - includes externally computed forward operators output from the ROMS - model using FGAT (First Guess At Time) during the model run. - Documentation in - [convert_roms_obs.f90](../../observations/obs_converters/ROMS/convert_roms_obs.f90) - (source). - - SSUSI - Added an obs_sequence converter for wind profiler observations. - Documentation in - [convert_f16_edr_dsk.html](../../observations/obs_converters/SSUSI/convert_f16_edr_dsk.html). - - tropical_cyclone - Added an obs_sequence converter for ASCII format tropical cyclone - track observations. Documentation in - [tc_to_obs.html](../../observations/obs_converters/tropical_cyclone/tc_to_obs.html). - - - -\[[top](#)\] - ------ - -## New Diagnostics and Documentation - -**Better Web Pages.** We've put a lot of effort into expanding our -documentation. For example, please check out -[the MATLAB diagnostics section](Diagnostics.html#mat_obs) -or the pages outlining the -[observation sequence file contents](Observations.html#obs_seq_overview). - - - The MATLAB® diagnostic routines have been updated to remove the - dependency on third-party toolboxes. These routines use the built-in - netCDF support that comes with basic MATLAB® (no other toolboxes needed). - - - The MATLAB® routines have been updated to work consistently across - many MATLAB® versions (which is no small trick considering how many non-backward-compatible - changes they have made!). - -But there's always more to add. -**Please use the GitHub Wiki or Issues facilities to let us know where we are lacking or what doesn't work.** - - - -\[[top](#)\] - ------ - -## New Utilities - -This section describes updates and changes to the tutorial materials, -scripting, setup, and build information since the Lanai release. - - - *obs_impact_tool* please refer to - [Website](https://www.image.ucar.edu/DAReS/DART/Manhattan/assimilation_code/programs/obs_impact_tool/obs_impact_tool.html) - or - [local file](../../assimilation_code/programs/obs_impact_tool/obs_impact_tool.html) - - *gen_sampling_error_table* now computes sampling error correction - tables for any ensemble size. - - *compute_error* - [Website](https://www.image.ucar.edu/DAReS/DART/Manhattan/assimilation_code/programs/compute_error/compute_error.html) - or - [local file](../../assimilation_code/programs/compute_error/compute_error.html) - - - -\[[top](#)\] - ------ - -## Known Problems - - - The move to provide DART through GitHub offers us the opportunity - to revise the way we distribute documentation. We are still exploring - options for the best way to provide content that is accessible both - offline and through GitHub Pages. Please bear with us, but please do - point out broken links and offer suggestions on ways we can improve. - - - There are many changes in this release and more updates are expected - to come soon. We are not aware of any obvious bugs, but if you - encounter any unexpected behavior please contact us. Please watch - the dart-users email list for announcements of updates to the - release code, and be prepared to issue a GitHub Pull Request from - the DART repository to your fork, or a git 'fetch' from the DART repository - to get updated files. - -\[[top](#)\] - ------ diff --git a/guide/pages/support.md b/guide/pages/support.md deleted file mode 100644 index 446eb84ea8..0000000000 --- a/guide/pages/support.md +++ /dev/null @@ -1,27 +0,0 @@ ---- -layout: default -title: Support ---- - -## Support - -Please send email to [dart@ucar.edu](mailto:dart@ucar.edu) rather than individuals. -It is the best way to ensure a timely response and sometimes generates productive -discussion among our group that leads to a better response. - -GitHub also provides a [wiki](https://github.com/NCAR/DART/wiki) that -we will be using to add examples and appropriate content. - -Lastly, GitHub also provides a [bug-reporting](https://github.com/NCAR/DART/issues) -facility. - -### links - -- [GitHub workflow](https://guides.github.com/introduction/flow/) - -### Svn to Git - -We will populate this with advice on how to migrate the changes from your local svn version of DART to -the equivalent git version. From there, you can choose to merge to a more current branch or tag. -Stay Tuned\! - diff --git a/guide/pages/time_manager_mod.md b/guide/pages/time_manager_mod.md deleted file mode 100644 index b4e501caff..0000000000 --- a/guide/pages/time_manager_mod.md +++ /dev/null @@ -1,1011 +0,0 @@ -[]{#TOP} - -MODULE time\_manager\_mod -========================= - - ------------------------------------------------------------------------ ------------------------------------------------------------------- - ![DART project logo](../../../docs/images/Dartboard7.png){height="70"} Jump to [DART Documentation Main Index](../../../docs/index.html) - ------------------------------------------------------------------------ ------------------------------------------------------------------- - -[INTERFACES](#Interface) / [NAMELIST](#Namelist) / [FILES](#FilesUsed) / -[REFERENCES](#References) / [ERRORS](#Errors) / [PLANS](#FuturePlans) / -[PRIVATE COMPONENTS](#PrivateComponents) / [TERMS OF USE](#Legalese) - -Overview --------- - -Provides a set of routines to manipulate both time and calendars of -various types.\ -\ -Time intervals are stored and defined in terms of integer number of days -and integer seconds. The minimum time resolution is 1 second. -Mathematical operations (e.g. addition, subtraction, multiplication) are -defined on these intervals. Seconds which roll over 86400 (the number of -seconds in a day) are converted into days.\ -\ -Calendars interpret time intervals in terms of years, months, days. -Various calendars commonly in use in the scientific community are -supported. - -[]{#OtherModulesUsed} - -
- -\[[](#top)\] - -
- ------------------------------------------------------------------------- - -OTHER MODULES USED ------------------- - - types_mod - utilities_mod - -[](#Interface) - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -PUBLIC INTERFACES ------------------ - - | module name | interface | - |------------------------------ | ---------------------------------------- - | use time_manager_mod, only : | [time\_type](#time_type) - | | [operator(+)](#op_type) - | | [operator(-)](#op_type) - | | [operator(\*)](#op_type) - | | [operator(/)](#op_type) - | | [operator(>)](#op_type) - | |  [operator(>=)](#op_type) - | |  [operator(==)](#op_type) - | |  [operator(/=)](#op_type) - | |  [operator(<)](#op_type) - | |  [operator(<=)](#op_type) - | |  [operator(//)](#op_type) - | |  [set\_time](#set_time) - | |  [set\_time\_missing](#set_time_missing) - | |  [increment\_time](#increment_time) - | |  [decrement\_time](#decrement_time) - | |  [get\_time](#get_time) - | |  [interval\_alarm](#interval_alarm) - | |  [repeat\_alarm](#repeat_alarm) - | |  [THIRTY\_DAY\_MONTHS](#cal_type) - | |  [JULIAN](#cal_type) - | |  [GREGORIAN](#cal_type) - | |  [NOLEAP](#cal_type) - | |  [NO\_CALENDAR](#cal_type) - | |  [GREGORIAN\_MARS](#cal_type) - | |  [set\_calendar\_type](#set_calendar_type) - | |  [get\_calendar\_type](#get_calendar_type) - | |  [get\_calendar\_string](#get_calendar_string) - | |  [set\_date](#set_date) - | |  [get\_date](#get_date) - | |  [increment\_date](#increment_date) - | |  [decrement\_date](#decrement_date) - | |  [days\_in\_month](#days_in_month) - | |  [leap\_year](#leap_year) - | |  [length\_of\_year](#length_of_year) - | |  [days\_in\_year](#days_in_year) - | |  [month\_name](#month_name) - | |  [julian\_day](#julian_day) - | |  [time\_manager\_init](#time_manager_init) - | |  [print\_time](#print_time) - | |  [print\_date](#print_date) - | | [write\_time](#write_time) - | |  [read\_time](#read_time) - | |  [interactive\_time](#interactive_time) - | | [generate\_seed](#generate_seed) - - - - --------------------------------------------------------------------------------- - -set_time --------- - -[](#set_time) - -
- - var = set_time(seconds [, days]) - - type(time_type) :: set_time - integer, intent(in) :: seconds - integer, optional, intent(in) :: days - -
- -
- -Fills a time type. If seconds are > 86400, they are converted into -the appropriate number of days. Note that seconds are specified first. - - | argument | description | - |-----------|-------------------------------------------------------------------------------------------------- - |*seconds* | Number of seconds. If larger than 86400, they are converted into the appropriate number of days. - |*days* | Number of days. Default is 0. - -
- - -set_time_missing ----------------- - -[](#set_time_missing) - -
- - var = set_time_missing() - - type(time_type) :: set_time_missing - -
- -
- -Set a time type to a missing value. The resulting time value will cause -an error if used for an arithmetic operation or if get\_time() is -called. - -
- - -\ -[]{#increment_time}\ - -
- -*var = increment\_time(time, seconds *\[, days\]*)* - type(time_type) :: increment_time - type(time_type), intent(in) :: time - integer, intent(in) :: seconds - integer, optional, intent(in) :: days - -
- -
- -Adds the specified number of seconds and optionally, days, to the given -time and returns the new time. Increments cannot be negative (see -decrement\_time below). - - ----------- ------------------------------------------------------- - *time* time value to be incremented. - *seconds* number of seconds to add to given time. - *days* optionally a number of days to add to the given time. - ----------- ------------------------------------------------------- - -
- -\ -[]{#decrement_time}\ - -
- -*var = decrement\_time(time, seconds *\[, days\]*)* - type(time_type) :: decrement_time - type(time_type), intent(in) :: time - integer, intent(in) :: seconds - integer, intent(in), optional :: days - -
- -
- -Subtract the specified number of seconds and optionally, days, to the -given time and returns the new time. Decrements cannot be negative (see -increment\_time above). - - ----------- -------------------------------------------------------------- - *time* time value to be decremented. - *seconds* number of seconds to subtract from the given time. - *days* optionally a number of days to subtract from the given time. - ----------- -------------------------------------------------------------- - -
- -\ -[]{#interval_alarm}\ - -
- -*var = interval\_alarm(time, time\_interval, alarm, alarm\_interval)* - logical :: interval_alarm - type(time_type), intent(in) :: time - type(time_type), intent(in) :: time_interval - type(time_type), intent(inout) :: alarm - type(time_type), intent(in) :: alarm_interval - -
- -
- -Supports a commonly used type of test on times for models. Given the -current time, and a time for an alarm, determines if this is the closest -time to the alarm time given a time step of time\_interval. If this is -the closest time (alarm - time <= time\_interval/2), the function -returns true and the alarm is incremented by the alarm\_interval. Watch -for problems if the new alarm time is less than time + time\_interval. - - ------------------- ---------------------------------------------------------------- - *time* Current time. - *time\_interval* Bin size for determining if alarm time is close enough to now. - *alarm* When alarm next goes off next. Updated by this routine. - *alarm\_interval* How often alarm goes off. - ------------------- ---------------------------------------------------------------- - -
- -\ -[]{#repeat_alarm}\ - -
- -*var = repeat\_alarm(time, alarm\_frequency, alarm\_length)* - type(time_type) :: repeat_alarm - type(time_type), intent(in) :: time - type(time_type), intent(in) :: alarm_frequency - type(time_type), intent(in) :: alarm_length - -
- -
- -Repeat\_alarm supports an alarm that goes off with alarm\_frequency and -lasts for alarm\_length. If the nearest occurence of an alarm time is -less than half an alarm\_length from the input time, repeat\_alarm is -true. For instance, if the alarm\_frequency is 1 day, and the -alarm\_length is 2 hours, then repeat\_alarm is true from time 2300 on -day n to time 0100 on day n + 1 for all n. - - -------------------- ------------------------------- - *time* Current time. - *alarm\_frequency* How often the alarm goes off. - *alarm\_length* How long the alarm is true. - -------------------- ------------------------------- - -
- -\ -[]{#get_calendar_type}\ - -
- -*var = get\_calendar\_type()* - integer :: get_calendar_type - -
- -
- -Returns default calendar type for mapping from time to date. Calendar -types are public integer parameters that define various calendars. See -elsewhere in this file for the list. - -
- -\ -[]{#set_date}\ - -
- -*var = set\_date(year, month, day *\[, hours, minutes, seconds\]*)* - type(time_type) :: set_date - integer, intent(in) :: year - integer, intent(in) :: month - integer, intent(in) :: day - integer, intent(in), optional :: hours - integer, intent(in), optional :: minutes - integer, intent(in), optional :: seconds - -
- -
- -Given a date interpreted using the current calendar type, compute the -corresponding time. - - ----------- -------------------------------- - *year* Integer year. - *month* Integer month number. - *day* Integer day number. - *hours* Integer hour. Default is 0. - *minutes* Integer minutes. Default is 0. - *seconds* Integer seconds. Default is 0. - ----------- -------------------------------- - -
- -\ -[]{#increment_date}\ - -
- -*var = increment\_date(time *\[, years, months, days, hours, minutes, -seconds\]*)* - type(time_type) :: increment_date - type(time_type), intent(in) :: time - integer, intent(in), optional :: years - integer, intent(in), optional :: months - integer, intent(in), optional :: days - integer, intent(in), optional :: hours - integer, intent(in), optional :: minutes - integer, intent(in), optional :: seconds - -
- -
- -Given a time and some date increment, compute a new time. The -interpretation of the date depends on the currently selected calendar -type. - - ----------- --------------------------------------- - *time* Current time. - *year* Integer years to add. Default is 0. - *month* Integer months to add. Default is 0. - *day* Integer days to add. Default is 0. - *hours* Integer hours to add. Default is 0. - *minutes* Integer minutes to add. Default is 0. - *seconds* Integer seconds to add. Default is 0. - ----------- --------------------------------------- - -
- -\ -[]{#decrement_date}\ - -
- -*var = decrement\_date(time *\[, years, months, days, hours, minutes, -seconds\]*)* - type(time_type) :: decrement_date - type(time_type), intent(in) :: time - integer, intent(in), optional :: years - integer, intent(in), optional :: months - integer, intent(in), optional :: days - integer, intent(in), optional :: hours - integer, intent(in), optional :: minutes - integer, intent(in), optional :: seconds - -
- -
- -Given a time and some date decrement, compute a new time. The -interpretation of the date depends on the currently selected calendar -type. - - ----------- -------------------------------------------- - *time* Current time. - *year* Integer years to subtract. Default is 0. - *month* Integer months to subtract. Default is 0. - *day* Integer days to subtract. Default is 0. - *hours* Integer hours to subtract. Default is 0. - *minutes* Integer minutes to subtract. Default is 0. - *seconds* Integer seconds to subtract. Default is 0. - ----------- -------------------------------------------- - -
- -\ -[]{#days_in_month}\ - -
- -*var = days\_in\_month(time)* - integer :: days_in_month - type(time_type), intent(in) :: time - -
- -
- -Given a time, determine the month based on the currently selected -calendar type and return the numbers of days in that month. - - -------- --------------- - *time* Current time. - -------- --------------- - -
- -\ -[]{#leap_year}\ - -
- -*var = leap\_year(time)* - logical :: leap_year - type(time_type),intent(in) :: time - -
- -
- -Given a time, determine if the current year is a leap year in the -currently selected calendar type. - - -------- --------------- - *time* Current time. - -------- --------------- - -
- -\ -[]{#length_of_year}\ - -
- -*var = length\_of\_year()* - integer :: length_of_year - -
- -
- -For the currently selected calendar type, return the number of days in a -year if that value is fixed (e.g. there are not leap years). For other -calendar types, see [days\_in\_year()](#days_in_year) which takes a time -argument to determine the current year. - -
- -\ -[]{#days_in_year}\ - -
- -*var = days\_in\_year(time)* - integer :: days_in_year - type(time_type), intent(in) :: time - -
- -
- -Given a time, determine the year based on the currently selected -calendar type and return the numbers of days in that year. - - -------- --------------- - *time* Current time. - -------- --------------- - -
- -\ -[]{#month_name}\ - -
- -*var = month\_name(n)* - character(len=9) :: month_name - integer, intent(in) :: n - -
- -
- -Return a character string containing the month name corresponding to the -given month number. - - ----- ---------------------------------------------------- - *n* Month number. Must be between 1 and 12, inclusive. - ----- ---------------------------------------------------- - -
- -\ -[]{#julian_day}\ - -
- -*var = julian\_day(year, month, day)* - integer :: julian_day - integer, intent(in) :: year - integer, intent(in) :: month - integer, intent(in) :: day - -
- -
- -Given a date in year/month/day format, compute the day number from the -beginning of the year. The currently selected calendar type must be -GREGORIAN. - - --------- ----------------------------------------- - *year* Year number in the Gregorian calendar. - *month* Month number in the Gregorian calendar. - *day* Day of month in the Gregorian calendar. - --------- ----------------------------------------- - -
- -\ -[]{#read_time}\ - -
- -*var = read\_time(file\_unit *\[, form, ios\_out\]*)* - type(time_type) :: read_time - integer, intent(in) :: file_unit - character(len=*), intent(in), optional :: form - integer, intent(out), optional :: ios_out - -
- -
- -Read a time from the given file unit number. The unit must already be -open. The default format is ascii/formatted. If an error is encountered -and ios\_out is specified, the error status will be returned to the -caller; otherwise the error is fatal. - - -------------- ----------------------------------------------------------------------------------------------------------------------------------------- - *file\_unit* Integer file unit number of an already open file. - *form* Format to read the time. Options are 'formatted' or 'unformatted'. Default is 'formatted'. - *ios\_out* On error, if specified, the error status code is returned here. If not specified, an error calls the standard error\_handler and exits. - -------------- ----------------------------------------------------------------------------------------------------------------------------------------- - -
- -\ -[]{#get_time}\ - -
- -*call get\_time(time, seconds *\[, days\]*)* - type(time_type), intent(in) :: time - integer, intent(out) :: seconds - integer, intent(out), optional :: days - -
- -
- -Returns days and seconds ( < 86400 ) corresponding to a time. If the -optional 'days' argument is not given, the days are converted to seconds -and the total time is returned as seconds. Note that seconds preceeds -days in the argument list. - - ----------- --------------------------------------------------------------------------------------------------------- - *time* Time to convert into seconds and days. - *seconds* If days is specified, number of seconds in the current day. Otherwise, total number of seconds in time. - *days* If specified, number of days in time. - ----------- --------------------------------------------------------------------------------------------------------- - -
- -\ -[]{#set_calendar_type}\ - -
- -*call set\_calendar\_type(mytype)* or -*call set\_calendar\_type(calstring)* - integer, intent(in) :: mytype - or - character(len=*), intent(in) :: calstring - -
- -
- -Selects the current calendar type, for converting between time and -year/month/day. The argument can either be one of the predefined -calendar integer parameter types (see elsewhere in this file for the -list of types), or a string which matches the name of the integer -parameters. The string interface is especially suitable for namelist -use. - - ---------- ------------------------------------------------ - *mytype* Integer parameter to select the calendar type. - ---------- ------------------------------------------------ - -or - ------------- -------------------------------------------------------------------------------------------------------- - *calstring* Character string to select the calendar type. Valid strings match the names of the integer parameters. - ------------- -------------------------------------------------------------------------------------------------------- - -
- -\ -[]{#get_calendar_string}\ - -
- -*call get\_calendar\_string(mystring)* - character(len=*), intent(out) :: mystring - -
- -
- -Return the character string corresponding to the currently selected -calendar type. - - ------------ -------------------------------------------------------------- - *mystring* Character string corresponding to the current calendar type. - ------------ -------------------------------------------------------------- - -
- -\ -[]{#get_date}\ - -
- -*call get\_date(time, year, month, day, hour, minute, second)* - type(time_type), intent(in) :: time - integer, intent(out) :: year - integer, intent(out) :: month - integer, intent(out) :: day - integer, intent(out) :: hour - integer, intent(out) :: minute - integer, intent(out) :: second - -
- -
- -Given a time, compute the corresponding date given the currently -selected calendar type. - - ---------- ------------------------------- - *time* Input time. - *year* Corresponding calendar year. - *month* Corresponding calendar month. - *day* Corresponding calendar day. - *hour* Corresponding hour. - *minute* Corresponding minute. - *second* Corresponding second. - ---------- ------------------------------- - -
- -\ -[]{#time_manager_init}\ - -
- -*call time\_manager\_init()* - -
- -
- -Initializes any internal data needed by the time manager code. Does not -need to be called before using any of the time manager routines; it will -be called internally before executing any of the other routines. - -
- -\ -[]{#print_time}\ - -
- -*call print\_time(time *\[, str, iunit\]*)* - type(time_type), intent(in) :: time - character(len=*), intent(in), optional :: str - integer, intent(in), optional :: iunit - - -
- -
- -Print the time as days and seconds. If the optional str argument is -specified, print that string as a label. If iunit is specified, write -output to that unit; otherwise write to standard output/terminal. - - --------- ------------------------------------------------------------------------------- - *time* Time to be printed as days/seconds. - *str* String label to print before days/seconds. Default: 'TIME: '. - *iunit* Unit number to write output on. Default is standard output/terminal (unit 6). - --------- ------------------------------------------------------------------------------- - -
- -\ -[]{#print_date}\ - -
- -*call print\_date(time *\[, str, iunit\]*)* - type(time_type), intent(in) :: time - character(len=*), intent(in), optional :: str - integer, intent(in), optional :: iunit - - -
- -
- -Print the time as year/month/day/hour/minute/second, as computed from -the currently selected calendar type. If the optional str argument is -specified, print that string as a label. If iunit is specified, write -output to that unit; otherwise write to standard output/terminal. - - --------- ------------------------------------------------------------------------------- - *time* Time to be printed as a calendar date/time. - *str* String label to print before date. Default: 'DATE: '. - *iunit* Unit number to write output on. Default is standard output/terminal (unit 6). - --------- ------------------------------------------------------------------------------- - -
- -\ -[]{#write_time}\ - -
- -*call write\_time(file\_unit, time *\[, form, ios\_out\]*)* - integer, intent(in) :: file_unit - type(time_type), intent(in) :: time - character(len=*), intent(in), optional :: form - integer, intent(out), optional :: ios_out - -
- -
- -Write a time to an already open file unit. The optional 'form' argument -controls whether it is formatted or unformatted. On error, the optional -'ios\_out' argument returns the error code; otherwise a fatal error is -triggered. - - -------------- ------------------------------------------------------------------------------------------------------------------------------------------- - *file\_unit* Integer unit number for an already open file. - *time* Time to write to the file. - *form* String format specifier; either 'unformatted' or 'formatted'. Defaults to 'formatted'. - *ios\_out* If specified, on error the i/o status error code is returned here. Otherwise, the standard error handler is called and the program exits. - -------------- ------------------------------------------------------------------------------------------------------------------------------------------- - -
- -\ -[]{#interactive_time}\ - -
- -*call interactive\_time(time)* - type(time_type), intent(inout) :: time - -
- -
- -Prompt the user for a time as a calendar date, based on the currently -selected calendar type. Writes prompt to standard output and reads from -standard input. - - -------- --------------------------- - *time* Time type to be returned. - -------- --------------------------- - -
- -[](#interactive_time) -
- - val = generate_seed(timestamp) - - type(time_type), intent(in) :: timestamp - integer :: generate_seed - -Given a time type, turn it into as unique an integer as possible. -Expected to be used to seed a random number generator in a way -that you can reproduce the same sequence if seeded again from -the same time value. - -| argument | description | ------------|--------------| -| timestamp | a dart time type | -| return | a unique integer seed | - -
- -\ -[]{#time_type}\ - -
- - type time_type - private - integer :: seconds - integer :: days - end type time_type - -
- -
- -This type is used to define a time interval. - -
- -\ -[]{#cal_type}\ - -
- - integer :: NO_CALENDAR - integer :: GREGORIAN - integer :: GREGORIAN_MARS - integer :: JULIAN - integer :: THIRTY_DAY_MONTHS - integer :: NOLEAP - -
- -
- -The public integer parameters which define different calendar types. The -same names defined as strings can be used to set the calendar type. - -
- -\ -[]{#op_type}\ - -
- - operator(+) - operator(-) - operator(*) - operator(/) - operator(>) - operator(>=) - operator(==) - operator(/=) - operator(<) - operator(<=) - operator(//) - -
- -
- -Arithmetic operations are defined for time types, so expressions like - - t3 = t1 + t2 - -can be constructed. To use these operators, they must be listed on the -module use statement in the form specified above.\ -\ -Multiplication is one time and one scalar.\ -\ -Division with a single slash is integer, and returns the largest integer -for which time1 >= time2 \* n. Division with a double slash returns a -double precision quotient of the two times. - -
- -\ -[]{#Namelist} - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -NAMELIST --------- - -No namelist is currently defined for the time manager code. - -[]{#FilesUsed} - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -FILES ------ - -- none - -[]{#References} - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -REFERENCES ----------- - -1. none - -[]{#Errors} - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -ERROR CODES and CONDITIONS --------------------------- - -
- -Routine -Message -Comment - -
- -KNOWN BUGS ----------- - -none at this time - -[]{#FuturePlans} - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -FUTURE PLANS ------------- - -none at this time - -[]{#PrivateComponents} - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -PRIVATE COMPONENTS ------------------- - -N/A - -[]{#Legalese} - -
- -\[[top](#)\] - -
- ------------------------------------------------------------------------- - -Terms of Use ------------- - -DART software - Copyright UCAR. This open source software is provided by -UCAR, "as is", without charge, subject to all terms of use at - From 532afd76c2879ccf561203a2d59f07d7013bffb7 Mon Sep 17 00:00:00 2001 From: Helen Kershaw Date: Tue, 24 Aug 2021 12:25:00 -0600 Subject: [PATCH 02/16] remvoving html boilerplate code because the documentation is no longer written in html --- guide/boilerplate/Makefile | 25 ---- guide/boilerplate/boilerplate.rst | 198 -------------------------- guide/boilerplate/html_function.f90 | 144 ------------------- guide/boilerplate/html_functoc.f90 | 65 --------- guide/boilerplate/html_namelist.f90 | 105 -------------- guide/boilerplate/html_subroutine.f90 | 135 ------------------ guide/boilerplate/html_type.f90 | 96 ------------- guide/boilerplate/template.rst | 139 ------------------ 8 files changed, 907 deletions(-) delete mode 100644 guide/boilerplate/Makefile delete mode 100644 guide/boilerplate/boilerplate.rst delete mode 100644 guide/boilerplate/html_function.f90 delete mode 100644 guide/boilerplate/html_functoc.f90 delete mode 100644 guide/boilerplate/html_namelist.f90 delete mode 100644 guide/boilerplate/html_subroutine.f90 delete mode 100644 guide/boilerplate/html_type.f90 delete mode 100644 guide/boilerplate/template.rst diff --git a/guide/boilerplate/Makefile b/guide/boilerplate/Makefile deleted file mode 100644 index 4d635f3a8b..0000000000 --- a/guide/boilerplate/Makefile +++ /dev/null @@ -1,25 +0,0 @@ - -include ../../../mkmf/mkmf.template - -ALL = html_namelist html_function html_subroutine html_type html_functoc - -default: $(ALL) - -html_namelist : html_namelist.f90 - $(FC) $(FFLAGS) $< -o $@ - -html_function: html_function.f90 - $(FC) $(FFLAGS) $< -o $@ - -html_subroutine: html_subroutine.f90 - $(FC) $(FFLAGS) $< -o $@ - -html_type: html_type.f90 - $(FC) $(FFLAGS) $< -o $@ - -html_functoc: html_functoc.f90 - $(FC) $(FFLAGS) $< -o $@ - - -clean: - rm -f $(ALL) fort.22 diff --git a/guide/boilerplate/boilerplate.rst b/guide/boilerplate/boilerplate.rst deleted file mode 100644 index b6868f5718..0000000000 --- a/guide/boilerplate/boilerplate.rst +++ /dev/null @@ -1,198 +0,0 @@ -PROGRAM OR MODULE name_of_thing -=============================== - -Overview --------- - -This is the place for the general description of the module or program or whatever. - -overview issues, how/when/why to use this thing, etc. - -more stuff about code, usage, etc. - -Namelist --------- - -DART namelists are always read from file ``input.nml``. - -We adhere to the F90 standard of starting a namelist with an ampersand '&' and terminating with a slash '/' for all our -namelist input. Character strings that contain a '/' must be enclosed in quotes to prevent them from prematurely -terminating the namelist. - -:: - - &NAMELIST_NML - name=value, - name=value, - name=value - / - -| - -.. container:: - - ==== ==== ============================== - Item Type Description - ==== ==== ============================== - name type (often multi-line) description - ==== ==== ============================== - -| - -Other modules used ------------------- - -:: - - types_mod - model_mod - assim_tools_mod - time_manager_mod - fms_mod - -Public interfaces ------------------ - -===================== =========== -*use xxxxxxx, only :* yyypubtype1 -\ yyyroutine1 -\ yyyroutine2 -\ yyyroutine3 -===================== =========== - -A note about documentation style. Optional arguments are enclosed in brackets *[like this]*. - -| - -.. container:: routine - - :: - - type location_type - private - real(r8) :: x - end type location_type - -.. container:: indent1 - - The location type is essential to the fabric of the universe. If we don't know where we are, how do we know which - exit to take? - - ========= ==================================== - Component Description - ========= ==================================== - x is the nondimensional distance [0,1] - ========= ==================================== - -| - -.. container:: routine - - *subroutine yyyroutine1( var1, var2, var3 [, global])* - :: - - type(time_type), intent(in) :: var1 - real(r8), dimension(:), intent(inout) :: var2 - real(r8), dimension(:,:), intent(out) :: var3 - real(r8), optional, intent(in) :: global - -.. container:: indent1 - - | Returns the resolution of compute domain for either the current processor or the global domain. - | This is the best thing since sliced bread. All you have to do is throw some arguments in the call and the - subroutine automatically slices, dices and makes julienne fries. *But wait!* There's more! - - +----------+----------------------------------------------------------------------------------------------------------+ - | ``var1`` | is the number of spark plugs in a '67 Cuda. | - +----------+----------------------------------------------------------------------------------------------------------+ - | ``var2`` | is used for both input and output. Upon input, this contains the furlongs per weekday for every vertical | - | | level. Upon exit, we now have the number of spotted gobies per square hectare at that level. | - +----------+----------------------------------------------------------------------------------------------------------+ - | ``var3`` | The leftmost dimension pertains to the number of feet in an orange-footed brush fowl, indiginous to | - | | Australia. The next dimension is the number of feathers on said fowl, naturally. | - +----------+----------------------------------------------------------------------------------------------------------+ - | *global* | is really, REALLY useful in certain situations. | - +----------+----------------------------------------------------------------------------------------------------------+ - - notes would go here - -| - -.. container:: routine - - *function yyyroutine2( var1, var2, var3, [,bob] )* - :: - - type(time_type), intent(in) :: var1 - real(r8), dimension(:), intent(in) :: var2 - real(r8), dimension(:,:), intent(in) :: var3 - real(r8), optional, intent(in) :: bob - integer, dimension(size(var2)) :: yyyroutine2 - -.. container:: indent1 - - | Returns the resolution of compute domain for either the current processor or the global domain. All input variables - are not changed. Otherwise, this would be a subroutine. - | This is the second-best thing since sliced bread. All you have to do is throw some arguments in the call and the - function automatically deep fries. - - +----------------+----------------------------------------------------------------------------------------------------+ - | ``var1`` | the first time you changed your oil. | - +----------------+----------------------------------------------------------------------------------------------------+ - | ``var2`` | miles between every oil change you've ever done. Don't lie. | - +----------------+----------------------------------------------------------------------------------------------------+ - | ``var3`` | the distances you've ridden. Each row corresponds to the hour-of-day, each column is a different | - | | day-of-the-week. | - +----------------+----------------------------------------------------------------------------------------------------+ - | *bob* | mean time between failures. in msec. | - +----------------+----------------------------------------------------------------------------------------------------+ - | ``yyroutine2`` | number of gray hairs as a function of time. in kilohairs. | - +----------------+----------------------------------------------------------------------------------------------------+ - - notes would go here - -| - -Files ------ - -This is the place to discuss the files that are associated with this module. They could be input files, output files, -data files, shell scripts ... anything. - -=========================== =========================================================================== -filename purpose -=========================== =========================================================================== -inputfile1 to read some input -input.nml to read namelists -preassim.nc the time-history of the model state before assimilation -analysis.nc the time-history of the model state after assimilation -dart_log.out [default name] the run-time diagnostic output -dart_log.nml [default name] the record of all the namelists actually USED - contains the default values -=========================== =========================================================================== - -References ----------- - -- Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Arellano, 2009: - The Data Assimilation Research Testbed: A Community Facility. Bull. Amer. Meteor. Soc., 90, 1283-1296. - `DOI: 10.1175/2009BAMS2618.1 `__ - -- none - -Private components ------------------- - -N/A - -Any routines or 'local' variables of interest may be discussed here. There are generally lots of 'internal' functions -that make life simpler, but you don't want to make them available outside the scope of the current module. This is the -place to point them out, if you like. - -.. container:: routine - - :: - - type location_type - private - real(r8) :: x - end type location_type diff --git a/guide/boilerplate/html_function.f90 b/guide/boilerplate/html_function.f90 deleted file mode 100644 index da6741d3c8..0000000000 --- a/guide/boilerplate/html_function.f90 +++ /dev/null @@ -1,144 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program html_function - -implicit none - -! Interactive creation of html for function documentation for DART -integer :: num, num_args, i, max_len, len, j -character(len = 299) :: c1, c2, function_name, function_type, spaces -character(len = 299), allocatable :: arg_name(:), arg_type(:) -logical :: ispointer - -write(22, *) '' -write(22, *) '' - -spaces = ' ' - -write(*, *) 'input the number of arguments' -read(*, *) num_args -allocate(arg_name(0:num_args), arg_type(0:num_args)) - -write(*, *) 'input the function name' -read(*, *) arg_name(0) -write(22, *) '' - -write(22, *) '
' -write(22, *) '
' - -! Loop to read in the argument names -do i = 1, num_args - write(*, *) 'input argument name ', i - read(*, *) arg_name(i) -end do - -c1 = '' -do i = 1, num_args - c1 = trim(c1) // trim(arg_name(i)) - if(i /= num_args) c1 = trim(c1) // ', ' -end do - -! Write out the command line -write(22, *) ' function ', trim(arg_name(0)), '(', trim(c1), ') ' -write(22, *) '
'
-
-max_len = 0
-do i = 0, num_args
-   write(*, *) 'input the type for ', trim(arg_name(i))
-   read(*, *) arg_type(i)
-   if (i > 0) then
-      write(*, *) 'input 1 if allocatable'
-      read(*, *) j
-      if(j == 1) arg_type(i) = trim(arg_type(i)) // ', allocatable'
-      write(*, *) 'input 1 if optional'
-      read(*, *) j
-      if(j == 1) arg_type(i) = trim(arg_type(i)) // ', optional'
-   endif
-   write(*, *) 'input 1 if pointer'
-   read(*, *) j
-   ispointer = (j == 1)
-   if(ispointer) arg_type(i) = trim(arg_type(i)) // ', pointer'
-   write(*, *) 'input dimension (0, 1, 2, 3)'
-   read(*, *) j
-   if(j == 1) then
-      arg_type(i) = trim(arg_type(i)) // ', dimension(:)'
-   else if (j == 2) then
-      arg_type(i) = trim(arg_type(i)) // ', dimension(:, :)'
-   else if (j == 3) then
-      arg_type(i) = trim(arg_type(i)) // ', dimension(:, :, :)'
-   endif
-   if (i > 0 .or. ispointer) then   ! cannot give intent on pointer args
-      write(*, *) '1=intent(in); 2=intent(out); 3=intent(inout)'
-      read(*, *) j
-      if(j == 1) then
-         arg_type(i) = trim(arg_type(i)) // ', intent(in)'
-      elseif(j == 2) then
-         arg_type(i) = trim(arg_type(i)) // ', intent(out)'
-      elseif(j == 3) then
-         arg_type(i) = trim(arg_type(i)) // ', intent(inout)'
-      endif
-   endif
-
-   len = len_trim(arg_type(i))
-   if(len > max_len) max_len = len
-end do
-
-do i = 0, num_args
-   c1 = trim(arg_type(i)) // spaces
-   write(22, *) c1(1:max_len) // ' :: ' // trim(arg_name(i)) // ''
-end do
-
-write(22, *) '
' -write(22, *) '
' -write(22, *) ' ' -write(22, *) '
' -write(22, *) '' - -write(22, *) ' ' -write(22, *) '

' -write(22, *) 'input function description here ' -write(22, *) '

' -write(22, *) ' ' - -! Write table header -write(22, *) '' -write(22, *) '' - - -! Write the table of argument descriptions -do i = 0, num_args - write(*, *) 'input description of ', trim(arg_name(i)) - read(*, 31) c1 - 31 format(a299) - write(22, *) '' - if (i == 0) then - write(22, *) ' ' - else - write(22, *) ' ' - endif - write(22, *) '' -end do - - -!End of table -write(22, *) '' -write(22, *) '
' // trim(arg_name(i)) // & - ' Function return value. ' // trim(c1) // '' // trim(c1) // '
' -write(22, *) '
' -write(22, *) '
' -write(22, *) '' -write(22, *) '' - -write(*,*) 'output file is fort.22 -- move or rename before running program again' - -end program html_function - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/guide/boilerplate/html_functoc.f90 b/guide/boilerplate/html_functoc.f90 deleted file mode 100644 index e573836b3d..0000000000 --- a/guide/boilerplate/html_functoc.f90 +++ /dev/null @@ -1,65 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program html_functoc - -implicit none - -! Interactive creation of html for interface list documentation for DART -integer :: num, num_items, i, max_len, len, j -character(len = 299) :: c1, c2, module_name, item_name, spaces - -write(22, *) '' -write(22, *) '' - -spaces = ' ' - -! get the name of the module -write(*, *) 'input the name of this module' -read(*, *) module_name - -! Put in the standard header stuff -write(22, *) '' -write(22, *) '
[top]
' -write(22, *) '

' -write(22, *) '' -write(22, *) "" -write(22, *) '' - -write(*, *) 'input the names of the subroutines and functions in this module' -write(*, *) 'input "end" when done' -readloop: do i=1, 10000 - write(*, *) 'next routine name ("end" to finish)' - read(*, *) item_name - if (trim(item_name) == "end") exit readloop - if (i==1) then - write(22, *) ' ' - else - write(22, *) '' - endif -end do readloop - -write(22, *) '
use ' // trim(module_name), ', only : '//trim(item_name)//'
  '//trim(item_name)//'
' - -write(22, *) '' -write(22, *) '

' -write(22, *) ' A note about documentation style.' -write(22, *) ' Optional arguments are enclosed in brackets' -write(22, *) ' [like this].' -write(22, *) '

' -write(22, *) '' - -write(22, *) '' - -write(*,*) 'output file is fort.22 -- move or rename before running program again' - -end program html_functoc - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/guide/boilerplate/html_namelist.f90 b/guide/boilerplate/html_namelist.f90 deleted file mode 100644 index 67f11a8f83..0000000000 --- a/guide/boilerplate/html_namelist.f90 +++ /dev/null @@ -1,105 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program html_namelist - -implicit none - -! Interactive creation of html for namelist documentation for DART -integer :: num, num_args, i, max_len, len, j -character(len = 299) :: c1, c2, function_name, function_type, spaces -character(len = 299), allocatable :: arg_name(:), arg_type(:) - -write(22, *) '' -write(22, *) '' - -spaces = ' ' - -! Put in the standard header stuff -write(22, *) '' -write(22, *) '
[top]
' -write(22, *) '

NAMELIST

' -write(22, *) '

' -write(22, *) 'This namelist is read from the file input.nml.' -write(22, *) 'Namelists start with an ampersand' -write(22, *) "'&' and terminate with a slash '/'." -write(22, *) "Character strings that contain a '/' must be" -write(22, *) 'enclosed in quotes to prevent them from' -write(22, *) 'prematurely terminating the namelist.' -write(22, *) '

' -write(22, *) "
" -write(22, *) "
"
-
-! Now write the namelist sequence
-write(*, *) 'input the number of namelist entries'
-read(*, *) num_args
-allocate(arg_name(0:num_args), arg_type(0:num_args))
-write(*, *) 'input the namelist name'
-read(*, *) arg_name(0)
-
-! Write out the namelist start
-write(22, *) '&' , trim(arg_name(0))
-
-! Loop to read in the argument names
-do i = 1, num_args
-   write(*, *) 'input namelist entry ', i, ' as name = value '
-   read(*, 31) c1
-   read(c1, *) arg_name(i)
-   write(22, *) '   ' // trim(c1) // ', '
-end do
-
-write(22, *) '/'
-write(22, *) '
' - -write(22, *) '

' -write(22, *) 'put in any general namelist discussion information' -write(22, *) '

' -write(22, *) '

' - -! Prepare the table header -write(22, *) '
' -write(22, *) '' -write(22, *) '' -write(22, *) '' -write(22, *) ' ' -write(22, *) ' ' -write(22, *) '' -write(22, *) '' -write(22, *) '' -do i = 1, num_args - write(*, *) 'input the type for ', trim(arg_name(i)) - read(*, *) arg_type(i) -end do -do i = 1, num_args - write(*, *) 'input description for ', trim(arg_name(i)) - read(*, 31) c1 - 31 format(a299) - - write(22, *) '' - write(22, *) ' ' - write(22, *) ' ' - write(22, *) '' -end do - - -! Write the end of the table -write(22, *) '' -write(22, *) '
Item Type Description
' // trim(arg_name(i)) // ' ' // trim(arg_type(i)) // ' ' // trim(c1) // '
' -write(22, *) '
' - -write(22, *) '

' - -write(22, *) '' - -write(*,*) 'output file is fort.22 -- move or rename before running program again' - -end program html_namelist - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/guide/boilerplate/html_subroutine.f90 b/guide/boilerplate/html_subroutine.f90 deleted file mode 100644 index fc51c1b324..0000000000 --- a/guide/boilerplate/html_subroutine.f90 +++ /dev/null @@ -1,135 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program html_subroutine - -implicit none - -! Interactive creation of html for subroutine documentation for DART -integer :: num, num_args, i, max_len, len, j -character(len = 299) :: c1, c2, function_name, function_type, spaces -character(len = 299), allocatable :: arg_name(:), arg_type(:) - -write(22, *) '' -write(22, *) '' - -spaces = ' ' - -write(*, *) 'input the number of arguments' -read(*, *) num_args -allocate(arg_name(0:num_args), arg_type(0:num_args)) - -write(*, *) 'input the subroutine name' -read(*, *) arg_name(0) -write(22, *) '' - -write(22, *) '
' -write(22, *) '
' - -! Loop to read in the argument names -do i = 1, num_args - write(*, *) 'input argument name ', i - read(*, *) arg_name(i) -end do - -c1 = '' -do i = 1, num_args - c1 = trim(c1) // trim(arg_name(i)) - if(i /= num_args) c1 = trim(c1) // ', ' -end do - -! Write out the command line -write(22, *) ' subroutine ', trim(arg_name(0)), '(', trim(c1), ') ' -write(22, *) '
'
-
-max_len = 0
-do i = 1, num_args
-   write(*, *) 'input the type for ', trim(arg_name(i))
-   read(*, *) arg_type(i)
-   write(*, *) 'input 1 if allocatable'
-   read(*, *) j
-   if(j == 1) arg_type(i) = trim(arg_type(i)) // ', allocatable'
-   write(*, *) 'input 1 if optional'
-   read(*, *) j
-   if(j == 1) arg_type(i) = trim(arg_type(i)) // ', optional'
-   write(*, *) 'input 1 if pointer'
-   read(*, *) j
-   if(j == 1) arg_type(i) = trim(arg_type(i)) // ', pointer'
-   write(*, *) 'input dimension (0, 1, 2, 3)'
-   read(*, *) j
-   if(j == 1) then
-      arg_type(i) = trim(arg_type(i)) // ', dimension(:)'
-   else if (j == 2) then
-      arg_type(i) = trim(arg_type(i)) // ', dimension(:, :)'
-   else if (j == 3) then
-      arg_type(i) = trim(arg_type(i)) // ', dimension(:, :, :)'
-   endif
-   write(*, *) '1=intent(in); 2=intent(out); 3=intent(inout)'
-   read(*, *) j
-   if(j == 1) then
-      arg_type(i) = trim(arg_type(i)) // ', intent(in)'
-   elseif(j == 2) then
-      arg_type(i) = trim(arg_type(i)) // ', intent(out)'
-   elseif(j == 3) then
-      arg_type(i) = trim(arg_type(i)) // ', intent(inout)'
-   endif
-
-
-   len = len_trim(arg_type(i))
-   if(len > max_len) max_len = len
-end do
-
-do i = 1, num_args
-   c1 = trim(arg_type(i)) // spaces
-   write(22, *) c1(1:max_len) // ' :: ' // trim(arg_name(i)) // ''
-end do
-
-write(22, *) '
' -write(22, *) '
' -write(22, *) ' ' -write(22, *) '
' -write(22, *) '' - -write(22, *) ' ' -write(22, *) '

' -write(22, *) 'input subroutine description here ' -write(22, *) '

' -write(22, *) ' ' - -! Write table header -write(22, *) '' -write(22, *) '' - - -! Write the table of argument descriptions -do i = 1, num_args - write(*, *) 'input description of ', trim(arg_name(i)) - read(*, 31) c1 - 31 format(a299) - write(22, *) '' - write(22, *) ' ' - write(22, *) '' -end do - - -!End of table -write(22, *) '' -write(22, *) '
' // trim(arg_name(i)) // & - ' ' // trim(c1) // '
' -write(22, *) '
' -write(22, *) '
' -write(22, *) '' -write(22, *) '' - -write(*,*) 'output file is fort.22 -- move or rename before running program again' - -end program html_subroutine - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/guide/boilerplate/html_type.f90 b/guide/boilerplate/html_type.f90 deleted file mode 100644 index 7dc9019cf1..0000000000 --- a/guide/boilerplate/html_type.f90 +++ /dev/null @@ -1,96 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program html_type - -implicit none - -! Interactive creation of html for derived type documentation for DART -integer :: num, num_items, i, max_len, len, j -character(len = 299) :: c1, c2, type_name, spaces -character(len = 299), allocatable :: item_name(:), item_type(:) - -write(22, *) '' -write(22, *) '' - -spaces = ' ' - -! get the count and name of the type -write(*, *) 'input the number of items in this type' -read(*, *) num_items -allocate(item_name(0:num_items), item_type(0:num_items)) -write(*, *) 'input the derived type name' -read(*, *) item_name(0) -write(*, *) 'enter 1 if contents are private' -read(*, *) j - -! Put in the standard header stuff -write(22, *) '' -write(22, *) '
' -write(22, *) '
'
-write(22, *) 'type ' // trim(item_name(0)) // ''
-if (j==1) write(22, *) '   private'
-
-! Loop to read in the item names
-do i = 1, num_items
-   write(*, *) 'input name for item ', i
-   read(*, *) item_name(i)
-   write(*, *) 'input the type for ', trim(item_name(i))
-   read(*, *) item_type(i)
-   write(22, *) '   ' // trim(item_type(i)) // ' :: ' // trim(item_name(i))
-end do
-
-write(22, *) 'end type ' // trim(item_name(0))
-write(22, *) '
' -write(22, *) '
' - -write(22, *) '' -write(22, *) '
' -write(22, *) '' -write(22, *) '' -write(22, *) '

' -write(22, *) 'put in any general type discussion information' -write(22, *) '

' - -! Prepare the table header -write(22, *) '' -write(22, *) '' -write(22, *) '' -write(22, *) ' ' -write(22, *) '' -write(22, *) '' -write(22, *) '' -write(22, *) '' -do i = 1, num_items - write(*, *) 'input description for ', trim(item_name(i)) - read(*, 31) c1 - 31 format(a299) - - write(22, *) '' - write(22, *) ' ' - write(22, *) '' -end do - - -! Write the end of the table -write(22, *) '' -write(22, *) '
Component Description
' // trim(item_name(i)) // ' ' // trim(c1) // '
' -write(22, *) '' -write(22, *) '
' -write(22, *) '
' -write(22, *) '' - -write(22, *) '' - -write(*,*) 'output file is fort.22 -- move or rename before running program again' - -end program html_type - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/guide/boilerplate/template.rst b/guide/boilerplate/template.rst deleted file mode 100644 index c3b60d6b24..0000000000 --- a/guide/boilerplate/template.rst +++ /dev/null @@ -1,139 +0,0 @@ -PROGRM OR MODULE name_of_thing -============================== - -Overview --------- - -Explain in general terms what this is. - -Namelist --------- - -DART namelists are always read from file ``input.nml``. - -We adhere to the F90 standard of starting a namelist with an ampersand '&' and terminating with a slash '/' for all our -namelist input. Character strings that contain a '/' must be enclosed in quotes to prevent them from prematurely -terminating the namelist. - -:: - - &NAMELIST_NML - name=value, - name=value, - name=value - / - -Any comments about the namelist as a whole. - -| - -.. container:: - - ==== ==== ============================== - Item Type Description - ==== ==== ============================== - name type (often multi-line) description - ==== ==== ============================== - -| - -Modules used ------------- - -:: - - types_mod - utilities_mod - random_seq_mod - time_manager_mod - ensemble_manager_mod - -Public interfaces ------------------ - -================================== ================== -*use this_module_name_mod, only :* subr/function name -\ name2 -\ name3 -================================== ================== - -A note about documentation style. Optional arguments are enclosed in brackets *[like this]*. - -| - -.. container:: routine - - *subroutine subroutine1(arg1, [, arg2])* - :: - - real(r8), intent(in) :: arg1 - real(r8), optional, intent(in) :: arg2 - -.. container:: indent1 - - describe what this subroutine does. - - ======== ======================= - ``arg1`` Describe arg1. - *arg2* Describe optional arg2. - ======== ======================= - -| - -.. container:: routine - - *function function1(arg1)* - :: - - logical, :: function1 - integer, intent(in) :: arg1 - -.. container:: indent1 - - Describe function. - - ============= =================================== - ``function1`` describe what this function returns - ``arg1`` describe function argument - ============= =================================== - -| - -.. container:: type - - :: - - type bob_type - private - integer :: bob1 - integer :: bob2 - end type bob_type - -.. container:: indent1 - - describe bob - - ========= ============== - Component Description - ========= ============== - bob1 Describe bob1. - bob2 Describe bob2. - ========= ============== - -| - -Files ------ - -describe files used by code - -References ----------- - -- author, title. publication, volume, pages. - `doi: nn.yyyy/rest_of_number `__ - -Private components ------------------- - -no discussion From c87b56bc6e9c3c553a8be3ea341520882641f26f Mon Sep 17 00:00:00 2001 From: Helen Kershaw Date: Tue, 24 Aug 2021 14:27:28 -0600 Subject: [PATCH 03/16] removed wrf unused code model_mod_vorticity.f90 is a draft from rma distributed_phb_mod.f90 and distributed_phb_model_mod.f90 are wrf with distributed static data (state sized phb array). Never used. --- models/wrf/distrib_phb_mod.f90 | 294 - models/wrf/distributed_phb_model_mod.f90 | 9074 --------------------- models/wrf/model_mod_vorticity.f90 | 9116 ---------------------- 3 files changed, 18484 deletions(-) delete mode 100644 models/wrf/distrib_phb_mod.f90 delete mode 100644 models/wrf/distributed_phb_model_mod.f90 delete mode 100644 models/wrf/model_mod_vorticity.f90 diff --git a/models/wrf/distrib_phb_mod.f90 b/models/wrf/distrib_phb_mod.f90 deleted file mode 100644 index 4c9378501a..0000000000 --- a/models/wrf/distrib_phb_mod.f90 +++ /dev/null @@ -1,294 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -!> Aim: to replace the array phb, with a distributed version -!> Current thinking: Only distribute the array as much as necessary. -!> Ideally, if memory was unliimited, every task would have the whole array. -!> -!> Splitting in x only, because this is simple, and I can't think -!> of a reason to split in more than one dimension yet. -module distrib_phb_mod - -use mpi_utilities_mod, only : datasize, my_task_id, task_count -use location_mod, only : location_type -use types_mod, only : r8 -use pnetcdf_utilities_mod, only : pnet_check - -use mpi - -implicit none -private - -public :: create_groups, read_phb, phb_filename, create_window, free_window -public :: phb ! for the model to see - -! grid groups global to module - -! grid window -real(r8), allocatable :: phb_array(:,:,:) !< local phb info -integer :: phb_win -real(r8) :: duplicate_phb(*) -pointer(aa, duplicate_phb) - -integer :: group_size = 4 !< should be namelist option -integer :: group_all !< mpi_comm_world group -integer :: subgroup !< subgroup for the grid -integer :: mpi_comm_grid -integer, allocatable :: group_members(:) -integer :: local_rank !< rank within group - -! pntecdf variables -character*50 :: phb_filename !< netcdf file containing grid info -integer :: ret !< return code for pnetcdf -integer :: ncfile !< ncfile -integer :: varId !< variable Id -integer(KIND=MPI_OFFSET_KIND) :: we_length, sn_length, bts_length !< lengths of dimensions -integer(KIND=MPI_OFFSET_KIND) :: start(4) -integer(KIND=MPI_OFFSET_KIND) :: count(4) -integer(KIND=MPI_OFFSET_KIND) :: stride(4) -integer(KIND=MPI_OFFSET_KIND) :: my_num_we !< splitting up by we - -integer :: we_dimId, sn_dimId, bts_dimId !< dimension ids - -contains - -!------------------------------------------------------------- -!> create groups for grid -!> make a communicator for the distributed grid -subroutine create_groups - -integer ierr ! all MPI errors are fatal anyway - -allocate(group_members(group_size)) ! this is module global - -call mpi_comm_group(mpi_comm_world, group_all, ierr) ! get the word group from mpi_comm_world -call build_my_group(my_task_id(), group_size, group_members) ! create a list of processors in the grid group -call mpi_group_incl(group_all, group_size, group_members, subgroup, ierr) -call mpi_comm_create(mpi_comm_world, subgroup, mpi_comm_grid, ierr) -call mpi_comm_rank(mpi_comm_grid, local_rank, ierr) ! rank within group - -end subroutine create_groups - -!----------------------------------------------------------- -!> build the group to store the grid -subroutine build_my_group(myrank, group_size, group_members) - -implicit none - -integer, intent(in) :: myrank ! why are you passing this in? -integer, intent(inout) :: group_size ! need to modify this if your #tasks does not divide by group size -integer, intent(out) :: group_members(group_size) - -integer bottom, top !< start and end members of the group -integer i - -! integer arithmatic. rouding down to the lowest group size -bottom = (myrank / group_size ) * group_size -top = bottom + group_size - 1 -if (top >= task_count()) then - top = task_count() - 1 - group_size = top - bottom + 1 - print*, 'rank', myrank, 'bottom top', bottom, top, 'group_size', group_size -endif - - -! fill up group members -group_members(1) = bottom -do i = 2, group_size - group_members(i) = group_members(i-1) + 1 -enddo - -end subroutine build_my_group - -!----------------------------------------------------------- -!> create window for phb -!> Need to add a loop for domain. Just single domain for now. -!> Window is filled with columns contiguous in memory, possibly -!> we will grab a whole column at once. -!> -!> If you want to change the order in the window, be sure to -!> change the subroutine who_has_grid_info -subroutine create_window - -integer ii, jj, kk, ierr, sizedouble, count -integer(KIND=MPI_ADDRESS_KIND) :: window_size - -call mpi_type_size(datasize, sizedouble, ierr) ! datasize comes from mpi_utilities_mod -window_size = my_num_we*sn_length*bts_length*sizedouble -aa = malloc(my_num_we*sn_length*bts_length) -call MPI_ALLOC_MEM(window_size, mpi_info_null, aa, ierr) - -! can't do array assignment with a cray pointer, so you need to loop -count = 1 -! which order should these be in? (z, x, y) so you can grab a column at a time? -do jj = 1, sn_length - do kk = 1, my_num_we - do ii = 1, bts_length - duplicate_phb(count) = phb_array(kk, jj, ii) - !print*, 'count ', count - count = count + 1 - enddo - enddo -enddo - -call mpi_win_create(duplicate_phb, window_size, sizedouble, MPI_INFO_NULL, mpi_comm_grid, phb_win, ierr) - -end subroutine create_window - -!----------------------------------------------------------- -!> read in phb in parallel -!> using pnetcdf. -!> How does the synchronization cost change with increasing task number? -!> I think it should only vary with group size, and that total task number is -!> irrelevant, but we'll see. -subroutine read_phb -use pnetcdf - -integer :: temp -integer(KIND=MPI_OFFSET_KIND) :: dummy_count -integer :: my_start !< start for we - -ret = nfmpi_open(mpi_comm_grid, phb_filename, NF_NOWRITE, mpi_info_null, ncfile) -call pnet_check(ret, 'read_phb', 'opening file') - -! get dimensions -ret = nfmpi_inq_dimid(ncfile, 'west_east', we_dimId) -call pnet_check(ret, 'read_phb', 'getting dimension id') - -ret = nfmpi_inq_dimid(ncfile, 'south_north', sn_dimId) -call pnet_check(ret, 'read_phb', 'getting dimension id') - -ret = nfmpi_inq_dimid(ncfile, 'bottom_top_stag', bts_dimId) -call pnet_check(ret, 'read_phb', 'getting dimension id') - -! get dimension lengths -ret = nfmpi_inq_dimlen(ncfile, we_dimId, we_length) -call pnet_check(ret, 'read_phb', 'getting dimension length') - -ret = nfmpi_inq_dimlen(ncfile, sn_dimId, sn_length) -call pnet_check(ret, 'read_phb', 'getting dimension length') - -ret = nfmpi_inq_dimlen(ncfile, bts_dimId, bts_length) -call pnet_check(ret, 'read_phb', 'getting dimension length') - -! allocate space to read the grid -! Q.how do you split a three d variable? Is this the place to use global arrays? -! split up in x dimension only? (or y or z) -temp = we_length / group_size -if (local_rank < group_size - 1) then - my_num_we = temp -else - my_num_we = we_length - temp*(group_size - 1) -endif - -allocate(phb_array(my_num_we, sn_length, bts_length)) ! local phb array - -!print*, 'lengths ', my_num_we, sn_length, bts_length -!print*, 'rank', my_task_id(), 'total num vars ', my_num_we*sn_length*bts_length - -my_start = local_rank*temp + 1 - -dummy_count = 1 - -! Why do you need the fouth dimension for pnetcdf, it seems like netcdf will just read the first three -start = (/my_start, 1, 1, 1/) -count = (/my_num_we, sn_length, bts_length, dummy_count/) -stride = (/1, 1, 1, 1/) - -! read in the grid -ret = nfmpi_inq_varid(ncfile, 'PHB', varId) ! get status of variable -call pnet_check(ret, 'read_phb', 'phb id') - -ret = nfmpi_get_vars_double_all(ncfile, varId, start, count, stride, phb_array) -call pnet_check(ret, 'read_phb', 'reading phb') - -! check against matlab read -!print*, phb_array(1,1, 1:5) - -! close netcdf file -ret = nfmpi_close(ncfile) ! close netcdf file -call pnet_check(ret, 'read_phb', 'closing file') - -end subroutine read_phb - -!--------------------------------------------------------- -!> Free the mpi windows you created -subroutine free_window -integer :: ierr - -call mpi_win_free(phb_win, ierr) -call MPI_FREE_MEM(duplicate_phb, ierr) - -deallocate(group_members, phb_array) - -end subroutine free_window - -!--------------------------------------------------------- -!> Function to get phb -function phb(dom, i, j, k) - -real(r8) :: phb !< geopotential height -integer, intent(in) :: i, j, k !< 3D location -integer, intent(in) :: dom !< domain number - -integer :: owner !< which task has the part of phb we need -integer(KIND=MPI_ADDRESS_KIND) :: target_disp !< displacement -integer :: ierr - -! caluclate who has the info -call who_has_grid_info(dom, i, j, k, owner, target_disp) - -! grab the info -call mpi_win_lock(MPI_LOCK_SHARED, owner, 0, phb_win, ierr) -call mpi_get(phb, 1, datasize, owner, target_disp, 1, datasize, phb_win, ierr) -call mpi_win_unlock(owner, phb_win, ierr) - -end function - -!--------------------------------------------------------- -!> get the owner and location in memory of the grid point -!> @todo How do we split up domains across the window? -subroutine who_has_grid_info(dom, i, j, k, owner, target_disp) - -integer, intent(in) :: dom -integer, intent(in) :: i -integer, intent(in) :: j -integer, intent(in) :: k -integer, intent(out) :: owner -integer(KIND=MPI_ADDRESS_KIND), intent(out) :: target_disp !< displacement - -integer :: temp -integer :: local_we !< local index of my slice of we -integer :: owner_num_we !< length of we slab on the owner -integer :: we - -temp = we_length / group_size ! phb is split only in we (west-east) - -we = i-1 -owner = we / temp ! we is i - -! last task in the group has the most grid points -if (owner > group_size - 1) owner = group_size - 1 - -if (owner < group_size - 1) then - owner_num_we = temp -else - owner_num_we = we_length - temp*(group_size - 1) -endif - -! phb is a 3d array. (z,x,y) window -local_we = i - temp*owner -target_disp = (j - 1)*bts_length*owner_num_we + (local_we- 1)*bts_length + k -1 ! target disp starts at 0 - -end subroutine who_has_grid_info - -end module distrib_phb_mod - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/models/wrf/distributed_phb_model_mod.f90 b/models/wrf/distributed_phb_model_mod.f90 deleted file mode 100644 index 07c9174441..0000000000 --- a/models/wrf/distributed_phb_model_mod.f90 +++ /dev/null @@ -1,9074 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -module model_mod - - -! Assimilation interface for WRF model -!> \defgroup wrf model_mod -!> Model mod -!> -!> Model mod for WRF -!> @{ -!----------------------------------------------------------------------- -! -! interface for WRF -! -!----------------------------------------------------------------------- -!---------------- m o d u l e i n f o r m a t i o n ------------------ -!----------------------------------------------------------------------- - -use types_mod, only : r8, i8, deg2rad, missing_r8, ps0, earth_radius, & - gas_constant, gas_constant_v, gravity, pi, & - digits12 - -use time_manager_mod, only : time_type, set_time, set_calendar_type, GREGORIAN,& - set_date, get_date - -use location_mod, only : location_type, get_location, set_location, & - horiz_dist_only, & - LocationDims, LocationName, LocationLName, & - query_location, vert_is_undef, vert_is_surface, & - vert_is_level, vert_is_pressure, vert_is_height, & - vert_is_scale_height, VERTISUNDEF, VERTISSURFACE, & - VERTISLEVEL, VERTISPRESSURE, VERTISHEIGHT, & - VERTISSCALEHEIGHT, & - get_close_type, get_dist, get_close_maxdist_init, & - get_close_obs_init, loc_get_close_obs => get_close_obs - -use utilities_mod, only : file_exist, open_file, close_file, & - register_module, error_handler, E_ERR, E_WARN, & - E_MSG, nmlfileunit, do_output, nc_check, & - find_namelist_in_file, check_namelist_read, & - find_textfile_dims, file_to_text, & - do_nml_file, do_nml_term, scalar - -use mpi_utilities_mod, only : my_task_id, task_count - -use random_seq_mod, only : random_seq_type, init_random_seq, random_gaussian - -use obs_kind_mod, only : QTY_U_WIND_COMPONENT, QTY_V_WIND_COMPONENT, & - QTY_SURFACE_PRESSURE, QTY_TEMPERATURE, & - QTY_SPECIFIC_HUMIDITY, QTY_SURFACE_ELEVATION, & - QTY_PRESSURE, QTY_VERTICAL_VELOCITY, & - QTY_DENSITY, QTY_FLASH_RATE_2D, & - QTY_RAINWATER_MIXING_RATIO, QTY_HAIL_MIXING_RATIO, & - QTY_GRAUPEL_MIXING_RATIO, QTY_SNOW_MIXING_RATIO, & - QTY_CLOUD_LIQUID_WATER, QTY_CLOUD_ICE, & - QTY_CONDENSATIONAL_HEATING, QTY_VAPOR_MIXING_RATIO, & - QTY_ICE_NUMBER_CONCENTRATION, QTY_GEOPOTENTIAL_HEIGHT, & - QTY_POTENTIAL_TEMPERATURE, QTY_SOIL_MOISTURE, & - QTY_DROPLET_NUMBER_CONCENTR, QTY_SNOW_NUMBER_CONCENTR, & - QTY_RAIN_NUMBER_CONCENTR, QTY_GRAUPEL_NUMBER_CONCENTR, & - QTY_HAIL_NUMBER_CONCENTR, QTY_HAIL_VOLUME, & - QTY_GRAUPEL_VOLUME, QTY_DIFFERENTIAL_REFLECTIVITY, & - QTY_RADAR_REFLECTIVITY, QTY_POWER_WEIGHTED_FALL_SPEED, & - QTY_SPECIFIC_DIFFERENTIAL_PHASE, & - QTY_VORTEX_LAT, QTY_VORTEX_LON, & - QTY_VORTEX_PMIN, QTY_VORTEX_WMAX, & - QTY_SKIN_TEMPERATURE, QTY_LANDMASK, & - get_index_for_quantity, get_num_quantities, & - get_name_for_quantity - -use ensemble_manager_mod, only : ensemble_type, map_pe_to_task, get_var_owner_index, & - get_my_vars, get_copy_owner_index - -use sort_mod, only : sort - -use window_mod ! for state windows: forward operator and height calculation - -use distrib_phb_mod ! distributed phb array - -use distributed_state_mod, only : get_state - -use state_structure_mod, only : add_domain, get_model_variable_indices, & - state_structure_info, & - get_index_start, get_index_end, & - get_dart_vector_index - -use mpi_utilities_mod, only : broadcast_minmax - -! FIXME: -! the kinds QTY_CLOUD_LIQUID_WATER should be QTY_CLOUDWATER_MIXING_RATIO, -! and kind QTY_CLOUD_ICE should be QTY_ICE_MIXING_RATIO, but for backwards -! compatibility with other models, they remain as is for now. at the next -! major dart release, the names will be made consistent. -! ditto QTY_ICE_NUMBER_CONCENTRATION, which should be QTY_ICE_NUMBER_CONCENTR -! to be consistent with the other concentration names. - -!nc -- module_map_utils split the declarations of PROJ_* into a separate module called -!nc -- misc_definitions_module -use map_utils, only : proj_info, map_init, map_set, latlon_to_ij, & - ij_to_latlon, gridwind_to_truewind - -use misc_definitions_module, only : PROJ_LATLON, PROJ_MERC, PROJ_LC, PROJ_PS, PROJ_CASSINI, & - PROJ_CYL - -use netcdf -use typesizes - -implicit none -private - - -!----- -! DART requires 16 specific public interfaces from model_mod.f90 -- Note -! that the last four are simply "stubs" since WRF currently requires use -! of system called shell scripts to advance the model. - -public :: get_model_size, & - get_state_meta_data, & - get_model_time_step, & - static_init_model, & - pert_model_copies, & - nc_write_model_atts, & - nc_write_model_vars, & - get_close_obs, & - get_close_maxdist_init, & - get_close_obs_init, & - model_interpolate, & - vert_convert, & - query_vert_localization_coord, & - read_model_time, & - write_model_time, & - convert_base_obs_location !HK - -! public stubs -public :: adv_1step, & - end_model, & - init_time, & - init_conditions - -!----- -! Here is the appropriate place for other users to make additional routines -! contained within model_mod available for public use: -public :: get_number_domains, & - get_wrf_static_data, & - model_pressure_distrib, & - model_height_distrib, & - pres_to_zk, & - height_to_zk, & - get_domain_info, & - get_wrf_state_variables, & - fill_default_state_table, & - read_wrf_dimensions, & - get_number_of_wrf_variables, & - get_variable_bounds, & - set_variable_bound_defaults, & - get_variable_size_from_file, & - trans_3Dto1D, trans_1Dto3D, & - trans_2Dto1D, trans_1Dto2D, & - get_wrf_date, set_wrf_date, & - height_diff_check - -! public parameters -public :: max_state_variables, & - num_state_table_columns, & - num_bounds_table_columns - -! types -public :: wrf_dom, wrf_static_data_for_dart - -! Interfaces for array and single value versions of subroutines/functions -! This is because the forward operator works on the whole ensemble, and the -! vertical conversion only uses the mean copy. - - -! HK ? interp_4pressure interface needed? - -!----------------------------------------------------------------------- -! version controlled file description for error handling, do not edit -character(len=256), parameter :: source = & - "$URL$" -character(len=32 ), parameter :: revision = "$Revision$" -character(len=128), parameter :: revdate = "$Date$" - -! miscellaneous -integer, parameter :: max_state_variables = 100 -integer, parameter :: num_state_table_columns = 5 -integer, parameter :: num_bounds_table_columns = 4 - -!----------------------------------------------------------------------- -! Model namelist parameters with default values. -! -! center_search_half_length: half length (in meter) of the searching box to locate -! minimum pressure at a grid point -! center_spline_scale: coarse grid to spline interp. fine grid ratio -!----------------------------------------------------------------------- - -logical :: output_state_vector = .false. ! output prognostic variables -logical :: default_state_variables = .true. ! use default state list? -character(len=129) :: wrf_state_variables(num_state_table_columns,max_state_variables) = 'NULL' -character(len=129) :: wrf_state_bounds(num_bounds_table_columns,max_state_variables) = 'NULL' -integer :: num_domains = 1 -integer :: calendar_type = GREGORIAN -integer :: assimilation_period_seconds = 21600 -! Max height a surface obs can be away from the actual model surface -! and still be accepted (in meters) -real (kind=r8) :: sfc_elev_max_diff = -1.0_r8 ! could be something like 200.0_r8 -real (kind=r8) :: center_search_half_length = 500000.0_r8 -real(r8) :: circulation_pres_level = 80000.0_r8 -real(r8) :: circulation_radius = 108000.0_r8 -integer :: center_spline_grid_scale = 10 -integer :: vert_localization_coord = VERTISHEIGHT -! Allow observations above the surface but below the lowest sigma level. -logical :: allow_obs_below_vol = .false. -! Do the interpolation of pressure values only after taking the log (.true.) -! vs doing a linear interpolation directly in pressure units (.false.) -logical :: log_vert_interp = .true. -logical :: log_horz_interpM = .false. -logical :: log_horz_interpQ = .false. -!nc -- we are adding these to the model.nml until they appear in the NetCDF files -logical :: polar = .false. ! wrap over the poles -logical :: periodic_x = .false. ! wrap in longitude or x -logical :: periodic_y = .false. ! used for single column model, wrap in y -!JPH -- single column model flag -logical :: scm = .false. ! using the single column model -logical :: allow_perturbed_ics = .false. ! should spin the model up for a while after - -! obsolete items; ignored by this code. -! non-backwards-compatible change. should be removed, -! but see note below about namelist. -integer :: num_moist_vars -logical :: surf_obs, soil_data, h_diab - -! adv_mod_command moved to dart_to_wrf namelist; ignored here. -character(len = 72) :: adv_mod_command = '' - -! num_moist_vars, surf_obs, soil_data, h_diab, and adv_mod_command -! are IGNORED no matter what their settings in the namelist are. -! they are obsolete, but removing them here will cause a fatal error -! until users remove them from their input.nml files as well. -namelist /model_nml/ output_state_vector, num_moist_vars, & - num_domains, calendar_type, surf_obs, soil_data, h_diab, & - default_state_variables, wrf_state_variables, & - wrf_state_bounds, sfc_elev_max_diff, & - adv_mod_command, assimilation_period_seconds, & - allow_obs_below_vol, vert_localization_coord, & - center_search_half_length, center_spline_grid_scale, & - circulation_pres_level, circulation_radius, polar, & - periodic_x, periodic_y, scm, allow_perturbed_ics - -! if you need to check backwards compatibility, set this to .true. -! otherwise, leave it as false to use the more correct geometric height -logical :: use_geopotential_height = .false. - -character(len = 20) :: wrf_nml_file = 'namelist.input' -logical :: have_wrf_nml_file = .false. -integer :: num_obs_kinds = 0 -logical, allocatable :: in_state_vector(:) -integer, allocatable :: domain_id(:) ! Global storage to interface with state_structure_mod. - -!----------------------------------------------------------------------- - -! Private definition of domain map projection use by WRF - -!nc -- added in CASSINI and CYL according to module_map_utils convention -!JPH -- change variable name from map_sphere to more specific map_latlon -integer, parameter :: map_latlon = 0, map_lambert = 1, map_polar_stereo = 2, map_mercator = 3 -integer, parameter :: map_cassini = 6, map_cyl = 5 - -! Private definition of model variable types - -real (kind=r8), PARAMETER :: kappa = 2.0_r8/7.0_r8 ! gas_constant / cp -real (kind=r8), PARAMETER :: ts0 = 300.0_r8 ! Base potential temperature for all levels. - -!---- private data ---- - -! Got rid of surf_var as global private variable for model_mod and just defined it locally -! within model_interpolate - -TYPE wrf_static_data_for_dart - - integer :: bt, bts, sn, sns, we, wes, sls - real(r8) :: dx, dy, dt, p_top - integer :: map_proj - real(r8) :: cen_lat,cen_lon - type(proj_info) :: proj - - ! Boundary conditions -- hopefully one day these will be in the global attributes of the - ! input NetCDF file ("periodic_x" and "polar" are namelist items in the &bdy_control - ! section of a standard WRF "namelist.input" file), but for now we have included them - ! in the "model_nml" group of DART's own "input.nml". Above, their default values are - ! both set to .true. (indicating a global domain). - logical :: periodic_x - logical :: periodic_y - logical :: polar - logical :: scm - - integer :: domain_size - integer :: localization_coord - real(r8), dimension(:), pointer :: znu, dn, dnw, zs, znw - real(r8), dimension(:,:), pointer :: mub, hgt - real(r8), dimension(:,:), pointer :: latitude, latitude_u, latitude_v - real(r8), dimension(:,:), pointer :: longitude, longitude_u, longitude_v - !real(r8), dimension(:,:,:), pointer :: phb ! leave in for compilation - - ! NEWVAR: Currently you have to add a new type here if you want to use - ! NEWVAR: a WRF variable which is not one of these types. This will go - ! NEWVAR: away eventually, we hope. Search for NEWVAR for other places - ! NEWVAR: the code has to change. - - ! JPH local variables to hold type indices - integer :: type_u, type_v, type_w, type_t, type_qv, type_qr, type_hdiab, & - type_qndrp, type_qnsnow, type_qnrain, type_qngraupel, type_qnice, & - type_qc, type_qg, type_qi, type_qs, type_gz, type_refl, type_fall_spd, & - type_dref, type_spdp, type_qh, type_qnhail, type_qhvol, type_qgvol - - integer :: type_u10, type_v10, type_t2, type_th2, type_q2, & - type_ps, type_mu, type_tsk, type_tslb, type_sh2o, & - type_smois, type_2dflash - - integer :: number_of_wrf_variables - integer(i8), dimension(:,:), pointer :: var_index - integer, dimension(:,:), pointer :: var_size - integer, dimension(:), pointer :: var_type - integer, dimension(:), pointer :: var_index_list - logical, dimension(:), pointer :: var_update_list - integer, dimension(:), pointer :: dart_kind - integer, dimension(:,:), pointer :: land - real(r8), dimension(:), pointer :: lower_bound,upper_bound - character(len=10), dimension(:),pointer :: clamp_or_fail - character(len=129),dimension(:),pointer :: description, units, stagger, coordinates - - integer, dimension(:,:,:,:), pointer :: dart_ind - -end type wrf_static_data_for_dart - -type wrf_dom - type(wrf_static_data_for_dart), pointer :: dom(:) - integer :: model_size -end type wrf_dom - -type(wrf_dom) :: wrf - -! JPH move map stuff into common (can move back into S/R later?) -real(r8) :: stdlon,truelat1,truelat2 !,latinc,loninc - -! have a single, module global error string (rather than -! replicate it in each subroutine and use up more stack space) -character(len=129) :: errstring, msgstring2, msgstring3 - -contains - -!####################################################################### - -subroutine static_init_model() - -! Initializes class data for WRF - -integer :: ncid -integer :: io, iunit - -character (len=1) :: idom -logical, parameter :: debug = .false. -integer :: ind, i, j, k, id, dart_index -integer :: my_index -integer :: var_element_list(max_state_variables) -logical :: var_update_list(max_state_variables) -real(r8) :: var_bounds_table(max_state_variables,2) -! holds the variable names for a domain when calling add_domain -character(len=129) :: netcdf_variable_names(max_state_variables) - -!---------------------------------------------------------------------- - -! Register the module -call register_module(source, revision, revdate) - -! Begin by reading the namelist input -call find_namelist_in_file("input.nml", "model_nml", iunit) -read(iunit, nml = model_nml, iostat = io) -call check_namelist_read(iunit, io, "model_nml") - -! Record the namelist values used for the run ... -if (do_nml_file()) write(nmlfileunit, nml=model_nml) -if (do_nml_term()) write( * , nml=model_nml) - -! Temporary warning until this namelist item is removed. -if (adv_mod_command /= '') then - msgstring2 = "Set the model advance command in the &dart_to_wrf_nml namelist" - call error_handler(E_MSG, 'static_init_model:', & - "WARNING: adv_mod_command ignored in &model_mod namelist", & - text2=msgstring2) -endif - -allocate(wrf%dom(num_domains)) -allocate(domain_id(num_domains)) - -! get default state variable table if asked -if ( default_state_variables ) then - wrf_state_variables = 'NULL' - call fill_default_state_table(wrf_state_variables) - msgstring2 = 'Set "default_state_variables" to .false. in the namelist' - msgstring3 = 'to use the "wrf_state_variables" list instead.' - call error_handler(E_MSG, 'static_init_model:', & - 'Using predefined wrf variable list for dart state vector.', & - text2=msgstring2, text3=msgstring3) - -endif - -if ( debug ) then - print*,'WRF state vector table' - print*,'default_state_variables = ',default_state_variables - print*,wrf_state_variables -endif - -!--------------------------- -! set this array so we know exactly which obs kinds are -! allowed to be interpolated (and can give a reasonably -! helpful error message if not). -!--------------------------- - -num_obs_kinds = get_num_quantities() -allocate(in_state_vector(num_obs_kinds)) -call fill_dart_kinds_table(wrf_state_variables, in_state_vector) - - -if ( debug ) then - if ( output_state_vector ) then - write(*,*)'netcdf file in state vector format' - else - write(*,*)'netcdf file in prognostic vector format' - endif -endif - -! set calendar type -call set_calendar_type(calendar_type) - -! Store vertical localization coordinate -! Only 4 are allowed: level(1), pressure(2), height(3), or scale height(4) -! Everything else is assumed height -if (vert_localization_coord == VERTISLEVEL) then - wrf%dom(:)%localization_coord = VERTISLEVEL -elseif (vert_localization_coord == VERTISPRESSURE) then - wrf%dom(:)%localization_coord = VERTISPRESSURE -elseif (vert_localization_coord == VERTISHEIGHT) then - wrf%dom(:)%localization_coord = VERTISHEIGHT -elseif (vert_localization_coord == VERTISSCALEHEIGHT) then - wrf%dom(:)%localization_coord = VERTISSCALEHEIGHT -else - write(msgstring2,*)'vert_localization_coord must be one of ', & - VERTISLEVEL, VERTISPRESSURE, VERTISHEIGHT, VERTISSCALEHEIGHT - write(errstring,*)'vert_localization_coord is ', vert_localization_coord - call error_handler(E_ERR,'static_init_model', errstring, source, revision,revdate, & - text2=msgstring2) -endif - -! the agreement amongst the dart/wrf users was that there was no need to -! read the wrf namelist, since the only thing it was extracting was the -! timestep, which is part of the wrf input netcdf file. -! call read_dt_from_wrf_nml() - -dart_index = 1 - -WRFDomains : do id=1,num_domains - - write( idom , '(I1)') id - - ! only print this once, no matter how many parallel tasks are running - if (do_output()) then - write(*,*) '******************' - write(*,*) '** DOMAIN # ',idom,' **' - write(*,*) '******************' - endif - - if(file_exist('wrfinput_d0'//idom)) then - - call nc_check( nf90_open('wrfinput_d0'//idom, NF90_NOWRITE, ncid), & - 'static_init_model','open wrfinput_d0'//idom ) - - else - - call error_handler(E_ERR,'static_init_model', & - 'Please put wrfinput_d0'//idom//' in the work directory.', source, revision,revdate) - - endif - - if(debug) write(*,*) ' ncid is ',ncid - -!------------------------------------------------------- -! read WRF dimensions -!------------------------------------------------------- - call read_wrf_dimensions(ncid,wrf%dom(id)%bt, wrf%dom(id)%bts, & - wrf%dom(id)%sn, wrf%dom(id)%sns, & - wrf%dom(id)%we, wrf%dom(id)%wes, & - wrf%dom(id)%sls) - -!------------------------------------------------------- -! read WRF file attributes -!------------------------------------------------------- - call read_wrf_file_attributes(ncid,id) - -!------------------------------------------------------- -! assign boundary condition flags -!------------------------------------------------------- - - call assign_boundary_conditions(id) - -!------------------------------------------------------- -! read static data -!------------------------------------------------------- - - call read_wrf_static_data(ncid,id) - - -!------------------------------------------------------- -! next block set up map -!------------------------------------------------------- - - call setup_map_projection(id) - -!------------------------------------------------------- -! end block set up map -!------------------------------------------------------- - -! get the number of wrf variables wanted in this domain's state - wrf%dom(id)%number_of_wrf_variables = get_number_of_wrf_variables(id,wrf_state_variables,var_element_list, var_update_list) - -! allocate and store the table locations of the variables valid on this domain - allocate(wrf%dom(id)%var_index_list(wrf%dom(id)%number_of_wrf_variables)) - wrf%dom(id)%var_index_list = var_element_list(1:wrf%dom(id)%number_of_wrf_variables) - -! allocation for wrf variable types - allocate(wrf%dom(id)%var_type(wrf%dom(id)%number_of_wrf_variables)) - -! allocation for update/nocopyback/noupdate - allocate(wrf%dom(id)%var_update_list(wrf%dom(id)%number_of_wrf_variables)) - wrf%dom(id)%var_update_list = var_update_list(1:wrf%dom(id)%number_of_wrf_variables) - -! allocation for dart kinds - allocate(wrf%dom(id)%dart_kind(wrf%dom(id)%number_of_wrf_variables)) - -! allocation of var size - allocate(wrf%dom(id)%var_size(3,wrf%dom(id)%number_of_wrf_variables)) - -! allocation for wrf variable metadata - allocate(wrf%dom(id)%stagger(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%description(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%units(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%coordinates(wrf%dom(id)%number_of_wrf_variables)) - -! set default bounds checking - allocate(wrf%dom(id)%lower_bound(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%upper_bound(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%clamp_or_fail(wrf%dom(id)%number_of_wrf_variables)) - call set_variable_bound_defaults(wrf%dom(id)%number_of_wrf_variables, & - wrf%dom(id)%lower_bound, & - wrf%dom(id)%upper_bound, & - wrf%dom(id)%clamp_or_fail) - -! build the variable indices -! this accounts for the fact that some variables might not be on all domains - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - - wrf%dom(id)%var_type(ind) = ind ! types are just the order for this domain - wrf%dom(id)%dart_kind(ind) = get_index_for_quantity(trim(wrf_state_variables(2,my_index))) - - if ( debug ) then - print*,'dart kind identified: ',trim(wrf_state_variables(2,my_index)),' ',wrf%dom(id)%dart_kind(ind) - endif - - ! get stagger and variable size - call get_variable_size_from_file(ncid,id, & - wrf_state_variables(1,my_index), & - wrf%dom(id)%bt, wrf%dom(id)%bts, & - wrf%dom(id)%sn, wrf%dom(id)%sns, & - wrf%dom(id)%we, wrf%dom(id)%wes, & - wrf%dom(id)%stagger(ind), & - wrf%dom(id)%var_size(:,ind)) - - ! get other variable metadata; units, coordinates and description - call get_variable_metadata_from_file(ncid,id, & - wrf_state_variables(1,my_index), & - wrf%dom(id)%description(ind), & - wrf%dom(id)%coordinates(ind), & - wrf%dom(id)%units(ind) ) - - if ( debug ) then - print*,'variable size ',trim(wrf_state_variables(1,my_index)),' ',wrf%dom(id)%var_size(:,ind) - endif - - ! add bounds checking information - call get_variable_bounds(wrf_state_bounds, wrf_state_variables(1,my_index), & - wrf%dom(id)%lower_bound(ind), wrf%dom(id)%upper_bound(ind), & - wrf%dom(id)%clamp_or_fail(ind)) - - if ( debug ) then - write(*,*) 'Bounds for variable ', & - trim(wrf_state_variables(1,my_index)), & - ' are ',wrf%dom(id)%lower_bound(ind), & - wrf%dom(id)%upper_bound(ind), & - wrf%dom(id)%clamp_or_fail(ind) - endif - - write(errstring, '(A,I4,2A)') 'state vector array ', ind, ' is ', trim(wrf_state_variables(1,my_index)) - call error_handler(E_MSG, 'static_init_model: ', errstring) - enddo - -! close data file, we have all we need - - call nc_check(nf90_close(ncid),'static_init_model','close wrfinput_d0'//idom) - -! indices into 1D array - hopefully this becomes obsolete -! JPH changed last dimension here from num_model_var_types - !HK allocate(wrf%dom(id)%dart_ind(wrf%dom(id)%wes,wrf%dom(id)%sns,wrf%dom(id)%bts,wrf%dom(id)%number_of_wrf_variables)) - -! start and stop of each variable in vector - allocate(wrf%dom(id)%var_index(2,wrf%dom(id)%number_of_wrf_variables)) - -!--------------------------- -! end block to be obsolete -!--------------------------- - -!--------------------------- -! at this point we need all information assigned to each variable -! then just loop thru the table -!--------------------------- - - !HK wrf%dom(id)%dart_ind = 0 - -! NOTE: this could be put into the loop above if wrf%dom(id)%dart_ind is -! eventually not needed -! Here we use ind instead of type as the 4th dimension. In is linked to the -! specific type via wrf%dom(id)%var_index_list(ind). This saves some -! space from the previous implementation but I am not yet sure of other -! problems that it might cause. - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - my_index = wrf%dom(id)%var_index_list(ind) - - if ( debug ) then - write(*,*) 'Assigning dart vector indices for var_type ',wrf%dom(id)%var_type(ind) - write(*,*) 'affiliated with WRF variable ',trim(wrf_state_variables(1,my_index)),' of size ',wrf%dom(id)%var_size(:,ind) - endif - - wrf%dom(id)%var_index(1,ind) = dart_index - do k=1,wrf%dom(id)%var_size(3,ind) - do j=1,wrf%dom(id)%var_size(2,ind) - do i=1,wrf%dom(id)%var_size(1,ind) - ! HK wrf%dom(id)%dart_ind(i,j,k,ind) & - ! = dart_index - dart_index = dart_index + 1 - enddo - enddo - enddo - wrf%dom(id)%var_index(2,ind) = dart_index - 1 - - if ( debug ) write(*,*) 'assigned start, stop ',wrf%dom(id)%var_index(:,ind) - - enddo ! loop through all viable state variables on this domain - if ( id == 1 ) then - wrf%dom(id)%domain_size = dart_index - 1 - else - wrf%dom(id)%domain_size = dart_index - 1 - do ind = id-1, 1, -1 - wrf%dom(id)%domain_size = wrf%dom(id)%domain_size - wrf%dom(ind)%domain_size - enddo - endif - - - ! NEWVAR: If you add a new wrf array type which is not yet in this list, currently - ! NEWVAR: you will have to add it here, and add a type_xx for it, and also add - ! NEWVAR: a in_state_vector case in the select statement. search for NEWVAR. - - ! JPH now that we have the domain ID just go ahead and get type indices once - ! NOTE: this is not strictly necessary - can use only stagger info in the future (???) - wrf%dom(id)%type_u = get_type_ind_from_type_string(id,'U') - wrf%dom(id)%type_v = get_type_ind_from_type_string(id,'V') - wrf%dom(id)%type_w = get_type_ind_from_type_string(id,'W') - wrf%dom(id)%type_t = get_type_ind_from_type_string(id,'T') - wrf%dom(id)%type_gz = get_type_ind_from_type_string(id,'PH') - wrf%dom(id)%type_qv = get_type_ind_from_type_string(id,'QVAPOR') - wrf%dom(id)%type_qr = get_type_ind_from_type_string(id,'QRAIN') - wrf%dom(id)%type_qc = get_type_ind_from_type_string(id,'QCLOUD') - wrf%dom(id)%type_qg = get_type_ind_from_type_string(id,'QGRAUP') - wrf%dom(id)%type_qh = get_type_ind_from_type_string(id,'QHAIL') - wrf%dom(id)%type_qi = get_type_ind_from_type_string(id,'QICE') - wrf%dom(id)%type_qs = get_type_ind_from_type_string(id,'QSNOW') - wrf%dom(id)%type_qgvol = get_type_ind_from_type_string(id,'QVGRAUPEL') - wrf%dom(id)%type_qhvol = get_type_ind_from_type_string(id,'QVHAIL') - wrf%dom(id)%type_qnice = get_type_ind_from_type_string(id,'QNICE') - wrf%dom(id)%type_qndrp = get_type_ind_from_type_string(id,'QNDRP') - wrf%dom(id)%type_qnsnow = get_type_ind_from_type_string(id,'QNSNOW') - wrf%dom(id)%type_qnrain = get_type_ind_from_type_string(id,'QNRAIN') - wrf%dom(id)%type_qngraupel = get_type_ind_from_type_string(id,'QNGRAUPEL') - wrf%dom(id)%type_qnhail = get_type_ind_from_type_string(id,'QNHAIL') - wrf%dom(id)%type_u10 = get_type_ind_from_type_string(id,'U10') - wrf%dom(id)%type_v10 = get_type_ind_from_type_string(id,'V10') - wrf%dom(id)%type_t2 = get_type_ind_from_type_string(id,'T2') - wrf%dom(id)%type_th2 = get_type_ind_from_type_string(id,'TH2') - wrf%dom(id)%type_q2 = get_type_ind_from_type_string(id,'Q2') - wrf%dom(id)%type_ps = get_type_ind_from_type_string(id,'PSFC') - wrf%dom(id)%type_mu = get_type_ind_from_type_string(id,'MU') - wrf%dom(id)%type_tsk = get_type_ind_from_type_string(id,'TSK') - wrf%dom(id)%type_2dflash = get_type_ind_from_type_string(id,'FLASH_RATE_2D') - wrf%dom(id)%type_tslb = get_type_ind_from_type_string(id,'TSLB') - wrf%dom(id)%type_smois = get_type_ind_from_type_string(id,'SMOIS') - wrf%dom(id)%type_sh2o = get_type_ind_from_type_string(id,'SH2O') - wrf%dom(id)%type_refl = get_type_ind_from_type_string(id,'REFL_10CM') - wrf%dom(id)%type_dref = get_type_ind_from_type_string(id,'DIFF_REFL_10CM') - wrf%dom(id)%type_spdp = get_type_ind_from_type_string(id,'SPEC_DIFF_10CM') - wrf%dom(id)%type_fall_spd = get_type_ind_from_type_string(id,'FALL_SPD_Z_WEIGHTED') - !wrf%dom(id)%type_fall_spd = get_type_ind_from_type_string(id,'VT_DBZ_WT') - wrf%dom(id)%type_hdiab = get_type_ind_from_type_string(id,'H_DIABATIC') - - ! variable bound table for setting upper and lower bounds of variables - var_bounds_table(1:wrf%dom(id)%number_of_wrf_variables,1) = wrf%dom(id)%lower_bound - var_bounds_table(1:wrf%dom(id)%number_of_wrf_variables,2) = wrf%dom(id)%upper_bound - - ! List of netcdf variable names in the domain - do i = 1, wrf%dom(id)%number_of_wrf_variables - my_index = wrf%dom(id)%var_index_list(i) ! index in wrf_state_variables - netcdf_variable_names(i) = wrf_state_variables(1, my_index) - enddo - - - ! add domain - not doing anything with domain_id yet so just overwriting it - domain_id(id) = add_domain( 'wrfinput_d0'//idom, & - wrf%dom(id)%number_of_wrf_variables, & - var_names = netcdf_variable_names(1:wrf%dom(id)%number_of_wrf_variables), & - clamp_vals = var_bounds_table(1:wrf%dom(id)%number_of_wrf_variables,:) ) - - if (debug) call state_structure_info(domain_id(id)) - -enddo WRFDomains - -wrf%model_size = dart_index - 1 -write(errstring,*) ' wrf model size is ',wrf%model_size -call error_handler(E_MSG, 'static_init_model', errstring) - -end subroutine static_init_model - -!####################################################################### -!> Convert from the state structure id to the wrf domain number. -!> These are the same if there is only WRF involved in the assimilation -!> The state structure id may be different if WRF is coupled with another -!> model. -function get_wrf_domain(state_id) - -integer, intent(in) :: state_id -integer :: get_wrf_domain - -integer :: i - -do i = 1, num_domains - if (domain_id(i) == state_id) then - get_wrf_domain = i - return - endif -enddo - -call error_handler(E_ERR, 'get_wrf_domain', 'not a valid domain') - -end function get_wrf_domain - -!####################################################################### - -function get_model_size() - -integer :: get_model_size - -get_model_size = wrf%model_size - -end function get_model_size - -!####################################################################### - -function get_number_domains() - -integer :: get_number_domains - -get_number_domains = num_domains - -end function get_number_domains - -!####################################################################### - -function get_wrf_static_data(dom_num) - -integer, intent(in) :: dom_num - -type(wrf_static_data_for_dart) :: get_wrf_static_data - -get_wrf_static_data = wrf%dom(dom_num) - -return -end function get_wrf_static_data - -!####################################################################### - -subroutine get_wrf_state_variables(state_var) - -character(len=129), intent(out) :: state_var(num_state_table_columns,max_state_variables) - -state_var = wrf_state_variables - -end subroutine get_wrf_state_variables - -!####################################################################### - -function get_model_time_step() -!------------------------------------------------------------------------ -! function get_model_time_step() -! -! Returns the time step of the model. In the long run should be replaced -! by a more general routine that returns details of a general time-stepping -! capability. -! -! toward that end ... we are now reading a namelist variable for the -! width of the assimilation time window. - -type(time_type) :: get_model_time_step -integer :: model_dt, assim_dt - -! We need to coordinate the desired assimilation window to be a -! multiple of the model time step (which has no precision past integer seconds). - -model_dt = nint(wrf%dom(1)%dt) - -! The integer arithmetic does its magic. -assim_dt = (assimilation_period_seconds / model_dt) * model_dt - -get_model_time_step = set_time(assim_dt) - -end function get_model_time_step - - -!####################################################################### - - -subroutine get_state_meta_data(state_handle, index_in, location, var_type_out, id_out) - -! Given an integer index into the DART state vector structure, returns the -! associated location. This is not a function because the more general -! form of the call has a second intent(out) optional argument kind. -! Maybe a functional form should be added? - -! this version has an optional last argument that is never called by -! any of the dart code, which can return the wrf domain number. - -type(ensemble_type), intent(in) :: state_handle -integer(i8), intent(in) :: index_in -type(location_type), intent(out) :: location -integer, optional, intent(out) :: var_type_out, id_out - -integer :: var_type, dart_type -integer(i8) :: index -integer :: ip, jp, kp -integer :: nz, ny, nx -logical :: var_found -real(r8) :: lon, lat, lev -character(len=129) :: string1 - -integer :: i, id, var_id, state_id -logical, parameter :: debug = .false. - -! from the dart index get the local variables indices -call get_model_variable_indices(index_in, ip, jp, kp, var_id=var_id, dom_id=state_id) - -! convert from state_structure domain number to wrf. -id = get_wrf_domain(state_id) - -! at this point, (ip,jp,kp) refer to indices in the variable's own grid - -if(debug) write(*,*) ' ip, jp, kp for index ',ip,jp,kp,index -if(debug) write(*,*) ' Var type: ',var_type - -var_type = wrf%dom(id)%var_type(var_id) -dart_type = wrf%dom(id)%dart_kind(var_id) - -! first obtain lat/lon from (ip,jp) -call get_wrf_horizontal_location( ip, jp, var_type, id, lon, lat ) - -!HK Not doing this conversion for now in the distributed version. Doing it on demand in -! vert_convert - NOPE I think that this is really slow, because each step in the sequential -! obs do loop is limited by the slowest processor doing vertical conversion. It makes -! more sense for everyone to blast through all their pieces of state. Idea: overlap this -! with task 0 writing the diagnostic files. - -! now convert to desired vertical coordinate (defined in the namelist) -if (wrf%dom(id)%localization_coord == VERTISLEVEL) then - ! here we need level index of mass grid - if( (var_type == wrf%dom(id)%type_w ) .or. (var_type == wrf%dom(id)%type_gz) ) then - lev = real(kp) - 0.5_r8 - else - lev = real(kp) - endif -elseif (wrf%dom(id)%localization_coord == VERTISPRESSURE) then - ! directly convert to pressure - lev = model_pressure_distrib(ip, jp, kp, id, var_type, state_handle) -elseif (wrf%dom(id)%localization_coord == VERTISHEIGHT) then - lev = model_height_distrib(ip, jp, kp, id, var_type, state_handle) -elseif (wrf%dom(id)%localization_coord == VERTISSCALEHEIGHT) then - lev = -log(model_pressure_distrib(ip, jp, kp, id, var_type, state_handle) / & - model_surface_pressure_distrib(ip, jp, id, var_type, state_handle)) -endif - -if(debug) write(*,*) 'lon, lat, lev: ',lon, lat, lev - -! convert to DART location type -location = set_location(lon, lat, lev, wrf%dom(id)%localization_coord) - -! return DART variable kind if requested -if(present(var_type_out)) var_type_out = dart_type - -! return domain id if requested -if(present(id_out)) id_out = id - -end subroutine get_state_meta_data - -!-------------------------------------------------------------------- -!> Distributed version of model interpolate -!> obs_kind is called as location type in assim model model -!> Storing the mean copy level location as the observation location -!> to save recomputation of v_p in vert_convert in get_close_obs -! -! Should this code be simplified so there is not so much repetition? -! This is the main forward operator subroutine for WRF. -! Given an ob (its DART location and kind), the corresponding model -! value is computed at nearest i,j,k. Thus, first i,j,k is obtained -! from ob lon,lat,z and then the state value that corresponds to -! the ob kind is interpolated. -! -! No location conversions are carried out in this subroutine. See -! get_close_obs, where ob vertical location information is converted -! to the requested vertical coordinate type. -subroutine model_interpolate(state_handle, ens_size, location, obs_kind, expected_obs, istatus) - -! x: Full DART state vector relevant to what's being updated -! in the filter (mean or individual members). -! istatus: Returned 0 if everything is OK, >0 if error occured. -! 1 = missing domain id -! 2 = bad vertical level -! 3 = unsupported obs kind -! 10 = polar observation while restrict_polar namelist true -! 99 = unknown reason (reached end of routine with missing_r8 -! as obs_val) - -! modified 26 June 2006 to accomodate vortex attributes -! modified 13 December 2006 to accomodate changes for the mpi version -! modified 22 October 2007 to accomodate global WRF (3.0) -! modified 18 November 2008 to allow unknown kinds to return without stopping -! modified 5 February 2010 to add circulation calc for vortex obs - -! Helen Kershaw - Aim: to not require the whole state vector - -! arguments -type(ensemble_type), intent(in) :: state_handle -integer, intent(in) :: ens_size -type(location_type), intent(in) :: location -integer, intent(in) :: obs_kind -real(r8), intent(out) :: expected_obs(ens_size) -integer, intent(out) :: istatus(ens_size) - -! local -logical, parameter :: debug = .false. -logical, parameter :: restrict_polar = .false. -logical, parameter :: use_old_vortex = .true. ! set to .false. to use circ code -real(r8), parameter :: drad = pi / 18.0_r8 -real(r8) :: xloc, yloc, xloc_u, yloc_v, xyz_loc(3) -integer :: i, i_u, j, j_v, k2 -real(r8) :: dx,dy,dxm,dym,dx_u,dxm_u,dy_v,dym_v -integer :: id -logical :: surf_var -real(r8) :: a1(ens_size) -real(r8) :: zloc(ens_size) -integer :: k(ens_size) -real(r8) :: dz(ens_size), dzm(ens_size) -real(r8) :: utrue(ens_size), vtrue(ens_size) - -! from getCorners -integer, dimension(2) :: ll, lr, ul, ur, ll_v, lr_v, ul_v, ur_v -integer :: rc, i1, i2 -integer(i8) :: ill, ilr, iul, iur - -real(r8) :: fld(2,ens_size) -real(r8), allocatable, dimension(:,:) :: v_h, v_p - -! local vars, used in finding sea-level pressure and vortex center -real(r8), allocatable, dimension(:, :) :: t1d, p1d, qv1d, z1d -real(r8), allocatable, dimension(:, :) :: z1d_1, z1d_2 -real(r8), allocatable, dimension(:,:) :: pp, pd -real(r8), allocatable, dimension(:,:,:) :: vfld -real(r8), allocatable, dimension(:,:,:) :: uwnd, vwnd, vort -real(r8), allocatable, dimension(:) :: x1d, y1d, xx1d, yy1d -integer :: center_search_half_size, center_track_xmin, center_track_ymin, & - center_track_xmax, center_track_ymax, circ_half_size, & - circ_xmin, circ_xmax, circ_ymin, circ_ymax, xlen, ylen, & - xxlen, yylen, ii1, ii2, cxlen, cylen, imax, jmax -real(r8) :: clat, clon, cxloc, cyloc, vcrit, & - circ_half_length, asum, distgrid, dgi1, dgi2 - -real(r8) :: magwnd(ens_size), maxwspd(ens_size), circ(ens_size) - -! local vars, used in calculating density, pressure, height -real(r8) :: rho1(ens_size) , rho2(ens_size) , rho3(ens_size), rho4(ens_size) -real(r8) :: pres1(ens_size), pres2(ens_size), pres3(ens_size), pres4(ens_size), pres(ens_size) -logical :: is_lev0(ens_size) - -! local var for terrain elevation check for surface stations -real(r8) :: mod_sfc_elevation - - -real(r8) :: x_ill(ens_size), x_iul(ens_size), x_ilr(ens_size), x_iur(ens_size), ugrid(ens_size), vgrid(ens_size) -real(r8) :: x_ugrid_1(ens_size), x_ugrid_2(ens_size), x_ugrid_3(ens_size), x_ugrid_4(ens_size) -real(r8) :: x_vgrid_1(ens_size), x_vgrid_2(ens_size), x_vgrid_3(ens_size), x_vgrid_4(ens_size) -integer :: e, count, uk !< index varibles for loop -real(r8) :: failedcopies(ens_size) -integer, allocatable :: uniquek(:) -integer :: ksort(ens_size) - -integer(i8) :: ugrid_1, ugrid_2, ugrid_3, ugrid_4, vgrid_1, vgrid_2, vgrid_3, vgrid_4 -integer(i8) :: z1d_ind1, z1d_ind2, t1d_ind, qv1d_ind - -id = 1 - -! HK printing out sizes of wrf_static_data_for_dart -!print*, '******** wrf_static_data_for_dart' -!print*, 'znu, dn, dnw, zs, znw ', size(wrf%dom(id)%znu), size(wrf%dom(id)%dn), size(wrf%dom(id)%dnw), size(wrf%dom(id)%zs), size(wrf%dom(id)%znw) -!print*, 'mub, hgt ', size(wrf%dom(id)%mub), size(wrf%dom(id)%hgt) -!print*, 'latitude, latitude_u, latitude_v ', size(wrf%dom(id)%latitude), size(wrf%dom(id)%latitude_u), size(wrf%dom(id)%latitude_v) -!print*, 'longitude, longitude_u, longitude_v ', size(wrf%dom(id)%longitude), size(wrf%dom(id)%longitude_u), size(wrf%dom(id)%longitude_v) -!print*, 'phb ', size(wrf%dom(id)%phb) - -!print*, 'var_index ', size(wrf%dom(id)%var_index) -!print*, 'var_size ', size(wrf%dom(id)%var_size) -!print*, 'var_type ', size(wrf%dom(id)%var_type) -!print*, 'var_index_list ', size(wrf%dom(id)%var_index_list) -!print*, 'var_update_list ', size(wrf%dom(id)%var_update_list) -!print*, 'dart_kind ', size(wrf%dom(id)%dart_kind) -!print*, 'land ', size(wrf%dom(id)%land) -!print*, 'lower_bound,upper_bound ', size(wrf%dom(id)%lower_bound), size(wrf%dom(id)%upper_bound) -!print*, 'clamp_or_fail ', size(wrf%dom(id)%clamp_or_fail) -!print*, 'description, units, stagger, coordinates ', size(wrf%dom(id)%description), size(wrf%dom(id)%units), size(wrf%dom(id)%stagger), size(wrf%dom(id)%coordinates) -!print*, 'dart_ind ', size(wrf%dom(id)%dart_ind) - - -! Initialize stuff -istatus(:) = 0 -fld(:,:) = missing_r8 -expected_obs(:) = missing_r8 !< array of obs_vals -failedcopies(:) = 1 - -! If identity observation (obs_kind < 0), then no need to interpolate -if ( obs_kind < 0 ) then - - ! identity observation -> -(obs_kind)=DART state vector index - ! obtain state value directly from index - expected_obs(:) = get_state(int(-1*obs_kind, i8), state_handle) - -! Otherwise, we need to do interpolation -else - - ! Is this a valid kind to interpolate? Set up in the static_init_model code, - ! based on entries in wrf_state_vector namelist item. - if (.not. in_state_vector(obs_kind)) then - write(errstring, *) 'cannot interpolate ' // trim(get_name_for_quantity(obs_kind)) & - // ' with the current WRF arrays in state vector' - call error_handler(E_ERR, 'model_interpolate', errstring, & - source, revision, revdate) - endif - - ! Unravel location_type information - xyz_loc = get_location(location) - - !---------------------------------- - ! 0. Prelude to Interpolation - !---------------------------------- - - ! 0.a Horizontal stuff - - ! first obtain domain id, and mass points (i,j) -! JPH --- scm is only defined for d1 - if ( .not. scm ) then - call get_domain_info(xyz_loc(1),xyz_loc(2),id,xloc,yloc) - else - id = 1 - xloc = 1.0_r8 - yloc = 1.0_r8 - endif - - ! check that we obtained a valid domain id number - if (id==0) then - istatus = 1 - return - endif - - if ( debug ) then - write(*,*) 'retreiving obs kind ',obs_kind,' on domain ',id - endif - - !***************************************************************************** - ! Check polar-b.c. constraints -- if restrict_polar = .true., then we are not - ! processing observations poleward of the 1st or last mass grid points. - ! If we have tried to pass a polar observation, then exit with istatus = 10 - if ( wrf%dom(id)%polar .and. restrict_polar ) then - if ( yloc < 1.0_r8 .or. yloc >= real(wrf%dom(id)%sn,r8) ) then - - ! Perhaps write to dart_log.out? - write(errstring,*)'Obs cannot be polar with restrict_polar on: yloc = ',yloc - call error_handler(E_WARN,'model_interpolate', errstring, & - source, revision, revdate) - - istatus = 10 ! istatus 10, if it's not used, will mean the observation is too polar - print*, 'model_mod.f90 :: model_interpolate :: No polar observations! istatus = ', istatus - return - endif - endif - !***************************************************************************** - - ! print info if debugging - if(debug) then - i = xloc - j = yloc - print*,xyz_loc(2), xyz_loc(1), xloc,yloc - write(*,*) ' corners of lat ' - write(*,*) wrf%dom(id)%latitude(i,j),wrf%dom(id)%latitude(i+1,j), & - wrf%dom(id)%latitude(i,j+1), & - wrf%dom(id)%latitude(i+1,j+1) - write(*,*) ' corners of long ' - write(*,*) wrf%dom(id)%longitude(i,j),wrf%dom(id)%longitude(i+1,j), & - wrf%dom(id)%longitude(i,j+1), & - wrf%dom(id)%longitude(i+1,j+1) - endif - - ! get integer (west/south) grid point and distances to neighboring grid points - ! distances are used as weights to carry out horizontal interpolations - call toGrid(xloc,i,dx,dxm) - call toGrid(yloc,j,dy,dym) - - ! 0.b Vertical stuff - - if ( debug ) then - write(*,*) 'vert_is_pressure ',vert_is_pressure(location) - write(*,*) 'vert_is_height ',vert_is_height(location) - endif - - ! HK - ! Allocate both a vertical height and vertical pressure coordinate -- 0:bt x ens_size - allocate(v_h(0:wrf%dom(id)%bt, ens_size), v_p(0:wrf%dom(id)%bt, ens_size)) - - ! Set surf_var to .false. and then change in vert_is_surface section if necessary - surf_var = .false. - - ! Determine corresponding model level for obs location - ! This depends on the obs vertical coordinate - ! From this we get a meaningful z-direction real-valued index number - if(vert_is_level(location)) then - ! Ob is by model level - zloc = xyz_loc(3) - - elseif(vert_is_pressure(location)) then - ! Ob is by pressure: get corresponding mass level zloc from - ! computed column pressure profile - call get_model_pressure_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_p,state_handle, ens_size) - - !print*, 'v_p distrib ', v_p - -! testing get_model_pressure_profile_distrib -! istatus = 0 -! expected_obs = -9 -! return - - ! get pressure vertical co-ordinate - call pres_to_zk_distrib(xyz_loc(3), v_p, wrf%dom(id)%bt, ens_size, zloc,is_lev0) - if(debug .and. obs_kind /= QTY_SURFACE_PRESSURE) & - print*,' obs is by pressure and zloc,lev0 =',zloc, is_lev0 - if(debug) print*,'model pressure profile' - if(debug) print*,v_p - - ! If location is above model surface but below the lowest sigma level, - ! the default is to reject it. But if the namelist value is true, then - ! accept the observation and later on extrapolate the values from levels - ! 1 and 2 downward. - - !HK ensemble loop, however do you reject the obs for all ensembles later anyway? - - do e = 1, ens_size - - if (is_lev0(e)) then - ! the pres_to_zk() routine has returned a valid zloc in case we - ! want to use it. the default is to reject the observation and so - ! we overwrite it with missing -- but, if the namelist value is set - ! to true, leave zloc alone. - if (.not. allow_obs_below_vol) zloc(e) = missing_r8 - if (debug .and. .not. allow_obs_below_vol) print*, 'setting zloc missing' - - ! else need to set a qc here? - endif - - enddo - - elseif(vert_is_height(location)) then - - ! Ob is by height: get corresponding mass level zloc from - ! computed column height profile - call get_model_height_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_h, state_handle, ens_size) - ! get height vertical co-ordinate - do e = 1, ens_size ! HK should there be a height_to_zk_distrib? - call height_to_zk(xyz_loc(3), v_h(:, e), wrf%dom(id)%bt,zloc(e),is_lev0(e)) - if(debug) print*,' obs is by height and zloc,lev0 =',zloc(e), is_lev0(e) - if(debug) print*,'model height profile' - if(debug) print*,v_h - - ! If location is above model surface but below the lowest sigma level, - ! the default is to reject it. But if the namelist value is true, then - ! accept the observation and later on extrapolate the values from levels - ! 1 and 2 downward. - if (is_lev0(e)) then - ! the height_to_zk() routine has returned a valid zloc in case we - ! want to use it. the default is to reject the observation and so - ! we overwrite it with missing. but if the namelist value is set - ! to true, leave zloc alone. - if (.not. allow_obs_below_vol) zloc(e) = missing_r8 - if (debug .and. .not. allow_obs_below_vol) print*, 'setting zloc missing member ', e - ! else need to set a qc here? - endif - - enddo - - elseif(vert_is_surface(location)) then - zloc = 1.0_r8 - surf_var = .true. - if(debug) print*,' obs is at the surface = ', xyz_loc(3) - - ! if you want to have a distance check to see if the station height - ! is too far away from the model surface height, here is the place to - ! reject the observation. - - ! Elevation check function drawn from Ryan's wrf preprocessing code. - ! The elevation is now passed in instead of calling model_interpolate: - ! sfc_elev_max_diff - if < 0 routine is skipped. - ! mod_sfc_elevation - interpolated model surface height at the lowest - ! - model layer. - ! z_loc - the third array element of xyz_loc - ! the station elevation against the estimated model surface height at the - ! station location, the maximum difference in elevation allowed (m), and the - ! observation location. There is no check for whether the third element for - ! the xyz_loc array is anything other than station height in meters. The - ! function returns a logical where .true. means the obs station elevation - ! 'passed' the height check. Here, if a height check fails, set an istatus - ! of '1' and bail out of this routine. - if ( sfc_elev_max_diff >= 0 ) then - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners HGT for sfc rc = ', rc - - ! Interpolation for the HGT field -- HGT is NOT part of state vector x, but rather - ! in the associated domain meta data - mod_sfc_elevation = dym*( dxm*wrf%dom(id)%hgt(ll(1), ll(2)) + & - dx*wrf%dom(id)%hgt(lr(1), lr(2)) ) + & - dy*( dxm*wrf%dom(id)%hgt(ul(1), ul(2)) + & - dx*wrf%dom(id)%hgt(ur(1), ur(2)) ) - endif - if ( .not. height_diff_check(sfc_elev_max_diff,xyz_loc(3),mod_sfc_elevation) ) zloc = missing_r8 - endif - - elseif(vert_is_undef(location)) then - ! the zloc value should not be used since there is no actual vertical - ! location for this observation, but give zloc a valid value to avoid - ! the error checks below for missing_r8 - zloc = 0.0_r8 - if(debug) print*,' obs height is intentionally undefined' - - else - write(errstring,*) 'wrong option for which_vert ', & - nint(query_location(location,'which_vert')) - call error_handler(E_ERR,'model_interpolate', errstring, & - source, revision, revdate) - - endif - - - ! Deal with missing vertical coordinates -- return with istatus .ne. 0 - ! HK This is annoying. Back to earlier question of QC if one ensemble fails do we - ! discard all the obervations? - ! For now, to replicate the original code behaviour keep track of the failed copies - - ! ********* bail out early code ********** - !if(any(zloc == missing_r8)) then - ! print*, 'One of the ensembles has failed the vertical co ordinate rank', my_task_id() - ! expected_obs = missing_r8 !HK this is redundant? expected_obs is initialized to missing_r8 - ! istatus = 2 - ! deallocate(v_h, v_p) - ! return - !endif - ! ********* endof bail out early code ******** - - do e = 1, ens_size - if(zloc(e) == missing_r8) then - failedcopies(e) = missing_r8 - istatus(e) = 2 - endif - enddo - - ! Set a working integer k value -- if (int(zloc) < 1), then k = 1 - k = max(1,int(zloc)) !HK k is now ensemble size - - - ! Find the unique k values - ksort = sort(k) - - count = 1 - do e = 2, ens_size - if ( ksort(e) /= ksort(e-1) ) count = count + 1 - enddo - - allocate(uniquek(count)) - - uk = 1 - do e = 1, ens_size - if ( all(uniquek /= k(e)) ) then - uniquek(uk) = k(e) - uk = uk + 1 - endif - enddo - - ! The big horizontal interp loop below computes the data values in the level - ! below and above the actual location, and then does a separate vertical - ! interpolation (if the obs is not a 2d surface obs). The two values are - ! stored in fld(1:2). Set them to missing here, and if the code below cannot - ! compute a value, it can just drop out and not have to explicitly set it to - ! missing anymore. - fld(:,:) = missing_r8 !HK - - !---------------------------------- - ! 1. Horizontal Interpolation - !---------------------------------- - - ! This part is the forward operator -- compute desired model state value for given point. - - ! Strategy is to do the horizontal interpolation on two different levels in the - ! vertical, and then to do the vertical interpolation afterwards, since it depends on - ! what the vertical coordinate is - - ! Large if-structure to select on obs_kind of desired field.... - ! Table of Contents: - ! 1.a Horizontal Winds (U, V, U10, V10) - ! 1.b Sensible Temperature (T, T2) - ! 1.c Potential Temperature (Theta, TH2) - ! 1.d Density (Rho) - ! 1.e Vertical Wind (W) - ! 1.f Specific Humidity (SH, SH2) - ! 1.g Vapor Mixing Ratio (QV, Q2) - ! 1.h Rainwater Mixing Ratio (QR) - ! 1.i.1 Graupel Mixing Ratio (QG) - ! 1.i.2 Hail Mixing Ratio (QH) - ! 1.j Snow Mixing Ratio (QS) - ! 1.k Ice Mixing Ratio (QI) - ! 1.l Cloud Mixing Ratio (QC) - ! 1.m Droplet Number Concentration (QNDRP) - ! 1.n Ice Number Concentration (QNICE) - ! 1.o Snow Number Concentration (QNSNOW) - ! 1.p Rain Number Concentration (QNRAIN) - ! 1.q.1 Graupel Number Concentration (QNGRAUPEL) - ! 1.q.2 Hail Number Concentration (QNHAIL) - ! 1.r Previous time step condensational heating (H_DIABATIC) - ! 1.s Reflectivity weighted precip fall speed (FALL_SPD_Z_WEIGHTED) - ! 1.t Pressure (P) - ! 1.u Vortex Center Stuff from Yongsheng - ! 1.v.1 Radar Reflectivity (REFL_10CM) - ! 1.v.2 Differential Reflectivity (DIFF_REFL_10CM) - ! 1.v.3 Specific Differential Phase (SPEC_DIFF_10CM) - ! 1.w Geopotential Height (GZ) - ! 1.x Surface Elevation (HGT) - ! 1.y Surface Skin Temperature (TSK) - ! 1.z Land Mask (XLAND) - - ! NEWVAR: Currently you have to add a new case here to tell the code what - ! field inside the state vector you will be interpolating in. the eventual - ! plan is for there to be a default case which all simple interps fall into, - ! but for now we still have to add code. - - ! NOTE: the previous version of this code checked for surface observations with the syntax: - ! "if(.not. vert_is_surface(location) .or. .not. surf_var) then" - ! We identified this as redundant because surf_var is changed from .false. only by - ! the above code (section 0.b), which must be traced through before one can arrive - ! at the following forward operator code. Hence, we can remove the call to - ! vert_is_surface. - - if (obs_kind == QTY_RAINWATER_MIXING_RATIO .or. & - obs_kind == QTY_GRAUPEL_MIXING_RATIO .or. & - obs_kind == QTY_HAIL_MIXING_RATIO .or. & - obs_kind == QTY_SNOW_MIXING_RATIO .or. & - obs_kind == QTY_CLOUD_ICE .or. & - obs_kind == QTY_CLOUD_LIQUID_WATER .or. & - obs_kind == QTY_DROPLET_NUMBER_CONCENTR .or. & - obs_kind == QTY_ICE_NUMBER_CONCENTRATION .or. & - obs_kind == QTY_SNOW_NUMBER_CONCENTR .or. & - obs_kind == QTY_RAIN_NUMBER_CONCENTR .or. & - obs_kind == QTY_GRAUPEL_NUMBER_CONCENTR .or. & - obs_kind == QTY_HAIL_NUMBER_CONCENTR .or. & - obs_kind == QTY_CONDENSATIONAL_HEATING .or. & - obs_kind == QTY_POWER_WEIGHTED_FALL_SPEED .or. & - obs_kind == QTY_RADAR_REFLECTIVITY .or. & - obs_kind == QTY_DIFFERENTIAL_REFLECTIVITY .or. & - obs_kind == QTY_SPECIFIC_DIFFERENTIAL_PHASE ) then - - call simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_handle) - if (all(fld == missing_r8)) goto 200 - - - ! don't accept negative fld - if (obs_kind == QTY_RAINWATER_MIXING_RATIO .or. & - obs_kind == QTY_GRAUPEL_MIXING_RATIO .or. & - obs_kind == QTY_HAIL_MIXING_RATIO .or. & - obs_kind == QTY_SNOW_MIXING_RATIO .or. & - obs_kind == QTY_CLOUD_ICE .or. & - obs_kind == QTY_CLOUD_LIQUID_WATER .or. & - obs_kind == QTY_DROPLET_NUMBER_CONCENTR .or. & - obs_kind == QTY_ICE_NUMBER_CONCENTRATION .or. & - obs_kind == QTY_SNOW_NUMBER_CONCENTR .or. & - obs_kind == QTY_RAIN_NUMBER_CONCENTR .or. & - obs_kind == QTY_GRAUPEL_NUMBER_CONCENTR .or. & - obs_kind == QTY_HAIL_NUMBER_CONCENTR ) then - - fld = max(0.0_r8, fld) ! Don't accept negative - - endif - - - !----------------------------------------------------- - ! 1.a Horizontal Winds (U, V, U10, V10) - - ! We need one case structure for both U & V because they comprise a vector which could need - ! transformation depending on the map projection (hence, the call to gridwind_to_truewind) - elseif( obs_kind == QTY_U_WIND_COMPONENT .or. obs_kind == QTY_V_WIND_COMPONENT) then ! U, V - - ! This is for 3D wind fields -- surface winds later - if(.not. surf_var) then - - if ( ( wrf%dom(id)%type_u >= 0 ) .and. ( wrf%dom(id)%type_v >= 0 ) ) then - - ! xloc and yloc are indices on mass-grid. If we are on a periodic longitude domain, - ! then xloc can range from [1 wes). This means that simply adding 0.5 to xloc has - ! the potential to render xloc_u out of the valid mass-grid index bounds (>wes). - ! To remedy this, we can either do periodicity check on xloc_u value, or we can - ! leave it to a subroutine or function to alter xloc to xloc_u if the observation - ! type requires it. - xloc_u = xloc + 0.5_r8 - yloc_v = yloc + 0.5_r8 - - ! Check periodicity if necessary -- but only subtract 'we' because the U-grid - ! cannot have an index < 1 (i.e., U(wes) = U(1) ). - if ( wrf%dom(id)%periodic_x .and. xloc_u > real(wrf%dom(id)%wes,r8) ) & - xloc_u = xloc_u - real(wrf%dom(id)%we,r8) - - ! Get South West gridpoint indices for xloc_u and yloc_v - call toGrid(xloc_u,i_u,dx_u,dxm_u) - call toGrid(yloc_v,j_v,dy_v,dym_v) - - do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i_u, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_u) .and. & - boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_v) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_u) .and. & - boundsCheck( j_v, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_v) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_u) ) then - - ! Need to get grid cell corners surrounding observation location -- with - ! periodicity, this could be non-consecutive (i.e., NOT necessarily i and i+1); - ! Furthermore, it could be different for the U-grid and V-grid. Remember, for - ! now, we are disallowing observations to be located poleward of the 1st and - ! last mass points. - - call getCorners(i_u, j, id, wrf%dom(id)%type_u, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners U rc = ', rc - - call getCorners(i, j_v, id, wrf%dom(id)%type_v, ll_v, ul_v, lr_v, ur_v, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners V rc = ', rc - - ! Now we want to get the corresponding DART state vector indices, and then - ! interpolate horizontally on TWO different vertical levels (so that we can - ! do the vertical interpolation properly later) - do k2 = 1, 2 - - ! Interpolation for the U field - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_u) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_u) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_u) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_u) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - ugrid = dym*( dxm_u*x_ill + dx_u*x_ilr ) + dy*( dxm_u*x_iul + dx_u*x_iur ) - - ! Interpolation for the V field - ill = get_dart_vector_index(ll_v(1), ll_v(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_v) - iul = get_dart_vector_index(ul_v(1), ul_v(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_v) - ilr = get_dart_vector_index(lr_v(1), lr_v(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_v) - iur = get_dart_vector_index(ur_v(1), ur_v(2), uniquek(uk)+k2-1, domain_id(id), wrf%dom(id)%type_v) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - vgrid = dym_v*( dxm*x_ill + dx*x_ilr ) + dy_v*( dxm*x_iul + dx*x_iur ) - - do e = 1, ens_size - - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - ! Certain map projections have wind on grid different than true wind (on map) - ! subroutine gridwind_to_truewind is in module_map_utils.f90 - call gridwind_to_truewind(xyz_loc(1), wrf%dom(id)%proj, ugrid(e), vgrid(e), & - utrue(e), vtrue(e)) - - ! Figure out which field was the actual desired observation and store that - ! field as one of the two elements of "fld" (the other element is the other - ! k-level) - if( obs_kind == QTY_U_WIND_COMPONENT) then - fld(k2, e) = utrue(e) - else ! must want v - fld(k2, e) = vtrue(e) - endif - endif - enddo - enddo - endif - - enddo - - endif - - - ! This is for surface wind fields -- NOTE: surface winds are on Mass grid - ! (therefore, TYPE_T), not U-grid & V-grid. - ! Also, because surface winds are at a given single vertical level, - ! only fld(1) will be filled. - else - - if ( ( wrf%dom(id)%type_u10 >= 0 ) .and. ( wrf%dom(id)%type_v10 >= 0 ) ) then - - ! JPH -- should test this for doubly periodic - ! JPH -- does not pass for SCM config, so just do it below - ! Check to make sure retrieved integer gridpoints are in valid range - if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners U10, V10 rc = ', rc - - ! Interpolation for the U10 field - ill = get_dart_vector_index(ll(1), ll(2), 1, domain_id(id), wrf%dom(id)%type_u10) - iul = get_dart_vector_index(ul(1), ul(2), 1, domain_id(id), wrf%dom(id)%type_u10) - ilr = get_dart_vector_index(lr(1), lr(2), 1, domain_id(id), wrf%dom(id)%type_u10) - iur = get_dart_vector_index(ur(1), ur(2), 1, domain_id(id), wrf%dom(id)%type_u10) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - ugrid = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - ! Interpolation for the V10 field - ill = get_dart_vector_index(ll(1), ll(2), 1, domain_id(id), wrf%dom(id)%type_v10) - iul = get_dart_vector_index(ul(1), ul(2), 1, domain_id(id), wrf%dom(id)%type_v10) - ilr = get_dart_vector_index(lr(1), lr(2), 1, domain_id(id), wrf%dom(id)%type_v10) - iur = get_dart_vector_index(ur(1), ur(2), 1, domain_id(id), wrf%dom(id)%type_v10) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - x_ilr = get_state(ilr, state_handle) - - vgrid = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - do e = 1, ens_size - call gridwind_to_truewind(xyz_loc(1), wrf%dom(id)%proj, ugrid(e), vgrid(e), & - utrue(e), vtrue(e)) - - ! U10 (U at 10 meters) - if( obs_kind == QTY_U_WIND_COMPONENT) then - fld(1, e) = utrue(e) - ! V10 (V at 10 meters) - else - fld(1, e) = vtrue(e) - endif - - enddo - - - endif - endif - endif - - !----------------------------------------------------- - ! 1.b Sensible Temperature (T, T2) - - elseif ( obs_kind == QTY_TEMPERATURE ) then - ! This is for 3D temperature field -- surface temps later - !print*, 'k ', k - - if(.not. surf_var) then - - if ( wrf%dom(id)%type_t >= 0 ) then - - do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners T rc = ', rc - - ! Interpolation for T field at level k - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_t) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_t) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_t) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_t) - - x_iul = get_state(iul, state_handle) - x_ill = get_state(ill, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - ! In terms of perturbation potential temperature - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk), id, state_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk), id, state_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk), id, state_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk), id, state_handle, ens_size) - - ! Pressure at location - pres = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - ! Full sensible temperature field - fld(1, e) = (ts0 + a1(e))*(pres(e)/ps0)**kappa - endif - enddo - - ! Interpolation for T field at level k+1 - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - x_ilr = get_state(ilr, state_handle) - - ! In terms of perturbation potential temperature - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk)+1, id, state_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk)+1, id, state_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk)+1, id, state_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk)+1, id, state_handle, ens_size) - - ! Pressure at location - pres = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - ! Full sensible temperature field - fld(2, e) = (ts0 + a1(e))*(pres(e)/ps0)**kappa - endif - enddo - - - endif - enddo - endif - - ! This is for surface temperature (T2) - else - - if ( wrf%dom(id)%type_t2 >= 0 ) then ! HK is there a better way to do this? - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_t2, dxm, dx, dy, dym, ens_size, state_handle) - if (all(fld == missing_r8)) goto 200 - endif - endif - - !----------------------------------------------------- - ! 1.c Potential Temperature (Theta, TH2) - - ! Note: T is perturbation potential temperature (potential temperature - ts0) - ! TH2 is potential temperature at 2 m - elseif ( obs_kind == QTY_POTENTIAL_TEMPERATURE ) then - ! This is for 3D potential temperature field -- surface pot temps later - if(.not. surf_var) then - - if ( wrf%dom(id)%type_t >= 0 ) then - - do uk = 1, count - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners Theta rc = ', rc - - ! Interpolation for Theta field at level k - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk), domain_id(id),wrf%dom(id)%type_t) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk), domain_id(id),wrf%dom(id)%type_t) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk), domain_id(id),wrf%dom(id)%type_t) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk), domain_id(id),wrf%dom(id)%type_t) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(1, e) = ts0 + dym*( dxm*x_ill(e) + dx*x_ilr(e)) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - - ! Interpolation for Theta field at level k+1 - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_t) - - x_ill = get_state(ill, state_handle) - x_ill = get_state(ill, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(2, e) = ts0 + dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - endif - enddo - endif - - ! This is for surface potential temperature (TH2) - else - - if ( wrf%dom(id)%type_th2 >= 0 ) then - - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_th2, dxm, dx, dy, dym, ens_size, state_handle) - if (all(fld == missing_r8)) goto 200 - - endif - endif - - !----------------------------------------------------- - ! 1.d Density (Rho) - elseif (obs_kind == QTY_DENSITY) then - - do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners Rho rc = ', rc - - ! calculate full rho at corners of interp box - ! and interpolate to desired horizontal location - - ! Hmmm, it does not appear that Rho is part of the DART state vector, so there - ! is not a reference to wrf%dom(id)%dart_ind -- we'll have to go right from - ! the corner indices - - ! Interpolation for the Rho field at level k - rho1 = model_rho_t_distrib(ll(1), ll(2), uniquek(uk), id, state_handle, ens_size) - rho2 = model_rho_t_distrib(lr(1), lr(2), uniquek(uk), id, state_handle, ens_size) - rho3 = model_rho_t_distrib(ul(1), ul(2), uniquek(uk), id, state_handle, ens_size) - rho4 = model_rho_t_distrib(ur(1), ur(2), uniquek(uk), id, state_handle, ens_size) - - do e = 1, ens_size - if (k(e) == uniquek(uk) ) then - fld(1, e) = dym*( dxm*rho1(e) + dx*rho2(e) ) + dy*( dxm*rho3(e) + dx*rho4(e) ) - endif - enddo - - ! Interpolation for the Rho field at level k+1 - rho1 = model_rho_t_distrib(ll(1), ll(2), uniquek(uk)+1, id, state_handle, ens_size) - rho2 = model_rho_t_distrib(lr(1), lr(2), uniquek(uk)+1, id, state_handle, ens_size) - rho3 = model_rho_t_distrib(ul(1), ul(2), uniquek(uk)+1, id, state_handle, ens_size) - rho4 = model_rho_t_distrib(ur(1), ur(2), uniquek(uk)+1, id, state_handle, ens_size) - - do e = 1, ens_size - if (k(e) == uniquek(uk) ) then - fld(2, e) = dym*( dxm*rho1(e) + dx*rho2(e) ) + dy*( dxm*rho3(e) + dx*rho4(e) ) - endif - enddo - - endif - - enddo - - !----------------------------------------------------- - ! 1.e Vertical Wind (W) - - elseif ( obs_kind == QTY_VERTICAL_VELOCITY ) then - - ! Adjust zloc for staggered ZNW grid (or W-grid, as compared to ZNU or M-grid) - zloc = zloc + 0.5_r8 - k = max(1,int(zloc)) - - call simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_handle ) - if (all(fld == missing_r8)) goto 200 - - !----------------------------------------------------- - ! 1.f Specific Humidity (SH, SH2) - ! Look at me - ! Convert water vapor mixing ratio to specific humidity: - else if( obs_kind == QTY_SPECIFIC_HUMIDITY ) then - - ! This is for 3D specific humidity -- surface spec humidity later - if(.not. surf_var) then - - ! First confirm that vapor mixing ratio is in the DART state vector - if ( wrf%dom(id)%type_qv >= 0 ) then - - UNIQUEK_LOOP: do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) ! HK why is this type_t - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners SH rc = ', rc - - ! Interpolation for SH field at level k - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_qv) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_qv) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_qv) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk), domain_id(id), wrf%dom(id)%type_qv) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct k - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) ! think this can go outside the k loop - fld(1,e) = a1(e) /(1.0_r8 + a1(e)) - endif - enddo - - ! Interpolation for SH field at level k+1 - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_qv) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_qv) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_qv) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk)+1, domain_id(id), wrf%dom(id)%type_qv) - - x_ill = get_state(ill, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - fld(2,e) = a1(e) /(1.0_r8 + a1(e)) - endif - enddo - endif - enddo UNIQUEK_LOOP - - endif - - ! This is for surface specific humidity (calculated from Q2) - else - - ! confirm that field is in the DART state vector - if ( wrf%dom(id)%type_q2 >= 0 ) then - ! Check to make sure retrieved integer gridpoints are in valid range - if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners SH2 rc = ', rc - - ! Interpolation for the SH2 field - ill = get_dart_vector_index(ll(1), ll(2), 1, domain_id(id), wrf%dom(id)%type_q2) - iul = get_dart_vector_index(ul(1), ul(2), 1, domain_id(id), wrf%dom(id)%type_q2) - ilr = get_dart_vector_index(lr(1), lr(2), 1, domain_id(id), wrf%dom(id)%type_q2) - iur = get_dart_vector_index(ur(1), ur(2), 1, domain_id(id), wrf%dom(id)%type_q2) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - x_ilr = get_state(ilr, state_handle) - - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - fld(1,:) = a1 /(1.0_r8 + a1) - - endif - endif - endif - - - !----------------------------------------------------- - ! 1.g Vapor Mixing Ratio (QV, Q2) - else if( obs_kind == QTY_VAPOR_MIXING_RATIO ) then - - ! This is for 3D vapor mixing ratio -- surface QV later - if(.not. surf_var) then - call simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_handle ) - if (all(fld == missing_r8)) goto 200 - else ! This is for surface QV (Q2) - ! Confirm that right field is in the DART state vector - if ( wrf%dom(id)%type_q2 >= 0 ) then - !HK I am not sure what the type should be - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_q2, dxm, dx, dy, dym, ens_size, state_handle) - if (all(fld == missing_r8)) goto 200 - endif - endif - - ! Don't accept negative water vapor amounts (?) - fld = max(0.0_r8, fld) - - !----------------------------------------------------- - ! 1.t Pressure (P) - else if( obs_kind == QTY_PRESSURE .or. obs_kind == QTY_SURFACE_PRESSURE ) then - ! This is for the 3D pressure field -- surface pressure later - if(.not. surf_var) then - - do uk = 1, count - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners P rc = ', rc - - ! Hmmm, it does not appear that P is part of the DART state vector, so there - ! is not a reference to wrf%dom(id)%dart_ind -- we'll have to go right from - ! the corner indices - - ! Interpolation for the P field at level k - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk), id, state_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk), id, state_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk), id, state_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk), id, state_handle, ens_size) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct k - fld(1, e) = dym*( dxm*pres1(e) + dx*pres2(e) ) + dy*( dxm*pres3(e) + dx*pres4(e) ) - endif - enddo - - - ! Interpolation for the P field at level k+1 - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk)+1, id, state_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk)+1, id, state_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk)+1, id, state_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk)+1, id, state_handle, ens_size) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct k - fld(2, e) = dym*( dxm*pres1(e) + dx*pres2(e) ) + dy*( dxm*pres3(e) + dx*pres4(e) ) - endif - enddo - - endif - enddo - - ! This is for surface pressure (PSFC) - else - - if ( wrf%dom(id)%type_ps >= 0 ) then - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners PS rc = ', rc - - ! Interpolation for the PS field - ill = get_dart_vector_index(ll(1), ll(2), 1, domain_id(id), wrf%dom(id)%type_ps) - iul = get_dart_vector_index(ul(1), ul(2), 1, domain_id(id), wrf%dom(id)%type_ps) - ilr = get_dart_vector_index(lr(1), lr(2), 1, domain_id(id), wrf%dom(id)%type_ps) - iur = get_dart_vector_index(ur(1), ur(2), 1, domain_id(id), wrf%dom(id)%type_ps) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - do e = 1, ens_size - ! I'm not quite sure where this comes from, but I will trust them on it.... - if ( x_ill(e) /= 0.0_r8 .and. x_ilr(e) /= 0.0_r8 .and. x_iul(e) /= 0.0_r8 .and. & - x_iur(e) /= 0.0_r8 ) then - - fld(1, e) = dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - - ! JPH special treatment for scm configuration, where PS is not defined - ! on the boundaries and the weights are already 1,0 - elseif ( wrf%dom(id)%scm ) then !HK is this redunant? What happens if you fail both conditions. - fld(1, e) = x_ill(e) - endif - enddo - endif - endif - endif - - - !----------------------------------------------------- - ! 1.u Vortex Center Stuff from Yongsheng - else if ( obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON .or. & - obs_kind == QTY_VORTEX_PMIN .or. obs_kind == QTY_VORTEX_WMAX ) then - - do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - !! define a search box bounded by center_track_*** - center_search_half_size = nint(center_search_half_length/wrf%dom(id)%dx) - if ( wrf%dom(id)%periodic_x ) then - center_track_xmin = i-center_search_half_size - center_track_xmax = i+center_search_half_size - else - center_track_xmin = max(1,i-center_search_half_size) - center_track_xmax = min(wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu),i+center_search_half_size) - endif - if ( wrf%dom(id)%periodic_y ) then - center_track_ymin = j-center_search_half_size - center_track_ymax = j+center_search_half_size - else - center_track_ymin = max(1,j-center_search_half_size) - center_track_ymax = min(wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu),j+center_search_half_size) - endif - if ( center_track_xmin<1 .or. center_track_xmax>wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) .or. & - center_track_ymin<1 .or. center_track_ymax>wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) .or. & - center_track_xmin >= center_track_xmax .or. center_track_ymin >= center_track_ymax) then - - print*,'i,j,center_search_half_length,center_track_xmin(max),center_track_ymin(max)' - print*,i,j,center_search_half_length,center_track_xmin,center_track_xmax,center_track_ymin,center_track_ymax - write(errstring,*)'Wrong setup in center_track_nml' - call error_handler(E_ERR,'model_interpolate', errstring, source, revision, revdate) - - endif - - if ( obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON .or. & - obs_kind == QTY_VORTEX_PMIN ) then - - !! define spline interpolation box dimensions - xlen = center_track_xmax - center_track_xmin + 1 - ylen = center_track_ymax - center_track_ymin + 1 - xxlen = (center_track_xmax - center_track_xmin)*center_spline_grid_scale + 1 - yylen = (center_track_ymax - center_track_ymin)*center_spline_grid_scale + 1 - - allocate(x1d(xlen), y1d(ylen)) ; allocate(xx1d(xxlen), yy1d(yylen)) - allocate(pd(xlen,ylen)) ; allocate(pp(xxlen,yylen)) - allocate(vfld(xlen,ylen, ens_size)) - - do i1 = 1,xlen - x1d(i1) = (i1-1)+center_track_xmin - enddo - do i2 = 1,ylen - y1d(i2) = (i2-1)+center_track_ymin - enddo - do ii1 = 1,xxlen - xx1d(ii1) = center_track_xmin+real(ii1-1,r8)*1.0_r8/real(center_spline_grid_scale,r8) - enddo - do ii2 = 1,yylen - yy1d(ii2) = center_track_ymin+real(ii2-1,r8)*1.0_r8/real(center_spline_grid_scale,r8) - enddo - - endif - - if ( (obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON) .and. (.not. use_old_vortex) ) then - - ! determine window that one would need wind components, thus circulation - circ_half_size = nint(circulation_radius/wrf%dom(id)%dx) - circ_half_length = circulation_radius / wrf%dom(id)%dx - - if ( wrf%dom(id)%periodic_x ) then - circ_xmin = i-center_search_half_size-circ_half_size - circ_xmax = i+center_search_half_size+circ_half_size - else - circ_xmin = max(1,i-center_search_half_size-circ_half_size) - circ_xmax = min(wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu),i+center_search_half_size+circ_half_size) - endif - - if ( wrf%dom(id)%periodic_y ) then - circ_ymin = j-center_search_half_size-circ_half_size - circ_ymax = j+center_search_half_size+circ_half_size - else - circ_ymin = max(1,j-center_search_half_size-circ_half_size) - circ_ymax = min(wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu),j+center_search_half_size+circ_half_size) - endif - - cxlen = circ_xmax-circ_xmin+1 - cylen = circ_ymax-circ_ymin+1 - allocate(uwnd(cxlen+2,cylen+2, ens_size)) - allocate(vwnd(cxlen+2,cylen+2, ens_size)) - allocate(z1d(0:wrf%dom(id)%bt, ens_size)) - - do i1 = circ_xmin-1, circ_xmax+1 - - ii1 = i1 - if ( wrf%dom(id)%periodic_x ) then - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = i1 - wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = i1 + wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - endif - else - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = 1 - endif - endif - - do i2 = circ_ymin-1, circ_ymax+1 - - ii2 = i2 - if ( wrf%dom(id)%periodic_y ) then - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - elseif ( i2 < 1 ) then - ii2 = i2 + wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - endif - else - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - elseif ( i2 < 1 ) then - ii2 = 1 - endif - endif - - ! calculate the wind components at the desired pressure level - do k2 = 1, wrf%dom(id)%var_size(3,wrf%dom(id)%type_t) - !z1d(k2) = model_pressure_t(i1,i2,k2,id,x) - z1d(k2, :) = model_pressure_t_distrib(i1,i2,k2,id, state_handle, ens_size) - enddo - z1d(0, :) = z1d(1, :) - !call pres_to_zk(circulation_pres_level, z1d, wrf%dom(id)%bt, zloc, is_lev0) - call pres_to_zk_distrib(circulation_pres_level, z1d, wrf%dom(id)%bt, ens_size, zloc, is_lev0) - - do e = 1, ens_size - - k2 = floor(zloc(e)) ; dxm = mod(zloc(e),1.0_r8) ; dx = 1.0_r8 - dxm - - if ( zloc(e) >= 1.0_r8 ) then ! vertically interpolate - -! ugrid = (dx * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2, wrf%dom(id)%type_u)) + & -! x(wrf%dom(id)%dart_ind(ii1+1,ii2, k2, wrf%dom(id)%type_u))) + & -! dxm * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2+1,wrf%dom(id)%type_u)) + & -! x(wrf%dom(id)%dart_ind(ii1+1,ii2, k2+1,wrf%dom(id)%type_u)))) * 0.5_r8 - - ugrid_1 = get_dart_vector_index(ii1, ii2, k2, domain_id(id), wrf%dom(id)%type_u) - ugrid_2 = get_dart_vector_index(ii1+1,ii2, k2, domain_id(id), wrf%dom(id)%type_u) - ugrid_3 = get_dart_vector_index(ii1, ii2, k2, domain_id(id), wrf%dom(id)%type_u) - ugrid_4 = get_dart_vector_index(ii1, ii2, k2+1, domain_id(id), wrf%dom(id)%type_u ) - - x_ugrid_1 = get_state(ugrid_1, state_handle) - x_ugrid_2 = get_state(ugrid_2, state_handle) - x_ugrid_3 = get_state(ugrid_3, state_handle) - x_ugrid_4 = get_state(ugrid_4, state_handle) - - ugrid = (dx * (x_ugrid_1 + x_ugrid_2) + dxm * (x_ugrid_3 + x_ugrid_4)) * 0.5_r8 - - vgrid_1 = get_dart_vector_index(ii1, ii2, k2, domain_id(id),wrf%dom(id)%type_v) - vgrid_2 = get_dart_vector_index(ii1, ii2+1,k2, domain_id(id),wrf%dom(id)%type_v) - vgrid_3 = get_dart_vector_index(ii1, ii2, k2+1, domain_id(id),wrf%dom(id)%type_v) - vgrid_4 = get_dart_vector_index(ii1, ii2+1,k2+1, domain_id(id),wrf%dom(id)%type_v) - - x_vgrid_1 = get_state(vgrid_1, state_handle) - x_vgrid_2 = get_state(vgrid_2, state_handle) - x_vgrid_3 = get_state(vgrid_3, state_handle) - x_vgrid_4 = get_state(vgrid_4, state_handle) - -! vgrid = (dx * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2, wrf%dom(id)%type_v)) + & -! x(wrf%dom(id)%dart_ind(ii1, ii2+1,k2, wrf%dom(id)%type_v))) + & -! dxm * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2+1,wrf%dom(id)%type_v)) + & -! x(wrf%dom(id)%dart_ind(ii1, ii2+1,k2+1,wrf%dom(id)%type_v)))) * 0.5_r8 - - vgrid = (dx * (vgrid_1 + vgrid_2) + dxm * (vgrid_3 + vgrid_4)) * 0.5_r8 - - else ! pressure level below ground. Take model level 1 winds - -! ugrid = (x(wrf%dom(id)%dart_ind(ii1, ii2, 1,wrf%dom(id)%type_u)) + & -! x(wrf%dom(id)%dart_ind(ii1+1,ii2, 1,wrf%dom(id)%type_u))) * 0.5_r8 - - ugrid_1 = get_dart_vector_index(ii1, ii2, 1, domain_id(id),wrf%dom(id)%type_u) - ugrid_2 = get_dart_vector_index(ii1+1,ii2, 1, domain_id(id),wrf%dom(id)%type_u) - - x_ugrid_1 = get_state(ugrid_1, state_handle) - x_ugrid_2 = get_state(ugrid_2, state_handle) - - ugrid = (x_ugrid_1 + x_ugrid_2) * 0.5_r8 - -! vgrid = (x(wrf%dom(id)%dart_ind(ii1, ii2, 1,wrf%dom(id)%type_v)) + & -! x(wrf%dom(id)%dart_ind(ii1, ii2+1,1,wrf%dom(id)%type_v))) * 0.5_r8 - - vgrid_1 = get_dart_vector_index(ii1, ii2, 1, domain_id(id),wrf%dom(id)%type_v) - vgrid_2 = get_dart_vector_index(ii1, ii2+1,1, domain_id(id),wrf%dom(id)%type_v) - - x_vgrid_1 = get_state(vgrid_1, state_handle) - x_vgrid_2 = get_state(vgrid_2, state_handle) - - vgrid = (x_vgrid_1 + x_vgrid_2) * 0.5_r8 - - endif - uwnd(i1-circ_xmin+2,i2-circ_ymin+2, :) = ugrid - vwnd(i1-circ_xmin+2,i2-circ_ymin+2, :) = vgrid - - enddo - - enddo - enddo - - allocate(vort(cxlen,cylen, ens_size)) - do i1 = circ_xmin, circ_xmax - - dgi1 = 2.0_r8 - if ( .not. wrf%dom(id)%periodic_x ) then - if ( i1 == 1 ) then - dgi1 = 1.0_r8 - elseif ( i1 == wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - dgi1 = 1.0_r8 - endif - endif - - do i2 = circ_ymin, circ_ymax - - dgi2 = 2.0_r8 - if ( .not. wrf%dom(id)%periodic_x ) then - if ( i2 == 1 ) then - dgi2 = 1.0_r8 - elseif ( i2 == wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - dgi2 = 1.0_r8 - endif - endif - - ii1 = i1-circ_xmin+1 - ii2 = i2-circ_ymin+1 - - ! compute the vorticity for each point needed to compute circulation - vort(ii1,ii2, :) = (vwnd(ii1+2,ii2+1, :) - vwnd(ii1+1,ii2+1, :)) / (wrf%dom(id)%dx * dgi2) + & - (vwnd(ii1+1,ii2+1, :) - vwnd(ii1 ,ii2+1, :)) / (wrf%dom(id)%dx * dgi2) - & - (uwnd(ii1+1,ii2+2, :) - uwnd(ii1+1,ii2+1, :)) / (wrf%dom(id)%dx * dgi1) - & - (uwnd(ii1+1,ii2+1, :) - uwnd(ii1+1,ii2, : )) / (wrf%dom(id)%dx * dgi1) - - enddo - enddo - - ! loop over all grid points in search area, compute average vorticity - ! (circulation) within a certain distance of that grid point - do i1 = center_track_xmin, center_track_xmax - do i2 = center_track_ymin, center_track_ymax - - asum = 0.0_r8 ; circ = 0.0_r8 - do ii1 = max(circ_xmin,i1-circ_half_size), min(circ_xmax,i1+circ_half_size) - do ii2 = max(circ_ymin,i2-circ_half_size), min(circ_ymax,i2+circ_half_size) - - distgrid = sqrt(real(ii1-i1,r8) ** 2 + real(ii2-i2,r8) ** 2) * wrf%dom(id)%dx - if ( distgrid <= circulation_radius ) then - - asum = asum + 1.0_r8 - circ = circ + vort(ii1-circ_xmin+1,ii2-circ_ymin+1, :) - - endif - - enddo - enddo - - vfld(i1-center_track_xmin+1,i2-center_track_ymin+1, :) = circ / asum - - enddo - enddo - - ! find maximum in circulation through spline interpolation - do e = 1, ens_size - call splie2(x1d,y1d,vfld(:,:,e),xlen,ylen,pd) - enddo - - vcrit = -1.0e20_r8 - cxloc = -1 - cyloc = -1 - do ii1 = 1, xxlen - do ii2 = 1, yylen - do e = 1, ens_size - call splin2(x1d,y1d,vfld(:,:,e),pd,xlen,ylen,xx1d(ii1),yy1d(ii2),pp(ii1,ii2)) - enddo - if (vcrit < pp(ii1,ii2)) then - vcrit = pp(ii1,ii2) - cxloc = xx1d(ii1) - cyloc = yy1d(ii2) - endif - enddo - enddo - - ! forward operator fails if maximum is at edge of search area - if ( cxloc-xx1d(1) < 1.0_r8 .or. xx1d(xxlen)-cxloc < 1.0_r8 .or. & - cyloc-yy1d(1) < 1.0_r8 .or. yy1d(yylen)-cyloc < 1.0_r8 ) then - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - fld(:, e) = missing_r8 - endif - enddo - - else - - call ij_to_latlon(wrf%dom(id)%proj, cxloc, cyloc, clat, clon) - - if ( obs_kind == QTY_VORTEX_LAT ) then - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - fld(1, e) = clat - endif - enddo - - else - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - fld(1, e) = clon - endif - enddo - - endif - - endif - - deallocate(uwnd, vwnd, vort, z1d) - deallocate(vfld, pd, pp, x1d, y1d, xx1d, yy1d) - - else if ( obs_kind == QTY_VORTEX_PMIN .or. (use_old_vortex .and. & - (obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON)) ) then - - allocate(p1d(wrf%dom(id)%bt, ens_size), t1d(wrf%dom(id)%bt, ens_size)) - allocate(qv1d(wrf%dom(id)%bt, ens_size), z1d(wrf%dom(id)%bt, ens_size)) - allocate(z1d_1(wrf%dom(id)%bt, ens_size), z1d_2(wrf%dom(id)%bt, ens_size)) - - ! compute SLP for each grid point within the search area - print*, center_track_xmin, center_track_xmax - print*, center_track_ymin, center_track_ymax - do i1 = center_track_xmin, center_track_xmax - - ii1 = i1 - if ( wrf%dom(id)%periodic_x ) then - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = i1 - wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = i1 + wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - endif - endif - - do i2 = center_track_ymin, center_track_ymax - - ii2 = i2 - if ( wrf%dom(id)%periodic_y ) then - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - elseif ( i2 < 1 ) then - ii2 = i2 + wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - endif - endif - - do k2 = 1,wrf%dom(id)%var_size(3,wrf%dom(id)%type_t) - -! p1d(k2) = model_pressure_t(ii1,ii2,k2,id,x) - p1d(k2, :) = model_pressure_t_distrib(ii1,ii2,k2, id, state_handle, ens_size) - !print*, 'p1d(k2, 1)', p1d(k2, 1) - -! t1d(k2) = x(wrf%dom(id)%dart_ind(ii1,ii2,k2,wrf%dom(id)%type_t)) + ts0 - t1d_ind = get_dart_vector_index(ii1,ii2,k2, domain_id(id),wrf%dom(id)%type_t) - t1d(k2, :) = get_state( t1d_ind, state_handle) - t1d(k2, :) = t1d(k2, :) + ts0 - !print*, 't1d(k2, 1)', t1d(k2, 1) - -! qv1d(k2)= x(wrf%dom(id)%dart_ind(ii1,ii2,k2,wrf%dom(id)%type_qv)) - qv1d_ind = get_dart_vector_index(ii1,ii2,k2, domain_id(id),wrf%dom(id)%type_qv) - qv1d(k2, :) = get_state(qv1d_ind, state_handle) - !print*, 'qv1d(k2, 1)', qv1d(k2, 1) - -! z1d(k2) = (x(wrf%dom(id)%dart_ind(ii1,ii2,k2, wrf%dom(id)%type_gz))+ & -! x(wrf%dom(id)%dart_ind(ii1,ii2,k2+1,wrf%dom(id)%type_gz))+ & -! wrf%dom(id)%phb(ii1,ii2,k2)+wrf%dom(id)%phb(ii1,ii2,k2+1))*0.5_r8/gravity - z1d_ind1 = get_dart_vector_index(ii1,ii2,k2, domain_id(id),wrf%dom(id)%type_gz) - z1d_ind2 = get_dart_vector_index(ii1,ii2,k2+1, domain_id(id),wrf%dom(id)%type_gz) - - z1d_1(k2, :) = get_state(z1d_ind1, state_handle) - z1d_2(k2, :) = get_state(z1d_ind2, state_handle) - - z1d(k2, :) = ( z1d_1(k2, :)+ z1d_2(k2, :) + & - phb(id,ii1,ii2,k2)+phb(id,ii1,ii2,k2+1))*0.5_r8/gravity - - !print*, 'z1d(k2, 1)', z1d(k2, 1) - - - enddo - do e = 1, ens_size - call compute_seaprs(wrf%dom(id)%bt, z1d(:,e), t1d(:,e), p1d(:,e), qv1d(:,e), & - vfld(i1-center_track_xmin+1,i2-center_track_ymin+1, e), debug) - enddo - enddo - - enddo - - !print*, 'vfld(1, 1)', vfld(1, 1, 1) - do e = 1, ens_size - - if ( k(e) == uniquek(uk) ) then - - ! find minimum in MSLP through spline interpolation - call splie2(x1d,y1d,vfld(:,:,e),xlen,ylen,pd) - - vcrit = 1.0e20_r8 - do ii1=1,xxlen - do ii2=1,yylen - call splin2(x1d,y1d,vfld(:,:,e),pd,xlen,ylen,xx1d(ii1),yy1d(ii2),pp(ii1,ii2)) - if ( vcrit > pp(ii1,ii2)) then - vcrit = pp(ii1,ii2) - cxloc = xx1d(ii1) - cyloc = yy1d(ii2) - endif - enddo - enddo - - ! forward operator fails if maximum is at edge of search area - if ( cxloc-xx1d(1) < 1.0_r8 .or. xx1d(xxlen)-cxloc < 1.0_r8 .or. & - cyloc-yy1d(1) < 1.0_r8 .or. yy1d(yylen)-cyloc < 1.0_r8 ) then - - fld(:, e) = missing_r8 - - else - - call ij_to_latlon(wrf%dom(id)%proj, cxloc, cyloc, clat, clon) - - if ( obs_kind == QTY_VORTEX_PMIN ) then - fld(1, e) = vcrit - else if ( obs_kind == QTY_VORTEX_LAT ) then - fld(1, e) = clat - else - fld(1, e) = clon - endif - - endif - - endif - - enddo - - - print*, 'fld', fld - deallocate(p1d, t1d, qv1d, z1d) - deallocate(vfld, pd, pp, x1d, y1d, xx1d, yy1d) - if (all(fld == missing_r8)) goto 200 - - else if ( obs_kind == QTY_VORTEX_WMAX ) then ! Maximum wind speed - - maxwspd = 0.0_r8 - do i1 = center_track_xmin, center_track_xmax - - ii1 = i1 - if ( wrf%dom(id)%periodic_x ) then - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = i1 - wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = i1 + wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - endif - endif - - do i2 = center_track_ymin, center_track_ymax - - ii2 = i2 - if ( wrf%dom(id)%periodic_y ) then - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - elseif ( i2 < 1 ) then - ii2 = i2 + wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - endif - endif - - if ( ( wrf%dom(id)%type_u10 >= 0 ) .and. ( wrf%dom(id)%type_v10 >= 0 ) ) then - !ugrid = x(wrf%dom(id)%dart_ind(ii1,ii2,1,wrf%dom(id)%type_u10)) - !vgrid = x(wrf%dom(id)%dart_ind(ii1,ii2,1,wrf%dom(id)%type_v10)) - - ugrid_1 = get_dart_vector_index(ii1,ii2,1, domain_id(id),wrf%dom(id)%type_u10) - ugrid = get_state(ugrid_1, state_handle) - - vgrid_1 = get_dart_vector_index(ii1,ii2,1, domain_id(id),wrf%dom(id)%type_v10) - vgrid = get_state(vgrid_1, state_handle) - - else - -! Same code as above -! ugrid = 0.5_r8*(x(wrf%dom(id)%dart_ind(ii1, ii2,1,wrf%dom(id)%type_u)) + & -! x(wrf%dom(id)%dart_ind(ii1+1,ii2,1,wrf%dom(id)%type_u))) -! vgrid = 0.5_r8*(x(wrf%dom(id)%dart_ind(ii1,ii2, 1,wrf%dom(id)%type_v)) + & -! x(wrf%dom(id)%dart_ind(ii1,ii2+1,1,wrf%dom(id)%type_v))) - ugrid_1 = get_dart_vector_index(ii1, ii2, 1, domain_id(id),wrf%dom(id)%type_u) - ugrid_2 = get_dart_vector_index(ii1+1,ii2, 1, domain_id(id),wrf%dom(id)%type_u) - - x_ugrid_1 = get_state(ugrid_1, state_handle) - x_ugrid_2 = get_state(ugrid_2, state_handle) - - ugrid = (x_ugrid_1 + x_ugrid_2) * 0.5_r8 - - vgrid_1 = get_dart_vector_index(ii1, ii2, 1, domain_id(id),wrf%dom(id)%type_v) - vgrid_2 = get_dart_vector_index(ii1, ii2+1,1, domain_id(id),wrf%dom(id)%type_v) - - x_vgrid_1 = get_state(vgrid_1, state_handle) - x_vgrid_2 = get_state(vgrid_2, state_handle) - - vgrid = (x_vgrid_1 + x_vgrid_2) * 0.5_r8 - - endif - - magwnd = sqrt(ugrid * ugrid + vgrid * vgrid) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - if ( magwnd(e) > maxwspd(e) ) then - imax = i1 - jmax = i2 - maxwspd(e) = magwnd(e) - endif - endif - enddo - - - - enddo - enddo - - ! forward operator fails if maximum is at edge of search area - if ( imax == center_track_xmin .or. jmax == center_track_ymin .or. & - imax == center_track_xmax .or. jmax == center_track_ymax ) then - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - fld(:, e) = missing_r8 - endif - enddo - else - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - fld(1, e) = maxwspd(e) - endif - enddo - endif - - endif ! if test on obs_kind - - endif ! bounds check failed. - - enddo - -!***************************************************************************** -! END OF VERBATIM BIT -!***************************************************************************** - - - !----------------------------------------------------- - ! 1.w Geopotential Height (GZ) - - ! GZ is on the ZNW grid (bottom_top_stagger), so its bottom-most level is defined to - ! be at eta = 1 (the surface). Thus, we have a 3D variable that contains a surface - ! variable; the same is true for W as well. If one wants to observe the surface value - ! of either of these variables, then one can simply operate on the full 3D field - ! (toGrid below should return dz ~ 0 and dzm ~ 1) - else if( obs_kind == QTY_GEOPOTENTIAL_HEIGHT ) then - !if( my_task_id() == 0 ) print*, '*** geopotential height forward operator not tested' - - ! make sure vector includes the needed field - if ( wrf%dom(id)%type_gz >= 0 ) then - - ! Adjust zloc for staggered ZNW grid (or W-grid, as compared to ZNU or M-grid) - zloc = zloc + 0.5_r8 - k = max(1,int(zloc)) ! Only 1 value of k across the ensemble? - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_gz ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_gz ) .and. & - boundsCheck( k(1), .false., id, dim=3, type=wrf%dom(id)%type_gz ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_gz, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners GZ rc = ', rc - - ! Interpolation for GZ field at level k - ill = get_dart_vector_index(ll(1), ll(2), k(1), domain_id(id), wrf%dom(id)%type_gz) - iul = get_dart_vector_index(ul(1), ul(2), k(1), domain_id(id), wrf%dom(id)%type_gz) - ilr = get_dart_vector_index(lr(1), lr(2), k(1), domain_id(id), wrf%dom(id)%type_gz) - iur = get_dart_vector_index(ur(1), ur(2), k(1), domain_id(id), wrf%dom(id)%type_gz) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - x_ilr = get_state(ilr, state_handle) - - fld(1,:) = ( dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) + & - dym*( dxm*phb(id, ll(1), ll(2), k(1)) + & - dx *phb(id, lr(1), lr(2), k(1)) ) + & - dy *( dxm*phb(id, ul(1), ul(2), k(1)) + & - dx *phb(id, ur(1), ur(2), k(1)) ) ) / gravity - - ! Interpolation for GZ field at level k+1 - ill = get_dart_vector_index(ll(1), ll(2), k(1)+1, domain_id(id), wrf%dom(id)%type_gz) - iul = get_dart_vector_index(ul(1), ul(2), k(1)+1, domain_id(id), wrf%dom(id)%type_gz) - ilr = get_dart_vector_index(lr(1), lr(2), k(1)+1, domain_id(id), wrf%dom(id)%type_gz) - iur = get_dart_vector_index(ur(1), ur(2), k(1)+1, domain_id(id), wrf%dom(id)%type_gz) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - x_ilr = get_state(ilr, state_handle) - - fld(2, :) = ( dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) + & - dym*( dxm*phb(id, ll(1), ll(2), k(1)+1) + & - dx *phb(id, lr(1), lr(2), k(1)+1) ) + & - dy *( dxm*phb(id, ul(1), ul(2), k(1)+1) + & - dx *phb(id, ur(1), ur(2), k(1)+1) ) ) / gravity - - endif - endif - - !----------------------------------------------------- - ! 1.x Surface Elevation (HGT) - - ! Surface Elevation has been added by Ryan Torn to accommodate altimeter observations. - ! HGT is not in the dart_ind vector, so get it from wrf%dom(id)%hgt. - else if( obs_kind == QTY_SURFACE_ELEVATION ) then - - if ( debug ) print*,'Getting surface elevation' - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners HGT rc = ', rc - - ! Interpolation for the HGT field -- HGT is NOT part of state vector x, but rather - ! in the associated domain meta data - fld(1, :) = dym*( dxm*wrf%dom(id)%hgt(ll(1), ll(2)) + & - dx*wrf%dom(id)%hgt(lr(1), lr(2)) ) + & - dy*( dxm*wrf%dom(id)%hgt(ul(1), ul(2)) + & - dx*wrf%dom(id)%hgt(ur(1), ur(2)) ) - - endif - - - !----------------------------------------------------- - ! 1.y Surface Skin Temperature (TSK) - - else if( obs_kind == QTY_SKIN_TEMPERATURE ) then - ! make sure vector includes the needed field - if ( wrf%dom(id)%type_tsk >= 0 ) then - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_tsk, dxm, dx, dy, dym, ens_size, state_handle) - if (all(fld == missing_r8)) goto 200 - endif - - !----------------------------------------------------- - ! 1.z Land Mask (XLAND) - - ! Land Mask has been added to accommodate satellite observations. - ! XLAND is not in the dart_ind vector, so get it from wrf%dom(id)%land - else if( obs_kind == QTY_LANDMASK ) then - if( my_task_id() == 0 ) print*, '*** Land mask forward operator not tested' - - if ( debug ) print*,'Getting land mask' - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners XLAND rc = ', rc - - ! Interpolation for the XLAND field -- XLAND is NOT part of state vector x, but rather - ! in the associated domain meta data - fld(1, :) = dym*( dxm*real(wrf%dom(id)%land(ll(1), ll(2))) + & - dx*real(wrf%dom(id)%land(lr(1), lr(2))) ) + & - dy*( dxm*real(wrf%dom(id)%land(ul(1), ul(2))) + & - dx*real(wrf%dom(id)%land(ur(1), ur(2))) ) - - endif - - !----------------------------------------------------- - ! If obs_kind is not negative (for identity obs), or if it is not one of the above 15 - ! explicitly checked-for kinds, then set error istatus and missing_r8. - else - - expected_obs = missing_r8 - istatus = 3 - if (debug) print*, 'unrecognized obs KIND, value = ', obs_kind - deallocate(v_h, v_p) - return - - endif - - - !---------------------------------- - ! 2. Vertical Interpolation - !---------------------------------- - - ! Do vertical interpolation -- only for non-surface, non-identity obs. - - ! The previous section (1. Horizontal Interpolation) has produced a variable called - ! "fld", which nominally has two entries in it. 3D fields have hopefully produced - ! 2 non-zero entries, whereas surface fields only have filled the first entry. - ! If a full 3D field, then do vertical interpolation between sandwiched model levels - ! (k and k+1). - - ! Check to make sure that we did something sensible in the Horizontal Interpolation - ! section above. All valid obs_kinds will have changed fld(1,e). - - !HK I am unsure as to whether this should be done on the array expected_obs or one ensemble - ! member (e) at a time. - do e = 1, ens_size - - if ( fld(1,e) == missing_r8 ) then - - expected_obs(e) = missing_r8 - - else ! We purposefully changed fld(1,e), so continue onward - - ! If a surface variable, or a variable with no particular vertical location - ! (basically the entire column) then no need to do any vertical interpolation - if ( surf_var .or. vert_is_undef(location) ) then - - !obs_val = fld(1) - expected_obs(e) = fld(1,e) !HK - - ! If an interior variable, then we DO need to do vertical interpolation - else - - ! First make sure fld(2,:) is no longer a missing value - if ( fld(2,e) == missing_r8 ) then !HK should be any? - - expected_obs(e) = missing_r8 - - ! Do vertical interpolation -- at this point zloc is >= 1 unless - ! the namelist value allow_obs_below_vol is true, in which case - ! it is >= 0, and < 1 is a request to extrapolate. - else - - ! Get fractional distances between grid points - call toGrid(zloc(e), k(e), dz(e), dzm(e)) - if (debug) print*, 'zloc(e), k(e), dz(e), dzm(e) = ', zloc(e), k(e), dz(e), dzm(e) - if (debug) print*, 'fld(1,e), fld(2,e) = ', fld(1,e), fld(2,e) - - ! If you get here and zloc < 1.0, then k will be 0, and - ! we should extrapolate. fld(1,:) and fld(2,:) where computed - ! at levels 1 and 2. - - if (k(e) >= 1) then - ! Linearly interpolate between grid points - expected_obs(e) = dzm(e)*fld(1,e) + dz(e)*fld(2,e) - if (debug) print*, 'interpolated obs_val = ', expected_obs(e) - else - ! Extrapolate below first level. - expected_obs(e) = fld(1,e) - (fld(2,e)-fld(1,e))*dzm(e) - if (debug) print*, 'extrapolated obs_val = ', expected_obs(e) - endif - - endif - endif - endif - - ! Fill in failed copies - if ( failedcopies(e) == missing_r8 ) expected_obs(e) = missing_r8 - - enddo - -endif ! end of "if ( obs_kind < 0 )" - -200 continue - -if (all(fld == missing_r8)) then - expected_obs(:) = missing_r8 - istatus(:) = 99 -endif - -! Now that we are done, check to see if a missing value somehow -! made it through without already having set an error return code. -do e = 1, ens_size - if ( expected_obs(e) == missing_r8 .and. istatus(e) == 0 ) then - istatus(e) = 99 - endif -enddo - -! Pring the observed value if in debug mode -if(debug) then - print*,'model_interpolate() return value for obs_kind ',obs_kind, ' = ',expected_obs -endif - -! Deallocate variables before exiting -deallocate(v_h, v_p) -deallocate(uniquek) - -end subroutine model_interpolate - -!####################################################################### -!> This is used in the filter_assim. The vertical conversion is done using the -!> mean state. -!> I think at the moment you are over communicating -subroutine vert_convert(state_handle, location, obs_kind, istatus) - -! This subroutine converts a given ob/state vertical coordinate to -! the vertical localization coordinate type requested through the -! model_mod namelist. -! -! Notes: (1) obs_kind is only necessary to check whether the ob -! is an identity ob. -! (2) This subroutine can convert both obs' and state points' -! vertical coordinates. Remember that state points get -! their DART location information from get_state_meta_data -! which is called by filter_assim during the assimilation -! process. -! (3) x is the relevant DART state vector for carrying out -! interpolations necessary for the vertical coordinate -! transformations. As the vertical coordinate is only used -! in distance computations, this is actually the "expected" -! vertical coordinate, so that computed distance is the -! "expected" distance. Thus, under normal circumstances, -! x that is supplied to vert_convert should be the -! ensemble mean. Nevertheless, the subroutine has the -! functionality to operate on any DART state vector that -! is supplied to it. - -type(ensemble_type), intent(in) :: state_handle -type(location_type), intent(inout) :: location -integer, intent(in) :: obs_kind -integer, intent(out) :: istatus - -! changed zloc to zin and zout, since the point of this routine -! is to convert zloc from one value to another. ztype{in,out} -! are the vertical types as defined by the 3d sphere locations mod. -real(r8) :: xloc, yloc, zin, xyz_loc(3), zout -integer :: id, i, j, rc, ztypein, ztypeout -real(r8) :: dx,dy,dxm,dym, dz, dzm -integer, dimension(2) :: ll, lr, ul, ur - -real(r8), allocatable, dimension(:) :: v_h -real(r8), allocatable, dimension(:) :: v_p !< only need the mean value - -! local vars, used in calculating pressure and height -real(r8) :: pres1, pres2, pres3, pres4 -real(r8) :: presa, presb, psurf -real(r8) :: hgt1, hgt2, hgt3, hgt4, hgta, hgtb - -!HK -real(r8) :: zk -integer :: k -logical :: lev0 - -! assume failure. -istatus = 1 - -! first off, check if ob is identity ob. if so get_state_meta_data() will -! return location information already in the requested vertical type. -!>@todo This in not true anymore if you don't convert all the state variables -! to the localization coordinate in get_state_meta_data -if (obs_kind < 0) then - call get_state_meta_data(state_handle, int(obs_kind,i8),location) - istatus = 0 - return -endif - -! if the existing coord is already in the requested vertical units -! or if the vert is 'undef' which means no specifically defined -! vertical coordinate, return now. -ztypein = nint(query_location(location, 'which_vert')) -ztypeout = vert_localization_coord -if ((ztypein == ztypeout) .or. (ztypein == VERTISUNDEF)) then - istatus = 0 - return -endif - -! we do need to convert the vertical. start by -! extracting the location lat/lon/vert values. -xyz_loc = get_location(location) - -!if(my_task_id() == 0) write(10, *) xyz_loc - -! the routines below will use zin as the incoming vertical value -! and zout as the new outgoing one. start out assuming failure -! (zout = missing) and wait to be pleasantly surprised when it works. -zin = xyz_loc(3) -zout = missing_r8 - -! if the vertical is missing to start with, return it the same way -! with the requested type as out. -if (zin == missing_r8) then - location = set_location(xyz_loc(1),xyz_loc(2),missing_r8,ztypeout) - !print*, 'missing rank', my_task_id() - return -endif - -! first obtain domain id, and where we are in the grid (xloc,yloc) -if ( .not. scm ) then - call get_domain_info(xyz_loc(1),xyz_loc(2),id,xloc,yloc) -else - id = 1 - xloc = 1.0_r8 - yloc = 1.0_r8 -endif - -! cannot find domain info, return error. set location to missing value -! but using requested vertical coord. istatus already set above. -if (id==0) then - location = set_location(xyz_loc(1),xyz_loc(2),missing_r8,ztypeout) - !print*, 'can not find domain rank', my_task_id() - - return -endif - -! get integer (west/south) grid point and distances to neighboring grid points -! distances are used as weights to carry out horizontal interpolations -call toGrid(xloc,i,dx,dxm) -call toGrid(yloc,j,dy,dym) - -! Check that integer indices of Mass grid are in valid ranges for the given -! boundary conditions (i.e., periodicity). if not, bail here. -if ( .not. boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .or. & - .not. boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - location = set_location(xyz_loc(1),xyz_loc(2),missing_r8,ztypeout) - !print*, 'out of bounds rank', my_task_id() - - return -endif - -!HK Note the result is not bitwise -!if(my_task_id() == 0) then -! write(10, *) '------' -! write(10, *) 'xyz_loc ', xyz_loc -! location = set_location(xyz_loc(1),xyz_loc(2),xyz_loc(3),ztypeout) -! write(10, *) 'set_location ', location%lon, location%lat, location%vloc -! xyz_loc = get_location(location) -! write(10, *) 'get_location ', xyz_loc -! location = set_location(xyz_loc(1),xyz_loc(2),xyz_loc(3),ztypeout) -! write(10, *) 'set_location ', location%lon, location%lat, location%vloc -!endif - - -! Get indices of corners (i,i+1,j,j+1), which depend on periodicities. -! since the boundsCheck routines succeeded, this call should never fail -! so make it a fatal error if it does return an error code. -call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) -if ( rc /= 0 ) then - write(errstring,*) 'for i,j: ', i, j, ' getCorners rc = ', rc - call error_handler(E_ERR,'model_mod.f90::vert_convert', errstring, & - source, revision, revdate) -endif - -! at this point we have set: xloc, yloc, i, j, ll, ul, lr, ur, zin, id, -! dx, dxm, dy, dym already, and i, j have been checked to verify they -! are valid values for this grid. if you need k, dz, dzm below you still -! need to compute and validate them first. - -! Convert the incoming vertical type (ztypein) into the vertical -! localization coordinate given in the namelist (ztypeout). - -! convert from: -select case (ztypein) - -! ------------------------------------------------------- -! incoming vertical coordinate is 'model level number' -! ---------------------------------------------------- --- -case (VERTISLEVEL) - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - - ! get neighboring mass level indices & compute weights to zin - call toGrid(zin,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! compute pressure at all neighboring mass points and interpolate - presa = scalar(model_pressure_t_distrib(ll(1), ll(2), k ,id, state_handle,1)) - presb = scalar(model_pressure_t_distrib(ll(1), ll(2), k+1,id, state_handle,1)) - pres1 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(lr(1), lr(2), k ,id, state_handle,1)) - presb = scalar(model_pressure_t_distrib(lr(1), lr(2), k+1,id, state_handle,1)) - pres2 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ul(1), ul(2), k ,id, state_handle,1)) - presb = scalar(model_pressure_t_distrib(ul(1), ul(2), k+1,id, state_handle,1)) - pres3 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ur(1), ur(2), k ,id, state_handle,1)) - presb = scalar(model_pressure_t_distrib(ur(1), ur(2), k+1,id, state_handle,1)) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - - ! get neighboring mass level indices & compute weights to zin - ! need to add half a grid to get to staggered vertical coordinate - call toGrid(zin+0.5_r8,k,dz,dzm) - !print*, 'k', k, 'zin ', zin - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_gz)) then - ! print*, 'bounds check fail' - goto 100 - endif - - ! HK if this is a piece of state, I believe you don't need to the four corners, - ! the location is the lower left corner. - ! compute height at all neighboring mass points and interpolate - ! You have already converted the state in get_state_meta_data - hgta = model_height_w_distrib(ll(1), ll(2), k ,id,state_handle) - hgtb = model_height_w_distrib(ll(1), ll(2), k+1,id,state_handle) - hgt1 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(lr(1), lr(2), k ,id,state_handle) - hgtb = model_height_w_distrib(lr(1), lr(2), k+1,id,state_handle) - hgt2 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ul(1), ul(2), k ,id,state_handle) - hgtb = model_height_w_distrib(ul(1), ul(2), k+1,id,state_handle) - hgt3 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ur(1), ur(2), k ,id,state_handle) - hgtb = model_height_w_distrib(ur(1), ur(2), k+1,id,state_handle) - hgt4 = dzm*hgta + dz*hgtb - zout = dym*( dxm*hgt1 + dx*hgt2 ) + dy*( dxm*hgt3 + dx*hgt4 ) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - - ! get neighboring mass level indices & compute weights to zin - call toGrid(zin,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! pressure at height - presa = scalar(model_pressure_t_distrib(ll(1), ll(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ll(1), ll(2), k+1,id,state_handle,1)) - pres1 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(lr(1), lr(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(lr(1), lr(2), k+1,id,state_handle,1)) - pres2 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ul(1), ul(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ul(1), ul(2), k+1,id,state_handle,1)) - pres3 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ur(1), ur(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ur(1), ur(2), k+1,id,state_handle,1)) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - ! surface pressure - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_handle) - zout = -log(zout / (dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ))) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was model level.') - - - end select ! incoming vert was model level - -! ------------------------------------------------------- -! incoming vertical coordinate is 'pressure' in Pa -! ------------------------------------------------------- -case (VERTISPRESSURE) - - ! If obs is by pressure: get corresponding mass level zk, - ! then get neighboring mass level indices and compute weights - - ! get model pressure profile and - ! get pressure vertical co-ordinate in model level number - allocate(v_p(0:wrf%dom(id)%bt)) - !HK This has already been called in model interpolate - ! - not for observations that were not in the assimilate catagory - call get_model_pressure_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_p, state_handle,1) - - !if (my_task_id() == 0) then - ! write(10, *) v_p - !endif - - call pres_to_zk(zin, v_p(:), wrf%dom(id)%bt, zk, lev0) - deallocate(v_p) - - !if (my_task_id() == 0) then - ! write(10, *) zk - !endif - - ! if you cannot get a model level out of the pressure profile, bail to end - if ( zk == missing_r8 ) goto 100 !HK I don't think zk = missing_r8 even if there is an error - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' in Pa - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - ! pres_to_zk() above converted pressure into a real number - ! of vertical model levels, including the fraction. - zout = zk - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' in Pa - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - ! adding 0.5 to get to the staggered vertical grid - ! because height is on staggered vertical grid - call toGrid(zk+0.5, k, dz, dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_gz)) goto 100 - - ! HK should check whether this is state or not. If it is state, there is no need to - ! do all four corners, it is the lower left corner. - - ! compute height at all neighboring mass points and interpolate - hgta = model_height_w_distrib(ll(1), ll(2), k ,id, state_handle) - hgtb = model_height_w_distrib(ll(1), ll(2), k+1,id, state_handle) - hgt1 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(lr(1), lr(2), k ,id, state_handle) - hgtb = model_height_w_distrib(lr(1), lr(2), k+1,id, state_handle) - hgt2 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ul(1), ul(2), k ,id, state_handle) - hgtb = model_height_w_distrib(ul(1), ul(2), k+1,id, state_handle) - hgt3 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ur(1), ur(2), k ,id, state_handle) - hgtb = model_height_w_distrib(ur(1), ur(2), k+1,id, state_handle) - hgt4 = dzm*hgta + dz*hgtb - zout = dym*( dxm*hgt1 + dx*hgt2 ) + dy*( dxm*hgt3 + dx*hgt4 ) - - !if (my_task_id() == 0) then - ! write(10, *) zout - !endif - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' in Pa - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - call toGrid(zk,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! compute surface pressure at all neighboring mass points and interpolate - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_handle) - zout = -log(zin / (dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ))) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was pressure.') - - - end select ! incoming vert was pressure - - -! ------------------------------------------------------- -! incoming vertical coordinate is 'height' in meters -! ------------------------------------------------------- -case (VERTISHEIGHT) - - ! If obs is by height: get corresponding mass level zk, - ! then get neighboring mass level indices and compute weights - - ! get model height profile and - ! get height vertical co-ordinate in model level number - allocate(v_h(0:wrf%dom(id)%bt)) - call get_model_height_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_h, state_handle,1) - call height_to_zk(zin, v_h, wrf%dom(id)%bt,zk,lev0) - deallocate(v_h) - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - ! height_to_zk() above converted pressure into a real number - ! of vertical model levels, including the fraction. - zout = zk - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - call toGrid(zk,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! compute pressure at all neighboring mass points and interpolate - presa = scalar(model_pressure_t_distrib(ll(1), ll(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ll(1), ll(2), k+1,id,state_handle,1)) - pres1 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(lr(1), lr(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(lr(1), lr(2), k+1,id,state_handle,1)) - pres2 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ul(1), ul(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ul(1), ul(2), k+1,id,state_handle,1)) - pres3 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ur(1), ur(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ur(1), ur(2), k+1,id,state_handle,1)) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - call toGrid(zk,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! pressure at height - presa = scalar(model_pressure_t_distrib(ll(1), ll(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ll(1), ll(2), k+1,id,state_handle,1)) - pres1 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(lr(1), lr(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(lr(1), lr(2), k+1,id,state_handle,1)) - pres2 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ul(1), ul(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ul(1), ul(2), k+1,id,state_handle,1)) - pres3 = dzm*presa + dz*presb - presa = scalar(model_pressure_t_distrib(ur(1), ur(2), k ,id,state_handle,1)) - presb = scalar(model_pressure_t_distrib(ur(1), ur(2), k+1,id,state_handle,1)) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - ! surface pressure - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_handle) - zout = -log(zout / (dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ))) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was height.') - - - end select ! incoming vert was height - - -! ------------------------------------------------------- -! incoming vertical coordinate is 'scale height' -! ------------------------------------------------------- -case (VERTISSCALEHEIGHT) - - ! If obs is by scale height: compute the surface pressure, - ! get corresponding mass level zk, then get neighboring mass - ! level indices and compute weights - - pres1 = model_pressure_s_distrib(ll(1), ll(2), id,state_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id,state_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id,state_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id,state_handle) - psurf = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - ! get model pressure profile and - ! get pressure vertical co-ordinate in model level number - allocate(v_p(0:wrf%dom(id)%bt)) - call get_model_pressure_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_p, state_handle, 1) - call pres_to_zk(exp(-zin)*psurf, v_p, wrf%dom(id)%bt,zk,lev0) - deallocate(v_p) - - ! if you cannot get a model level out of the pressure profile, bail to end - if (zk == missing_r8) goto 100 - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - ! pres_to_zk() above converted scale height/pressure into - ! a real number of vertical model levels, including the fraction. - zout = zk - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - ! adding 0.5 to get to the staggered vertical grid - ! because height is on staggered vertical grid - call toGrid(zk+0.5,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_gz)) goto 100 - - ! compute height at all neighboring mass points and interpolate - hgta = model_height_w_distrib(ll(1), ll(2), k ,id,state_handle) - hgtb = model_height_w_distrib(ll(1), ll(2), k+1,id,state_handle) - hgt1 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(lr(1), lr(2), k ,id,state_handle) - hgtb = model_height_w_distrib(lr(1), lr(2), k+1,id,state_handle) - hgt2 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ul(1), ul(2), k ,id,state_handle) - hgtb = model_height_w_distrib(ul(1), ul(2), k+1,id,state_handle) - hgt3 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ur(1), ur(2), k ,id,state_handle) - hgtb = model_height_w_distrib(ur(1), ur(2), k+1,id,state_handle) - hgt4 = dzm*hgta + dz*hgtb - zout = dym*( dxm*hgt1 + dx*hgt2 ) + dy*( dxm*hgt3 + dx*hgt4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - zout = exp(-zin)*psurf - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was scale height.') - - - end select ! incoming vert was scale height - -! ------------------------------------------------------- -! incoming vertical coordinate is 'surface' (assumes zin is height in meters) -! ------------------------------------------------------- -case(VERTISSURFACE) - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - zout = 1.0_r8 - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - - ! compute surface pressure at all neighboring mass points - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_handle) - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - zout = -log(1.0_r8) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - ! a surface ob is assumed to have height as vertical coordinate. - ! this code needs to be revised if this is not true - ! (in that case uncomment lines below to get terrain height - ! from model) - zout = zin - !! or: directly interpolate terrain height at neighboring mass points - !zout = dym*( dxm*wrf%dom(id)%hgt(i, j) + & - ! dx*wrf%dom(id)%hgt(i+1,j) ) + & - ! dy*( dxm*wrf%dom(id)%hgt(i, j+1) + & - ! dx*wrf%dom(id)%hgt(i+1,j+1) ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was surface.') - - - end select ! incoming vert was surface - -! ------------------------------------------------------- -! incoming vertical coordinate has no case section -! ------------------------------------------------------- -case default - write(errstring,*) 'Incoming vertical coordinate type not recognized: ',ztypein - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate) - -end select ! incoming z vertical type - - -! on error, come here. istatus was set to 1 and zout to missing_r8 -! so unless they have been reset to good values, things did not work. -100 continue - -! Returned location -location = set_location(xyz_loc(1),xyz_loc(2),zout,ztypeout) - -!if(my_task_id() == 0 ) print*, xyz_loc(1), xyz_loc(2), zout, ztypeout - -! Set successful return code only if zout has good value -if(zout /= missing_r8) istatus = 0 - -end subroutine vert_convert - -!####################################################################### - - -function get_wrf_index( i,j,k,var_type,id ) - -integer, intent(in) :: i,j,k,var_type,id - -integer :: get_wrf_index -integer :: in - -write(errstring,*)'function get_wrf_index should not be called -- still needs updating!' -call error_handler(E_ERR,'get_wrf_index', errstring, & - source, revision, revdate) - -do in = 1, wrf%dom(id)%number_of_wrf_variables - if(var_type == wrf%dom(id)%var_type(in) ) then - exit - endif -enddo - -! If one decides to use get_wrf_index, then the following test should be updated -! to take periodicity into account at the boundaries -- or should it? -if(i >= 1 .and. i <= wrf%dom(id)%var_size(1,in) .and. & - j >= 1 .and. j <= wrf%dom(id)%var_size(2,in) .and. & - k >= 1 .and. k <= wrf%dom(id)%var_size(3,in)) then - - get_wrf_index = wrf%dom(id)%dart_ind(i,j,k,var_type) - -!!$ get_wrf_index = wrf%dom(id)%var_index(1,in)-1 + & -!!$ i + wrf%dom(id)%var_size(1,in)*((j-1) + & -!!$ wrf%dom(id)%var_size(2,in)*(k-1)) - -else - - write(errstring,*)'Indices ',i,j,k,' exceed grid dimensions: ', & - wrf%dom(id)%var_size(1,in), & - wrf%dom(id)%var_size(2,in),wrf%dom(id)%var_size(3,in) - call error_handler(E_ERR,'get_wrf_index', errstring, & - source, revision, revdate) - -endif - -end function get_wrf_index - - -!*********************************************************************** - - -subroutine get_wrf_horizontal_location( i, j, var_type, id, long, lat ) - -integer, intent(in) :: i,j,var_type, id -real(r8), intent(out) :: long, lat - -! given i, j indices into the horizontal grid return the lat/long. -! if u or v staggering use the staggered grids, otherwise use the mass -! grid. this code has changed -- earlier versions only had the mass -! grid available and used it to compute cell midpoints and called them -! the staggered points. now that all three grids are being read, look -! up the point locations directly from the appropriate array. - -if (var_type == wrf%dom(id)%type_u) then - long = wrf%dom(id)%longitude_u(i,j) - lat = wrf%dom(id)%latitude_u(i,j) -elseif (var_type == wrf%dom(id)%type_v) then - long = wrf%dom(id)%longitude_v(i,j) - lat = wrf%dom(id)%latitude_v(i,j) -else - long = wrf%dom(id)%longitude(i,j) - lat = wrf%dom(id)%latitude(i,j) -endif - -do while (long < 0.0_r8) - long = long + 360.0_r8 -end do -do while (long > 360.0_r8) - long = long - 360.0_r8 -end do - -end subroutine get_wrf_horizontal_location - - - -!*********************************************************************** - - -function nc_write_model_atts( ncFileID, model_mod_writes_state_variables ) result (ierr) -!----------------------------------------------------------------- -! Writes the model-specific attributes to a netCDF file -! A. Caya May 7 2003 -! T. Hoar Mar 8 2004 writes prognostic flavor - -logical, parameter :: write_precip = .false. - -integer, intent(in) :: ncFileID ! netCDF file identifier -logical, intent(out) :: model_mod_writes_state_variables -integer :: ierr ! return value of function - -!----------------------------------------------------------------- - -integer :: nDimensions, nVariables, nAttributes, unlimitedDimID -integer :: StateVarDimID, StateVarID, TimeDimID - -integer, dimension(num_domains) :: weDimID, weStagDimID, snDimID, snStagDimID, & - btDimID, btStagDimID, slSDimID, tmp - -integer :: MemberDimID, DomDimID -integer :: DXVarID, DYVarID, TRUELAT1VarID, TRUELAT2VarID, STAND_LONVarID -integer :: CEN_LATVarID, CEN_LONVarID, MAP_PROJVarID -integer :: PERIODIC_XVarID, POLARVarID -integer :: metadataID, wrfStateID, wrfDimID, WRFStateVarID, WRFStateDimID - -integer, dimension(num_domains) :: DNVarID, ZNUVarID, DNWVarID, phbVarID, & - MubVarID, LonVarID, LatVarID, ilevVarID, XlandVarID, hgtVarID , LatuVarID, & - LatvVarID, LonuVarID, LonvVarID, ZNWVarID - -! currently unused, but if needed could be added back in. these fields -! only appear to be supported in certain projections, so the code should -! test to be sure they exist before trying to read them from the netcdf file. -!integer, dimension(num_domains) :: MapFacMVarID, MapFacUVarID, MapFacVVarID - -integer :: var_id -integer :: i, id - -character(len=129) :: title - -character(len=8) :: crdate ! needed by F90 DATE_AND_TIME intrinsic -character(len=10) :: crtime ! needed by F90 DATE_AND_TIME intrinsic -character(len=5) :: crzone ! needed by F90 DATE_AND_TIME intrinsic -integer, dimension(8) :: values ! needed by F90 DATE_AND_TIME intrinsic -character(len=NF90_MAX_NAME) :: str1 - -character (len=1) :: idom - -character(len=129), allocatable, dimension(:) :: textblock -integer :: nlines, linelen -integer :: linelenDimID, nlinesDimID, nmlVarID -integer :: ind, my_index -character(len=NF90_MAX_NAME) :: attname, varname -character(len=129) :: unitsval, descriptionval, coordinatesval, long_nameval, coordinate_char -integer, dimension(5) :: dimids_3D -integer, dimension(4) :: dimids_2D -logical :: debug = .false. - -!----------------------------------------------------------------- - -ierr = 0 ! assume normal termination -model_mod_writes_state_variables = .true. - -!----------------------------------------------------------------- -! make sure ncFileID refers to an open netCDF file, -! and then put into define mode. -!----------------------------------------------------------------- - -call nc_check(nf90_get_att(ncFileID, NF90_GLOBAL, 'title', title), 'nc_write_model_atts','get_att') -call nc_check(nf90_Inquire(ncFileID, nDimensions, nVariables, nAttributes, unlimitedDimID), & - 'nc_write_model_atts','inquire') -call nc_check(nf90_Redef(ncFileID),'nc_write_model_atts','redef') - -!----------------------------------------------------------------- -! We need the dimension ID for the number of copies -!----------------------------------------------------------------- - -call nc_check(nf90_inq_dimid(ncid=ncFileID, name="NMLlinelen", dimid=linelenDimID), & - 'nc_write_model_atts','inq_dimid NMLlinelen') -call nc_check(nf90_inq_dimid(ncid=ncFileID, name="copy", dimid=MemberDimID), & - 'nc_write_model_atts','inq_dimid copy') -call nc_check(nf90_inq_dimid(ncid=ncFileID, name="time", dimid= TimeDimID), & - 'nc_write_model_atts','inq_dimid time') - -if ( TimeDimID /= unlimitedDimId ) then - write(errstring,*)'Time Dimension ID ',TimeDimID, & - ' must match Unlimited Dimension ID ',unlimitedDimID - call error_handler(E_ERR,'nc_write_model_atts', errstring, source, revision, revdate) -endif - -!----------------------------------------------------------------- -! Define the model size, state variable dimension ... whatever ... -!----------------------------------------------------------------- -call nc_check(nf90_def_dim(ncid=ncFileID, name="StateVariable", & - len=wrf%model_size, dimid = StateVarDimID), & - 'nc_write_model_atts','def_dim StateVariable') - -!----------------------------------------------------------------- -! Write Global Attributes -!----------------------------------------------------------------- -call DATE_AND_TIME(crdate,crtime,crzone,values) -write(str1,'(''YYYY MM DD HH MM SS = '',i4,5(1x,i2.2))') & - values(1), values(2), values(3), values(5), values(6), values(7) - -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "creation_date",str1), & - 'nc_write_model_atts','put_att creation_date') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model","WRF"), & - 'nc_write_model_atts','put_att model') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model_source",source), & - 'nc_write_model_atts','put_att model_source') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model_revision",revision), & - 'nc_write_model_atts','put_att model_revision') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model_revdate",revdate), & - 'nc_write_model_atts','put_att model_revdate') - -!----------------------------------------------------------------- -! how about namelist input? might be nice to save ... -! long lines are truncated when read into textblock -!----------------------------------------------------------------- - -call find_textfile_dims(wrf_nml_file, nlines, linelen) -if (nlines <= 0 .or. linelen <= 0) have_wrf_nml_file = .false. - -if (have_wrf_nml_file) then - allocate(textblock(nlines)) - textblock = '' - - call nc_check(nf90_def_dim(ncid=ncFileID, name="nlines", & - len = nlines, dimid = nlinesDimID), & - 'nc_write_model_atts', 'def_dim nlines ') -endif - -!----------------------------------------------------------------- -! Define the dimensions IDs -!----------------------------------------------------------------- - -call nc_check(nf90_def_dim(ncid=ncFileID, name="domain", & - len = num_domains, dimid = DomDimID), & - 'nc_write_model_atts','def_dim domain') - -do id=1,num_domains - write( idom , '(I1)') id - call nc_check(nf90_def_dim(ncid=ncFileID, name="west_east_d0"//idom, & - len = wrf%dom(id)%we, dimid = weDimID(id)), & - 'nc_write_model_atts','def_dim west_east_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="west_east_stag_d0"//idom, & - len = wrf%dom(id)%wes, dimid = weStagDimID(id)), & - 'nc_write_model_atts','def_dim west_east_stag_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="south_north_d0"//idom, & - len = wrf%dom(id)%sn, dimid = snDimID(id)), & - 'nc_write_model_atts','def_dim south_north_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="south_north_stag_d0"//idom, & - len = wrf%dom(id)%sns, dimid = snStagDimID(id)), & - 'nc_write_model_atts','def_dim south_north_stag_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="bottom_top_d0"//idom, & - len = wrf%dom(id)%bt, dimid = btDimID(id)), & - 'nc_write_model_atts','def_dim bottom_top_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="bottom_top_stag_d0"//idom, & - len = wrf%dom(id)%bts, dimid = btStagDimID(id)), & - 'nc_write_model_atts','def_dim bottom_top_stag_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="soil_layers_stag_d0"//idom, & - len = wrf%dom(id)%sls, dimid = slSDimID(id)), & - 'nc_write_model_atts','def_dim soil_layers_stag_d0'//idom) -enddo - -!----------------------------------------------------------------- -! Create the (empty) Variables and the Attributes -!----------------------------------------------------------------- - -!----------------------------------------------------------------- -! Create the (empty) static variables and their attributes -! Commented block is from wrfinput -!----------------------------------------------------------------- - -if (have_wrf_nml_file) then - call nc_check(nf90_def_var(ncFileID,name="WRFnml", xtype=nf90_char, & - dimids = (/ linelenDimID, nlinesDimID /), varid=nmlVarID), & - 'nc_write_model_atts', 'def_var WRFnml') - call nc_check(nf90_put_att(ncFileID, nmlVarID, "long_name", & - "namelist.input contents"), 'nc_write_model_atts', 'put_att WRFnml') -endif - -call nc_check(nf90_def_var(ncFileID, name="DX", xtype=nf90_real, & - dimids= DomDimID, varid=DXVarID), & - 'nc_write_model_atts','def_var DX') -call nc_check(nf90_put_att(ncFileID, DXVarID, "long_name", "X HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DX long_name') -call nc_check(nf90_put_att(ncFileID, DXVarID, "description", "X HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DX description') -call nc_check(nf90_put_att(ncFileID, DXVarID, "units", "m"), & - 'nc_write_model_atts','put_att DX units') - -call nc_check(nf90_def_var(ncFileID, name="DY", xtype=nf90_real, & - dimids= DomDimID, varid=DYVarID), & - 'nc_write_model_atts','def_var DY') -call nc_check(nf90_put_att(ncFileID, DYVarID, "long_name", "Y HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DY long_name') -call nc_check(nf90_put_att(ncFileID, DYVarID, "description", "Y HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DY description') -call nc_check(nf90_put_att(ncFileID, DYVarID, "units", "m"), & - 'nc_write_model_atts','put_att DY units') - -call nc_check(nf90_def_var(ncFileID, name="TRUELAT1", xtype=nf90_real, & - dimids= DomDimID, varid=TRUELAT1VarID), & - 'nc_write_model_atts','def_var TRUELAT1') -call nc_check(nf90_put_att(ncFileID, TRUELAT1VarID, "long_name", & - "first standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT1 long_name') -call nc_check(nf90_put_att(ncFileID, TRUELAT1VarID, "description", & - "first standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT1 description') -call nc_check(nf90_put_att(ncFileID, TRUELAT1VarID, "units", & - "degrees, negative is south"), & - 'nc_write_model_atts','put_att TRUELAT1 units') - -call nc_check(nf90_def_var(ncFileID, name="TRUELAT2", xtype=nf90_real, & - dimids= DomDimID, varid=TRUELAT2VarID), & - 'nc_write_model_atts','def_var TRUELAT2') -call nc_check(nf90_put_att(ncFileID, TRUELAT2VarID, "long_name", & - "second standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT2 long_name') -call nc_check(nf90_put_att(ncFileID, TRUELAT2VarID, "description", & - "second standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT2 description') -call nc_check(nf90_put_att(ncFileID, TRUELAT2VarID, "units", & - "degrees, negative is south"), & - 'nc_write_model_atts','put_att TRUELAT2 units') - -call nc_check(nf90_def_var(ncFileID, name="STAND_LON", xtype=nf90_real, & - dimids= DomDimID, varid=STAND_LONVarID), & - 'nc_write_model_atts','def_var STAND_LON') -call nc_check(nf90_put_att(ncFileID, STAND_LONVarID, "long_name", & - "standard longitude"), & - 'nc_write_model_atts','put_att STAND_LON long_name') -call nc_check(nf90_put_att(ncFileID, STAND_LONVarID, "description", & - "standard longitude"), & - 'nc_write_model_atts','put_att STAND_LON description') -call nc_check(nf90_put_att(ncFileID, STAND_LONVarID, "units", & - "degrees, negative is west"), & - 'nc_write_model_atts','put_att STAND_LON units') - -call nc_check(nf90_def_var(ncFileID, name="CEN_LAT", xtype=nf90_real, & - dimids= DomDimID, varid=CEN_LATVarID), & - 'nc_write_model_atts','def_var CEN_LAT') -call nc_check(nf90_put_att(ncFileID, CEN_LATVarID, "long_name", & - "center latitude"), & - 'nc_write_model_atts','put_att CEN_LAT long_name') -call nc_check(nf90_put_att(ncFileID, CEN_LATVarID, "description", & - "center latitude"), & - 'nc_write_model_atts','put_att CEN_LAT description') -call nc_check(nf90_put_att(ncFileID, CEN_LATVarID, "units", & - "degrees, negative is south"), & - 'nc_write_model_atts','put_att CEN_LAT units') - -call nc_check(nf90_def_var(ncFileID, name="CEN_LON", xtype=nf90_real, & - dimids= DomDimID, varid=CEN_LONVarID), & - 'nc_write_model_atts','def_var CEN_LON') -call nc_check(nf90_put_att(ncFileID, CEN_LONVarID, "long_name", & - "central longitude"), & - 'nc_write_model_atts','put_att CEN_LON long_name') -call nc_check(nf90_put_att(ncFileID, CEN_LONVarID, "description", & - "central longitude"), & - 'nc_write_model_atts','put_att CEN_LON description') -call nc_check(nf90_put_att(ncFileID, CEN_LONVarID, "units", & - "degrees, negative is west"), & - 'nc_write_model_atts','put_att CEN_LON units') - -call nc_check(nf90_def_var(ncFileID, name="MAP_PROJ", xtype=nf90_real, & - dimids= DomDimID, varid=MAP_PROJVarID), & - 'nc_write_model_atts','def_var MAP_PROJ') -call nc_check(nf90_put_att(ncFileID, MAP_PROJVarID, "long_name", & - "domain map projection"), & - 'nc_write_model_atts','put_att MAP_PROJ long_name') -call nc_check(nf90_put_att(ncFileID, MAP_PROJVarID, "description", & - "domain map projection"), & - 'nc_write_model_atts','put_att MAP_PROJ description') -call nc_check(nf90_put_att(ncFileID, MAP_PROJVarID, "units", & - "0=none, 1=Lambert, 2=polar, 3=Mercator, 5=Cylindrical, 6=Cassini"), & - 'nc_write_model_atts','put_att MAP_PROJ units') - -!nc -- we need to add in code here to report the domain values for the -! boundary condition flags periodic_x and polar. Since these are -! carried internally as logicals, they will first need to be -! converted back to integers. -call nc_check(nf90_def_var(ncFileID, name="PERIODIC_X", xtype=nf90_int, & - dimids= DomDimID, varid=PERIODIC_XVarID), & - 'nc_write_model_atts','def_var PERIODIC_X') -call nc_check(nf90_put_att(ncFileID, PERIODIC_XVarID, "long_name", & - "Longitudinal periodic b.c. flag"), & - 'nc_write_model_atts','put_att PERIODIC_X long_name') -call nc_check(nf90_put_att(ncFileID, PERIODIC_XVarID, "description", & - "Longitudinal periodic b.c. flag"), & - 'nc_write_model_atts','put_att PERIODIC_X desciption') -call nc_check(nf90_put_att(ncFileID, PERIODIC_XVarID, "units", & - "logical: 1 = .true., 0 = .false."), & - 'nc_write_model_atts','put_att PERIODIC_X units') - -call nc_check(nf90_def_var(ncFileID, name="POLAR", xtype=nf90_int, & - dimids= DomDimID, varid=POLARVarID), & - 'nc_write_model_atts','def_var POLAR') -call nc_check(nf90_put_att(ncFileID, POLARVarID, "long_name", & - "Polar periodic b.c. flag"), & - 'nc_write_model_atts','put_att POLAR long_name') -call nc_check(nf90_put_att(ncFileID, POLARVarID, "description", & - "Polar periodic b.c. flag"), & - 'nc_write_model_atts','put_att POLAR description') -call nc_check(nf90_put_att(ncFileID, POLARVarID, "units", & - "logical: 1 = .true., 0 = .false."), & - 'nc_write_model_atts','put_att POLAR units') - - - -do id=1,num_domains - write( idom , '(I1)') id - - call nc_check(nf90_def_var(ncFileID, name="DN_d0"//idom, xtype=nf90_real, & - dimids= btDimID(id), varid=DNVarID(id)), & - 'nc_write_model_atts','def_var DN_do'//idom) - call nc_check(nf90_put_att(ncFileID, DNVarID(id), "long_name", & - "dn values on half (mass) levels"), & - 'nc_write_model_atts','put_att DN_do'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, DNVarID(id), "description", & - "dn values on half (mass) levels"), & - 'nc_write_model_atts','put_att DN_do'//idom//' description') - call nc_check(nf90_put_att(ncFileID, DNVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att DN_do'//idom//' units') - - call nc_check(nf90_def_var(ncFileID, name="ZNU_d0"//idom, xtype=nf90_real, & - dimids= btDimID(id), varid=ZNUVarID(id)), & - 'nc_write_model_atts','def_var ZNU_d0'//idom) - call nc_check(nf90_put_att(ncFileID, ZNUVarID(id), "long_name", & - "eta values on half (mass) levels"), & - 'nc_write_model_atts','put_att ZNU_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, ZNUVarID(id), "description", & - "eta values on half (mass) levels"), & - 'nc_write_model_atts','put_att ZNU_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, ZNUVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att ZNU_d0'//idom//' units') - - call nc_check(nf90_def_var(ncFileID, name="ZNW_d0"//idom, xtype=nf90_real, & - dimids= btStagDimID(id), varid=ZNWVarID(id)), & - 'nc_write_model_atts','def_var ZNW_d0'//idom) - call nc_check(nf90_put_att(ncFileID, ZNWVarID(id), "long_name", & - "eta values on full (mass) levels"), & - 'nc_write_model_atts','put_att ZNW_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, ZNWVarID(id), "description", & - "eta values on full (mass) levels"), & - 'nc_write_model_atts','put_att ZNW_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, ZNWVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att ZNW_d0'//idom//' units') - - call nc_check(nf90_def_var(ncFileID, name="DNW_d0"//idom, xtype=nf90_real, & - dimids= btDimID(id), varid=DNWVarID(id)), & - 'nc_write_model_atts','def_var DNW_d0'//idom) - call nc_check(nf90_put_att(ncFileID, DNWVarID(id), "long_name", & - "dn values on full (w) levels"), & - 'nc_write_model_atts','def_var DNW_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, DNWVarID(id), "description", & - "dn values on full (w) levels"), & - 'nc_write_model_atts','def_var DNW_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, DNWVarID(id), "units", & - ""), & - 'nc_write_model_atts','def_var DNW_d0'//idom//' units') - -! -! float MUB(Time, south_north, west_east) ; -! MUB:FieldType = 104 ; -! MUB:MemoryOrder = "XY " ; -! MUB:stagger = "" ; - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - call nc_check(nf90_def_var(ncFileID, name="MUB_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snDimID(id) /), varid=MubVarID(id)), & - 'nc_write_model_atts','def_var MUB_d0'//idom) - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "long_name", & - "base state dry air mass in column"), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "description", & - "base state dry air mass in column"), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "units", "Pa"), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "coordinates", & - trim(coordinate_char)), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' coordinates') - -! Longitudes -! float XLONG(Time, south_north, west_east) ; -! XLONG:FieldType = 104 ; -! XLONG:MemoryOrder = "XY " ; -! XLONG:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLONG_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snDimID(id) /), varid=LonVarID(id)), & - 'nc_write_model_atts','def_var XLONG_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "long_name", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "units", "degrees_east"), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "valid_range", & - (/ -180.0_r8, 180.0_r8 /)), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "description", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' description') - -! float XLONG_U(Time, south_north, west_east_stag) ; -! XLONG:FieldType = 104 ; -! XLONG:MemoryOrder = "XY " ; -! XLONG:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLONG_U_d0"//idom, xtype=nf90_real, & - dimids= (/ weStagDimID(id), snDimID(id) /), varid=LonuVarID(id)), & - 'nc_write_model_atts','def_var XLONG_U_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "long_name", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LonuVarID(id), "units", "degrees_east"), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LonuVarID(id), "valid_range", & - (/ -180.0_r8, 180.0_r8 /)), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LonuVarID(id), "description", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' description') - -! float XLONG_V(Time, south_north_stag, west_east) ; -! XLONG:FieldType = 104 ; -! XLONG:MemoryOrder = "XY " ; -! XLONG:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLONG_V_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snStagDimID(id) /), varid=LonvVarID(id)), & - 'nc_write_model_atts','def_var XLONG_V_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "long_name", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "units", "degrees_east"), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "valid_range", & - (/ -180.0_r8, 180.0_r8 /)), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "description", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' description') - -! Latitudes -! float XLAT(Time, south_north, west_east) ; -! XLAT:FieldType = 104 ; -! XLAT:MemoryOrder = "XY " ; -! XLAT:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLAT_d0"//idom, xtype=nf90_real, & - dimids=(/ weDimID(id), snDimID(id) /), varid=LatVarID(id)), & - 'nc_write_model_atts','def_var XLAT_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "long_name", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "units", "degrees_north"), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "valid_range", & - (/ -90.0_r8, 90.0_r8 /)), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "description", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' description') - -! float XLAT_U(Time, south_north, west_east_stag) ; -! XLAT_U:FieldType = 104 ; -! XLAT_U:MemoryOrder = "XY " ; -! XLAT_U:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLAT_U_d0"//idom, xtype=nf90_real, & - dimids=(/ weStagDimID(id), snDimID(id) /), varid=LatuVarID(id)), & - 'nc_write_model_atts','def_var XLAT_U_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "long_name", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "units", "degrees_north"), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "valid_range", & - (/ -90.0_r8, 90.0_r8 /)), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "description", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' description') - -! float XLAT_V(Time, south_north_stag, west_east) ; -! XLAT_V:FieldType = 104 ; -! XLAT_V:MemoryOrder = "XY " ; -! XLAT_V:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLAT_V_d0"//idom, xtype=nf90_real, & - dimids=(/ weDimID(id), snStagDimID(id) /), varid=LatvVarID(id)), & - 'nc_write_model_atts','def_var XLAT_V_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "long_name", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "units", "degrees_north"), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "valid_range", & - (/ -90.0_r8, 90.0_r8 /)), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "description", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' description') - -! grid levels - call nc_check(nf90_def_var(ncFileID, name="level_d0"//idom, xtype=nf90_short, & - dimids=btDimID(id), varid=ilevVarID(id)), & - 'nc_write_model_atts','def_var level_d0'//idom) - call nc_check(nf90_put_att(ncFileID, ilevVarID(id), "long_name", & - "level index"), & - 'nc_write_model_atts','put_att level_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, ilevVarID(id), "description", & - "level index"), & - 'nc_write_model_atts','put_att level_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, ilevVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att level_d0'//idom//' units') - -! Land Mask -! float XLAND(Time, south_north, west_east) ; -! XLAND:FieldType = 104 ; -! XLAND:MemoryOrder = "XY " ; -! XLAND:units = "NA" ; -! XLAND:stagger = "" ; - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - call nc_check(nf90_def_var(ncFileID, name="XLAND_d0"//idom, xtype=nf90_short, & - dimids= (/ weDimID(id), snDimID(id) /), varid=XlandVarID(id)), & - 'nc_write_model_atts','def_var XLAND_d0'//idom) - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "long_name", & - "LAND MASK (1 FOR LAND, 2 FOR WATER)"), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "units", " "), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "coordinates", & - trim(coordinate_char)), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' coordinates') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "valid_range", (/ 1, 2 /)), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "description", & - "LAND MASK (1 FOR LAND, 2 FOR WATER)"), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' description') - -! PHB -! float PHB(Time, bottom_top_stag, south_north, west_east) ; -! PHB:FieldType = 104 ; -! PHB:MemoryOrder = "XYZ" ; -! PHB:stagger = "Z" ; -! *** phb not complete on any processor - ! coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - !call nc_check(nf90_def_var(ncFileID, name="PHB_d0"//idom, xtype=nf90_real, & - ! dimids= (/ weDimID(id), snDimID(id), btStagDimID(id) /), varid=phbVarId(id)), & - ! 'nc_write_model_atts','def_var PHB_d0'//idom) - !call nc_check(nf90_put_att(ncFileID, phbVarId(id), "long_name", & - ! "base-state geopotential"), & - ! 'nc_write_model_atts','put_att PHB_d0'//idom//' long_name') - !call nc_check(nf90_put_att(ncFileID, phbVarId(id), "description", & - ! "base-state geopotential"), & - ! 'nc_write_model_atts','put_att PHB_d0'//idom//' description') - !call nc_check(nf90_put_att(ncFileID, phbVarId(id), "units", "m2/s2"), & - ! 'nc_write_model_atts','put_att PHB_d0'//idom//' units') - !call nc_check(nf90_put_att(ncFileID, phbVarId(id), "coordinates", & - ! trim(coordinate_char)), & - ! 'nc_write_model_atts','put_att PHB_d0'//idom//' coordinates') - !call nc_check(nf90_put_att(ncFileID, phbVarId(id), "units_long_name", "m{2} s{-2}"), & - ! 'nc_write_model_atts','put_att PHB_d0'//idom//' units_long_name') -! *** - - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - call nc_check(nf90_def_var(ncFileID, name="HGT_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snDimID(id) /), varid=hgtVarId(id)), & - 'nc_write_model_atts','def_var HGT_d0'//idom) - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "long_name", "Terrain Height"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "description", "Terrain Height"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "units", "m"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "coordinates", & - trim(coordinate_char)), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' coordinates') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "units_long_name", "meters"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' units_long_name') - - ! can't deal with precip yet - need to leave hard coded - - if ( trim(adjustl(title(1:2))) == 'pr' .and. write_precip ) then - - call nc_check(nf90_def_var(ncid=ncFileID, name="RAINC_d0"//idom, xtype=nf90_real, & - dimids = (/ weDimID(id), snDimID(id), MemberDimID, unlimitedDimID /), & - varid = var_id),'nc_write_model_atts','def_var RAINC_d0'//idom) - call nc_check(nf90_put_att(ncFileID, var_id, "units", "mm"), & - 'nc_write_model_atts','put_att RAINC_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, var_id, "long_name", & - "ACCUMULATED TOTAL CUMULUS PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINC_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, var_id, "description", & - "ACCUMULATED TOTAL CUMULUS PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINC_d0'//idom//' description') - - call nc_check(nf90_def_var(ncid=ncFileID, name="RAINNC_d0"//idom, xtype=nf90_real, & - dimids = (/ weDimID(id), snDimID(id), MemberDimID, unlimitedDimID /), & - varid = var_id),'nc_write_model_atts','def_var RAINNC_d0'//idom) - call nc_check(nf90_put_att(ncFileID, var_id, "units", "mm"), & - 'nc_write_model_atts','put_att RAINNC_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, var_id, "long_name", & - "ACCUMULATED TOTAL GRID POINT PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINNC_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, var_id, "description", & - "ACCUMULATED TOTAL GRID POINT PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINNC_d0'//idom//' description') - - endif - -enddo - -if ( output_state_vector ) then - - !----------------------------------------------------------------- - ! Create attributes for the state vector - !----------------------------------------------------------------- - - call nc_check(nf90_inq_dimid(ncFileID, "metadatalength", metadataID), & - 'nc_write_model_atts','inq_dimid metadatalength') - - call nc_check(nf90_def_dim(ncid=ncFileID, name="WRFStateVariables", & - len = wrf%dom(1)%number_of_wrf_variables, dimid = wrfStateID), & - 'nc_write_model_atts','def_dim WRFStateVariables') - - call nc_check(nf90_def_dim(ncid=ncFileID, name="WRFVarDimension", & - len = 3, dimid = wrfDimID), & - 'nc_write_model_atts','def_dim WRFVarDimensionID') - - ! Define the state variable name variable - call nc_check(nf90_def_var(ncid=ncFileID,name="WRFStateVariables", xtype=nf90_char, & - dimids=(/ metadataID, wrfStateID /), varid=WRFStateVarID), & - 'nc_write_model_atts','def_var WRFStateVariables') - - call nc_check(nf90_put_att(ncFileID, WRFStateVarID, "long_name", & - "WRF State Variable Name"), & - 'nc_write_model_atts','put_att WRFStateVariables long_name') - - ! Define the WRF state variable dimension lengths - call nc_check(nf90_def_var(ncid=ncFileID,name="WRFStateDimensions", xtype=nf90_int, & - dimids=(/ wrfDimID, wrfStateID, DomDimID /), varid=WRFStateDimID), & - 'nc_write_model_atts','def_var WRFStateDimensions') - - call nc_check(nf90_put_att(ncFileID, WRFStateDimID, "long_name", & - "WRF State Variable Dimensions"), & - 'nc_write_model_atts','put_att WRFStateDimensions long_name') - - ! Define the actual state vector - - call nc_check(nf90_def_var(ncid=ncFileID, name="state", xtype=nf90_real, & - dimids = (/ StateVarDimID, MemberDimID, unlimitedDimID /), & - varid=StateVarID), & - 'nc_write_model_atts','def_var state') - call nc_check(nf90_put_att(ncFileID, StateVarID, "long_name", & - "model state or fcopy"), & - 'nc_write_model_atts','put_att state long_name') - - ! only define those that are present in the state vector - do id = 1,num_domains ! this makes sure we get them all - write( idom , '(I1)') id - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - ! units - attname = trim(wrf_state_variables(1,my_index))//'_units' - - ! if we didn't already write it, do it now - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - - unitsval = trim(wrf%dom(id)%units(ind)) - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - unitsval), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - ! description - attname = trim(wrf_state_variables(1,my_index))//'_description' - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - descriptionval = trim(wrf%dom(id)%description(ind)) - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - descriptionval), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - ! long_name - same as description - attname = trim(wrf_state_variables(1,my_index))//'_long_name' - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - long_nameval = trim(wrf%dom(id)%description(ind)) - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - long_nameval), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - ! coordinates - define the name of the (sometimes staggered) - ! coordinate variables to use to decode locations - attname = trim(wrf_state_variables(1,my_index))//'_coordinates' - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - coordinatesval = trim(wrf%dom(id)%coordinates(ind)) - if (coordinatesval(1:7) .eq. 'XLONG_U') then - coordinate_char = "XLONG_U_d0"//idom//" XLAT_U_d0"//idom - else if (coordinatesval(1:7) .eq. 'XLONG_V') then - coordinate_char = "XLONG_V_d0"//idom//" XLAT_V_d0"//idom - else - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - end if - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - trim(coordinate_char)), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - enddo - enddo - - ! Leave define mode so we can actually fill the variables. - - call nc_check(nf90_enddef(ncfileID),'nc_write_model_atts','enddef') - - do ind = 1,wrf%dom(1)%number_of_wrf_variables - my_index = wrf%dom(1)%var_index_list(ind) - call nc_check(nf90_put_var(ncFileID,WRFStateVarID,trim(wrf_state_variables(1,my_index)), & - start = (/ 1, ind /), count = (/ len_trim(wrf_state_variables(1,my_index)), 1 /)), & - 'nc_write_model_atts', 'put_var WRFStateVariables') - enddo - - do id = 1, num_domains - do ind = 1,wrf%dom(id)%number_of_wrf_variables - call nc_check(nf90_put_var(ncFileID,WRFStateDimID,wrf%dom(id)%var_size(:,ind), & - start = (/ 1, ind, id /), count = (/ 3, 1, 1 /)), & - 'nc_write_model_atts', 'put_var WRFStateDimensions') - enddo - enddo - -else ! physical arrays - -do id=1,num_domains - write( idom , '(I1)') id - - !---------------------------------------------------------------------------- - ! Create the (empty) Prognostic Variables and their attributes - !---------------------------------------------------------------------------- - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - - varname = trim(wrf_state_variables(1,my_index))//'_d0'//idom - - if ( debug ) write(*,*) 'Defining variable ',varname - - if ( wrf%dom(id)%var_size(3,ind) > 1 ) then ! 3D variable - - dimids_3D(4:5) = (/MemberDimID,unlimitedDimID/) - - ! get first two dimensions based on stagger (could do this - ! differently) - select case (trim(wrf%dom(id)%stagger(ind))) - case ('Z') ! mass grid staggered vertical - dimids_3D(1:2)=(/weDimID(id),snDimId(id)/) - case ('X') ! U grid unstaggered vertical - dimids_3D(1:2)=(/weStagDimID(id),snDimId(id)/) - case ('Y') ! V grid unstaggered vertical - dimids_3D(1:2)=(/weDimID(id),snStagDimId(id)/) - case default - dimids_3D(1:2)=(/weDimID(id),snDimId(id)/) - end select - - ! vertical dimension can be stag, unstag, or staggered soil - ! need to use if/then/else instead of select because testing - ! is against variables - if ( wrf%dom(id)%var_size(3,my_index) == wrf%dom(id)%bts ) then - dimids_3D(3)=btStagDimID(id) - elseif ( wrf%dom(id)%var_size(3,my_index) == wrf%dom(id)%bt ) then - dimids_3D(3)=btDimID(id) - elseif ( wrf%dom(id)%var_size(3,my_index) == wrf%dom(id)%sls ) then - dimids_3D(3)=slSDimID(id) - else - write(errstring,*)'Could not determine dim_id for vertical dimension to output variable '//varname - call error_handler(E_ERR,'nc_write_model_atts',errstring,source, revision,revdate) - endif - - if ( debug ) write(*,*) '3D with dim ids ',dimids_3D - - call nc_check(nf90_def_var(ncid=ncFileID, name=varname, & - xtype=nf90_real,dimids=dimids_3D, varid=var_id), & - 'nc_write_model_atts','def_var '//varname) - - else ! must be 2D - - dimids_2D(3:4) = (/MemberDimID,unlimitedDimID/) - - select case (trim(wrf%dom(id)%stagger(ind))) - case ('Z') ! mass grid staggered vertical - dimids_2D(1:2)=(/weDimID(id),snDimId(id)/) - case ('X') ! U grid unstaggered vertical - dimids_2D(1:2)=(/weStagDimID(id),snDimId(id)/) - case ('Y') ! V grid unstaggered vertical - dimids_2D(1:2)=(/weDimID(id),snStagDimId(id)/) - case default - dimids_2D(1:2)=(/weDimID(id),snDimId(id)/) - end select - - if ( debug ) write(*,*) '2D with dim ids ',dimids_2D - - call nc_check(nf90_def_var(ncid=ncFileID, name=varname, & - xtype=nf90_real,dimids=dimids_2D, varid=var_id), & - 'nc_write_model_atts','def_var '//varname) - - endif ! 3D or 2D - - unitsval = trim(wrf%dom(id)%units(my_index)) - - call nc_check(nf90_put_att(ncFileID, var_id, "units", trim(unitsval)), & - 'nc_write_model_atts','put_att '//varname//' units') - - descriptionval = trim(wrf%dom(id)%description(my_index)) - - call nc_check(nf90_put_att(ncFileID, var_id, "description", trim(descriptionval)), & - 'nc_write_model_atts','put_att '//varname//' description') - - long_nameval = trim(wrf%dom(id)%description(my_index)) - - call nc_check(nf90_put_att(ncFileID, var_id, "long_name", trim(long_nameval)), & - 'nc_write_model_atts','put_att '//varname//' long_name') - - coordinatesval = trim(wrf%dom(id)%coordinates(my_index)) - if (coordinatesval(1:7) .eq. 'XLONG_U') then - coordinate_char = "XLONG_U_d0"//idom//" XLAT_U_d0"//idom - else if (coordinatesval(1:7) .eq. 'XLONG_V') then - coordinate_char = "XLONG_V_d0"//idom//" XLAT_V_d0"//idom - else - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - end if - call nc_check(nf90_put_att(ncFileID, var_id, "coordinates", trim(coordinate_char)), & - 'nc_write_model_atts','put_att '//varname//' coordinates') - - - !------No info on long_name or units_long_name right now! -! call nc_check(nf90_put_att(ncFileID, var_id, "long_name", "x-wind component"), & -! 'nc_write_model_atts','put_att U_d0'//idom//' long_name') -! call nc_check(nf90_put_att(ncFileID, var_id, "units_long_name", "m s{-1}"), & -! 'nc_write_model_atts','put_att U_d0'//idom//' units_long_name') - - enddo ! variables - -enddo ! domains - -! Leave define mode so we can actually fill the variables. -call nc_check(nf90_enddef(ncfileID),'nc_write_model_atts','enddef') - -endif - -!----------------------------------------------------------------- -! Fill the variables we can -!----------------------------------------------------------------- - -if (have_wrf_nml_file) then - call file_to_text(wrf_nml_file, textblock) - call nc_check(nf90_put_var(ncFileID, nmlVarID, textblock ), & - 'nc_write_model_atts', 'put_var nmlVarID') - deallocate(textblock) -endif - -call nc_check(nf90_put_var(ncFileID, DXVarID, wrf%dom(1:num_domains)%dx), & - 'nc_write_model_atts','put_var dx') -call nc_check(nf90_put_var(ncFileID, DYVarID, wrf%dom(1:num_domains)%dy), & - 'nc_write_model_atts','put_var dy') -call nc_check(nf90_put_var(ncFileID, TRUELAT1VarID, wrf%dom(1:num_domains)%proj%truelat1), & - 'nc_write_model_atts','put_var truelat1') -call nc_check(nf90_put_var(ncFileID, TRUELAT2VarID, wrf%dom(1:num_domains)%proj%truelat2), & - 'nc_write_model_atts','put_var truelat2') -call nc_check(nf90_put_var(ncFileID, STAND_LONVarID, wrf%dom(1:num_domains)%proj%stdlon), & - 'nc_write_model_atts','put_var stdlon') -call nc_check(nf90_put_var(ncFileID, CEN_LATVarID, wrf%dom(1:num_domains)%cen_lat), & - 'nc_write_model_atts','put_var cen_lat') -call nc_check(nf90_put_var(ncFileID, CEN_LONVarID, wrf%dom(1:num_domains)%cen_lon), & - 'nc_write_model_atts','put_var cen_lon') -call nc_check(nf90_put_var(ncFileID, MAP_PROJVarID, wrf%dom(1:num_domains)%map_proj), & - 'nc_write_model_atts','put_var map_proj') - -!nc -- convert internally logical boundary condition variables into integers before filling -do id=1,num_domains - if ( wrf%dom(id)%periodic_x ) then - tmp(id) = 1 - else - tmp(id) = 0 - endif -end do -call nc_check(nf90_put_var(ncFileID, PERIODIC_XVarID, tmp(1:num_domains) ), & - 'nc_write_model_atts','put_var PERIODIC_XVarID') - -do id=1,num_domains - if ( wrf%dom(id)%polar ) then - tmp(id) = 1 - else - tmp(id) = 0 - endif -end do -call nc_check(nf90_put_var(ncFileID, POLARVarID, tmp(1:num_domains) ), & - 'nc_write_model_atts','put var POLARVarID') - - -do id=1,num_domains - -! defining grid levels - call nc_check(nf90_put_var(ncFileID, DNVarID(id), wrf%dom(id)%dn), & - 'nc_write_model_atts','put_var dn') - call nc_check(nf90_put_var(ncFileID, ZNUVarID(id), wrf%dom(id)%znu), & - 'nc_write_model_atts','put_var znu') - call nc_check(nf90_put_var(ncFileID, ZNWVarID(id), wrf%dom(id)%znw), & - 'nc_write_model_atts','put_var znw') - call nc_check(nf90_put_var(ncFileID, DNWVarID(id), wrf%dom(id)%dnw), & - 'nc_write_model_atts','put_var dnw') - -! defining horizontal - call nc_check(nf90_put_var(ncFileID, mubVarID(id), wrf%dom(id)%mub), & - 'nc_write_model_atts','put_var mub') - call nc_check(nf90_put_var(ncFileID, LonVarID(id), wrf%dom(id)%longitude), & - 'nc_write_model_atts','put_var longitude') - call nc_check(nf90_put_var(ncFileID, LonuVarID(id), wrf%dom(id)%longitude_u), & - 'nc_write_model_atts','put_var longitude_u') - call nc_check(nf90_put_var(ncFileID, LonvVarID(id), wrf%dom(id)%longitude_v), & - 'nc_write_model_atts','put_var longitude_v') - call nc_check(nf90_put_var(ncFileID, LatVarID(id), wrf%dom(id)%latitude), & - 'nc_write_model_atts','put_var latitude') - call nc_check(nf90_put_var(ncFileID, LatuVarID(id), wrf%dom(id)%latitude_u), & - 'nc_write_model_atts','put_var latitude_u') - call nc_check(nf90_put_var(ncFileID, LatvVarID(id), wrf%dom(id)%latitude_v), & - 'nc_write_model_atts','put_var latitude_v') - call nc_check(nf90_put_var(ncFileID, ilevVarID(id), (/ (i,i=1,wrf%dom(id)%bt) /)), & - 'nc_write_model_atts','put_var bt') - call nc_check(nf90_put_var(ncFileID, XlandVarID(id), wrf%dom(id)%land), & - 'nc_write_model_atts','put_var land') -! call nc_check(nf90_put_var(ncFileID, MapFacMVarID(id), wrf%dom(id)%mapfac_m), & -! 'nc_write_model_atts','put_var mapfac_m') -! call nc_check(nf90_put_var(ncFileID, MapFacUVarID(id), wrf%dom(id)%mapfac_u), & -! 'nc_write_model_atts','put_var mapfac_u') -! call nc_check(nf90_put_var(ncFileID, MapFacVVarID(id), wrf%dom(id)%mapfac_v), & -! 'nc_write_model_atts','put_var mapfac_v') - -! *** phb no longer on one processor -! call nc_check(nf90_put_var(ncFileID, phbVarID(id), wrf%dom(id)%phb), & -! 'nc_write_model_atts','put_var phb') -! call nc_check(nf90_put_var(ncFileID, hgtVarID(id), wrf%dom(id)%hgt), & -! 'nc_write_model_atts','put_var hgt') -! *** phb no longer on one processor - -enddo - -!----------------------------------------------------------------- -! Flush the buffer and leave netCDF file open -!----------------------------------------------------------------- - -call nc_check(nf90_sync(ncFileID),'nc_write_model_atts','sync') - -end function nc_write_model_atts - - - -function nc_write_model_vars( ncFileID, statevec, copyindex, timeindex ) result (ierr) -!----------------------------------------------------------------- -! Writes the model-specific variables to a netCDF file -! TJH 25 June 2003 -! -! TJH 29 July 2003 -- for the moment, all errors are fatal, so the -! return code is always '0 == normal', since the fatal errors stop execution. - - -integer, intent(in) :: ncFileID ! netCDF file identifier -real(r8), dimension(:), intent(in) :: statevec -integer, intent(in) :: copyindex -integer, intent(in) :: timeindex -integer :: ierr ! return value of function - -!----------------------------------------------------------------- - -logical, parameter :: debug = .false. -integer :: nDimensions, nVariables, nAttributes, unlimitedDimID -integer :: StateVarID, VarID, id, ind, my_index -integer :: i,j -real(r8), allocatable, dimension(:,:) :: temp2d -real(r8), allocatable, dimension(:,:,:) :: temp3d -character(len=NF90_MAX_NAME) :: varname -character(len=1) :: idom -integer, dimension(2) :: dimsizes_2D -integer, dimension(3) :: dimsizes_3D - -ierr = 0 ! assume normal termination - -!----------------------------------------------------------------- -! make sure ncFileID refers to an open netCDF file, -! then get all the Variable ID's we need. -!----------------------------------------------------------------- - -call nc_check(nf90_Inquire(ncFileID, nDimensions, nVariables, nAttributes, unlimitedDimID), & - 'nc_write_model_vars','inquire') - -if ( output_state_vector ) then - - call nc_check(nf90_inq_varid(ncFileID, "state", StateVarID), & - 'nc_write_model_vars','inq_varid state') - call nc_check(nf90_put_var(ncFileID, StateVarID, statevec, & - start=(/ 1, copyindex, timeindex /)), & - 'nc_write_model_vars','put_var statevec') - -else - -j = 0 - -do id=1,num_domains - - write( idom , '(I1)') id - - !---------------------------------------------------------------------------- - ! Fill the variables - !---------------------------------------------------------------------------- - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - - varname = trim(wrf_state_variables(1,my_index))//'_d0'//idom - - call nc_check(nf90_inq_varid(ncFileID, trim(varname), VarID), & - 'nc_write_model_vars','inq_varid '//trim(varname)) - - i = j + 1 - j = i + wrf%dom(id)%var_size(1,ind) * & - wrf%dom(id)%var_size(2,ind) * & - wrf%dom(id)%var_size(3,ind) - 1 - - if (debug) write(*,'(a10,'' = statevec('',i7,'':'',i7,'') with dims '',3(1x,i3))') & - trim(varname),i,j,wrf%dom(id)%var_size(1,ind),wrf%dom(id)%var_size(2,ind),wrf%dom(id)%var_size(3,ind) - - if ( wrf%dom(id)%var_size(3,ind) > 1 ) then - - dimsizes_3D = (/wrf%dom(id)%var_size(1,ind), & - wrf%dom(id)%var_size(2,ind), & - wrf%dom(id)%var_size(3,ind)/) - - allocate ( temp3d(dimsizes_3D(1),dimsizes_3D(2),dimsizes_3D(3)) ) - temp3d = reshape(statevec(i:j), (/ dimsizes_3D(1),dimsizes_3D(2),dimsizes_3D(3) /) ) - call nc_check(nf90_put_var( ncFileID, VarID, temp3d, & - start=(/ 1, 1, 1, copyindex, timeindex /) ), & - 'nc_write_model_vars','put_var '//trim(varname)) - deallocate(temp3d) - - else ! must be 2D - - dimsizes_2D = (/wrf%dom(id)%var_size(1,ind), & - wrf%dom(id)%var_size(2,ind)/) - - allocate ( temp2d(dimsizes_2D(1),dimsizes_2D(2)) ) - temp2d = reshape(statevec(i:j), (/ dimsizes_2D(1),dimsizes_2D(2) /) ) - call nc_check(nf90_put_var( ncFileID, VarID, temp2d, & - start=(/ 1, 1, copyindex, timeindex /) ), & - 'nc_write_model_vars','put_var '//trim(varname)) - deallocate(temp2d) - - - endif - - enddo ! variables - -enddo ! domains - -endif - -!----------------------------------------------------------------- -! Flush the buffer and leave netCDF file open -!----------------------------------------------------------------- - -call nc_check(nf90_sync(ncFileID), 'nc_write_model_vars','sync') - -end function nc_write_model_vars - -!------------------------------- - -! public stubs - -!********************************************** - -subroutine adv_1step(x, Time) - -! Does single time-step advance with vector state as -! input and output. - - real(r8), intent(inout) :: x(:) - -! Time is needed for more general models like this; need to add in to -! low-order models - type(time_type), intent(in) :: Time - -call error_handler(E_ERR,'adv_1step', & - 'WRF model cannot be called as a subroutine; async cannot = 0', & - source, revision, revdate) - - -end subroutine adv_1step - -!********************************************** -!> close the phb window -subroutine end_model() - -call free_window -deallocate(domain_id) - -end subroutine end_model - -!********************************************** - -subroutine init_time(i_time) -! For now returns value of Time_init which is set in initialization routines. - - type(time_type), intent(out) :: i_time - -!Where should initial time come from here? -! WARNING: CURRENTLY SET TO 0 - i_time = set_time(0, 0) - -end subroutine init_time - -!********************************************** - -subroutine init_conditions(x) -! Reads in restart initial conditions and converts to vector - -! Following changed to intent(inout) for ifc compiler;should be like this - real(r8), intent(inout) :: x(:) - -msgstring2 = "cannot run with 'start_from_restart = .false.' " -msgstring3 = 'use ensemble_init in the WRF utils dir, or use wrf_to_dart' -call error_handler(E_ERR,'init_conditions', & - 'WARNING!! WRF model has no built-in default state', & - source, revision, revdate, & - text2=msgstring2, text3=msgstring3) - -end subroutine init_conditions - - - -!####################################################################### - -subroutine toGrid (x, j, dx, dxm) - -! Transfer obs. x to grid j and calculate its -! distance to grid j and j+1 - - real(r8), intent(in) :: x - real(r8), intent(out) :: dx, dxm - integer, intent(out) :: j - - j = int (x) - - dx = x - real (j) - - dxm= 1.0_r8 - dx - -end subroutine toGrid - -!####################################################################### - -subroutine toGrid_distrib(x, ens_size, j, dx, dxm) -!HK I don't think this is needed. -! Transfer obs. x to grid j and calculate its -! distance to grid j and j+1 - - integer, intent(in) :: ens_size - real(r8), intent(in) :: x(ens_size) - real(r8), intent(out) :: dx(ens_size), dxm(ens_size) - integer, intent(out) :: j(ens_size) - - j = int (x) - - dx = x - real (j) - - dxm= 1.0_r8 - dx - -end subroutine toGrid_distrib - -!####################################################################### - -subroutine pres_to_zk(pres, mdl_v, n3, zk, lev0) - -! Calculate the model level "zk" on half (mass) levels, -! corresponding to pressure "pres". - - integer, intent(in) :: n3 - real(r8), intent(in) :: pres - real(r8), intent(in) :: mdl_v(0:n3) - real(r8), intent(out) :: zk - logical, intent(out) :: lev0 - - integer :: k - - zk = missing_r8 - lev0 = .false. - - ! if out of range completely, return missing_r8 and lev0 false - if (pres > mdl_v(0) .or. pres < mdl_v(n3)) return - - ! if above surface but below lowest sigma level, return the - ! sigma value but set lev0 true - if(pres <= mdl_v(0) .and. pres > mdl_v(1)) then - lev0 = .true. - if (log_vert_interp) then - zk = (log(mdl_v(0)) - log(pres))/(log(mdl_v(0)) - log(mdl_v(1))) - else - zk = (mdl_v(0) - pres)/(mdl_v(0) - mdl_v(1)) - endif - return - endif - - ! find the 2 sigma levels the value is between and return that - ! as a real number, including the fraction between the levels. - do k = 1,n3-1 - if(pres <= mdl_v(k) .and. pres >= mdl_v(k+1)) then - if (log_vert_interp) then - zk = real(k) + (log(mdl_v(k)) - log(pres))/(log(mdl_v(k)) - log(mdl_v(k+1))) - else - zk = real(k) + (mdl_v(k) - pres)/(mdl_v(k) - mdl_v(k+1)) - endif - exit - endif - enddo - -end subroutine pres_to_zk - -!####################################################################### - -subroutine pres_to_zk_distrib(pres, mdl_v, n3, ens_size, zk, lev0) -! Calculate the model level "zk" on half (mass) levels, -! corresponding to pressure "pres". - - integer, intent(in) :: n3 - real(r8), intent(in) :: pres - integer, intent(in) :: ens_size - real(r8), intent(in) :: mdl_v(0:n3, ens_size) - real(r8), intent(out) :: zk(ens_size) - logical, intent(out) :: lev0(ens_size) - - integer :: k, e - - zk = missing_r8 - lev0 = .false. - - do e = 1, ens_size - - ! if out of range completely, return missing_r8 and lev0 false - if (pres > mdl_v(0,e) .or. pres < mdl_v(n3,e)) then - ! do nothing, keep zk(e) = missing_r8 and lev0(e) = false - - ! if above surface but below lowest sigma level, return the - ! sigma value but set lev0 true - elseif(pres <= mdl_v(0,e) .and. pres > mdl_v(1,e)) then - lev0(e) = .true. - if (log_vert_interp) then - zk(e) = (log(mdl_v(0,e)) - log(pres))/(log(mdl_v(0,e)) - log(mdl_v(1,e))) - else - zk(e) = (mdl_v(0,e) - pres)/(mdl_v(0,e) - mdl_v(1,e)) - endif - - else - - ! find the 2 sigma levels the value is between and return that - ! as a real number, including the fraction between the levels. - do k = 1, n3-1 - if(pres <= mdl_v(k,e) .and. pres >= mdl_v(k+1,e)) then - if (log_vert_interp) then - zk(e) = real(k) + (log(mdl_v(k,e)) - log(pres))/(log(mdl_v(k,e)) - log(mdl_v(k+1,e))) - else - zk(e) = real(k) + (mdl_v(k,e) - pres)/(mdl_v(k,e) - mdl_v(k+1,e)) - endif - exit - endif - enddo - - endif - - enddo - -end subroutine pres_to_zk_distrib - -!####################################################################### - -subroutine height_to_zk(obs_v, mdl_v, n3, zk, lev0) - -! Calculate the model level "zk" on half (mass) levels, -! corresponding to height "obs_v". - - real(r8), intent(in) :: obs_v - integer, intent(in) :: n3 - real(r8), intent(in) :: mdl_v(0:n3) - real(r8), intent(out) :: zk - logical, intent(out) :: lev0 - - integer :: k - - zk = missing_r8 - lev0 = .false. - - ! if out of range completely, return missing_r8 and lev0 false - if (obs_v < mdl_v(0) .or. obs_v > mdl_v(n3)) return - - ! if above surface but below lowest 3-d height level, return the - ! height value but set lev0 true - if(obs_v >= mdl_v(0) .and. obs_v < mdl_v(1)) then - lev0 = .true. - zk = (mdl_v(0) - obs_v)/(mdl_v(0) - mdl_v(1)) - return - endif - - ! find the 2 height levels the value is between and return that - ! as a real number, including the fraction between the levels. - do k = 1,n3-1 - if(obs_v >= mdl_v(k) .and. obs_v <= mdl_v(k+1)) then - zk = real(k) + (mdl_v(k) - obs_v)/(mdl_v(k) - mdl_v(k+1)) - exit - endif - enddo - -end subroutine height_to_zk - -!####################################################### - -subroutine get_model_pressure_profile_distrib(i,j,dx,dy,dxm,dym,n,id,v_p, state_handle, ens_size) - -! Calculate the full model pressure profile on half (mass) levels, -! horizontally interpolated at the observation location. - -integer, intent(in) :: i,j,n,id -real(r8), intent(in) :: dx,dy,dxm,dym -integer, intent(in) :: ens_size -real(r8), intent(out) :: v_p(0:n, ens_size) -type(ensemble_type), intent(in) :: state_handle -integer e !< for ensemble loop - -integer, dimension(2) :: ll, lr, ul, ur -integer(i8) :: ill, ilr, iul, iur -integer :: k, rc -real(r8), allocatable :: pres1(:), pres2(:), pres3(:), pres4(:) -logical :: debug = .false. - -!HK -real(r8), allocatable :: x_ill(:), x_ilr(:), x_iul(:), x_iur(:) - -allocate(pres1(ens_size), pres2(ens_size), pres3(ens_size), pres4(ens_size)) -allocate(x_ill(ens_size), x_ilr(ens_size), x_iul(ens_size), x_iur(ens_size)) - -if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: getCorners rc = ', rc - - - do k=1,n - pres1 = model_pressure_t_distrib(ll(1), ll(2), k,id,state_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), k,id,state_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), k,id,state_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), k,id,state_handle, ens_size) - - v_p(k, :) = interp_4pressure_distrib(pres1, pres2, pres3, pres4, dx, dxm, dy, dym, ens_size) - enddo - - - if (debug) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: n, v_p() ', n, v_p(1:n, :) - - if ( wrf%dom(id)%type_ps >= 0 ) then - - ill = get_dart_vector_index(ll(1), ll(2), 1, domain_id(id), wrf%dom(id)%type_ps) - ilr = get_dart_vector_index(lr(1), lr(2), 1, domain_id(id), wrf%dom(id)%type_ps) - iul = get_dart_vector_index(ul(1), ul(2), 1, domain_id(id), wrf%dom(id)%type_ps) - iur = get_dart_vector_index(ur(1), ur(2), 1, domain_id(id), wrf%dom(id)%type_ps) - - x_ill = get_state(ill, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - - ! I'm not quite sure where this comes from, but I will trust them on it.... - ! Do you have to do this per ensemble? - !>@todo This is messy - do e = 1,ens_size - - if ( x_ill(e) /= 0.0_r8 .and. x_ilr(e) /= 0.0_r8 .and. x_iul(e) /= 0.0_r8 .and. & - x_iur(e) /= 0.0_r8 ) then - - v_p(0,e:e) = interp_4pressure_distrib(x_ill(e:e), x_ilr(e:e), x_iul(e:e), x_iur(e:e), dx, dxm, dy, dym, 1) - - else - - ! HK I think this is a bug, you are just going to grab the first copy - ! in each iteration of the loop - call error_handler(E_WARN, 'model_mod.f90 check for correctness', 'Helen') - pres1 = model_pressure_t_distrib(ll(1), ll(2), 2,id,state_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), 2,id,state_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), 2,id,state_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), 2,id,state_handle, ens_size) - - v_p(0,e:e) = interp_4pressure_distrib(pres1(e:e), pres2(e:e), pres3(e:e), pres4(e:e), dx, dxm, dy, dym, 1, & - extrapolate=.true., edgep=v_p(1,e)) - - endif - - enddo - - else - - pres1 = model_pressure_t_distrib(ll(1), ll(2), 2,id,state_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), 2,id,state_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), 2,id,state_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), 2,id,state_handle, ens_size) - - v_p(0,:) = interp_4pressure_distrib(pres1, pres2, pres3, pres4, dx, dxm, dy, dym, ens_size, & - extrapolate=.true., edgep=v_p(1,:)) - - endif - - if (debug) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: v_p(0) ', v_p(0, :) -else - v_p(:,:) = missing_r8 - -endif - -deallocate(pres1, pres2, pres3, pres4, x_ill, x_ilr, x_iul, x_iur) - -end subroutine get_model_pressure_profile_distrib - -!####################################################### -!> Only for the mean value. -!> Used in get_state_meta_data for the vertical conversion -function model_pressure_distrib(i, j, k, id, var_type, state_handle) - -! Calculate the pressure at grid point (i,j,k), domain id. -! The grid is defined according to var_type. - -type(ensemble_type), intent(in) :: state_handle -integer, intent(in) :: i,j,k,id,var_type -real(r8) :: model_pressure_distrib - -integer :: off, type_x -real(r8) :: pres1, pres2 -integer :: ens_size - -model_pressure_distrib = missing_r8 - -! since we are only working with the mean copy -ens_size = 1 - -! If W-grid (on ZNW levels), then we need to average in vertical, unless -! we are at the upper or lower boundary in which case we will extrapolate. -if( (var_type == wrf%dom(id)%type_w) .or. (var_type == wrf%dom(id)%type_gz) ) then - - if (var_type == wrf%dom(id)%type_w) then - type_x = wrf%dom(id)%type_w - else - type_x = wrf%dom(id)%type_gz - endif - - if( k == 1 ) then - - pres1 = scalar(model_pressure_t_distrib(i, j, k, id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i, j, k+1,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true.) - - elseif( k == wrf%dom(id)%var_size(3,type_x) ) then - - pres1 = scalar(model_pressure_t_distrib(i,j,k-1,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i,j,k-2,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true.) - - else - - pres1 = scalar(model_pressure_t_distrib(i, j, k, id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i, j, k-1,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2) - - endif - -! If U-grid, then pressure is defined between U points, so average -- -! averaging depends on longitude periodicity -elseif( var_type == wrf%dom(id)%type_u ) then - - if( i == wrf%dom(id)%var_size(1,wrf%dom(id)%type_u) ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = scalar(model_pressure_t_distrib(i-1,j,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(1, j,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = scalar(model_pressure_t_distrib(i-1,j,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i-2,j,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( i == 1 ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = scalar(model_pressure_t_distrib(i, j,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(wrf%dom(id)%we,j,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = scalar(model_pressure_t_distrib(i, j,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i+1,j,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = scalar(model_pressure_t_distrib(i, j,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i-1,j,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -! If V-grid, then pressure is defined between V points, so average -- -! averaging depends on polar periodicity -elseif( var_type == wrf%dom(id)%type_v ) then - - if( j == wrf%dom(id)%var_size(2,wrf%dom(id)%type_v) ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The upper corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = scalar(model_pressure_t_distrib(off,j-1,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i ,j-1,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = scalar(model_pressure_t_distrib(i,j-1,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i,j-2,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( j == 1 ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The lower corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = scalar(model_pressure_t_distrib(off,j,k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i, j,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = scalar(model_pressure_t_distrib(i,j, k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i,j+1,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = scalar(model_pressure_t_distrib(i,j, k,id, state_handle, ens_size)) - pres2 = scalar(model_pressure_t_distrib(i,j-1,k,id, state_handle, ens_size)) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -elseif( var_type == wrf%dom(id)%type_mu .or. var_type == wrf%dom(id)%type_tslb .or. & - var_type == wrf%dom(id)%type_ps .or. var_type == wrf%dom(id)%type_u10 .or. & - var_type == wrf%dom(id)%type_v10 .or. var_type == wrf%dom(id)%type_t2 .or. & - var_type == wrf%dom(id)%type_th2 .or. & - var_type == wrf%dom(id)%type_q2 .or. var_type == wrf%dom(id)%type_tsk .or. & - var_type == wrf%dom(id)%type_smois .or. var_type == wrf%dom(id)%type_sh2o) then - - model_pressure_distrib = model_pressure_s_distrib(i,j,id, state_handle) - -else - - pres1 = scalar(model_pressure_t_distrib(i,j,k,id, state_handle, ens_size)) - model_pressure_distrib = pres1 - -endif - -end function model_pressure_distrib - -!####################################################### - -function model_surface_pressure_distrib(i, j, id, var_type, state_handle) - -! Calculate the surface pressure at grid point (i,j), domain id. -! The grid is defined according to var_type. - -type(ensemble_type), intent(in) :: state_handle -integer, intent(in) :: i,j,id,var_type -real(r8) :: model_surface_pressure_distrib - -integer :: off -real(r8) :: pres1, pres2 - -model_surface_pressure_distrib = missing_r8 - - -! If U-grid, then pressure is defined between U points, so average -- -! averaging depends on longitude periodicity -if( var_type == wrf%dom(id)%type_u ) then - - if( i == wrf%dom(id)%var_size(1,wrf%dom(id)%type_u) ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = model_pressure_s_distrib(i-1,j,id, state_handle) - pres2 = model_pressure_s_distrib(1, j,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = model_pressure_s_distrib(i-1,j,id, state_handle) - pres2 = model_pressure_s_distrib(i-2,j,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( i == 1 ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = model_pressure_s_distrib(i, j,id, state_handle) - pres2 = model_pressure_s_distrib(wrf%dom(id)%we,j,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = model_pressure_s_distrib(i, j,id, state_handle) - pres2 = model_pressure_s_distrib(i+1,j,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = model_pressure_s_distrib(i, j,id, state_handle) - pres2 = model_pressure_s_distrib(i-1,j,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -! If V-grid, then pressure is defined between V points, so average -- -! averaging depends on polar periodicity -elseif( var_type == wrf%dom(id)%type_v ) then - - if( j == wrf%dom(id)%var_size(2,wrf%dom(id)%type_v) ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The upper corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = model_pressure_s_distrib(off,j-1,id, state_handle) - pres2 = model_pressure_s_distrib(i ,j-1,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = model_pressure_s_distrib(i,j-1,id, state_handle) - pres2 = model_pressure_s_distrib(i,j-2,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( j == 1 ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The lower corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = model_pressure_s_distrib(off,j,id, state_handle) - pres2 = model_pressure_s_distrib(i, j,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = model_pressure_s_distrib(i,j, id, state_handle) - pres2 = model_pressure_s_distrib(i,j+1,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = model_pressure_s_distrib(i,j, id, state_handle) - pres2 = model_pressure_s_distrib(i,j-1,id, state_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -else - - model_surface_pressure_distrib = model_pressure_s_distrib(i,j,id, state_handle) - -endif - -end function model_surface_pressure_distrib - -!####################################################### - -function model_pressure_t_distrib(i,j,k,id,state_handle, ens_size) - -! Calculate total pressure on mass point (half (mass) levels, T-point). - -integer, intent(in) :: ens_size -integer, intent(in) :: i,j,k,id -type(ensemble_type), intent(in) :: state_handle -real(r8) :: model_pressure_t_distrib(ens_size) - -real (kind=r8), PARAMETER :: rd_over_rv = gas_constant / gas_constant_v -real (kind=r8), PARAMETER :: cpovcv = 1.4_r8 ! cp / (cp - gas_constant) - -integer(i8) :: iqv !< I think this is i for index -integer(i8) :: it !< change to array -real(r8) :: qvf1(ens_size),rho(ens_size), x_iqv(ens_size), x_it(ens_size) - -model_pressure_t_distrib = missing_r8 - -! Adapted the code from WRF module_big_step_utilities_em.F ---- -! subroutine calc_p_rho_phi Y.-R. Guo (10/20/2004) - -! Simplification: alb*mub = (phb(i,j,k+1) - phb(i,j,k))/dnw(k) - -if (wrf%dom(id)%type_qv < 0 .or. wrf%dom(id)%type_t < 0) then - call error_handler(E_ERR, 'model_pressure_t:', & - 'BOTH QVAPOR and T must be in state vector to compute total pressure', & - source, revision, revdate) -endif - -iqv = get_dart_vector_index(i,j,k, domain_id(id), wrf%dom(id)%type_qv) -it = get_dart_vector_index(i,j,k, domain_id(id), wrf%dom(id)%type_t) - -x_iqv = get_state(iqv, state_handle) -x_it = get_state(it, state_handle) - -qvf1(:) = 1.0_r8 + x_iqv(:) / rd_over_rv -!print*, 'qvf1 ', qvf1 - -rho(:) = model_rho_t_distrib(i,j,k,id,state_handle, ens_size) -!print*, 'rho ', rho - -! .. total pressure: -model_pressure_t_distrib = ps0 * ( (gas_constant*(ts0+x_it)*qvf1) / & - (ps0/rho) )**cpovcv - -end function model_pressure_t_distrib - -!####################################################### - -function model_pressure_s_distrib(i, j, id, state_handle) - -! compute pressure at surface at mass point - -integer, intent(in) :: i,j,id -type(ensemble_type), intent(in) :: state_handle - -real(r8) :: model_pressure_s_distrib - -integer(i8) :: ips, imu -real(r8) :: x_imu, x_ips - - -! make sure one of these is good. -if ( wrf%dom(id)%type_mu < 0 .and. wrf%dom(id)%type_ps < 0 ) then - call error_handler(E_ERR, 'model_pressure_s:', & - 'One of MU or PSFC must be in state vector to compute surface pressure', & - source, revision, revdate) -endif - -if ( wrf%dom(id)%type_ps >= 0 ) then - ips = get_dart_vector_index(i,j,1, domain_id(id), wrf%dom(id)%type_ps) - x_ips = scalar(get_state(ips, state_handle)) - model_pressure_s_distrib = x_ips - -else - imu = get_dart_vector_index(i,j,1, domain_id(id), wrf%dom(id)%type_mu) - x_imu = minval(get_state(imu, state_handle)) - model_pressure_s_distrib = wrf%dom(id)%p_top + wrf%dom(id)%mub(i,j) + x_imu - -endif - -end function model_pressure_s_distrib - -!####################################################### - -function interp_pressure(p1, p2, extrapolate, vertical) - -! interpolate halfway between pressures 1 and 2 in log units. -! if extrapolate is true, extrapolate where 1 is the edge and -! 2 is the inner value, going 1/2 grid cell out. - -real(r8), intent(in) :: p1, p2 -logical, intent(in), optional :: extrapolate -logical, intent(in), optional :: vertical -real(r8) :: interp_pressure - -logical :: do_interp -logical :: is_vert -real(r8) :: intermediate - -! default is to do interpolation; only extrapolate if the optional -! arg is specified and if it is true. -do_interp = .true. -if (present(extrapolate)) then - if (extrapolate) do_interp = .false. -endif - -! if vert is specified and is false, check log_horz_interpM instead -! of log_vert_interp to decide log vs linear interpolation for the -! Midpoint value. default is to do vertical interpolation. -is_vert = .true. -if (present(vertical)) then - is_vert = vertical -endif - -! once we like the results, remove the log_vert_interp test. -if (do_interp) then - if (( is_vert .and. log_vert_interp ) .or. & - (.not. is_vert .and. log_horz_interpM)) then - interp_pressure = exp((log(p1) + log(p2))/2.0_r8) - else - interp_pressure = (p1 + p2)/2.0_r8 - endif -else - if (( is_vert .and. log_vert_interp ) .or. & - (.not. is_vert .and. log_horz_interpM)) then - intermediate = (3.0_r8*log(p1) - log(p2))/2.0_r8 - if (intermediate <= 0.0_r8) then - interp_pressure = p1 - else - interp_pressure = exp(intermediate) - endif - else - interp_pressure = (3.0_r8*p1 - p2)/2.0_r8 - endif -endif - -end function interp_pressure - -!####################################################### - -function interp_4pressure_distrib(p1, p2, p3, p4, dx, dxm, dy, dym, ens_size, extrapolate, edgep) - -! given 4 corners of a quad, where the p1, p2, p3 and p4 points are -! respectively: lower left, lower right, upper left, upper right -! and dx is the distance in x, dxm is 1.0-dx, dy is distance in y -! and dym is 1.0-dy, interpolate the pressure while converted to log. -! if extrapolate is true, extrapolate where edgep is the edge pressure -! and the 4 points and dx/dy give the location of the inner point. - -integer, intent(in) :: ens_size -real(r8), intent(in) :: p1(ens_size), p2(ens_size), p3(ens_size), p4(ens_size) -real(r8), intent(in) :: dx, dxm, dy, dym -logical, intent(in), optional :: extrapolate -real(r8), intent(in), optional :: edgep(ens_size) -real(r8) :: interp_4pressure_distrib(ens_size) - -logical :: do_interp -real(r8) :: intermediate(ens_size) -real(r8) :: l1(ens_size), l2(ens_size), l3(ens_size), l4(ens_size) - -!HK -integer :: i - -! default is to do interpolation; only extrapolate if the optional -! arg is specified and if it is true. for extrapolation 'edgep' is -! required; it is unused for interpolation. -do_interp = .true. -if (present(extrapolate)) then - if (extrapolate) do_interp = .false. -endif - -if (.not. do_interp .and. .not. present(edgep)) then - call error_handler(E_ERR, 'interp_4pressure:', & - 'edgep must be specified for extrapolation. internal error.', & - source, revision, revdate) -endif - -if (log_horz_interpQ) then - l1 = log(p1) - l2 = log(p2) - l3 = log(p3) - l4 = log(p4) -endif - - -! once we like the results, remove the log_horz_interpQ test. -if (do_interp) then - if (log_horz_interpQ) then - interp_4pressure_distrib = exp(dym*( dxm*l1 + dx*l2 ) + dy*( dxm*l3 + dx*l4 )) - else - interp_4pressure_distrib = dym*( dxm*p1 + dx*p2 ) + dy*( dxm*p3 + dx*p4 ) - endif -else - if (log_horz_interpQ) then - intermediate = (3.0_r8*log(edgep) - & - dym*( dxm*l1 + dx*l2 ) - dy*( dxm*l3 + dx*l4 ))/2.0_r8 - - do i = 1, size(intermediate) - if (intermediate(i) <= 0.0_r8) then - !>@todo is this correct? - interp_4pressure_distrib(i) = edgep(i) - else - interp_4pressure_distrib(i) = exp(intermediate(i)) - endif - enddo - else - interp_4pressure_distrib = (3.0_r8*edgep - & - dym*( dxm*p1 + dx*p2 ) - dy*( dxm*p3 + dx*p4 ))/2.0_r8 - endif -endif - -end function interp_4pressure_distrib - -!####################################################### -!> Interpolate function -function interp_4pressure(p1, p2, p3, p4, dx, dxm, dy, dym, extrapolate, edgep) - -! given 4 corners of a quad, where the p1, p2, p3 and p4 points are -! respectively: lower left, lower right, upper left, upper right -! and dx is the distance in x, dxm is 1.0-dx, dy is distance in y -! and dym is 1.0-dy, interpolate the pressure while converted to log. -! if extrapolate is true, extrapolate where edgep is the edge pressure -! and the 4 points and dx/dy give the location of the inner point. - -real(r8), intent(in) :: p1, p2, p3, p4 -real(r8), intent(in) :: dx, dxm, dy, dym -logical, intent(in), optional :: extrapolate -real(r8), intent(in), optional :: edgep -real(r8) :: interp_4pressure - -logical :: do_interp -real(r8) :: intermediate -real(r8) :: l1, l2, l3, l4 - -! default is to do interpolation; only extrapolate if the optional -! arg is specified and if it is true. for extrapolation 'edgep' is -! required; it is unused for interpolation. -do_interp = .true. -if (present(extrapolate)) then - if (extrapolate) do_interp = .false. -endif - -if (.not. do_interp .and. .not. present(edgep)) then - call error_handler(E_ERR, 'interp_4pressure:', & - 'edgep must be specified for extrapolation. internal error.', & - source, revision, revdate) -endif - -if (log_horz_interpQ) then - l1 = log(p1) - l2 = log(p2) - l3 = log(p3) - l4 = log(p4) -endif - - -! once we like the results, remove the log_horz_interpQ test. -if (do_interp) then - if (log_horz_interpQ) then - interp_4pressure = exp(dym*( dxm*l1 + dx*l2 ) + dy*( dxm*l3 + dx*l4 )) - else - interp_4pressure = dym*( dxm*p1 + dx*p2 ) + dy*( dxm*p3 + dx*p4 ) - endif -else - if (log_horz_interpQ) then - intermediate = (3.0_r8*log(edgep) - & - dym*( dxm*l1 + dx*l2 ) - dy*( dxm*l3 + dx*l4 ))/2.0_r8 - if (intermediate <= 0.0_r8) then - interp_4pressure = edgep - else - interp_4pressure = exp(intermediate) - endif - else - interp_4pressure = (3.0_r8*edgep - & - dym*( dxm*p1 + dx*p2 ) - dy*( dxm*p3 + dx*p4 ))/2.0_r8 - endif -endif - -end function interp_4pressure - -!####################################################### - -function model_rho_t_distrib(i,j,k,id,state_handle, ens_size) - -! Calculate the total density on mass point (half (mass) levels, T-point). - -integer, intent(in) :: ens_size -integer, intent(in) :: i,j,k,id -type(ensemble_type), intent(in) :: state_handle -real(r8) :: model_rho_t_distrib(ens_size) - -integer(i8) :: imu,iph,iphp1 -real(r8) :: ph_e(ens_size), x_imu(ens_size), x_iph(ens_size), x_iphp1(ens_size) - -model_rho_t_distrib(:) = missing_r8 - -! Adapted the code from WRF module_big_step_utilities_em.F ---- -! subroutine calc_p_rho_phi Y.-R. Guo (10/20/2004) - -! Simplification: alb*mub = (phb(i,j,k+1) - phb(i,j,k))/dnw(k) - -if (wrf%dom(id)%type_mu < 0 .or. wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'model_rho_t:', & - 'BOTH MU and PH must be in state vector to compute total density', & - source, revision, revdate) -endif - -imu = get_dart_vector_index(i,j,1, domain_id(id), wrf%dom(id)%type_mu) -iph = get_dart_vector_index(i,j,k, domain_id(id), wrf%dom(id)%type_gz) -iphp1 = get_dart_vector_index(i,j,k+1, domain_id(id), wrf%dom(id)%type_gz) - -x_imu = get_state(imu, state_handle) -x_iph = get_state(iph, state_handle) -x_iphp1 = get_state(iphp1, state_handle) - -ph_e = ( (x_iphp1 + phb(id, i,j,k+1)) & - - (x_iph + phb(id, i,j,k )) ) / wrf%dom(id)%dnw(k) - -! now calculate rho = - mu / dphi/deta - -model_rho_t_distrib(:) = - (wrf%dom(id)%mub(i,j)+x_imu) / ph_e - -end function model_rho_t_distrib - -!####################################################### - -subroutine get_model_height_profile_distrib(i,j,dx,dy,dxm,dym,n,id,v_h, state_handle, ens_size) - -! Calculate the model height profile on half (mass) levels, -! horizontally interpolated at the observation location. -! This routine used to compute geopotential heights; it now -! computes geometric heights. - -integer, intent(in) :: i,j,n,id -real(r8), intent(in) :: dx,dy,dxm,dym -integer, intent(in) :: ens_size -real(r8), intent(out) :: v_h(0:n, ens_size) -type(ensemble_type), intent(in) :: state_handle -integer e !< for ensemble loop - -real(r8) :: fll(n+1, ens_size), geop(ens_size), lat(ens_size) -integer(i8) :: ill, iul, ilr, iur -integer :: k, rc -integer, dimension(2) :: ll, lr, ul, ur -logical :: debug = .false. - -real(r8), allocatable :: x_ill(:), x_ilr(:), x_iul(:), x_iur(:) - -allocate(x_ill(ens_size), x_ilr(ens_size), x_iul(ens_size), x_iur(ens_size)) - -if (wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'get_model_height_profile:', & - 'PH must be in state vector to compute height profile', & - source, revision, revdate) -endif - -if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_gz ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_gz ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_gz, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: get_model_height_profile :: getCorners rc = ', rc - - do k = 1, wrf%dom(id)%var_size(3,wrf%dom(id)%type_gz) - - ill = get_dart_vector_index(ll(1), ll(2), k, domain_id(id), wrf%dom(id)%type_gz) - iul = get_dart_vector_index(ul(1), ul(2), k, domain_id(id), wrf%dom(id)%type_gz) - ilr = get_dart_vector_index(lr(1), lr(2), k, domain_id(id), wrf%dom(id)%type_gz) - iur = get_dart_vector_index(ur(1), ur(2), k, domain_id(id), wrf%dom(id)%type_gz) - - x_ill = get_state(ill, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - - geop(:) = ( dym*( dxm*( phb(id, ll(1),ll(2),k) + x_ill ) + & - dx*( phb(id, lr(1),lr(2),k) + x_ilr ) ) + & - dy*( dxm*( phb(id, ul(1),ul(2),k) + x_iul ) + & - dx*( phb(id, ur(1),ur(2),k) + x_iur ) ) )/gravity - - lat(:) = ( wrf%dom(id)%latitude(ll(1),ll(2)) + & - wrf%dom(id)%latitude(lr(1),lr(2)) + & - wrf%dom(id)%latitude(ul(1),ul(2)) + & - wrf%dom(id)%latitude(ur(1),ur(2)) ) / 4.0_r8 - - do e = 1, ens_size - fll(k, e) = compute_geometric_height(geop(e), lat(e)) - enddo - - end do - - do k=1,n - v_h(k, :) = 0.5_r8*(fll(k, :) + fll(k+1, :) ) - end do - - v_h(0, :) = dym*( dxm*wrf%dom(id)%hgt(ll(1), ll(2)) + & - dx*wrf%dom(id)%hgt(lr(1), lr(2)) ) + & - dy*( dxm*wrf%dom(id)%hgt(ul(1), ul(2)) + & - dx*wrf%dom(id)%hgt(ur(1), ur(2)) ) - - if (debug) & - print*, 'model_mod.f90 :: get_model_height_profile :: n, v_h() ', n, v_h(1:n, :) - - if (debug) & - print*, 'model_mod.f90 :: get_model_height_profile :: v_h(0) ', v_h(0, :) - -! If the boundsCheck functions return an unsatisfactory integer index, then set -! fld as missing data -else - - print*,'Not able the get height_profile' - print*,i,j,dx,dy,dxm,dym,n,id,wrf%dom(id)%var_size(1,wrf%dom(id)%type_gz), & - wrf%dom(id)%var_size(2,wrf%dom(id)%type_gz) - - v_h(:, :) = missing_r8 - -endif - -end subroutine get_model_height_profile_distrib - -!####################################################### - -function model_height_distrib(i,j,k,id,var_type, state_handle) - -! This routine used to compute geopotential heights; it now -! computes geometric heights. - -type(ensemble_type), intent(in) :: state_handle -integer, intent(in) :: i,j,k,id,var_type -real(r8) :: model_height_distrib - -integer(i8) :: i1, i2, i3, i4 -integer :: off -real(r8) :: x_i1(1), x_i2(1), x_i3(1), x_i4(1) -real(r8) :: geop, lat - -model_height_distrib = missing_r8 - -if (wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'model_height:', & - 'PH must be in state vector to compute total pressure', & - source, revision, revdate) -endif - -! If W-grid (on ZNW levels), then we are fine because it is native to GZ -if( (var_type == wrf%dom(id)%type_w) .or. (var_type == wrf%dom(id)%type_gz) ) then - - i1 = get_dart_vector_index(i,j,k, domain_id(id),wrf%dom(id)%type_gz) - x_i1 = get_state(i1, state_handle) - - geop = minval(((phb(id, i,j,k)+x_i1)/gravity)) - model_height_distrib = compute_geometric_height(geop, wrf%dom(id)%latitude(i, j)) - -! If U-grid, then height is defined between U points, both in horizontal -! and in vertical, so average -- averaging depends on longitude periodicity -elseif( var_type == wrf%dom(id)%type_u ) then - - if( i == wrf%dom(id)%var_size(1,wrf%dom(id)%type_u) ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at the seam in longitude, so take first and last mass points - i1 = get_dart_vector_index(i-1,j,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i-1,j,k+1, domain_id(id),wrf%dom(id)%type_gz) - i3 = get_dart_vector_index(1, j,k , domain_id(id),wrf%dom(id)%type_gz) - i4 = get_dart_vector_index(1, j,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i3, state_handle) - x_i4 = get_state(i4, state_handle) - - - geop = minval(((phb(id,i-1,j,k ) + x_i1) & - +(phb(id,i-1,j,k+1) + x_i2) & - +(phb(id,1 ,j,k ) + x_i3) & - +(phb(id,1 ,j,k+1) + x_i4) )/(4.0_r8*gravity)) - - lat = ( wrf%dom(id)%latitude(i-1,j) & - +wrf%dom(id)%latitude(i-1,j) & - +wrf%dom(id)%latitude(1 ,j) & - +wrf%dom(id)%latitude(1 ,j) ) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = get_dart_vector_index(i-1,j,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i-1,j,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i1 -1, state_handle) - x_i4 = get_state(i2 -1, state_handle) - - - geop = minval((3.0_r8*(phb(id,i-1,j,k )+x_i1) & - +3.0_r8*(phb(id,i-1,j,k+1)+x_i2) & - -(phb(id,i-2,j,k )+x_i3) & - -(phb(id,i-2,j,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i-1,j) & - +3.0_r8*wrf%dom(id)%latitude(i-1,j) & - -wrf%dom(id)%latitude(i-2,j) & - -wrf%dom(id)%latitude(i-2,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - elseif( i == 1 ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at the seam in longitude, so take first and last mass points - off = wrf%dom(id)%we - i1 = get_dart_vector_index(i ,j,k ,domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i ,j,k+1,domain_id(id),wrf%dom(id)%type_gz) - i3 = get_dart_vector_index(off,j,k ,domain_id(id),wrf%dom(id)%type_gz) - i4 = get_dart_vector_index(off,j,k+1,domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i3, state_handle) - x_i4 = get_state(i4, state_handle) - - geop = minval(((phb(id,i ,j,k ) + x_i1) & - +(phb(id,i ,j,k+1) + x_i2) & - +(phb(id,off,j,k ) + x_i3) & - +(phb(id,off,j,k+1) + x_i4) )/(4.0_r8*gravity)) - - lat = ( wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(off,j) & - +wrf%dom(id)%latitude(off,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = get_dart_vector_index(i,j,k ,domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i,j,k+1,domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i1 +1, state_handle) - x_i4 = get_state(i2 +1, state_handle) - - - geop = minval((3.0_r8*(phb(id,i ,j,k )+x_i1) & - +3.0_r8*(phb(id,i ,j,k+1)+x_i2) & - -(phb(id,i+1,j,k )+x_i3) & - -(phb(id,i+1,j,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i ,j) & - +3.0_r8*wrf%dom(id)%latitude(i ,j) & - -wrf%dom(id)%latitude(i+1,j) & - -wrf%dom(id)%latitude(i+1,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - else - - i1 = get_dart_vector_index(i,j,k ,domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i,j,k+1,domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i1 -1, state_handle) - x_i4 = get_state(i2 -1, state_handle) - - - geop = minval(((phb(id,i ,j,k )+x_i1) & - +(phb(id,i ,j,k+1)+x_i2) & - +(phb(id,i-1,j,k )+x_i3) & - +(phb(id,i-1,j,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i-1,j) & - +wrf%dom(id)%latitude(i-1,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - -! If V-grid, then pressure is defined between V points, both in horizontal -! and in vertical, so average -- averaging depends on polar periodicity -elseif( var_type == wrf%dom(id)%type_v ) then - - if( j == wrf%dom(id)%var_size(2,wrf%dom(id)%type_v) ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The upper corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - i1 = get_dart_vector_index(off,j-1,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(off,j-1,k+1, domain_id(id),wrf%dom(id)%type_gz) - i3 = get_dart_vector_index(i ,j-1,k , domain_id(id),wrf%dom(id)%type_gz) - i4 = get_dart_vector_index(i ,j-1,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i3, state_handle) - x_i4 = get_state(i4, state_handle) - - geop = minval(((phb(id,off,j-1,k )+x_i1) & - +(phb(id,off,j-1,k+1)+x_i2) & - +(phb(id,i ,j-1,k )+x_i3) & - +(phb(id,i ,j-1,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( wrf%dom(id)%latitude(off,j-1) & - +wrf%dom(id)%latitude(off,j-1) & - +wrf%dom(id)%latitude(i ,j-1) & - +wrf%dom(id)%latitude(i ,j-1)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = get_dart_vector_index(i,j-1,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i,j-1,k+1, domain_id(id),wrf%dom(id)%type_gz) - i3 = get_dart_vector_index(i,j-2,k , domain_id(id),wrf%dom(id)%type_gz) - i4 = get_dart_vector_index(i,j-2,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i3, state_handle) - x_i4 = get_state(i4, state_handle) - - geop = minval((3.0_r8*(phb(id,i,j-1,k )+x_i1) & - +3.0_r8*(phb(id,i,j-1,k+1)+x_i2) & - -(phb(id,i,j-2,k )+x_i3) & - -(phb(id,i,j-2,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i,j-1) & - +3.0_r8*wrf%dom(id)%latitude(i,j-1) & - -wrf%dom(id)%latitude(i,j-2) & - -wrf%dom(id)%latitude(i,j-2)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - elseif( j == 1 ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The lower corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - i1 = get_dart_vector_index(off,j,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(off,j,k+1, domain_id(id),wrf%dom(id)%type_gz) - i3 = get_dart_vector_index(i ,j,k , domain_id(id),wrf%dom(id)%type_gz) - i4 = get_dart_vector_index(i ,j,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i3, state_handle) - x_i4 = get_state(i4, state_handle) - - geop = minval(((phb(id,off,j,k )+x_i1) & - +(phb(id,off,j,k+1)+x_i2) & - +(phb(id,i ,j,k )+x_i3) & - +(phb(id,i ,j,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( wrf%dom(id)%latitude(off,j) & - +wrf%dom(id)%latitude(off,j) & - +wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i ,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = get_dart_vector_index(i,j ,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i,j ,k+1, domain_id(id),wrf%dom(id)%type_gz) - i3 = get_dart_vector_index(i,j+1,k , domain_id(id),wrf%dom(id)%type_gz) - i4 = get_dart_vector_index(i,j+1,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i3, state_handle) - x_i4 = get_state(i4, state_handle) - - geop = minval((3.0_r8*(phb(id,i,j ,k )+x_i1) & - +3.0_r8*(phb(id,i,j ,k+1)+x_i2) & - -(phb(id,i,j+1,k )+x_i3) & - -(phb(id,i,j+1,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i,j ) & - +3.0_r8*wrf%dom(id)%latitude(i,j ) & - -wrf%dom(id)%latitude(i,j+1) & - -wrf%dom(id)%latitude(i,j+1)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - else - - i1 = get_dart_vector_index(i,j ,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i,j ,k+1, domain_id(id),wrf%dom(id)%type_gz) - i3 = get_dart_vector_index(i,j-1,k , domain_id(id),wrf%dom(id)%type_gz) - i4 = get_dart_vector_index(i,j-1,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - x_i3 = get_state(i3, state_handle) - x_i4 = get_state(i4, state_handle) - - geop = minval(((phb(id,i,j ,k )+x_i1) & - +(phb(id,i,j ,k+1)+x_i2) & - +(phb(id,i,j-1,k )+x_i3) & - +(phb(id,i,j-1,k+1)+x_i4) )/(4.0_r8*gravity)) - - lat = ( wrf%dom(id)%latitude(i,j ) & - +wrf%dom(id)%latitude(i,j ) & - +wrf%dom(id)%latitude(i,j-1) & - +wrf%dom(id)%latitude(i,j-1)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - -elseif( var_type == wrf%dom(id)%type_mu .or. & - var_type == wrf%dom(id)%type_ps .or. & - var_type == wrf%dom(id)%type_tsk) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) - -elseif( var_type == wrf%dom(id)%type_tslb .or. & - var_type == wrf%dom(id)%type_smois .or. & - var_type == wrf%dom(id)%type_sh2o ) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) - wrf%dom(id)%zs(k) - -elseif( var_type == wrf%dom(id)%type_u10 .or. & - var_type == wrf%dom(id)%type_v10 ) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) + 10.0_r8 - -elseif( var_type == wrf%dom(id)%type_t2 .or. & - var_type == wrf%dom(id)%type_th2 .or. & - var_type == wrf%dom(id)%type_q2 ) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) + 2.0_r8 - -else - - i1 = get_dart_vector_index(i,j,k , domain_id(id),wrf%dom(id)%type_gz) - i2 = get_dart_vector_index(i,j,k+1, domain_id(id),wrf%dom(id)%type_gz) - - x_i1 = get_state(i1, state_handle) - x_i2 = get_state(i2, state_handle) - - geop = minval(((phb(id,i,j,k )+x_i1) & - +(phb(id, i,j,k+1)+x_i2) )/(2.0_r8*gravity)) - - lat = wrf%dom(id)%latitude(i,j) - - model_height_distrib = compute_geometric_height(geop, lat) - -endif - -end function model_height_distrib - -!####################################################### - -!> Distributed version of model_height_w -!> Only one value, the mean, is used because model_height_w_distrib -!> is only used in the vertical conversion -function model_height_w_distrib(i, j, k, id, state_handle) - -! return total height at staggered vertical coordinate -! and horizontal mass coordinates - -type(ensemble_type), intent(in) :: state_handle -integer, intent(in) :: i,j,k,id -real(r8) :: x_i1 -real(r8) :: model_height_w_distrib - -integer(i8) :: i1 -real(r8) :: geop - -if (wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'model_height_w:', & - 'PH must be in state vector to compute staggered model height', & - source, revision, revdate) -endif - -i1 = get_dart_vector_index(i,j,k, domain_id(id),wrf%dom(id)%type_gz) - -x_i1 = minval(get_state(i1, state_handle)) - -geop = (phb(id,i,j,k) + x_i1)/gravity -model_height_w_distrib = compute_geometric_height(geop, wrf%dom(id)%latitude(i, j)) - -end function model_height_w_distrib - -!####################################################### - -subroutine pert_model_copies(ens_handle, ens_size, dummy_pert_amp, interf_provided) - -type(ensemble_type), intent(inout) :: ens_handle -integer, intent(in) :: ens_size -real(r8), intent(in) :: dummy_pert_amp ! not used -logical, intent(out) :: interf_provided - -! Perturbs model states for generating initial ensembles. -! Because this requires some care when using - see the comments in the -! code below - you must set a namelist variable to enable this functionality. - -! Using this routine is not a substitute for a good set of real initial -! condition files. Intended as a last resort, this routine should be used -! to start a period of free-running to allow the model to spin-up a set of -! internally consistent states with their own structure before assimilating -! a set of observations. A good ensemble of boundary conditions is important -! to evolve the ensemble members differently, which is the goal. -! - -real(r8) :: pert_amount = 0.005 ! 0.5% - -real(r8) :: pert_ampl, range -real(r8) :: minv, maxv, temp -type(random_seq_type) :: random_seq -integer :: id, i, j, s, e -logical, allocatable :: within_range(:) -integer(i8), allocatable :: var_list(:) -integer :: num_variables -real(r8), allocatable :: min_var(:), max_var(:) -integer :: start_ind, end_ind -integer :: copy -integer :: count -logical :: bitwise_lanai - -! generally you do not want to just perturb a single state to begin an -! experiment, especially for a regional weather model, because the -! resulting fields will have spread but they won't have organized features. -! we have had good luck with some global atmosphere models where there is -! a lot of model divergence; after a few days of running they evolve into -! plausible conditions that allow assimilation of real obs. -! -! if you really need to start with a single state and proceed, the suggestion -! is to start with small magnitude perturbations and then get a good ensemble -! of boundary conditions and run the model for a while (many days) to let it -! evolve into plausible weather patterns. then start assimilating real obs. -! -! using this routine requires you to set the new namelist item -! 'allow_perturbed_ics' to true so you have to read the warnings here or -! in the html docs. -! -! this code will add random noise field by field (T, U, V, etc), and new values -! will not exceed the original max or min values for each field. this means -! it will not generate illegal values (e.g. negatives for percentages or -! number concentrations) but it also means that if all values in a field are -! identical (e.g. all 0.0) this routine will not change those values. the code -! can easily be modified to set allowed min and max values here instead of -! using the incoming field min and max values; but you will have to modify -! the code below to enable that functionality. - -if (.not. allow_perturbed_ics) then -call error_handler(E_ERR,'pert_model_copies', & - 'starting WRF model from a single vector requires additional steps', & - source, revision, revdate, & - text2='see comments in wrf/model_mod.f90::pert_model_copies()') -endif - -! NOT REACHED unless allow_perturbed_ics is true in the namelist - -! start of pert code -interf_provided = .true. - -! Get min and max of each variable in each domain -allocate(var_list(ens_handle%my_num_vars)) - -num_variables = 0 -do id = 1, num_domains - num_variables = num_variables + wrf%dom(id)%number_of_wrf_variables -enddo - -allocate(min_var(num_variables), max_var(num_variables)) -allocate(within_range(ens_handle%my_num_vars)) - -do id = 1, num_domains - do i = 1, wrf%dom(id)%number_of_wrf_variables - - start_ind = get_index_start(domain_id(id), i) - end_ind = get_index_end(domain_id(id), i) - - call get_my_vars(ens_handle, var_list) - within_range = var_list >= start_ind .and. var_list <= end_ind ! & - !.and. var_list >= start_domain .and. var_list <= end_domain - min_var(i) = minval(ens_handle%copies(1,:), MASK=within_range) - max_var(i) = maxval(ens_handle%copies(1,:), MASK=within_range) - - enddo -enddo - -call broadcast_minmax(min_var, max_var, num_variables) - -bitwise_lanai = .true. -if (bitwise_lanai) then - - call pert_copies_lanai_bitwise(ens_handle, ens_size, pert_amount, min_var, max_var) - -else - - call init_random_seq(random_seq, my_task_id()) - - count = 1 ! min and max are numbered 1 to n, where n is the total number of variables (all domains) - do id = 1, num_domains - do i = 1, num_variables - - start_ind = get_index_start(domain_id(id), i) - end_ind = get_index_end(domain_id(id), i) - - !! Option 1: - !! make the perturbation amplitude N% of the total - !! range of this variable. values could vary a lot - !! over some of the types, like pressure - !range = max_var(count) - min_var(count) - !pert_ampl = pert_amount * range - - do j=1, ens_handle%my_num_vars - if (ens_handle%my_vars(j) >= start_ind .and. ens_handle%my_vars(j) <= end_ind) then - do copy = 1, ens_size - ! once you change pert_state, state is changed as well - ! since they are the same storage as called from filter. - ! you have to save it if you want to use it again. - ! Option 2: perturb each value individually - !! make the perturbation amplitude N% of this value - pert_ampl = pert_amount * ens_handle%copies(copy, j) - ens_handle%copies(copy, j) = random_gaussian(random_seq, ens_handle%copies(copy, j), pert_ampl) - enddo - - ! keep variable from exceeding the original range - ens_handle%copies(1:ens_size,j) = max(min_var(count), ens_handle%copies(1:ens_size,j)) - ens_handle%copies(1:ens_size,j) = min(max_var(count), ens_handle%copies(1:ens_size,j)) - - endif - enddo - - count = count + 1 - - enddo - enddo - -endif - -end subroutine pert_model_copies - -!####################################################### -!> Perturb copies such that the result is bitwise -!> with Lanai -!> Note that (like Lanai) this is not bitwise with itself across tasks -!> for task_count < ens_size -subroutine pert_copies_lanai_bitwise(ens_handle, ens_size, pert_amount, min_var, max_var) - -type(ensemble_type), intent(inout) :: ens_handle -integer, intent(in) :: ens_size -real(r8), intent(in) :: pert_amount -real(r8), intent(in) :: min_var(:) -real(r8), intent(in) :: max_var(:) - - -integer :: start_ind ! start index variable in state -integer :: end_ind ! end index variable in state -integer :: owner ! pe that owns the state element -integer :: owner_index ! local index on pe -integer :: copy_owner -integer :: copy, id, i ! loop index -integer(i8) :: j ! loop index -integer :: count ! keep track of which variable you are perturbing -real(r8) :: pert_ampl -type(random_seq_type) :: random_seq(ens_size) -integer :: sequence_to_use -integer, allocatable :: counter(:) -real(r8) :: random_number - -! the first time through get the task id (0:N-1) and set a unique seed -! per task. this should reproduce from run to run if you keep the number -! of MPI tasks the same. it WILL NOT reproduce if the number of tasks changes. -! if this routine could at some point get the global ensemble member number -! as an argument, that would be unique and the right thing to use as a seed. -! -! the line below only executes the first time since counter gets incremented -! after the first use and the value is saved between calls. it is trying to -! generate a unique base number, and then just increments by 1 each subsequent -! time it is called (which only happens if there are multiple ensemble -! members/task). it is assuming there are no more than 1000 ensembles/task, -! which seems safe given the current sizes of state vecs and hardware memory. - -!if (counter == 0) counter = ((my_task_id()+1) * 1000) ! this is the code in Lanai -allocate(counter(task_count())) - -! initialize ens_size random number sequences -counter(:) = 0 - - -do copy = 1, ens_size - - call get_copy_owner_index(ens_handle, copy, owner, owner_index) - if (counter(owner+1)==0) counter(owner+1) = ((map_pe_to_task(ens_handle, owner)+1) * 1000) - call init_random_seq(random_seq(copy), counter(owner+1)) - counter(owner+1) = counter(owner+1) + 1 - - - count = 1 ! min_var and max_var are numbered 1 to n, where n is the total number of variables (all domains) - - do id = 1, num_domains - do i = 1, wrf%dom(id)%number_of_wrf_variables - - start_ind = get_index_start(domain_id(id), i) - end_ind = get_index_end(domain_id(id), i) - - !! Option 1: - !! make the perturbation amplitude N% of the total - !! range of this variable. values could vary a lot - !! over some of the types, like pressure - !range = max_var(count) - min_var(count) - !pert_ampl = pert_amount * range - - do j = start_ind, end_ind - - call get_var_owner_index(ens_handle, j, owner, owner_index) - - ! once you change pert_state, state is changed as well - ! since they are the same storage as called from filter. - ! you have to save it if you want to use it again. - ! Option 2: perturb each value individually - !! make the perturbation amplitude N% of this value - - ! pert_ampl is only important on the task that uses it, but need to keep - ! the random number sequence in the same order on each task (call the same amount of times) - pert_ampl = pert_amount * ens_handle%copies(copy, min(owner_index, ens_handle%my_num_vars)) - random_number = random_gaussian(random_seq(copy), 0.0_r8, pert_ampl) - - if (ens_handle%my_pe == owner) then - - ens_handle%copies(copy, owner_index) = ens_handle%copies(copy, owner_index) + random_number - - ! keep variable from exceeding the original range - ens_handle%copies(copy,owner_index) = max(min_var(count), ens_handle%copies(copy,owner_index)) - ens_handle%copies(copy,owner_index) = min(max_var(count), ens_handle%copies(copy,owner_index)) - - endif - enddo - - - count = count + 1 - - enddo - enddo - -enddo - -deallocate(counter) - -end subroutine pert_copies_lanai_bitwise - -!####################################################### -! !WARNING:: at the moment, this code is *not* called -! !so there is no requirement to have a wrf namelist in -! !the current directory. the only thing it was extracting -! !was the dt, and that exists in the wrf input netcdf file -! !and is now read from there. -! -! subroutine read_dt_from_wrf_nml() -! -! real(r8) :: dt -! -! integer :: time_step, time_step_fract_num, time_step_fract_den -! integer :: max_dom, feedback, smooth_option -! integer, dimension(3) :: s_we, e_we, s_sn, e_sn, s_vert, e_vert -! integer, dimension(3) :: dx, dy, ztop, grid_id, parent_id -! integer, dimension(3) :: i_parent_start, j_parent_start, parent_grid_ratio -! integer, dimension(3) :: parent_time_step_ratio -! integer :: io, iunit, id -! integer :: num_metgrid_levels, p_top_requested, nproc_x, nproc_y -! -! !nc -- we added "num_metgrid_levels" to the domains nml to make all well with the -! ! namelist.input file belonging to global WRF, -! ! also "p_top_requested" in domains nml -! ! also "nproc_x" & "nproc_y" -! !nc -- we notice that "ztop" is unused in code -- perhaps get rid of later? -! namelist /domains/ time_step, time_step_fract_num, time_step_fract_den -! namelist /domains/ max_dom -! namelist /domains/ s_we, e_we, s_sn, e_sn, s_vert, e_vert -! namelist /domains/ dx, dy, ztop, grid_id, parent_id -! namelist /domains/ i_parent_start, j_parent_start, parent_grid_ratio -! namelist /domains/ parent_time_step_ratio -! namelist /domains/ feedback, smooth_option -! namelist /domains/ num_metgrid_levels, p_top_requested, nproc_x, nproc_y -! -! ! Begin by reading the namelist input -! call find_namelist_in_file("namelist.input", "domains", iunit) -! read(iunit, nml = domains, iostat = io) -! call check_namelist_read(iunit, io, "domains") -! -! ! Record the namelist values used for the run ... -! if (do_nml_file()) write(nmlfileunit, nml=domains) -! if (do_nml_term()) write( * , nml=domains) -! -! if (max_dom /= num_domains) then -! -! write(*,*) 'max_dom in namelist.input = ',max_dom -! write(*,*) 'num_domains in input.nml = ',num_domains -! call error_handler(E_ERR,'read_dt_from_wrf_nml', & -! 'Make them consistent.', source, revision,revdate) -! -! endif -! -! if (time_step_fract_den /= 0) then -! dt = real(time_step) + real(time_step_fract_num) / real(time_step_fract_den) -! else -! dt = real(time_step) -! endif -! -! do id=1,num_domains -! wrf%dom(id)%dt = dt / real(parent_time_step_ratio(id)) -! enddo -! -! end subroutine read_dt_from_wrf_nml - - - -subroutine compute_seaprs ( nz, z, t, p , q , & - sea_level_pressure, debug) -!------------------------------------------------------------------------- -! compute_seaprs Estimate sea level pressure. -! -! This routines has been taken "as is" from wrf_user_fortran_util_0.f -! -! This routine assumes -! index order is (i,j,k) -! wrf staggering -! units: pressure (Pa), temperature(K), height (m), mixing ratio (kg kg{-1}) -! availability of 3d p, t, and qv; 2d terrain; 1d half-level zeta string -! output units of SLP are Pa, but you should divide that by 100 for the -! weather weenies. -! virtual effects are included -! -! Dave -! -! cys: change to 1d -! TJH: verified intent() qualifiers, declaration syntax, uses error_handler - - IMPLICIT NONE - INTEGER, intent(in) :: nz - REAL(r8), intent(in) :: z(nz), p(nz), q(nz) - REAL(r8), intent(inout) :: t(nz) - REAL(r8), intent(out) :: sea_level_pressure - LOGICAL, intent(in) :: debug - - INTEGER :: level - REAL(r8) :: t_surf, t_sea_level - -! Some required physical constants: - - REAL(r8) :: R, G, GAMMA - PARAMETER (R=287.04_r8, G=9.81_r8, GAMMA=0.0065_r8) - -! Specific constants for assumptions made in this routine: - - REAL(r8) :: TC, PCONST - PARAMETER (TC=273.16_r8 + 17.5_r8, PCONST = 10000.0_r8) - - LOGICAL :: ridiculous_mm5_test - PARAMETER (ridiculous_mm5_test = .TRUE.) -! PARAMETER (ridiculous_mm5_test = .false.) - -! Local variables: - - INTEGER :: k - INTEGER :: klo, khi - - REAL(r8) :: plo, phi, tlo, thi, zlo, zhi - REAL(r8) :: p_at_pconst, t_at_pconst, z_at_pconst - REAL(r8) :: z_half_lowest - - REAL(r8), PARAMETER :: cp = 7.0_r8*R/2.0_r8 - REAL(r8), PARAMETER :: rcp = R/cp - REAL(r8), PARAMETER :: p1000mb = 100000.0_r8 - - LOGICAL :: l1 , l2 , l3, found - -! Find least zeta level that is PCONST Pa above the surface. We later use this -! level to extrapolate a surface pressure and temperature, which is supposed -! to reduce the effect of the diurnal heating cycle in the pressure field. - - t = t*(p/p1000mb)**rcp - - level = -1 - - k = 1 - found = .false. - do while( (.not. found) .and. (k.le.nz)) - IF ( p(k) .LT. p(1)-PCONST ) THEN - level = k - found = .true. - END IF - k = k+1 - END DO - - IF ( level .EQ. -1 ) THEN - PRINT '(A,I4,A)','Troubles finding level ', & - NINT(PCONST)/100,' above ground.' - print*, 'p=',p - print*, 't=',t - print*, 'z=',z - print*, 'q=',q - write(errstring,*)'Error_in_finding_100_hPa_up' - call error_handler(E_ERR,'compute_seaprs',errstring,' ',' ',' ') - END IF - - -! Get temperature PCONST Pa above surface. Use this to extrapolate -! the temperature at the surface and down to sea level. - - klo = MAX ( level - 1 , 1 ) - khi = MIN ( klo + 1 , nz - 1 ) - - IF ( klo .EQ. khi ) THEN - PRINT '(A)','Trapping levels are weird.' - PRINT '(A,I3,A,I3,A)','klo = ',klo,', khi = ',khi, & - ': and they should not be equal.' - write(errstring,*)'Error_trapping_levels' - call error_handler(E_ERR,'compute_seaprs',errstring,' ',' ',' ') - END IF - - plo = p(klo) - phi = p(khi) - tlo = t(klo)*(1. + 0.608 * q(klo) ) - thi = t(khi)*(1. + 0.608 * q(khi) ) -! zlo = zetahalf(klo)/ztop*(ztop-terrain(i,j))+terrain(i,j) -! zhi = zetahalf(khi)/ztop*(ztop-terrain(i,j))+terrain(i,j) - zlo = z(klo) - zhi = z(khi) - - p_at_pconst = p(1) - pconst - t_at_pconst = thi-(thi-tlo)*LOG(p_at_pconst/phi)*LOG(plo/phi) - z_at_pconst = zhi-(zhi-zlo)*LOG(p_at_pconst/phi)*LOG(plo/phi) - - t_surf = t_at_pconst*(p(1)/p_at_pconst)**(gamma*R/g) - t_sea_level = t_at_pconst+gamma*z_at_pconst - - -! If we follow a traditional computation, there is a correction to the sea level -! temperature if both the surface and sea level temnperatures are *too* hot. - - IF ( ridiculous_mm5_test ) THEN - l1 = t_sea_level .LT. TC - l2 = t_surf .LE. TC - l3 = .NOT. l1 - IF ( l2 .AND. l3 ) THEN - t_sea_level = TC - ELSE - t_sea_level = TC - 0.005*(t_surf-TC)**2 - END IF - END IF - -! The grand finale: ta da! - -! z_half_lowest=zetahalf(1)/ztop*(ztop-terrain(i,j))+terrain(i,j) - z_half_lowest=z(1) - sea_level_pressure = p(1) * & - EXP((2.*g*z_half_lowest)/ & - (R*(t_sea_level+t_surf))) - -! sea_level_pressure(i,j) = sea_level_pressure(i,j)*0.01 - - if (debug) then - print *,'slp=',sea_level_pressure - endif -! print *,'t=',t(10:15,10:15,1),t(10:15,2,1),t(10:15,3,1) -! print *,'z=',z(10:15,1,1),z(10:15,2,1),z(10:15,3,1) -! print *,'p=',p(10:15,1,1),p(10:15,2,1),p(10:15,3,1) -! print *,'slp=',sea_level_pressure(10:15,10:15), & -! sea_level_pressure(10:15,10:15),sea_level_pressure(20,10:15) - -end subroutine compute_seaprs - - - -SUBROUTINE splin2(x1a,x2a,ya,y2a,m,n,x1,x2,y) - INTEGER m,n,NN - REAL(r8), intent(in) :: x1,x2,x1a(m),x2a(n),y2a(m,n),ya(m,n) - real(r8), intent(out) :: y - PARAMETER (NN=200) -! USES spline,splint - INTEGER j,k - REAL(r8) y2tmp(NN),ytmp(NN),yytmp(NN) - do 12 j=1,m - do 11 k=1,n - ytmp(k)=ya(j,k) - y2tmp(k)=y2a(j,k) -11 continue - call splint(x2a,ytmp,y2tmp,n,x2,yytmp(j)) -12 continue - call spline(x1a,yytmp,m,1.e30_r8,1.e30_r8,y2tmp) - call splint(x1a,yytmp,y2tmp,m,x1,y) - return -END subroutine splin2 - -SUBROUTINE splie2(x1a,x2a,ya,m,n,y2a) - INTEGER m,n,NN - REAL(r8), intent(in) :: x1a(m),x2a(n),ya(m,n) - REAL(r8), intent(out) :: y2a(m,n) - PARAMETER (NN=200) -! USES spline - INTEGER j,k - REAL(r8) y2tmp(NN),ytmp(NN) - do 13 j=1,m - do 11 k=1,n - ytmp(k)=ya(j,k) -11 continue - call spline(x2a,ytmp,n,1.e30_r8,1.e30_r8,y2tmp) - do 12 k=1,n - y2a(j,k)=y2tmp(k) -12 continue -13 continue - return -END subroutine splie2 - -SUBROUTINE spline(x,y,n,yp1,ypn,y2) - INTEGER n,NMAX - REAL(r8), intent(in) :: yp1,ypn,x(n),y(n) - REAL(r8), intent(out) :: y2(n) - PARAMETER (NMAX=800) - INTEGER i,k - REAL(r8) p,qn,sig,un,u(NMAX) - if (yp1.gt..99e30) then - y2(1)=0. - u(1)=0. - else - y2(1)=-0.5 - u(1)=(3./(x(2)-x(1)))*((y(2)-y(1))/(x(2)-x(1))-yp1) - endif - do 11 i=2,n-1 - sig=(x(i)-x(i-1))/(x(i+1)-x(i-1)) - p=sig*y2(i-1)+2. - y2(i)=(sig-1.)/p - u(i)=(6.*((y(i+1)-y(i))/(x(i+ & - 1)-x(i))-(y(i)-y(i-1))/(x(i)-x(i-1)))/(x(i+1)-x(i-1))-sig* & - u(i-1))/p -11 continue - if (ypn.gt..99e30) then - qn=0. - un=0. - else - qn=0.5 - un=(3./(x(n)-x(n-1)))*(ypn-(y(n)-y(n-1))/(x(n)-x(n-1))) - endif - y2(n)=(un-qn*u(n-1))/(qn*y2(n-1)+1.) - do 12 k=n-1,1,-1 - y2(k)=y2(k)*y2(k+1)+u(k) -12 continue - return -END subroutine spline - - -SUBROUTINE splint(xa,ya,y2a,n,x,y) - INTEGER n - REAL(r8),intent(in) :: x,xa(n),y2a(n),ya(n) - REAL(r8),intent(out) :: y - INTEGER k,khi,klo - REAL(r8) a,b,h - klo=1 - khi=n -1 if (khi-klo.gt.1) then - k=(khi+klo)/2 - if(xa(k).gt.x)then - khi=k - else - klo=k - endif - goto 1 - endif - h=xa(khi)-xa(klo) - if (h.eq.0.) then - write(*,*) 'bad xa input in splint' - stop - endif - a=(xa(khi)-x)/h - b=(x-xa(klo))/h - y=a*ya(klo)+b*ya(khi)+((a**3-a)*y2a(klo)+(b**3-b)*y2a(khi))*(h**2)/6. - return -END subroutine splint - - -!####################################################################### - -subroutine get_domain_info(obslon,obslat,id,iloc,jloc,domain_id_start) - -real(r8), intent(in) :: obslon, obslat -integer, intent(out) :: id -real(r8), intent(out) :: iloc, jloc -integer, intent(in), optional :: domain_id_start - -logical :: dom_found - -! given arbitrary lat and lon values, returns closest domain id and -! horizontal mass point grid points (xloc,yloc) - -dom_found = .false. - -! the default is to start at the innermost domain and stop when -! the location is found. however if you want to start at a particular -! domain id number, pass it in as the last optional arg. -id = num_domains -if (present(domain_id_start)) then - if (domain_id_start < 1 .or. domain_id_start > num_domains) then - write(errstring, '(A,I1)') 'bad domain_id_start: ', domain_id_start - write(msgstring2, '(A,I1)') 'must be between 1 and ', num_domains - call error_handler(E_ERR, 'model_mod', errstring, & - source, revision, revdate, text2=msgstring2) - endif - id = domain_id_start -endif - -do while (.not. dom_found) - - ! Checking for exact equality on real variable types is generally a bad idea. - - if( (wrf%dom(id)%proj%hemi == 1.0_r8 .and. obslat < -90.0_r8) .or. & - (wrf%dom(id)%proj%hemi == -1.0_r8 .and. obslat > 90.0_r8) .or. & - (wrf%dom(id)%proj%code == PROJ_MERC .and. abs(obslat) > 90.0_r8) ) then - - ! catch latitudes that are out of range - ignore them but print out a warning. - write(errstring, *) 'obs with latitude out of range: ', obslat - call error_handler(E_MSG, 'model_mod', errstring) - - else - call latlon_to_ij(wrf%dom(id)%proj,min(max(obslat,-89.9999999_r8),89.9999999_r8),obslon,iloc,jloc) - - ! Array bound checking depends on whether periodic or not -- these are - ! real-valued indices here, so we cannot use boundsCheck :( - - if ( wrf%dom(id)%periodic_x .and. .not. wrf%dom(id)%periodic_y ) then - if ( wrf%dom(id)%polar ) then - ! Periodic X & M_grid ==> [1 we+1) - ! Periodic Y & M_grid ==> [0.5 sn+0.5] - if ( iloc >= 1.0_r8 .and. iloc < real(wrf%dom(id)%we,r8)+1.0_r8 .and. & - jloc >= 0.5_r8 .and. jloc <= real(wrf%dom(id)%sn,r8)+0.5_r8 ) & - dom_found = .true. - else - ! Periodic X & M_grid ==> [1 we+1) - ! NOT Periodic Y & M_grid ==> [1 sn] - if ( iloc >= 1.0_r8 .and. iloc < real(wrf%dom(id)%we,r8)+1.0_r8 .and. & - jloc >= 1.0_r8 .and. jloc <= real(wrf%dom(id)%sn,r8) ) & - dom_found = .true. - endif - - elseif ( wrf%dom(id)%periodic_x .and. wrf%dom(id)%periodic_y ) then - ! Periodic X & M_grid ==> [1 we+1) - ! Periodic Y & M_grid ==> [1 sn+1] - if ( iloc >= 1.0_r8 .and. iloc < real(wrf%dom(id)%we,r8)+1.0_r8 .and. & - jloc >= 1.0_r8 .and. jloc <= real(wrf%dom(id)%sn,r8)+1.0_r8 ) & - dom_found = .true. - - else - if ( wrf%dom(id)%polar ) then - ! NOT Periodic X & M_grid ==> [1 we] - ! Periodic Y & M_grid ==> [0.5 sn+0.5] - if ( iloc >= 1.0_r8 .and. iloc <= real(wrf%dom(id)%we,r8) .and. & - jloc >= 0.5_r8 .and. jloc <= real(wrf%dom(id)%sn,r8)+0.5_r8 ) & - dom_found = .true. - else - ! NOT Periodic X & M_grid ==> [1 we] - ! NOT Periodic Y & M_grid ==> [1 sn] - if ( iloc >= 1.0_r8 .and. iloc <= real(wrf%dom(id)%we,r8) .and. & - jloc >= 1.0_r8 .and. jloc <= real(wrf%dom(id)%sn,r8) ) & - dom_found = .true. - endif - endif - - endif - - if (.not. dom_found) then - id = id - 1 - if (id == 0) return - endif - -end do - -end subroutine get_domain_info - -!####################################################################### -!> Distributed version of get_close_obs -subroutine get_close_obs(gc, base_obs_loc, base_obs_kind, obs_loc, & - obs_kind, num_close, close_ind, dist, state_handle) - -! Given a DART ob (referred to as "base") and a set of obs priors or state variables -! (obs_loc, obs_kind), returns the subset of close ones to the "base" ob, their -! indices, and their distances to the "base" ob... - -! For vertical distance computations, general philosophy is to convert all vertical -! coordinates to a common coordinate. This coordinate type is defined in the namelist -! with the variable "vert_localization_coord". - -! Vertical conversion is carried out by the subroutine vert_convert. - -! Note that both base_obs_loc and obs_loc are intent(inout), meaning that these -! locations are possibly modified here and returned as such to the calling routine. -! The calling routine is always filter_assim and these arrays are local arrays -! within filter_assim. In other words, these modifications will only matter within -! filter_assim, but will not propagate backwards to filter. - -!HK -type(ensemble_type), intent(in) :: state_handle -type(get_close_type), intent(in) :: gc -type(location_type), intent(inout) :: base_obs_loc, obs_loc(:) -integer, intent(in) :: base_obs_kind, obs_kind(:) -integer, intent(out) :: num_close, close_ind(:) -real(r8), intent(out) :: dist(:) - -integer :: t_ind, istatus1, istatus2, k -integer :: base_which, local_obs_which -real(r8), dimension(3) :: base_array, local_obs_array -type(location_type) :: local_obs_loc - - -! Initialize variables to missing status -num_close = 0 -close_ind = -99 -dist = 1.0e9 - -istatus1 = 0 -istatus2 = 0 - -! Convert base_obs vertical coordinate to requested vertical coordinate if necessary - -base_array = get_location(base_obs_loc) -base_which = nint(query_location(base_obs_loc)) - -if (.not. horiz_dist_only) then - if (base_which /= wrf%dom(1)%localization_coord) then - !print*, 'base_which ', base_which, 'loc coord ', wrf%dom(1)%localization_coord - call vert_convert(state_handle, base_obs_loc, base_obs_kind, istatus1) - !call error_handler(E_ERR, 'you should not call this ', 'get_close_obs') - elseif (base_array(3) == missing_r8) then - istatus1 = 1 - endif -endif - -if (istatus1 == 0) then - - ! Get all the potentially close obs but no dist (optional argument dist(:) is not present) - ! This way, we are decreasing the number of distance computations that will follow. - ! This is a horizontal-distance operation and we don't need to have the relevant vertical - ! coordinate information yet (for obs_loc). - call loc_get_close_obs(gc, base_obs_loc, base_obs_kind, obs_loc, obs_kind, & - num_close, close_ind) - - ! Loop over potentially close subset of obs priors or state variables - do k = 1, num_close - - t_ind = close_ind(k) - local_obs_loc = obs_loc(t_ind) - local_obs_which = nint(query_location(local_obs_loc)) - - ! Convert local_obs vertical coordinate to requested vertical coordinate if necessary. - ! This should only be necessary for obs priors, as state location information already - ! contains the correct vertical coordinate (filter_assim's call to get_state_meta_data). - if (.not. horiz_dist_only) then - if (local_obs_which /= wrf%dom(1)%localization_coord) then - call vert_convert(state_handle, local_obs_loc, obs_kind(t_ind), istatus2) - ! Store the "new" location into the original full local array - obs_loc(t_ind) = local_obs_loc !HK Overwritting the location - else - istatus2 = 0 - endif - endif - - ! Compute distance - set distance to a very large value if vert coordinate is missing - ! or vert_convert returned error (istatus2=1) - local_obs_array = get_location(local_obs_loc) - if (((.not. horiz_dist_only).and.(local_obs_array(3) == missing_r8)).or.(istatus2 == 1)) then - dist(k) = 1.0e9 - else - dist(k) = get_dist(base_obs_loc, local_obs_loc, base_obs_kind, obs_kind(t_ind)) - endif - - !print*, 'k ', k, 'rank ', my_task_id() - - end do -endif - -end subroutine get_close_obs - -!####################################################################### -!nc -- additional function from Greg Lawson & Nancy Collins -! -! logical function boundsCheck determines whether real-valued location indices are -! within a sensible range based on the assumed (un)staggered grid and based on -! whether the domain is assumed to be periodic in a given direction. - -function boundsCheck ( ind, periodic, id, dim, type ) - - integer, intent(in) :: ind, id, dim, type - logical, intent(in) :: periodic - - logical :: boundsCheck -! logical, parameter :: restrict_polar = .true. - logical, parameter :: restrict_polar = .false. - - ! Consider cases in REAL-VALUED indexing: - ! - ! I. Longitude -- x-direction - ! A. PERIODIC (period_x = .true.) - ! - ! Consider Mass-grid (& V-grid) longitude grid with 4 west-east gridpoints - ! Values :: [ -135 -45 45 135 ] .. {225} - ! Indices :: [ 1 2 3 4 ] .. {1,5} - ! Complementary U-grid - ! Values :: [ -180 -90 0 90 180 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 5) ---> [1 we+1) - ! ---> [-135 225) - ! 2. U-grid ---> [1 5) ---> [1 wes) - ! ---> [-180 180) - ! [Note that above "allowable values" reflect that one should be able to have - ! an observation anywhere on a given longitude circle -- the information - ! exists in order to successfully interpolate to anywhere over [0 360).] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & U-grid. Hence, two examples: - ! a. If there is an observation location at -165 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 4.667 - ! * An observation of TYPE_U (on the U-grid) would have ind = 1.167 - ! b. If there is an observation location at 0 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 2.5 - ! * An observation of TYPE_U (on the U-grid) would have ind = 3.0 - ! - ! B. NOT periodic (period_x = .false.) - ! - ! Consider Mass-grid (& V-grid) longitude grid with 4 west-east gridpoints - ! Values :: [ 95 105 115 125 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary U-grid - ! Values :: [ 90 100 110 120 130 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 4] ---> [1 we] - ! ---> [95 125] - ! 2. U-grid ---> [1.5 4.5] ---> [1.5 we+0.5] - ! ---> [95 125] - ! [Note that above "allowable values" reflect that one should only be able to - ! have an observation within the M-grid, since that is the only way to - ! guarantee that the necessary information exists in order to successfully - ! interpolate to a specified location.] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & U-grid. Hence, two examples: - ! a. If there is an observation location at 96 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 1.1 - ! * An observation of TYPE_U (on the U-grid) would have ind = 1.6 - ! b. If there is an observation location at 124 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 3.9 - ! * An observation of TYPE_U (on the U-grid) would have ind = 4.4 - ! - ! II. Latitude -- y-direction - ! A. PERIODIC (polar = .true.) - ! - ! Consider Mass-grid (& U-Grid) latitude grid with 4 south-north gridpoints - ! Values :: [ -67.5 -22.5 22.5 67.5 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary V-grid - ! Values :: [ -90 -45 0 45 90 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [0.5 4.5] ---> [0.5 sn+0.5] - ! ---> [-90 90] - ! 2. U-grid ---> [1 5] ---> [1 sns] - ! ---> [-90 90] - ! [Note that above "allowable values" reflect that one should be able to have - ! an observation anywhere along a give latitude circle -- the information - ! exists in order to successfully interpolate to anywhere over [-90 90]; - ! however, in latitude this poses a special challenge since the seams join - ! two separate columns of data over the pole, as opposed to in longitude - ! where the seam wraps back on a single row of data.] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & V-grid. Hence, two examples: - ! a. If there is an observation location at -75 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 0.833 - ! * An observation of TYPE_V (on the V-grid) would have ind = 1.333 - ! b. If there is an observation location at 0 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 2.5 - ! * An observation of TYPE_V (on the V-grid) would have ind = 3.0 - ! - ! B. NOT periodic (polar = .false.) - ! - ! Consider Mass-grid (& U-Grid) latitude grid with 4 south-north gridpoints - ! Values :: [ 10 20 30 40 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary V-grid - ! Values :: [ 5 15 25 35 45 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 4] ---> [1 sn] - ! ---> [10 40] - ! 2. U-grid ---> [1.5 4.5] ---> [1.5 sn+0.5] - ! ---> [10 40] - ! [Note that above "allowable values" reflect that one should only be able to - ! have an observation within the M-grid, since that is the only way to - ! guarantee that the necessary information exists in order to successfully - ! interpolate to a specified location.] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & V-grid. Hence, two examples: - ! a. If there is an observation location at 11 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 1.1 - ! * An observation of TYPE_V (on the V-grid) would have ind = 1.6 - ! b. If there is an observation location at 25 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 2.5 - ! * An observation of TYPE_V (on the V-grid) would have ind = 3.0 - ! - ! III. Vertical -- z-direction (periodicity not an issue) - ! - ! Consider Mass vertical grid with 4 bottom-top gridpoints - ! Values :: [ 0.875 0.625 0.375 0.125 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary W-grid - ! Values :: [ 1 0.75 0.50 0.25 0 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 4] ---> [1 bt] - ! ---> [0.875 0.125] - ! 2. W-grid ---> [1.5 4.5] ---> [1.5 bt+0.5] - ! ---> [0.875 0.125] - ! - ! [Note that above "allowable values" reflect that one should only be able to - ! have an observation within the M-grid, since that is the only way to - ! guarantee that the necessary information exists in order to successfully - ! interpolate to a specified location.] - ! - - ! Summary of Allowable REAL-VALUED Index Values ==> INTEGER Index Values - ! - ! In longitude (x) direction - ! Periodic & M_grid ==> [1 we+1) ==> [1 wes) - ! Periodic & U_grid ==> [1 wes) ==> [1 wes) - ! NOT Periodic & M_grid ==> [1 we] ==> [1 we) - ! NOT Periodic & U_grid ==> [1.5 we+0.5] ==> [1 wes) - ! In latitude (y) direction - ! Periodic & M_grid ==> [0.5 sn+0.5] ==> [0 sns) *though in practice, [1 sn)* - ! Periodic & V_grid ==> [1 sns] ==> [1 sns) *though allowable range, [1.5 sn+.5]* - ! NOT Periodic & M_grid ==> [1 sn] ==> [1 sn) - ! NOT Periodic & V_grid ==> [1.5 sn+0.5] ==> [1 sns) - ! In vertical (z) direction - ! M_grid ==> [1 bt] ==> [1 bt) - ! W_grid ==> [1.5 bt+0.5] ==> [1 bts) - - - ! Assume boundsCheck is false unless we can prove otherwise - boundsCheck = .false. - - ! First check direction (dimension) - ! Longitude (x-direction) has dim == 1 - if ( dim == 1 ) then - - ! Next check periodicity - if ( periodic ) then - - ! If periodic in longitude, then no need to check staggering because both - ! M and U grids allow integer indices from [1 wes) - if ( ind >= 1 .and. ind < wrf%dom(id)%wes ) boundsCheck = .true. - - else - - ! If NOT periodic in longitude, then we need to check staggering because - ! M and U grids allow different index ranges - - ! Check staggering by comparing var_size(dim,type) to the staggered dimension - ! for dim == 1 stored in wrf%dom(id) - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%wes ) then - ! U-grid allows integer range of [1 wes) - if ( ind >= 1 .and. ind < wrf%dom(id)%wes ) boundsCheck = .true. - else - ! M & V-grid allow [1 we) - if ( ind >= 1 .and. ind < wrf%dom(id)%we ) boundsCheck = .true. - endif - - endif - - ! Latitude (y-direction) has dim == 2 - elseif ( dim == 2 ) then - - ! Next check periodicity - if ( periodic ) then - - ! We need to check staggering because M and V grids allow different indices - -!*** NOTE: For now are disallowing observation locations that occur poleward of the -! first and last M-grid gridpoints. This means that this function will -! return false for polar observations. This need not be the case because -! the information should be available for proper interpolation across the -! poles, but it will require more clever thinking. Hopefully this can -! be added in later. - - ! Check staggering by comparing var_size(dim,type) to the staggered dimension - ! for dim == 2 stored in wrf%dom(id) - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%sns ) then - ! V-grid allows integer range [1 sns) - if ( ind >= 1 .and. ind < wrf%dom(id)%sns ) boundsCheck = .true. - else - ! For now we will set a logical flag to more restrictively check the array - ! bounds under our no-polar-obs assumptions - if ( restrict_polar ) then - ! M & U-grid allow integer range [1 sn) in practice (though properly, [0 sns) ) - if ( ind >= 1 .and. ind < wrf%dom(id)%sn ) boundsCheck = .true. - else - ! M & U-grid allow integer range [0 sns) in unrestricted circumstances - if ( ind >= 0 .and. ind < wrf%dom(id)%sns ) boundsCheck = .true. - endif - endif - - else - - ! We need to check staggering because M and V grids allow different indices - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%sns ) then - ! V-grid allows [1 sns) - if ( ind >= 1 .and. ind < wrf%dom(id)%sns ) boundsCheck = .true. - else - ! M & U-grid allow [1 sn) - if ( ind >= 1 .and. ind < wrf%dom(id)%sn ) boundsCheck = .true. - endif - - endif - - elseif ( dim == 3 ) then - - ! No periodicity to worry about in the vertical! However, we still need to check - ! staggering because the ZNU and ZNW grids allow different index ranges - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%bts ) then - ! W vertical grid allows [1 bts) - if ( ind >= 1 .and. ind < wrf%dom(id)%bts ) boundsCheck = .true. - else - ! M vertical grid allows [1 bt) - if ( ind >= 1 .and. ind < wrf%dom(id)%bt ) boundsCheck = .true. - endif - - else - - print*, 'model_mod.f90 :: function boundsCheck :: dim must equal 1, 2, or 3!' - - endif - - -end function boundsCheck - -!####################################################################### -! get_orners takes in an i and j index, information about domain and grid staggering, -! and then returns the four cornering gridpoints' 2-element integer indices. -subroutine getCorners(i, j, id, type, ll, ul, lr, ur, rc) - - integer, intent(in) :: i, j, id, type - integer, dimension(2), intent(out) :: ll, ul, lr, ur - integer, intent(out) :: rc - -! logical, parameter :: restrict_polar = .true. - logical, parameter :: restrict_polar = .false. - - ! set return code to 0, and change this if necessary - rc = 0 - - !---------------- - ! LOWER LEFT - !---------------- - - ! i and j are the lower left (ll) corner already - ! - ! NOTE :: once we allow for polar periodicity, the incoming j index could actually - ! be 0, which would imply a ll(2) value of 1, with a ll(1) value 180 degrees - ! of longitude away from the incoming i index! But we have not included - ! this possibility yet. - - ! As of 22 Oct 2007, this option is not allowed! - ! Note that j = 0 can only happen if we are on the M (or U) wrt to latitude - if ( wrf%dom(id)%polar .and. j == 0 .and. .not. restrict_polar ) then - - ! j = 0 should be mapped to j = 1 (ll is on other side of globe) - ll(2) = 1 - - ! Need to map i index 180 degrees away - ll(1) = i + wrf%dom(id)%we/2 - - ! Check validity of bounds & adjust by periodicity if necessary - if ( ll(1) > wrf%dom(id)%we ) ll(1) = ll(1) - wrf%dom(id)%we - - ! We shouldn't be able to get this return code if restrict_polar = .true. -! rc = 1 -! print*, 'model_mod.f90 :: getCorners :: Tried to do polar bc -- rc = ', rc - - else - - ll(1) = i - ll(2) = j - - endif - - - !---------------- - ! LOWER RIGHT - !---------------- - - ! Most of the time, the lower right (lr) corner will simply be (i+1,j), but we need to check - ! Summary of x-direction corners: - ! Periodic & M_grid has ind = [1 wes) - ! ind = [1 we) ==> ind_p_1 = ind + 1 - ! ind = [we wes) ==> ind_p_1 = 1 - ! Periodic & U_grid has ind = [1 wes) - ! ind = [1 we) ==> ind_p_1 = ind + 1 - ! ind = [we wes) ==> ind_p_1 = wes ( keep in mind that U(1) = U(wes) if periodic ) - ! NOT Periodic & M_grid has ind = [1 we) - ! ind = [1 we-1) ==> ind_p_1 = ind + 1 - ! ind = [we-1 we) ==> ind_p_1 = we - ! NOT Periodic & U_grid has ind = [1 wes) - ! ind = [1 we) ==> ind_p_1 = ind + 1 - ! ind = [we wes) ==> ind_p_1 = wes - - if ( wrf%dom(id)%periodic_x ) then - - ! Check to see what grid we have, M vs. U - if ( wrf%dom(id)%var_size(1,type) == wrf%dom(id)%wes ) then - ! U-grid is always i+1 -- do this in reference to already adjusted ll points - lr(1) = ll(1) + 1 - lr(2) = ll(2) - else - ! M-grid is i+1 except if we <= ind < wes, in which case it's 1 - if ( i < wrf%dom(id)%we ) then - lr(1) = ll(1) + 1 - else - lr(1) = 1 - endif - lr(2) = ll(2) - endif - - else - - ! Regardless of grid, NOT Periodic always has i+1 - lr(1) = ll(1) + 1 - lr(2) = ll(2) - - endif - - - !---------------- - ! UPPER LEFT - !---------------- - -!*** NOTE: For now are disallowing observation locations that occur poleward of the -! first and last M-grid gridpoints. This need not be the case because -! the information should be available for proper interpolation across the -! poles, but it will require more clever thinking. Hopefully this can -! be added in later. - - ! Most of the time, the upper left (ul) corner will simply be (i,j+1), but we need to check - ! Summary of y-direction corners: - ! Periodic & M_grid has ind = [0 sns) *though in practice, [1 sn)* - ! ind = [1 sn-1) ==> ind_p_1 = ind + 1 - ! ind = [sn-1 sn) ==> ind_p_1 = sn - ! Periodic & V_grid has ind = [1 sns) - ! ind = [1 sn) ==> ind_p_1 = ind + 1 - ! ind = [sn sns) ==> ind_p_1 = sns - ! NOT Periodic & M_grid has ind = [1 sn) - ! ind = [1 sn-1) ==> ind_p_1 = ind + 1 - ! ind = [sn-1 sn) ==> ind_p_1 = sn - ! NOT Periodic & V_grid has ind = [1 sns) - ! ind = [1 sn) ==> ind_p_1 = ind + 1 - ! ind = [sn sns) ==> ind_p_1 = sns - ! - ! Hence, with our current polar obs restrictions, all four possible cases DO map into - ! ul = (i,j+1). But this will not always be the case. - - if ( wrf%dom(id)%polar ) then - - ! Check to see what grid we have, M vs. V - if ( wrf%dom(id)%var_size(2,type) == wrf%dom(id)%sns ) then - ! V-grid is always j+1, even if we allow for full [1 sns) range - ul(1) = ll(1) - ul(2) = ll(2) + 1 - else - ! M-grid changes depending on polar restriction - if ( restrict_polar ) then - ! If restricted, then we can simply add 1 - ul(1) = ll(1) - ul(2) = ll(2) + 1 - else - ! If not restricted, then we can potentially wrap over the north pole, which - ! means that ul(2) is set to sn and ul(1) is shifted by 180 deg. - - if ( j == wrf%dom(id)%sn ) then - ! j > sn should be mapped to j = sn (ul is on other side of globe) - ul(2) = wrf%dom(id)%sn - - ! Need to map i index 180 degrees away - ul(1) = ll(1) + wrf%dom(id)%we/2 - - ! Check validity of bounds & adjust by periodicity if necessary - if ( ul(1) > wrf%dom(id)%we ) ul(1) = ul(1) - wrf%dom(id)%we - - ! We shouldn't be able to get this return code if restrict_polar = .true. -! rc = 1 -! print*, 'model_mod.f90 :: getCorners :: Tried to do polar bc -- rc = ', rc - - elseif ( j == 0 ) then - ! In this case, we have place ll on the other side of the globe, so we - ! cannot reference ul to ll - ul(1) = i - ul(2) = 1 - - else - ! We can confidently set to j+1 - ul(1) = ll(1) - ul(2) = ll(2) + 1 - endif - - endif - endif - - elseif ( wrf%dom(id)%periodic_y ) then - - ! Check to see what grid we have, M vs. U - if ( wrf%dom(id)%var_size(2,type) == wrf%dom(id)%sns ) then - ! V-grid is always j+1 -- do this in reference to already adjusted ll points - ul(1) = ll(1) - ul(2) = ll(2)+1 - else - ! M-grid is j+1 except if we <= ind < wes, in which case it's 1 - if ( j < wrf%dom(id)%sn ) then - ul(2) = ll(2) + 1 - else - ul(2) = 1 - endif - ul(1) = ll(1) - endif - - else - - ! Regardless of grid, NOT Periodic always has j+1 - ul(1) = ll(1) - ul(2) = ll(2) + 1 - - endif - - - !---------------- - ! UPPER RIGHT - !---------------- - -!*** NOTE: For now are disallowing observation locations that occur poleward of the -! first and last M-grid gridpoints. This need not be the case because -! the information should be available for proper interpolation across the -! poles, but it will require more clever thinking. Hopefully this can -! be added in later. - - ! Most of the time, the upper right (ur) corner will simply be (i+1,j+1), but we need to check - ! In fact, we can largely get away with ur = (lr(1),ul(2)). Where this will NOT work is - ! where we have had to re-map the i index to the other side of the globe (180 deg) due to - ! the polar boundary condition. There are no situations where ur(2) will not be equal to - ! ul(2). - - ur(2) = ul(2) - - ! Need to check if ur(1) .ne. lr(1) - if ( wrf%dom(id)%polar .and. .not. restrict_polar ) then - - ! Only if j == 0 or j == sn - if ( j == 0 .or. j == wrf%dom(id)%sn) then - ! j == 0 means that ll(1) = i + 180 deg, so we cannot use lr(1) -- hence, we will - ! add 1 to ul(1), unless doing so spans the longitude seam point. - ! j == sn means that ul(1) = i + 180 deg. Here we cannot use lr(1) either because - ! it will be half a domain away from ul(1)+1. Be careful of longitude seam point. - - ! Here we need to check longitude periodicity and the type of grid - if ( wrf%dom(id)%periodic_x ) then - - ! Check to see what grid we have, M vs. U - if ( wrf%dom(id)%var_size(1,type) == wrf%dom(id)%wes ) then - ! U-grid is always i+1 -- do this in reference to already adjusted ll points - ur(1) = ul(1) + 1 - else - ! M-grid is i+1 except if we <= ind < wes, in which case it's 1 - if ( ul(1) < wrf%dom(id)%we ) then - ur(1) = ul(1) + 1 - else - ur(1) = 1 - endif - endif - - else - - ! Regardless of grid, NOT Periodic always has i+1 - ur(1) = ul(1) + 1 - - endif - - ! If not a special j value, then we are set for the ur(1) = lr(1) - else - - ur(1) = lr(1) - - endif - - ! If not an unrestricted polar periodic domain, then we have nothing to worry about - else - - ur(1) = lr(1) - - endif - -end subroutine getCorners - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine read_wrf_dimensions(ncid,bt,bts,sn,sns,we,wes,sls) - -! ncid: input, file handl -! id: input, domain id - -integer, intent(in) :: ncid -integer, intent(out) :: bt,bts,sn,sns,we,wes,sls -logical, parameter :: debug = .false. -integer :: var_id -character (len=NF90_MAX_NAME) :: name - -! get wrf grid dimensions - - call nc_check( nf90_inq_dimid(ncid, "bottom_top", var_id), & - 'static_init_model','inq_dimid bottom_top') - call nc_check( nf90_inquire_dimension(ncid, var_id, name, bt), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "bottom_top_stag", var_id), & - 'static_init_model','inq_dimid bottom_top_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, bts), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "south_north", var_id), & - 'static_init_model','inq_dimid south_north') - call nc_check( nf90_inquire_dimension(ncid, var_id, name, sn), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "south_north_stag", var_id), & - 'static_init_model','inq_dimid south_north_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, sns), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "west_east", var_id), & - 'static_init_model','inq_dimid west_east') - call nc_check( nf90_inquire_dimension(ncid, var_id, name, we), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "west_east_stag", var_id), & - 'static_init_model','inq_dimid west_east_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, wes), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "soil_layers_stag", var_id), & - 'static_init_model','inq_dimid soil_layers_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, sls), & - 'static_init_model','inquire_dimension '//trim(name)) - - if(debug) then - write(*,*) ' dimensions bt, sn, we are ',bt, & - sn, we - write(*,*) ' staggered bt, sn, we are ',bts, & - sns,wes - endif - - RETURN - -end subroutine read_wrf_dimensions - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine read_wrf_file_attributes(ncid,id) - -! ncid: input, file handl -! id: input, domain id - -integer, intent(in) :: ncid, id -logical, parameter :: debug = .false. - -! get meta data and static data we need - - call nc_check( nf90_get_att(ncid, nf90_global, 'DX', wrf%dom(id)%dx), & - 'static_init_model', 'get_att DX') - call nc_check( nf90_get_att(ncid, nf90_global, 'DY', wrf%dom(id)%dy), & - 'static_init_model', 'get_att DY') - call nc_check( nf90_get_att(ncid, nf90_global, 'DT', wrf%dom(id)%dt), & - 'static_init_model', 'get_att DT') - write(errstring, *) 'dt from wrfinput_d0X file is: ', wrf%dom(id)%dt - call error_handler(E_MSG, ' ', errstring) - if(debug) write(*,*) ' dx, dy, dt are ',wrf%dom(id)%dx, & - wrf%dom(id)%dy, wrf%dom(id)%dt - - call nc_check( nf90_get_att(ncid, nf90_global, 'MAP_PROJ', wrf%dom(id)%map_proj), & - 'static_init_model', 'get_att MAP_PROJ') - if(debug) write(*,*) ' map_proj is ',wrf%dom(id)%map_proj - - call nc_check( nf90_get_att(ncid, nf90_global, 'CEN_LAT', wrf%dom(id)%cen_lat), & - 'static_init_model', 'get_att CEN_LAT') - if(debug) write(*,*) ' cen_lat is ',wrf%dom(id)%cen_lat - - call nc_check( nf90_get_att(ncid, nf90_global, 'CEN_LON', wrf%dom(id)%cen_lon), & - 'static_init_model', 'get_att CEN_LON') - if(debug) write(*,*) ' cen_lon is ',wrf%dom(id)%cen_lon - - call nc_check( nf90_get_att(ncid, nf90_global, 'TRUELAT1', truelat1), & - 'static_init_model', 'get_att TRUELAT1') - if(debug) write(*,*) ' truelat1 is ',truelat1 - - call nc_check( nf90_get_att(ncid, nf90_global, 'TRUELAT2', truelat2), & - 'static_init_model', 'get_att TRUELAT2') - if(debug) write(*,*) ' truelat2 is ',truelat2 - - call nc_check( nf90_get_att(ncid, nf90_global, 'STAND_LON', stdlon), & - 'static_init_model', 'get_att STAND_LON') - if(debug) write(*,*) ' stdlon is ',stdlon - - RETURN - -end subroutine read_wrf_file_attributes - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine assign_boundary_conditions(id) - -! id: input, domain id - -integer, intent(in) :: id -logical, parameter :: debug = .false. - -!nc -- fill in the boundary conditions (periodic_x and polar) here. This will -! need to be changed once these are taken from the NetCDF input instead -! of the model namelist -! NOTE :: because NetCDF cannot handle logicals, these boundary conditions -! are likely to be read in as integers. The agreed upon strategy is to -! test whether the integers are equal to 0 (for .false.) or 1 (for .true.) -! and set the wrf%dom(id)% values to logicals to be used internally within -! model_mod.f90. -! -! Jeff Anderson points out that not everyone will convert to wrf3.0 and this -! global attribute convention may not be backward-compatible. So we should -! test for existence of attributes and have defaults (from model_mod -! namelist) ready if they do not exist. Note that defaults are currently -! true (as of 24 Oct 2007), but once the attributes arrive, the defaults -! should be false. - if ( id == 1 ) then - wrf%dom(id)%periodic_x = periodic_x - wrf%dom(id)%periodic_y = periodic_y - wrf%dom(id)%polar = polar - wrf%dom(id)%scm = scm - else - wrf%dom(id)%periodic_x = .false. - wrf%dom(id)%periodic_y = .false. - wrf%dom(id)%polar = .false. - wrf%dom(id)%scm = .false. - endif - if(debug) write(*,*) ' periodic_x ',wrf%dom(id)%periodic_x - if(debug) write(*,*) ' periodic_y ',wrf%dom(id)%periodic_y - if(debug) write(*,*) ' polar ',wrf%dom(id)%polar - if(debug) write(*,*) ' scm ',wrf%dom(id)%scm - -end subroutine assign_boundary_conditions - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine read_wrf_static_data(ncid,id) - -! ncid: input, file handle -! id: input, domain id - -integer, intent(in) :: ncid, id -logical, parameter :: debug = .false. -integer :: var_id - - call nc_check( nf90_inq_varid(ncid, "P_TOP", var_id), & - 'read_wrf_static_data','inq_varid P_TOP') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%p_top), & - 'read_wrf_static_data','get_var P_TOP') - -! get 1D (z) static data defining grid levels - - allocate(wrf%dom(id)%dn(1:wrf%dom(id)%bt)) - call nc_check( nf90_inq_varid(ncid, "DN", var_id), & - 'read_wrf_static_data','inq_varid DN') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%dn), & - 'read_wrf_static_data','get_var DN') - if(debug) write(*,*) ' dn ',wrf%dom(id)%dn - - allocate(wrf%dom(id)%znu(1:wrf%dom(id)%bt)) - call nc_check( nf90_inq_varid(ncid, "ZNU", var_id), & - 'read_wrf_static_data','inq_varid ZNU') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%znu), & - 'read_wrf_static_data','get_var ZNU') - if(debug) write(*,*) ' znu is ',wrf%dom(id)%znu - - allocate(wrf%dom(id)%znw(1:wrf%dom(id)%bts)) - call nc_check( nf90_inq_varid(ncid, "ZNW", var_id), & - 'read_wrf_static_data','inq_varid ZNW') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%znw), & - 'read_wrf_static_data','get_var ZNW') - if(debug) write(*,*) ' znw is ',wrf%dom(id)%znw - - allocate(wrf%dom(id)%dnw(1:wrf%dom(id)%bt)) - call nc_check( nf90_inq_varid(ncid, "DNW", var_id), & - 'read_wrf_static_data','inq_varid DNW') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%dnw), & - 'read_wrf_static_data','get_var DNW') - if(debug) write(*,*) ' dnw is ',wrf%dom(id)%dnw - - allocate(wrf%dom(id)%zs(1:wrf%dom(id)%sls)) - call nc_check( nf90_inq_varid(ncid, "ZS", var_id), & - 'read_wrf_static_data','inq_varid ZS') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%zs), & - 'read_wrf_static_data','get_var ZS') - -! get 2D (x,y) base state for mu, latitude, longitude - - allocate(wrf%dom(id)%mub(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "MUB", var_id), & - 'read_wrf_static_data','inq_varid MUB') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%mub), & - 'read_wrf_static_data','get_var MUB') - if(debug) then - write(*,*) ' corners of mub ' - write(*,*) wrf%dom(id)%mub(1,1),wrf%dom(id)%mub(wrf%dom(id)%we,1), & - wrf%dom(id)%mub(1,wrf%dom(id)%sn),wrf%dom(id)%mub(wrf%dom(id)%we, & - wrf%dom(id)%sn) - endif - - allocate(wrf%dom(id)%longitude(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLONG", var_id), & - 'read_wrf_static_data','inq_varid XLONG') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%longitude), & - 'read_wrf_static_data','get_var XLONG') - - allocate(wrf%dom(id)%longitude_u(1:wrf%dom(id)%wes,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLONG_U", var_id), & - 'read_wrf_static_data','inq_varid XLONG_U') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%longitude_u), & - 'read_wrf_static_data','get_var XLONG_U') - - allocate(wrf%dom(id)%longitude_v(1:wrf%dom(id)%we,1:wrf%dom(id)%sns)) - call nc_check( nf90_inq_varid(ncid, "XLONG_V", var_id), & - 'read_wrf_static_data','inq_varid XLONG_V') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%longitude_v), & - 'read_wrf_static_data','get_var XLONG_V') - - allocate(wrf%dom(id)%latitude(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLAT", var_id), & - 'read_wrf_static_data','inq_varid XLAT') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%latitude), & - 'read_wrf_static_data','get_var XLAT') - - allocate(wrf%dom(id)%latitude_u(1:wrf%dom(id)%wes,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLAT_U", var_id), & - 'read_wrf_static_data','inq_varid XLAT_U') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%latitude_u), & - 'read_wrf_static_data','get_var XLAT_U') - - allocate(wrf%dom(id)%latitude_v(1:wrf%dom(id)%we,1:wrf%dom(id)%sns)) - call nc_check( nf90_inq_varid(ncid, "XLAT_V", var_id), & - 'read_wrf_static_data','inq_varid XLAT_V') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%latitude_v), & - 'read_wrf_static_data','get_var XLAT_V') - - allocate(wrf%dom(id)%land(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLAND", var_id), & - 'read_wrf_static_data','inq_varid XLAND') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%land), & - 'read_wrf_static_data','get_var XLAND') - if(debug) then - write(*,*) ' corners of land ' - write(*,*) wrf%dom(id)%land(1,1),wrf%dom(id)%land(wrf%dom(id)%we,1), & - wrf%dom(id)%land(1,wrf%dom(id)%sn),wrf%dom(id)%land(wrf%dom(id)%we, & - wrf%dom(id)%sn) - endif - - if(debug) then - write(*,*) ' corners of lat ' - write(*,*) wrf%dom(id)%latitude(1,1),wrf%dom(id)%latitude(wrf%dom(id)%we,1), & - wrf%dom(id)%latitude(1,wrf%dom(id)%sn), & - wrf%dom(id)%latitude(wrf%dom(id)%we,wrf%dom(id)%sn) - write(*,*) ' corners of long ' - write(*,*) wrf%dom(id)%longitude(1,1),wrf%dom(id)%longitude(wrf%dom(id)%we,1), & - wrf%dom(id)%longitude(1,wrf%dom(id)%sn), & - wrf%dom(id)%longitude(wrf%dom(id)%we,wrf%dom(id)%sn) - endif - - allocate(wrf%dom(id)%hgt(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "HGT", var_id), & - 'read_wrf_static_data','inq_varid HGT') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%hgt), & - 'read_wrf_static_data','get_var HGT') - -! get 3D base state geopotential - - !allocate(wrf%dom(id)%phb(1:wrf%dom(id)%we,1:wrf%dom(id)%sn,1:wrf%dom(id)%bts)) - !call nc_check( nf90_inq_varid(ncid, "PHB", var_id), & - ! 'read_wrf_static_data','inq_varid PHB') - !call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%phb), & - ! 'read_wrf_static_data','get_var PHB') - !if(debug) then - ! write(*,*) ' corners of phb ' - ! write(*,*) wrf%dom(id)%phb(1,1,1),wrf%dom(id)%phb(wrf%dom(id)%we,1,1), & - ! wrf%dom(id)%phb(1,wrf%dom(id)%sn,1),wrf%dom(id)%phb(wrf%dom(id)%we, & - ! wrf%dom(id)%sn,1) - ! write(*,*) wrf%dom(id)%phb(1,1,wrf%dom(id)%bts), & - ! wrf%dom(id)%phb(wrf%dom(id)%we,1,wrf%dom(id)%bts), & - ! wrf%dom(id)%phb(1,wrf%dom(id)%sn,wrf%dom(id)%bts), & - ! wrf%dom(id)%phb(wrf%dom(id)%we,wrf%dom(id)%sn,wrf%dom(id)%bts) - !endif - -!>@todo filenames -phb_filename = 'wrfinput_d01' -call create_groups ! each group reads in the grid info in parallel -call read_phb ! read in the grid info -call create_window ! phb - -end subroutine read_wrf_static_data - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine setup_map_projection(id) - -! id: input, domain id - -integer, intent(in) :: id -logical, parameter :: debug = .false. - -integer :: proj_code -real(r8) :: latinc,loninc - -! Initializes the map projection structure to missing values - - call map_init(wrf%dom(id)%proj) - -! Populate the map projection structure - -!nc -- added in case structures for CASSINI and CYL -!nc -- global wrfinput_d01 has truelat1 = 1.e20, so we need to change it where needed -!nc -- for PROJ_LATLON stdlon and truelat1 have different meanings -- -!nc -- stdlon --> loninc and truelat1 --> latinc -!JPH --- this latinc/loninc calculations are only valid for global domains - - if ( wrf%dom(id)%scm ) then -! JPH -- set to zero which should cause the map utils to return NaNs if called - latinc = 0.0_r8 - loninc = 0.0_r8 - else - latinc = 180.0_r8/wrf%dom(id)%sn - loninc = 360.0_r8/wrf%dom(id)%we - endif - - if(wrf%dom(id)%map_proj == map_latlon) then - truelat1 = latinc - stdlon = loninc - proj_code = PROJ_LATLON - elseif(wrf%dom(id)%map_proj == map_lambert) then - proj_code = PROJ_LC - elseif(wrf%dom(id)%map_proj == map_polar_stereo) then - proj_code = PROJ_PS - elseif(wrf%dom(id)%map_proj == map_mercator) then - proj_code = PROJ_MERC - elseif(wrf%dom(id)%map_proj == map_cyl) then - proj_code = PROJ_CYL - elseif(wrf%dom(id)%map_proj == map_cassini) then - proj_code = PROJ_CASSINI - else - call error_handler(E_ERR,'static_init_model', & - 'Map projection no supported.', source, revision, revdate) - endif - -!nc -- specified inputs to hopefully handle ALL map projections -- hopefully map_set will -! just ignore the inputs it doesn't need for its map projection of interest (?) -! -! NOTE:: We are NOT yet supporting the Gaussian grid or the Rotated Lat/Lon, so we -! are going to skip the entries: nlon, nlat, ixdim, jydim, stagger, phi, lambda -! -! + Gaussian grid uses nlat & nlon -! + Rotated Lat/Lon uses ixdim, jydim, stagger, phi, & lambda -! - call map_set( proj_code=proj_code, & - proj=wrf%dom(id)%proj, & - lat1=wrf%dom(id)%latitude(1,1), & - lon1=wrf%dom(id)%longitude(1,1), & - lat0=90.0_r8, & - lon0=0.0_r8, & - knowni=1.0_r8, & - knownj=1.0_r8, & - dx=wrf%dom(id)%dx, & - latinc=latinc, & - loninc=loninc, & - stdlon=stdlon, & - truelat1=truelat1, & - truelat2=truelat2 ) - -end subroutine setup_map_projection - -!-------------------------------------------- -!-------------------------------------------- - -subroutine fill_default_state_table(default_table) - -character(len=129), intent(out) :: default_table(num_state_table_columns,max_state_variables) - -integer :: row - -default_table = 'NULL' -row = 0 - -! fill default state variable table here. -row = row+1 -default_table(:,row) = (/ 'U ', & - 'QTY_U_WIND_COMPONENT', & - 'TYPE_U ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'V ', & - 'QTY_V_WIND_COMPONENT', & - 'TYPE_V ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'W ', & - 'QTY_VERTICAL_VELOCITY', & - 'TYPE_W ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'PH ', & - 'QTY_GEOPOTENTIAL_HEIGHT', & - 'TYPE_GZ ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'T ', & - 'QTY_POTENTIAL_TEMPERATURE', & - 'TYPE_T ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'MU ', & - 'QTY_PRESSURE', & - 'TYPE_MU ', & - 'UPDATE ', & - '999 ' /) - -row = row+1 -default_table(:,row) = (/ 'QVAPOR ', & - 'QTY_VAPOR_MIXING_RATIO', & - 'TYPE_QV ', & - 'UPDATE ', & - '999 ' /) - -return - -end subroutine fill_default_state_table - -!-------------------------------------------- -!-------------------------------------------- - -subroutine fill_dart_kinds_table(wrf_state_variables, in_state_vector) - -! for each row in the kinds table, tick them off in an array -! of all possible kinds. then do some simple error checks for -! kinds we know have interactions -- like both wind vectors are -! required to convert directions from projection to lat/lon, etc - -character(len=*), intent(in) :: wrf_state_variables(:, :) -logical, intent(inout) :: in_state_vector(:) - -integer :: row, i, nextkind - -in_state_vector = .false. - -row = size(wrf_state_variables, 2) - -! NEWVAR: for a simple new variable you do not need to do anything here. -! NEWVAR: but if a new kind has interactions - like if you have an obs -! NEWVAR: of this kind, you actually interpolate in multiple fields in -! NEWVAR: the state vector (e.g. wind needs both U and V), then you -! NEWVAR: might need to add some code here. -! NEWVAR: see each of part1, part 2, and part 3 below. - -! part 1: mark off all the kinds that the user specifies, plus the -! kinds that are related and can be interpolated from the given kind. - -do i = 1, row - - ! end of the list? - if (wrf_state_variables(2, i) == 'NULL') exit - - nextkind = get_index_for_quantity(trim(wrf_state_variables(2, i))) - select case(nextkind) - - ! wrf stores potential temperature (temperature perturbations around a - ! threshold) but we can interpolate sensible temperature from it - case (QTY_POTENTIAL_TEMPERATURE) - in_state_vector(QTY_TEMPERATURE) = .true. - in_state_vector(QTY_POTENTIAL_TEMPERATURE) = .true. - - ! we use vapor mixing ratio to compute specific humidity - case (QTY_VAPOR_MIXING_RATIO) - in_state_vector(QTY_VAPOR_MIXING_RATIO) = .true. - in_state_vector(QTY_SPECIFIC_HUMIDITY) = .true. - - ! unrecognized kind string in namelist. - ! 0 is actually QTY_STATE_VARIABLE and not supported here. - case (-1, 0) - write(errstring, *) 'unrecognized KIND string: ' // trim(wrf_state_variables(2, i)) - call error_handler(E_ERR, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) - - ! everything else, set it to be supported - case default - in_state_vector(nextkind) = .true. - - end select - - ! NOTE: PSFC can be labeled either QTY_PRESSURE or QTY_SURFACE_PRESSURE - ! in the namelist, but make sure however it is labeled that for now we - ! allow surface pressure interpolation. this may go away once we work out - ! QTY_FOO vs QTY_SURFACE_FOO - are they fundamentally different things - ! or should the decision be made based on a QTY_FOO and the vertical - ! location type -- if it is VERTISSURFACE, then you do the 2d calc in the - ! surface field, otherwise you do the full-up 3d interpolation. - if ( wrf_state_variables(1, i) == 'PSFC' ) then - in_state_vector(QTY_SURFACE_PRESSURE) = .true. - endif - -enddo - - -! part 2: if you specified one kind but the interpolation is going to -! require another, make sure the combinations make sense. i.e. if you -! have U wind, you also have to have V wind, etc. - -do i = 1, size(in_state_vector) - - select case(i) - - ! the vortex center computations require wind speeds and phb? - case (QTY_VORTEX_LAT, QTY_VORTEX_LON, QTY_VORTEX_PMIN, QTY_VORTEX_WMAX) - if ((.not. in_state_vector(QTY_U_WIND_COMPONENT)) .or. & - (.not. in_state_vector(QTY_V_WIND_COMPONENT)) .or. & - (.not. in_state_vector(QTY_TEMPERATURE)) .or. & - (.not. in_state_vector(QTY_VAPOR_MIXING_RATIO)) .or. & - (.not. in_state_vector(QTY_PRESSURE))) then - write(errstring, *) 'VORTEX kinds will require U,V,T,QVAPOR,MU in state vector' - ! FIXME: not fatal error, just informative at this point. - call error_handler(E_MSG, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) - endif - - ! if you have one wind component you have to have both - case (QTY_U_WIND_COMPONENT, QTY_V_WIND_COMPONENT) - if (in_state_vector(QTY_U_WIND_COMPONENT) .neqv. in_state_vector(QTY_V_WIND_COMPONENT)) then - write(errstring, *) 'WIND kinds will require both U,V in state vector' - ! FIXME: not fatal error, just informative at this point. - call error_handler(E_MSG, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) - endif - - ! by default anything else is fine - - end select -enddo - - -! part 3: fields you just have to have, always, and other exceptions -! and things that break the rules. -if (.not. in_state_vector(QTY_GEOPOTENTIAL_HEIGHT)) then - write(errstring, *) 'PH is always a required field' - call error_handler(E_ERR, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) -endif - -! FIXME: is this true? or is pressure always required, and surface -! pressure required only if you have any of the surface obs? -if ((.not. in_state_vector(QTY_PRESSURE)) .and. & - (.not. in_state_vector(QTY_SURFACE_PRESSURE))) then - write(errstring, *) 'One of MU or PSFC is a required field' - call error_handler(E_ERR, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) -endif - -! surface elevation and land mask are read outside the state vector mechanism, -! directly from the wrfinput template file, and does not vary from -! one ensemble member to another. -in_state_vector(QTY_SURFACE_ELEVATION) = .true. -in_state_vector(QTY_LANDMASK) = .true. - -! there is no field that directly maps to the vortex measurements. -! if you have all the fields it needs, allow them. -if (in_state_vector(QTY_U_WIND_COMPONENT) .and. & - in_state_vector(QTY_V_WIND_COMPONENT) .and. & - in_state_vector(QTY_TEMPERATURE) .and. & - in_state_vector(QTY_VAPOR_MIXING_RATIO) .and. & - in_state_vector(QTY_PRESSURE)) then ! ok to add vortex types - in_state_vector(QTY_VORTEX_LAT) = .true. - in_state_vector(QTY_VORTEX_LON) = .true. - in_state_vector(QTY_VORTEX_PMIN) = .true. - in_state_vector(QTY_VORTEX_WMAX) = .true. -endif - -! if you have geopotential height and pressure, you can compute -! a density value. -if (in_state_vector(QTY_GEOPOTENTIAL_HEIGHT) .and. & - in_state_vector(QTY_PRESSURE) ) in_state_vector(QTY_DENSITY) = .true. - - -! allow reflectivity to be asked for, because the obs_def has an alternative -! way to interpolate it even if it is not in the state vector. if this -! is not allowed it will error exit instead of returning with an invalid istatus -! to indicate to the caller that the interpolation failed. -! ditto for power weighted fall speed. -in_state_vector(QTY_RADAR_REFLECTIVITY) = .true. -in_state_vector(QTY_POWER_WEIGHTED_FALL_SPEED) = .true. - - - -! FIXME: i was going to suggest nuking this routine all together because it makes -! the default behavior be to exit with an error when requesting to interpolate an -! unknown kind, instead of simply returning with a failed interpolation and letting -! the calling code handle it. but it does turn out to be amazingly useful when -! trying to interpolate something you do expect to be there, so now i'm unsure what -! to suggest here. nsc 30jul09 - -end subroutine fill_dart_kinds_table - -!-------------------------------------------- -!-------------------------------------------- - -integer function get_number_of_wrf_variables(id, state_table, var_element_list, var_update_list) - -integer, intent(in) :: id -character(len=*), intent(in) :: state_table(num_state_table_columns,max_state_variables) -integer, intent(out), optional :: var_element_list(max_state_variables) -logical, intent(out), optional :: var_update_list(max_state_variables) - -integer :: ivar, num_vars -logical :: debug = .false. - -if ( present(var_element_list) ) var_element_list = -1 - -ivar = 1 -num_vars = 0 -do while ( trim(state_table(5,ivar)) /= 'NULL' ) - - if ( variable_is_on_domain(state_table(5,ivar),id) ) then - num_vars = num_vars + 1 - if ( present(var_element_list) ) var_element_list(num_vars) = ivar - - if (present(var_update_list)) then - if (state_table(4,ivar) == 'NO_COPY_BACK') then - var_update_list(num_vars) = .false. - else - var_update_list(num_vars) = .true. - endif - endif - - endif - - ivar = ivar + 1 - -enddo ! ivar - -if ( debug ) then - print*,'In function get_number_of_wrf_variables' - print*,'Found ',num_vars,' state variables for domain id ',id -endif - -get_number_of_wrf_variables = num_vars - -return - -end function get_number_of_wrf_variables - -!-------------------------------------------- -!-------------------------------------------- - -subroutine set_variable_bound_defaults(nbounds,lb,ub,instructions) - - integer, intent(in) :: nbounds - real(r8), dimension(nbounds), intent(out) :: lb, ub - character(len=10), dimension(nbounds), intent(out) :: instructions - - lb(:) = missing_r8 - ub(:) = missing_r8 - instructions(:) = 'NULL' - - return - -end subroutine set_variable_bound_defaults - -!-------------------------------------------- -!-------------------------------------------- - -subroutine get_variable_bounds(bounds_table,wrf_var_name,lb,ub,instructions) - -! matches WRF variable name in bounds table to input name, and assigns -! the bounds and instructions if they exist - - character(len=*), intent(in) :: bounds_table(num_bounds_table_columns,max_state_variables) - character(len=*), intent(in) :: wrf_var_name - real(r8), intent(out) :: lb,ub - character(len=10),intent(out) :: instructions - - character(len=50) :: wrf_varname_trim, bounds_varname_trim - character(len=50) :: bound_trim - integer :: ivar - logical :: debug = .false. - - wrf_varname_trim = '' - wrf_varname_trim = trim(wrf_var_name) - - ivar = 1 - do while ( trim(bounds_table(1,ivar)) /= 'NULL' ) - - bounds_varname_trim = trim(bounds_table(1,ivar)) - - if ( bounds_varname_trim == wrf_varname_trim ) then - - bound_trim = trim(bounds_table(2,ivar)) - if ( bound_trim /= 'NULL' ) then - read(bound_trim,'(d16.8)') lb - else - lb = missing_r8 - endif - - bound_trim = trim(bounds_table(3,ivar)) - if ( bound_trim /= 'NULL' ) then - read(bound_trim,'(d16.8)') ub - else - ub = missing_r8 - endif - - ! instructions are required - instructions = trim(bounds_table(4,ivar)) - - if ( instructions == 'NULL' ) then - call error_handler(E_ERR,'get_variable_bounds','instructions for bounds on '//wrf_varname_trim//' are required',& - source,revision,revdate) - endif - - endif - - ivar = ivar + 1 - - enddo !ivar - - if ( debug ) then - write(*,*) 'In get_variable_bounds assigned ',wrf_varname_trim - write(*,*) ' bounds ',lb,ub,instructions - endif - - return - -end subroutine get_variable_bounds - -!-------------------------------------------- -!-------------------------------------------- - -logical function variable_is_on_domain(domain_id_string, id) - -integer, intent(in) :: id -character(len=*), intent(in) :: domain_id_string - -integer :: domain_int, i - -variable_is_on_domain = .false. - - ! if '999' then counts all domains - if ( trim(domain_id_string) == '999' ) then - variable_is_on_domain = .true. - else - i = 1 - do while ( domain_id_string(i:i) /= ' ' ) - read(domain_id_string(i:i),'(i1)') domain_int - if ( domain_int == id ) variable_is_on_domain = .true. - i = i+1 - enddo - endif - -return - -end function variable_is_on_domain - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine get_variable_size_from_file(ncid,id,wrf_var_name,bt,bts,sn,sns, & - we,wes,stagger,var_size) - -!NOTE: only supports 2D and 3D variables (ignoring time dimension) - -! ncid: input, file handle -! id: input, domain index - -integer, intent(in) :: ncid, id -integer, intent(in) :: bt, bts, sn, sns, we, wes -character(len=*), intent(in) :: wrf_var_name -integer, intent(out) :: var_size(3) -character(len=129),intent(out) :: stagger - -logical, parameter :: debug = .false. -integer :: var_id, ndims, dimids(10) -integer :: idim - - stagger = '' - -! get variable ID - call nc_check( nf90_inq_varid(ncid, trim(wrf_var_name), var_id), & - 'get_variable_size_from_file', & - 'inq_varid '//wrf_var_name) - -! get number of dimensions and dimension IDs - call nc_check( nf90_inquire_variable(ncid, var_id,ndims=ndims, & - dimids=dimids), & - 'get_variable_size_from_file', & - 'inquire_variable '//wrf_var_name) - -! get dimension length, ignoring first dimension (time) - do idim = 1,ndims-1 - call nc_check( nf90_inquire_dimension(ncid, dimids(idim), & - len=var_size(idim)), & - 'get_variable_size_from_file', & - 'inquire_dimension '//wrf_var_name) - enddo - -! if a 2D variable fill the vertical dimension with 1 - if ( ndims < 4 ) var_size(ndims) = 1 - - if ( debug ) then - print*,'In get_variable_size_from_file got variable size ',var_size - endif - - -! get variable attribute stagger - call nc_check( nf90_get_att(ncid, var_id, 'stagger', stagger), & - 'get_variable_size_from_file', & - 'get_att '//wrf_var_name//' '//stagger) - - if ( debug ) then - print*,'In get_variable_size_from_file got stagger ',trim(stagger),' for variable ',trim(wrf_var_name) - endif - -return - -end subroutine get_variable_size_from_file - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine get_variable_metadata_from_file(ncid,id,wrf_var_name,description, & - coordinates,units) - -! ncid: input, file handle -! id: input, domain index - -integer, intent(in) :: ncid, id -character(len=*), intent(in) :: wrf_var_name -character(len=129), intent(out) :: description, coordinates, units - -logical, parameter :: debug = .false. -integer :: var_id - - call nc_check( nf90_inq_varid(ncid, trim(wrf_var_name), var_id), & - 'get_variable_metadata_from_file', & - 'inq_varid '//wrf_var_name) - - description = '' - call nc_check( nf90_get_att(ncid, var_id, 'description', description), & - 'get_variable_metadata_from_file', & - 'get_att '//wrf_var_name//' '//description) - - coordinates = '' - call nc_check( nf90_get_att(ncid, var_id, 'coordinates', coordinates), & - 'get_variable_metadata_from_file', & - 'get_att '//wrf_var_name//' '//coordinates) - - units = '' - call nc_check( nf90_get_att(ncid, var_id, 'units', units), & - 'get_variable_metadata_from_file', & - 'get_att '//wrf_var_name//' '//units) - -return - -end subroutine get_variable_metadata_from_file - -!-------------------------------------------- -!-------------------------------------------- -! Note get_dart_vector_index depends on this function -integer function get_type_ind_from_type_string(id, wrf_varname) - -! simply loop through the state variable table to get the index of the -! type for this domain. returns -1 if not found - - integer, intent(in) :: id - character(len=*), intent(in) :: wrf_varname - - integer :: ivar, my_index - logical :: debug = .false. - character(len=50) :: wrf_varname_trim, wrf_state_var_trim - - get_type_ind_from_type_string = -1 - - do ivar = 1,wrf%dom(id)%number_of_wrf_variables - - my_index = wrf%dom(id)%var_index_list(ivar) - - wrf_state_var_trim = trim(wrf_state_variables(1,my_index)) - wrf_varname_trim = trim(wrf_varname) - - if ( wrf_state_var_trim == wrf_varname_trim ) then - - get_type_ind_from_type_string = ivar - - endif - - enddo ! ivar - - if ( debug ) write(*,*) 'get_type_from_ind ',trim(wrf_varname),' ',get_type_ind_from_type_string - - return - -end function get_type_ind_from_type_string - -!------------------------------------------------------------- - -subroutine trans_2Dto1D( a1d, a2d, nx, ny ) - -integer, intent(in) :: nx,ny -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a2d(nx,ny) - -!--- - -integer :: i,j,m - -i=size(a2d,1) -j=size(a2d,2) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - m < nx*ny) then - write(errstring,*)'nx, ny, not compatible ',i,j,nx,ny - call error_handler(E_ERR,'trans_2d',errstring,source,revision,revdate) -endif - -do j=1,ny - do i=1,nx - a1d(i + nx*(j-1)) = a2d(i,j) - enddo -enddo - -end subroutine trans_2Dto1D - -!------------------------------------------------------------- - -subroutine trans_3Dto1D( a1d, a3d, nx, ny, nz ) - -integer, intent(in) :: nx,ny,nz -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a3d(:,:,:) - -!--- - -integer :: i,j,k,m - -i=size(a3d,1) -j=size(a3d,2) -k=size(a3d,3) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - k /= nz .or. & - m < nx*ny*nz) then - write(errstring,*)'nx, ny, nz, not compatible ',i,j,k,nx,ny,nz,m - call error_handler(E_ERR,'trans_3d',errstring,source,revision,revdate) -endif - -do k=1,nz - do j=1,ny - do i=1,nx - a1d(i + nx*(j-1) + nx*ny*(k-1) ) = a3d(i,j,k) - enddo - enddo -enddo - -end subroutine trans_3Dto1D - -!------------------------------------------------------------- - -subroutine trans_1Dto2D( a1d, a2d, nx, ny ) - -integer, intent(in) :: nx,ny -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a2d(nx,ny) - -!--- - -integer :: i,j,m - -i=size(a2d,1) -j=size(a2d,2) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - m < nx*ny) then - write(errstring,*)'nx, ny, not compatible ',i,j,nx,ny - call error_handler(E_ERR,'trans_2d',errstring,source,revision,revdate) -endif - -do j=1,ny - do i=1,nx - a2d(i,j) = a1d(i + nx*(j-1)) - enddo -enddo - -end subroutine trans_1Dto2D - -!------------------------------------------------------------- - -subroutine trans_1Dto3D( a1d, a3d, nx, ny, nz ) - -integer, intent(in) :: nx,ny,nz -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a3d(:,:,:) - -!--- - -integer :: i,j,k,m - -i=size(a3d,1) -j=size(a3d,2) -k=size(a3d,3) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - k /= nz .or. & - m < nx*ny*nz) then - write(errstring,*)'nx, ny, nz, not compatible ',i,j,k,nx,ny,nz,m - call error_handler(E_ERR,'trans_3d',errstring,source,revision,revdate) -endif - -do k=1,nz - do j=1,ny - do i=1,nx - a3d(i,j,k) = a1d(i + nx*(j-1) + nx*ny*(k-1) ) - enddo - enddo -enddo - -end subroutine trans_1Dto3D - -!---------------------------------------------------------------------- - -subroutine get_wrf_date (tstring, year, month, day, hour, minute, second) - -!-------------------------------------------------------- -! Returns integers taken from tstring -! It is assumed that the tstring char array is as YYYY-MM-DD_hh:mm:ss - -integer, intent(out) :: year, month, day, hour, minute, second -character(len=19), intent(in) :: tstring - -read(tstring( 1: 4),'(i4)') year -read(tstring( 6: 7),'(i2)') month -read(tstring( 9:10),'(i2)') day -read(tstring(12:13),'(i2)') hour -read(tstring(15:16),'(i2)') minute -read(tstring(18:19),'(i2)') second - -return - -end subroutine get_wrf_date - -!---------------------------------------------------------------------- - -subroutine set_wrf_date (tstring, year, month, day, hour, minute, second) - -!-------------------------------------------------------- -! Returns integers taken from tstring -! It is assumed that the tstring char array is as YYYY-MM-DD_hh:mm:ss - -integer, intent(in) :: year, month, day, hour, minute, second -character(len=19), intent(out) :: tstring - -character(len=4) :: ch_year -character(len=2) :: ch_month, ch_day, ch_hour, ch_minute, ch_second - -write(ch_year,'(i4)') year -write(ch_month,'(i2)') month -if (ch_month(1:1) == " ") ch_month(1:1) = "0" -write(ch_day,'(i2)') day -if (ch_day(1:1) == " ") ch_day(1:1) = "0" -write(ch_hour,'(i2)') hour -if (ch_hour(1:1) == " ") ch_hour(1:1) = "0" -write(ch_minute,'(i2)') minute -if (ch_minute(1:1) == " ") ch_minute(1:1) = "0" -write(ch_second,'(i2)') second -if (ch_second(1:1) == " ") ch_second(1:1) = "0" -tstring(1:4) = ch_year -tstring(5:5) = "-" -tstring(6:7) = ch_month -tstring(8:8) = "-" -tstring(9:10) = ch_day -tstring(11:11) = "_" -tstring(12:13) = ch_hour -tstring(14:14) = ":" -tstring(15:16) = ch_minute -tstring(17:17) = ":" -tstring(18:19) = ch_second - -end subroutine set_wrf_date - - -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -! -! height_diff_check - function that determines whether a pair of heights -! in meters are closer than the given tolerance. -! returns .TRUE. if closer or equal to limit -! -! max_diff_meters - maximum difference between 2 elevations (m) -! height1 - first height (m) -! height2 - second height (m) -! -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -function height_diff_check(max_diff_meters, height1, height2) - real(r8), intent(in) :: max_diff_meters, height1, height2 - logical :: height_diff_check - -height_diff_check = .true. - -if ( abs(height1 - height2) > max_diff_meters ) height_diff_check = .false. - -end function height_diff_check - -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -! -! compute_geometric_ht - subroutine the converts geopotential height -! into geometric height. -! -! geopot -- input real value geopotential height -! lat -- latitude of input (longitude not needed) -! -! output is real value geometric height -! -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -function compute_geometric_height(geopot, lat) - real(r8), intent(in) :: geopot - real(r8), intent(in) :: lat - real(r8) :: compute_geometric_height - - -! -----------------------------------------------------------------------*/ - real(digits12) :: pi2, latr - real(digits12) :: semi_major_axis, semi_minor_axis, grav_polar, grav_equator - real(digits12) :: earth_omega, grav_constant, flattening, somigliana - real(digits12) :: grav_ratio, sin2, termg, termr, grav, eccentricity - -! Parameters below from WGS-84 model software inside GPS receivers. - parameter(semi_major_axis = 6378.1370d3) ! (m) - parameter(semi_minor_axis = 6356.7523142d3) ! (m) - parameter(grav_polar = 9.8321849378) ! (m/s2) - parameter(grav_equator = 9.7803253359) ! (m/s2) - parameter(earth_omega = 7.292115d-5) ! (rad/s) - parameter(grav_constant = 3.986004418d14) ! (m3/s2) - parameter(grav = 9.80665d0) ! (m/s2) WMO std g at 45 deg lat - parameter(eccentricity = 0.081819d0) ! unitless - parameter(pi2 = 3.14159265358979d0/180.d0) - -! Derived geophysical constants - parameter(flattening = (semi_major_axis-semi_minor_axis) / semi_major_axis) - - parameter(somigliana = (semi_minor_axis/semi_major_axis)*(grav_polar/grav_equator)-1.d0) - - parameter(grav_ratio = (earth_omega*earth_omega * & - semi_major_axis*semi_major_axis * semi_minor_axis)/grav_constant) - - - latr = lat * (pi2) ! in radians - sin2 = sin(latr) * sin(latr) - termg = grav_equator * ( (1.d0+somigliana*sin2) / & - sqrt(1.d0-eccentricity*eccentricity*sin2) ) - termr = semi_major_axis / (1.d0 + flattening + grav_ratio - 2.d0*flattening*sin2) - - ! geometric height conversion. in the special case that you're trying to - ! reproduce an old result and want to use the original geopotential height - ! instead of converting it, set 'use_geopotential_height' to .true. at the - ! top of this file. Otherwise the normal conversion to geometric height is - ! what you want here; we agreed that height was going to be in terms of - ! geometric height when we did the conversions. - - if (use_geopotential_height) then - compute_geometric_height = geopot - else - compute_geometric_height = (termr*geopot) / ( (termg/grav) * termr - geopot ) - endif - -end function compute_geometric_height - -!-------------------------------------------------------------------------- -!> Perform interpolation across the ensemble -subroutine simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_handle) - -integer, intent(in) :: ens_size -type(ensemble_type), intent(in) :: state_handle -type(wrf_dom), intent(in) :: wrf -integer, intent(in) :: id -integer, intent(in) :: i,j -integer, intent(in) :: uniquek(:) -integer, intent(in) :: k(ens_size) -integer, intent(in) :: obs_kind -real(r8), intent(in) :: dxm, dx, dy, dym -real(r8), intent(out) :: fld(2, ens_size) - -integer(i8) :: ill, iul, ilr, iur -integer :: rc -integer, dimension(2) :: ll, ul, lr, ur -integer :: uk, e -logical :: in_state -integer :: wrf_type - -real(r8), dimension(ens_size) ::x_ill, x_iul, x_ilr, x_iur - -! Confirm that the obs kind is in the DART state vector and return the wrf_type -!>@todo should boundsCheck always be temperatue type? This is what it is in the original code -call obs_kind_in_state_vector(in_state, wrf_type, obs_kind, id) - -if ( in_state ) then - - ! An observation could be on different levels for each ensemble member. - ! But, you don't want to do ens_size*communication, so just do it for the levels you need. - UNIQUEK_LOOP: do uk = 1, size(uniquek) - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf_type ) .and.& - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf_type ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf_type ) ) then - - call getCorners(i, j, id, wrf_type, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners QNSNOW rc = ', rc - - ! Interpolation for QNSNOW field at level k - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk), domain_id(id), wrf_type) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk), domain_id(id), wrf_type) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk), domain_id(id), wrf_type) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk), domain_id(id), wrf_type) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(1,e) = dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - - ! Interpolation for QNSNOW field at level k+1 - ill = get_dart_vector_index(ll(1), ll(2), uniquek(uk)+1, domain_id(id), wrf_type) - iul = get_dart_vector_index(ul(1), ul(2), uniquek(uk)+1, domain_id(id), wrf_type) - ilr = get_dart_vector_index(lr(1), lr(2), uniquek(uk)+1, domain_id(id), wrf_type) - iur = get_dart_vector_index(ur(1), ur(2), uniquek(uk)+1, domain_id(id), wrf_type) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_ilr = get_state(ilr, state_handle) - x_iur = get_state(iur, state_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(2, e) = dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - - endif - - enddo UNIQUEK_LOOP - -else ! not in state - - call error_handler(E_MSG, 'simple_interp_distrib', & - 'obs_kind "'//trim(get_name_for_quantity(obs_kind))//'" is not in state vector', & - source, revision, revdate) - fld(2, ens_size) = missing_r8 - return - -endif - -end subroutine simple_interp_distrib - -!------------------------------------------------------------------------ -!> interpolation for surface fields -subroutine surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf_surf_type, dxm, dx, dy, dym, ens_size, state_handle) - -integer, intent(in) :: ens_size -type(ensemble_type), intent(in) :: state_handle -type(wrf_dom), intent(in) :: wrf -integer, intent(in) :: id -integer, intent(in) :: obs_kind -integer, intent(in) :: wrf_surf_type !< this has to be passed in is beccause obs_kind does not give a unique wrf_type ( could check obs_kind then if surf var?) -integer, intent(in) :: i,j -real(r8), intent(in) :: dxm, dx, dy, dym -real(r8), intent(out) :: fld(2, ens_size) - -integer(i8) :: ill, iul, ilr, iur -integer :: rc -integer, dimension(2) :: ll, ul, lr, ur - -logical :: in_state -integer :: wrf_type - -real(r8), dimension(ens_size) ::x_ill, x_iul, x_ilr, x_iur - -fld(:,:) = missing_r8 - -! Find the wrf_type from the obs kind -! check for in state is performed before surface_interp_distrib is called -!>@todo should boundsCheck always be temperatue type? This is what it is in the original code -call obs_kind_in_state_vector(in_state, wrf_type, obs_kind, id) - -! Check to make sure retrieved integer gridpoints are in valid range -if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf_type ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf_type ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf_type, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners T2 rc = ', rc - - ! Interpolation for the T2 field - ill = get_dart_vector_index(ll(1), ll(2), 1, domain_id(id), wrf_surf_type) - iul = get_dart_vector_index(ul(1), ul(2), 1, domain_id(id), wrf_surf_type) - ilr = get_dart_vector_index(lr(1), lr(2), 1, domain_id(id), wrf_surf_type) - iur = get_dart_vector_index(ur(1), ur(2), 1, domain_id(id), wrf_surf_type) - - x_ill = get_state(ill, state_handle) - x_iul = get_state(iul, state_handle) - x_iur = get_state(iur, state_handle) - x_ilr = get_state(ilr, state_handle) - - fld(1, :) = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - -endif - -end subroutine surface_interp_distrib - -!-------------------------------------------------------------------------- -!> test if an obs kind is in the state vector and set wrf_type -subroutine obs_kind_in_state_vector(part_of_state_vector, wrf_type, obs_kind, id) - -logical, intent(out) :: part_of_state_vector -integer, intent(out) :: wrf_type !< WRF -integer, intent(in) :: obs_kind !< DART -integer, intent(in) :: id - - -part_of_state_vector = .false. ! assume not in state vector - - -if ( ( obs_kind == QTY_VERTICAL_VELOCITY) .and. ( wrf%dom(id)%type_w >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_w -else if( ( obs_kind == QTY_RAINWATER_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qr >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qr -else if( ( obs_kind == QTY_GRAUPEL_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qg >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qg -else if( ( obs_kind == QTY_HAIL_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qh >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qh -else if( ( obs_kind == QTY_SNOW_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qs >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qs -else if( ( obs_kind == QTY_CLOUD_ICE ) .and. ( wrf%dom(id)%type_qi >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qi -else if( ( obs_kind == QTY_CLOUD_LIQUID_WATER ) .and. ( wrf%dom(id)%type_qc >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qc -else if( ( obs_kind == QTY_DROPLET_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qndrp >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qndrp -else if( ( obs_kind == QTY_ICE_NUMBER_CONCENTRATION ) .and. ( wrf%dom(id)%type_qnice >= 0 ) )then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnice -else if( ( obs_kind == QTY_SNOW_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qnsnow >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnsnow -else if( ( obs_kind == QTY_RAIN_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qnrain >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnrain -else if( ( obs_kind == QTY_GRAUPEL_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qngraupel >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qngraupel -else if( ( obs_kind == QTY_HAIL_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qnhail >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnhail -else if( ( obs_kind == QTY_CONDENSATIONAL_HEATING ) .and. ( wrf%dom(id)%type_hdiab >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_hdiab -else if( ( obs_kind == QTY_POWER_WEIGHTED_FALL_SPEED ) .and. ( wrf%dom(id)%type_fall_spd >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_fall_spd -else if( ( obs_kind == QTY_RADAR_REFLECTIVITY ) .and. ( wrf%dom(id)%type_refl >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_refl -else if( ( obs_kind == QTY_DIFFERENTIAL_REFLECTIVITY ) .and. ( wrf%dom(id)%type_dref >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_dref -else if( ( obs_kind == QTY_SPECIFIC_DIFFERENTIAL_PHASE ) .and. ( wrf%dom(id)%type_spdp >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_spdp -else if ( ( obs_kind == QTY_VAPOR_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qv >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qv -else if ( ( obs_kind == QTY_TEMPERATURE ) .and. ( wrf%dom(id)%type_t >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_t -else if ( ( obs_kind == QTY_POTENTIAL_TEMPERATURE ) .and. ( wrf%dom(id)%type_t >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_t -else if ( ( obs_kind == QTY_SKIN_TEMPERATURE ) .and. ( wrf%dom(id)%type_tsk >= 0 ) )then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_tsk -else - call error_handler(E_MSG, 'obs_kind_in_state_vector', & - 'obs_kind "'//trim(get_name_for_quantity(obs_kind))//'" is not in state vector', & - source, revision, revdate) - part_of_state_vector = .false. - wrf_type = -1 -endif - -end subroutine obs_kind_in_state_vector - -!-------------------------------------------------------------------- -!> pass the vertical localization coordinate to assim_tools_mod -function query_vert_localization_coord() - -integer :: query_vert_localization_coord - -query_vert_localization_coord = vert_localization_coord - -end function query_vert_localization_coord - -!-------------------------------------------------------------------- -!> read the time from the input file -function read_model_time(filename) - -character(len=256), intent(in) :: filename -integer :: year, month, day, hour, minute, second -integer :: ret !< netcdf return code -integer :: ndims, dimids(2), ivtype, ncid, var_id -character(len=80) :: varname -character(len=19) :: timestring -integer :: i, idims(2) - -type(time_type) :: read_model_time - - -call nc_check( nf90_open(filename, NF90_NOWRITE, ncid), & - 'opening', filename ) - -call nc_check( nf90_inq_varid(ncid, "Times", var_id), 'read_model_time', & - 'inq_varid Times' ) -call nc_check( nf90_inquire_variable(ncid, var_id, varname, xtype=ivtype, & - ndims=ndims, dimids=dimids), 'read_model_time', & - 'inquire_variable Times' ) - -do i=1,ndims ! isnt this just 1? - call nc_check( nf90_inquire_dimension(ncid, dimids(i), & - len=idims(i)),'read_model_time','inquire_dimensions Times' ) -enddo - -call nc_check( nf90_get_var(ncid, var_id, timestring, & - start = (/ 1, idims(2) /)), 'read_model_time','get_var Times' ) - -call get_wrf_date(timestring, year, month, day, hour, minute, second) -read_model_time = set_date(year, month, day, hour, minute, second) - - -call nc_check( nf90_close(ncid) , 'closing', filename) - -end function read_model_time - -!-------------------------------------------------------------------- -!> write the time from the input file -subroutine write_model_time(ncid, dart_time) - -use typeSizes -use netcdf - -integer, intent(in) :: ncid -type(time_type), intent(in) :: dart_time - -integer :: dim_ids(2), var_id, ret -integer :: year, month, day, hour, minute, second -character(len=19) :: timestring - -call get_date(dart_time, year, month, day, hour, minute, second) -call set_wrf_date(timestring, year, month, day, hour, minute, second) - -call nc_check(nf90_Redef(ncid),"redef") - -! Define Times variable if it does not exist -ret = nf90_inq_varid(ncid, "Times", var_id) -if (ret /= NF90_NOERR) then - - ! check to see if there is a time and date_str_length - ret = nf90_inq_dimid(ncid, "Time", dim_ids(2)) - ! if Time dimension does not exist create it - if (ret /= NF90_NOERR) then - call nc_check(nf90_def_dim(ncid, "Time", nf90_unlimited, dim_ids(2)), & - "write_model_time def_var dimension Time") - endif - - ret = nf90_inq_dimid(ncid, "DateStrLen", dim_ids(1)) - if (ret /= NF90_NOERR) then - ! if DateStrLen dimension does not exist create it. - call nc_check(nf90_def_dim(ncid, "DateStrLen", len(timestring), dim_ids(1)), & - "write_model_time def_var dimension dateStrLength") - endif - - ! use id's to set Times(Time, DateStrLen) - call nc_check(nf90_def_var(ncid, name="Times", xtype=nf90_char, & - dimids=dim_ids, varid=var_id), "write_model_time def_var Times") -endif - -call nc_check(nf90_Enddef(ncid),"Enddef") - -call nc_check( nf90_put_var(ncid, var_id, timestring), & - 'write_model_time', 'put_var Times' ) - -end subroutine write_model_time - -!-------------------------------------------------------------------- -!> This returns the vertical coordinate of an observation in the -!> requested vertical localization coordinate. -!> Aim: to have only the process who owns the observation do this calulation, -!> rather than all processeses doing the same calculation in get_close_obs -!> I don't know whether this is a good idea. - -subroutine convert_base_obs_location(obs_loc, state_handle, vert_coord, istatus) - -type(location_type), intent(inout) :: obs_loc -type(ensemble_type), intent(in) :: state_handle -real(r8), intent(out) :: vert_coord -integer, intent(out) :: istatus - -real(r8), dimension(3) :: base_array -!>@todo Should check for identity obs -integer :: base_obs_kind -integer :: base_which ! vertical coorardiate -integer :: istatus_v - -base_obs_kind = 1 ! dummy for now, should check for identity obs - -!base_which = nint(query_location(observation)) - -if (base_which /= wrf%dom(1)%localization_coord) then - call vert_convert(state_handle, obs_loc, base_obs_kind, istatus_v) -endif - -istatus = istatus_v - -base_array = get_location(obs_loc) -vert_coord = base_array(3) - -!>@todo set location so you don't redo this calculation in get_close_obs NOPE they are two different structures !obs_loc = set_location(base_array(1), base_array(2), base_array(3), wrf%dom(1)%localization_coord ) - -end subroutine convert_base_obs_location - -end module model_mod - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/models/wrf/model_mod_vorticity.f90 b/models/wrf/model_mod_vorticity.f90 deleted file mode 100644 index 128b793fd6..0000000000 --- a/models/wrf/model_mod_vorticity.f90 +++ /dev/null @@ -1,9116 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -!> @mainpage Remote Memory Access version of DART code. -!> -!> Forward operator, vertical conversion -!> -!> WRF -!> -!> For distributed phb array see distributed_phb_model_mod.f90 -!> \todo To do list -!> @author dart@ucar.edu -!> -!> \subpage test -!> -!> \page test - -!> WRF model mod -module model_mod - - -! Assimilation interface for WRF model -!> \defgroup wrf model_mod -!> Model mod -!> -!> Model mod for WRF -!> @{ -!----------------------------------------------------------------------- -! -! interface for WRF -! -!----------------------------------------------------------------------- -!---------------- m o d u l e i n f o r m a t i o n ------------------ -!----------------------------------------------------------------------- - -use types_mod, only : r8, deg2rad, missing_r8, ps0, earth_radius, & - gas_constant, gas_constant_v, gravity, pi, & - digits12 - -use time_manager_mod, only : time_type, set_time, set_calendar_type, GREGORIAN,& - set_date - -use location_mod, only : location_type, get_location, set_location, & - horiz_dist_only, & - LocationDims, LocationName, LocationLName, & - query_location, vert_is_undef, vert_is_surface, & - vert_is_level, vert_is_pressure, vert_is_height, & - vert_is_scale_height, VERTISUNDEF, VERTISSURFACE, & - VERTISLEVEL, VERTISPRESSURE, VERTISHEIGHT, & - VERTISSCALEHEIGHT, & - get_close_type, get_dist, get_close_maxdist_init, & - get_close_obs_init, loc_get_close_obs => get_close_obs, & - get_vert, set_vert, set_which_vert - -use utilities_mod, only : file_exist, open_file, close_file, & - register_module, error_handler, E_ERR, E_WARN, & - E_MSG, nmlfileunit, do_output, nc_check, & - find_namelist_in_file, check_namelist_read, & - find_textfile_dims, file_to_text, & - do_nml_file, do_nml_term - -use mpi_utilities_mod, only : my_task_id - -use random_seq_mod, only : random_seq_type, init_random_seq, random_gaussian - -use obs_kind_mod, only : QTY_U_WIND_COMPONENT, QTY_V_WIND_COMPONENT, & - QTY_SURFACE_PRESSURE, QTY_TEMPERATURE, & - QTY_SPECIFIC_HUMIDITY, QTY_SURFACE_ELEVATION, & - QTY_PRESSURE, QTY_VERTICAL_VELOCITY, & - QTY_DENSITY, QTY_FLASH_RATE_2D, & - QTY_RAINWATER_MIXING_RATIO, QTY_HAIL_MIXING_RATIO, & - QTY_GRAUPEL_MIXING_RATIO, QTY_SNOW_MIXING_RATIO, & - QTY_CLOUD_LIQUID_WATER, QTY_CLOUD_ICE, & - QTY_CONDENSATIONAL_HEATING, QTY_VAPOR_MIXING_RATIO, & - QTY_ICE_NUMBER_CONCENTRATION, QTY_GEOPOTENTIAL_HEIGHT, & - QTY_POTENTIAL_TEMPERATURE, QTY_SOIL_MOISTURE, & - QTY_DROPLET_NUMBER_CONCENTR, QTY_SNOW_NUMBER_CONCENTR, & - QTY_RAIN_NUMBER_CONCENTR, QTY_GRAUPEL_NUMBER_CONCENTR, & - QTY_HAIL_NUMBER_CONCENTR, QTY_HAIL_VOLUME, & - QTY_GRAUPEL_VOLUME, QTY_DIFFERENTIAL_REFLECTIVITY, & - QTY_RADAR_REFLECTIVITY, QTY_POWER_WEIGHTED_FALL_SPEED, & - QTY_SPECIFIC_DIFFERENTIAL_PHASE, & - QTY_VORTEX_LAT, QTY_VORTEX_LON, & - QTY_VORTEX_PMIN, QTY_VORTEX_WMAX, & - QTY_SKIN_TEMPERATURE, QTY_LANDMASK, & - get_index_for_quantity, get_num_quantities, & - get_name_for_quantity - -!HK should model_mod know about the number of copies? -use ensemble_manager_mod, only : ensemble_type, map_pe_to_task, get_var_owner_index, & - copies_in_window - -use sort_mod, only : sort - -use distributed_state_mod - -! FIXME: -! the kinds QTY_CLOUD_LIQUID_WATER should be QTY_CLOUDWATER_MIXING_RATIO, -! and kind QTY_CLOUD_ICE should be QTY_ICE_MIXING_RATIO, but for backwards -! compatibility with other models, they remain as is for now. at the next -! major dart release, the names will be made consistent. -! ditto QTY_ICE_NUMBER_CONCENTRATION, which should be QTY_ICE_NUMBER_CONCENTR -! to be consistent with the other concentration names. - -!nc -- module_map_utils split the declarations of PROJ_* into a separate module called -!nc -- misc_definitions_module -use map_utils, only : proj_info, map_init, map_set, latlon_to_ij, & - ij_to_latlon, gridwind_to_truewind - -use misc_definitions_module, only : PROJ_LATLON, PROJ_MERC, PROJ_LC, PROJ_PS, PROJ_CASSINI, & - PROJ_CYL - -use netcdf -use typesizes - -implicit none -private - - -!----- -! DART requires 16 specific public interfaces from model_mod.f90 -- Note -! that the last four are simply "stubs" since WRF currently requires use -! of system called shell scripts to advance the model. - -public :: get_model_size, & - get_state_meta_data_distrib, & - get_model_time_step, & - static_init_model, & - pert_model_state, & - nc_write_model_atts, & - nc_write_model_vars, & - get_close_obs_distrib, & - ens_mean_for_model, & - get_close_maxdist_init, & - get_close_obs_init, & - model_interpolate_distrib, & - vert_convert_distrib, & - query_vert_localization_coord, & - variables_domains, & - fill_variable_list, & - get_vert, set_vert, set_which_vert, & - info_file_name, construct_file_name, & - get_model_time - -! public stubs -public :: adv_1step, & - end_model, & - init_time, & - init_conditions - -!----- -! Here is the appropriate place for other users to make additional routines -! contained within model_mod available for public use: -public :: get_number_domains, & - get_wrf_static_data, & - model_pressure_distrib, & - model_height_distrib, & - pres_to_zk, & - height_to_zk, & - get_domain_info, & - get_wrf_state_variables, & - fill_default_state_table, & - read_wrf_dimensions, & - get_number_of_wrf_variables, & - get_variable_bounds, & - set_variable_bound_defaults, & - get_variable_size_from_file, & - trans_3Dto1D, trans_1Dto3D, & - trans_2Dto1D, trans_1Dto2D, & - get_wrf_date, set_wrf_date, & - height_diff_check - -! public parameters -public :: max_state_variables, & - num_state_table_columns, & - num_bounds_table_columns - -! types -public :: wrf_dom, wrf_static_data_for_dart - -! Interfaces for array and single value versions of subroutines/functions -! This is because the forward operator works on the whole ensemble, and the -! vertical conversion only uses the mean copy. - -interface get_model_pressure_profile_distrib - module procedure get_model_pressure_profile_distrib_fwd, get_model_pressure_profile_distrib_mean -end interface - -interface model_pressure_t_distrib - module procedure model_pressure_t_distrib_fwd, model_pressure_t_distrib_mean -end interface - -interface model_rho_t_distrib - module procedure model_rho_t_distrib_fwd, model_rho_t_distrib_mean -end interface - -interface get_model_height_profile_distrib - module procedure get_model_height_profile_distrib_fwd, get_model_height_profile_distrib_mean -end interface - -! HK ? interp_4pressure interface needed? - -!----------------------------------------------------------------------- -! version controlled file description for error handling, do not edit -character(len=256), parameter :: source = & - "$URL$" -character(len=32 ), parameter :: revision = "$Revision$" -character(len=128), parameter :: revdate = "$Date$" - -! miscellaneous -integer, parameter :: max_state_variables = 100 -integer, parameter :: num_state_table_columns = 5 -integer, parameter :: num_bounds_table_columns = 4 - -!----------------------------------------------------------------------- -! Model namelist parameters with default values. -! -! center_search_half_length: half length (in meter) of the searching box to locate -! minimum pressure at a grid point -! center_spline_scale: coarse grid to spline interp. fine grid ratio -!----------------------------------------------------------------------- - -logical :: output_state_vector = .false. ! output prognostic variables -logical :: default_state_variables = .true. ! use default state list? -character(len=129) :: wrf_state_variables(num_state_table_columns,max_state_variables) = 'NULL' -character(len=129) :: wrf_state_bounds(num_bounds_table_columns,max_state_variables) = 'NULL' -integer :: num_domains = 1 -integer :: calendar_type = GREGORIAN -integer :: assimilation_period_seconds = 21600 -! Max height a surface obs can be away from the actual model surface -! and still be accepted (in meters) -real (kind=r8) :: sfc_elev_max_diff = -1.0_r8 ! could be something like 200.0_r8 -real (kind=r8) :: center_search_half_length = 500000.0_r8 -real(r8) :: circulation_pres_level = 80000.0_r8 -real(r8) :: circulation_radius = 108000.0_r8 -integer :: center_spline_grid_scale = 10 -integer :: vert_localization_coord = VERTISHEIGHT -! Allow observations above the surface but below the lowest sigma level. -logical :: allow_obs_below_vol = .false. -! Do the interpolation of pressure values only after taking the log (.true.) -! vs doing a linear interpolation directly in pressure units (.false.) -logical :: log_vert_interp = .true. -logical :: log_horz_interpM = .false. -logical :: log_horz_interpQ = .false. -!nc -- we are adding these to the model.nml until they appear in the NetCDF files -logical :: polar = .false. ! wrap over the poles -logical :: periodic_x = .false. ! wrap in longitude or x -logical :: periodic_y = .false. ! used for single column model, wrap in y -!JPH -- single column model flag -logical :: scm = .false. ! using the single column model - -! obsolete items; ignored by this code. -! non-backwards-compatible change. should be removed, -! but see note below about namelist. -integer :: num_moist_vars -logical :: surf_obs, soil_data, h_diab - -! adv_mod_command moved to dart_to_wrf namelist; ignored here. -character(len = 72) :: adv_mod_command = '' - -! num_moist_vars, surf_obs, soil_data, h_diab, and adv_mod_command -! are IGNORED no matter what their settings in the namelist are. -! they are obsolete, but removing them here will cause a fatal error -! until users remove them from their input.nml files as well. -namelist /model_nml/ output_state_vector, num_moist_vars, & - num_domains, calendar_type, surf_obs, soil_data, h_diab, & - default_state_variables, wrf_state_variables, & - wrf_state_bounds, sfc_elev_max_diff, & - adv_mod_command, assimilation_period_seconds, & - allow_obs_below_vol, vert_localization_coord, & - center_search_half_length, center_spline_grid_scale, & - circulation_pres_level, circulation_radius, polar, & - periodic_x, periodic_y, scm - -! if you need to check backwards compatibility, set this to .true. -! otherwise, leave it as false to use the more correct geometric height -logical :: use_geopotential_height = .false. - -character(len = 20) :: wrf_nml_file = 'namelist.input' -logical :: have_wrf_nml_file = .false. -integer :: num_obs_kinds = 0 -logical, allocatable :: in_state_vector(:) - -!----------------------------------------------------------------------- - -! Private definition of domain map projection use by WRF - -!nc -- added in CASSINI and CYL according to module_map_utils convention -!JPH -- change variable name from map_sphere to more specific map_latlon -integer, parameter :: map_latlon = 0, map_lambert = 1, map_polar_stereo = 2, map_mercator = 3 -integer, parameter :: map_cassini = 6, map_cyl = 5 - -! Private definition of model variable types - -real (kind=r8), PARAMETER :: kappa = 2.0_r8/7.0_r8 ! gas_constant / cp -real (kind=r8), PARAMETER :: ts0 = 300.0_r8 ! Base potential temperature for all levels. - -!---- private data ---- - -! Got rid of surf_var as global private variable for model_mod and just defined it locally -! within model_interpolate - -TYPE wrf_static_data_for_dart - - integer :: bt, bts, sn, sns, we, wes, sls - real(r8) :: dx, dy, dt, p_top - integer :: map_proj - real(r8) :: cen_lat,cen_lon - type(proj_info) :: proj - - ! Boundary conditions -- hopefully one day these will be in the global attributes of the - ! input NetCDF file ("periodic_x" and "polar" are namelist items in the &bdy_control - ! section of a standard WRF "namelist.input" file), but for now we have included them - ! in the "model_nml" group of DART's own "input.nml". Above, their default values are - ! both set to .true. (indicating a global domain). - logical :: periodic_x - logical :: periodic_y - logical :: polar - logical :: scm - - integer :: domain_size - integer :: localization_coord - real(r8), dimension(:), pointer :: znu, dn, dnw, zs, znw - real(r8), dimension(:,:), pointer :: mub, hgt - real(r8), dimension(:,:), pointer :: latitude, latitude_u, latitude_v - real(r8), dimension(:,:), pointer :: longitude, longitude_u, longitude_v - real(r8), dimension(:,:,:), pointer :: phb - - ! NEWVAR: Currently you have to add a new type here if you want to use - ! NEWVAR: a WRF variable which is not one of these types. This will go - ! NEWVAR: away eventually, we hope. Search for NEWVAR for other places - ! NEWVAR: the code has to change. - - ! JPH local variables to hold type indices - integer :: type_u, type_v, type_w, type_t, type_qv, type_qr, type_hdiab, & - type_qndrp, type_qnsnow, type_qnrain, type_qngraupel, type_qnice, & - type_qc, type_qg, type_qi, type_qs, type_gz, type_refl, type_fall_spd, & - type_dref, type_spdp, type_qh, type_qnhail, type_qhvol, type_qgvol - - integer :: type_u10, type_v10, type_t2, type_th2, type_q2, & - type_ps, type_mu, type_tsk, type_tslb, type_sh2o, & - type_smois, type_2dflash - - integer :: number_of_wrf_variables - integer, dimension(:,:), pointer :: var_index - integer, dimension(:,:), pointer :: var_size - integer, dimension(:), pointer :: var_type - integer, dimension(:), pointer :: var_index_list - logical, dimension(:), pointer :: var_update_list - integer, dimension(:), pointer :: dart_kind - integer, dimension(:,:), pointer :: land - real(r8), dimension(:), pointer :: lower_bound,upper_bound - character(len=10), dimension(:),pointer :: clamp_or_fail - character(len=129),dimension(:),pointer :: description, units, stagger, coordinates - - integer, dimension(:,:,:,:), pointer :: dart_ind - -end type wrf_static_data_for_dart - -type wrf_dom - type(wrf_static_data_for_dart), pointer :: dom(:) - integer :: model_size -end type wrf_dom - -type(wrf_dom) :: wrf - -! JPH move map stuff into common (can move back into S/R later?) -real(r8) :: stdlon,truelat1,truelat2 !,latinc,loninc - -! have a single, module global error string (rather than -! replicate it in each subroutine and use up more stack space) -character(len=129) :: errstring, msgstring2, msgstring3 - -contains - -!####################################################################### - -subroutine static_init_model() - -! Initializes class data for WRF - -integer :: ncid -integer :: io, iunit - -character (len=1) :: idom -logical, parameter :: debug = .false. -integer :: ind, i, j, k, id, dart_index -integer :: my_index -integer :: var_element_list(max_state_variables) -logical :: var_update_list(max_state_variables) - - -!---------------------------------------------------------------------- - -! Register the module -call register_module(source, revision, revdate) - -! Begin by reading the namelist input -call find_namelist_in_file("input.nml", "model_nml", iunit) -read(iunit, nml = model_nml, iostat = io) -call check_namelist_read(iunit, io, "model_nml") - -! Record the namelist values used for the run ... -if (do_nml_file()) write(nmlfileunit, nml=model_nml) -if (do_nml_term()) write( * , nml=model_nml) - -! Temporary warning until this namelist item is removed. -if (adv_mod_command /= '') then - msgstring2 = "Set the model advance command in the &dart_to_wrf_nml namelist" - call error_handler(E_MSG, 'static_init_model:', & - "WARNING: adv_mod_command ignored in &model_mod namelist", & - text2=msgstring2) -endif - -allocate(wrf%dom(num_domains)) - -! get default state variable table if asked -if ( default_state_variables ) then - wrf_state_variables = 'NULL' - call fill_default_state_table(wrf_state_variables) - msgstring2 = 'Set "default_state_variables" to .false. in the namelist' - msgstring3 = 'to use the "wrf_state_variables" list instead.' - call error_handler(E_MSG, 'static_init_model:', & - 'Using predefined wrf variable list for dart state vector.', & - text2=msgstring2, text3=msgstring3) - -endif - -if ( debug ) then - print*,'WRF state vector table' - print*,'default_state_variables = ',default_state_variables - print*,wrf_state_variables -endif - -!--------------------------- -! set this array so we know exactly which obs kinds are -! allowed to be interpolated (and can give a reasonably -! helpful error message if not). -!--------------------------- - -num_obs_kinds = get_num_quantities() -allocate(in_state_vector(num_obs_kinds)) -call fill_dart_kinds_table(wrf_state_variables, in_state_vector) - - -if ( debug ) then - if ( output_state_vector ) then - write(*,*)'netcdf file in state vector format' - else - write(*,*)'netcdf file in prognostic vector format' - endif -endif - -! set calendar type -call set_calendar_type(calendar_type) - -! Store vertical localization coordinate -! Only 4 are allowed: level(1), pressure(2), height(3), or scale height(4) -! Everything else is assumed height -if (vert_localization_coord == VERTISLEVEL) then - wrf%dom(:)%localization_coord = VERTISLEVEL -elseif (vert_localization_coord == VERTISPRESSURE) then - wrf%dom(:)%localization_coord = VERTISPRESSURE -elseif (vert_localization_coord == VERTISHEIGHT) then - wrf%dom(:)%localization_coord = VERTISHEIGHT -elseif (vert_localization_coord == VERTISSCALEHEIGHT) then - wrf%dom(:)%localization_coord = VERTISSCALEHEIGHT -else - write(msgstring2,*)'vert_localization_coord must be one of ', & - VERTISLEVEL, VERTISPRESSURE, VERTISHEIGHT, VERTISSCALEHEIGHT - write(errstring,*)'vert_localization_coord is ', vert_localization_coord - call error_handler(E_ERR,'static_init_model', errstring, source, revision,revdate, & - text2=msgstring2) -endif - -! the agreement amongst the dart/wrf users was that there was no need to -! read the wrf namelist, since the only thing it was extracting was the -! timestep, which is part of the wrf input netcdf file. -! call read_dt_from_wrf_nml() - -dart_index = 1 - -WRFDomains : do id=1,num_domains - - write( idom , '(I1)') id - - ! only print this once, no matter how many parallel tasks are running - if (do_output()) then - write(*,*) '******************' - write(*,*) '** DOMAIN # ',idom,' **' - write(*,*) '******************' - endif - - if(file_exist('wrfinput_d0'//idom)) then - - call nc_check( nf90_open('wrfinput_d0'//idom, NF90_NOWRITE, ncid), & - 'static_init_model','open wrfinput_d0'//idom ) - - else - - call error_handler(E_ERR,'static_init_model', & - 'Please put wrfinput_d0'//idom//' in the work directory.', source, revision,revdate) - - endif - - if(debug) write(*,*) ' ncid is ',ncid - -!------------------------------------------------------- -! read WRF dimensions -!------------------------------------------------------- - call read_wrf_dimensions(ncid,wrf%dom(id)%bt, wrf%dom(id)%bts, & - wrf%dom(id)%sn, wrf%dom(id)%sns, & - wrf%dom(id)%we, wrf%dom(id)%wes, & - wrf%dom(id)%sls) - -!------------------------------------------------------- -! read WRF file attributes -!------------------------------------------------------- - call read_wrf_file_attributes(ncid,id) - -!------------------------------------------------------- -! assign boundary condition flags -!------------------------------------------------------- - - call assign_boundary_conditions(id) - -!------------------------------------------------------- -! read static data -!------------------------------------------------------- - - call read_wrf_static_data(ncid,id) - - -!------------------------------------------------------- -! next block set up map -!------------------------------------------------------- - - call setup_map_projection(id) - -!------------------------------------------------------- -! end block set up map -!------------------------------------------------------- - -! get the number of wrf variables wanted in this domain's state - wrf%dom(id)%number_of_wrf_variables = get_number_of_wrf_variables(id,wrf_state_variables,var_element_list, var_update_list) - -! allocate and store the table locations of the variables valid on this domain - allocate(wrf%dom(id)%var_index_list(wrf%dom(id)%number_of_wrf_variables)) - wrf%dom(id)%var_index_list = var_element_list(1:wrf%dom(id)%number_of_wrf_variables) - -! allocation for wrf variable types - allocate(wrf%dom(id)%var_type(wrf%dom(id)%number_of_wrf_variables)) - -! allocation for update/nocopyback/noupdate - allocate(wrf%dom(id)%var_update_list(wrf%dom(id)%number_of_wrf_variables)) - wrf%dom(id)%var_update_list = var_update_list(1:wrf%dom(id)%number_of_wrf_variables) - -! allocation for dart kinds - allocate(wrf%dom(id)%dart_kind(wrf%dom(id)%number_of_wrf_variables)) - -! allocation of var size - allocate(wrf%dom(id)%var_size(3,wrf%dom(id)%number_of_wrf_variables)) - -! allocation for wrf variable metadata - allocate(wrf%dom(id)%stagger(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%description(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%units(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%coordinates(wrf%dom(id)%number_of_wrf_variables)) - -! set default bounds checking - allocate(wrf%dom(id)%lower_bound(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%upper_bound(wrf%dom(id)%number_of_wrf_variables)) - allocate(wrf%dom(id)%clamp_or_fail(wrf%dom(id)%number_of_wrf_variables)) - call set_variable_bound_defaults(wrf%dom(id)%number_of_wrf_variables, & - wrf%dom(id)%lower_bound, & - wrf%dom(id)%upper_bound, & - wrf%dom(id)%clamp_or_fail) - -! build the variable indices -! this accounts for the fact that some variables might not be on all domains - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - - wrf%dom(id)%var_type(ind) = ind ! types are just the order for this domain - wrf%dom(id)%dart_kind(ind) = get_index_for_quantity(trim(wrf_state_variables(2,my_index))) - - if ( debug ) then - print*,'dart kind identified: ',trim(wrf_state_variables(2,my_index)),' ',wrf%dom(id)%dart_kind(ind) - endif - - ! get stagger and variable size - call get_variable_size_from_file(ncid,id, & - wrf_state_variables(1,my_index), & - wrf%dom(id)%bt, wrf%dom(id)%bts, & - wrf%dom(id)%sn, wrf%dom(id)%sns, & - wrf%dom(id)%we, wrf%dom(id)%wes, & - wrf%dom(id)%stagger(ind), & - wrf%dom(id)%var_size(:,ind)) - - ! get other variable metadata; units, coordinates and description - call get_variable_metadata_from_file(ncid,id, & - wrf_state_variables(1,my_index), & - wrf%dom(id)%description(ind), & - wrf%dom(id)%coordinates(ind), & - wrf%dom(id)%units(ind) ) - - if ( debug ) then - print*,'variable size ',trim(wrf_state_variables(1,my_index)),' ',wrf%dom(id)%var_size(:,ind) - endif - - ! add bounds checking information - call get_variable_bounds(wrf_state_bounds, wrf_state_variables(1,my_index), & - wrf%dom(id)%lower_bound(ind), wrf%dom(id)%upper_bound(ind), & - wrf%dom(id)%clamp_or_fail(ind)) - - if ( debug ) then - write(*,*) 'Bounds for variable ', & - trim(wrf_state_variables(1,my_index)), & - ' are ',wrf%dom(id)%lower_bound(ind), & - wrf%dom(id)%upper_bound(ind), & - wrf%dom(id)%clamp_or_fail(ind) - endif - - write(errstring, '(A,I4,2A)') 'state vector array ', ind, ' is ', trim(wrf_state_variables(1,my_index)) - call error_handler(E_MSG, 'static_init_model: ', errstring) - enddo - -! close data file, we have all we need - - call nc_check(nf90_close(ncid),'static_init_model','close wrfinput_d0'//idom) - -! indices into 1D array - hopefully this becomes obsolete -! JPH changed last dimension here from num_model_var_types - !HK allocate(wrf%dom(id)%dart_ind(wrf%dom(id)%wes,wrf%dom(id)%sns,wrf%dom(id)%bts,wrf%dom(id)%number_of_wrf_variables)) - -! start and stop of each variable in vector - allocate(wrf%dom(id)%var_index(2,wrf%dom(id)%number_of_wrf_variables)) - -!--------------------------- -! end block to be obsolete -!--------------------------- - -!--------------------------- -! at this point we need all information assigned to each variable -! then just loop thru the table -!--------------------------- - - !HK wrf%dom(id)%dart_ind = 0 - -! NOTE: this could be put into the loop above if wrf%dom(id)%dart_ind is -! eventually not needed -! Here we use ind instead of type as the 4th dimension. In is linked to the -! specific type via wrf%dom(id)%var_index_list(ind). This saves some -! space from the previous implementation but I am not yet sure of other -! problems that it might cause. - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - my_index = wrf%dom(id)%var_index_list(ind) - - if ( debug ) then - write(*,*) 'Assigning dart vector indices for var_type ',wrf%dom(id)%var_type(ind) - write(*,*) 'affiliated with WRF variable ',trim(wrf_state_variables(1,my_index)),' of size ',wrf%dom(id)%var_size(:,ind) - endif - - wrf%dom(id)%var_index(1,ind) = dart_index - do k=1,wrf%dom(id)%var_size(3,ind) - do j=1,wrf%dom(id)%var_size(2,ind) - do i=1,wrf%dom(id)%var_size(1,ind) - ! HK wrf%dom(id)%dart_ind(i,j,k,ind) & - ! = dart_index - dart_index = dart_index + 1 - enddo - enddo - enddo - wrf%dom(id)%var_index(2,ind) = dart_index - 1 - - if ( debug ) write(*,*) 'assigned start, stop ',wrf%dom(id)%var_index(:,ind) - - enddo ! loop through all viable state variables on this domain - if ( id == 1 ) then - wrf%dom(id)%domain_size = dart_index - 1 - else - wrf%dom(id)%domain_size = dart_index - 1 - do ind = id-1, 1, -1 - wrf%dom(id)%domain_size = wrf%dom(id)%domain_size - wrf%dom(ind)%domain_size - enddo - endif - - - ! NEWVAR: If you add a new wrf array type which is not yet in this list, currently - ! NEWVAR: you will have to add it here, and add a type_xx for it, and also add - ! NEWVAR: a in_state_vector case in the select statement. search for NEWVAR. - - ! JPH now that we have the domain ID just go ahead and get type indices once - ! NOTE: this is not strictly necessary - can use only stagger info in the future (???) - wrf%dom(id)%type_u = get_type_ind_from_type_string(id,'U') - wrf%dom(id)%type_v = get_type_ind_from_type_string(id,'V') - wrf%dom(id)%type_w = get_type_ind_from_type_string(id,'W') - wrf%dom(id)%type_t = get_type_ind_from_type_string(id,'T') - wrf%dom(id)%type_gz = get_type_ind_from_type_string(id,'PH') - wrf%dom(id)%type_qv = get_type_ind_from_type_string(id,'QVAPOR') - wrf%dom(id)%type_qr = get_type_ind_from_type_string(id,'QRAIN') - wrf%dom(id)%type_qc = get_type_ind_from_type_string(id,'QCLOUD') - wrf%dom(id)%type_qg = get_type_ind_from_type_string(id,'QGRAUP') - wrf%dom(id)%type_qh = get_type_ind_from_type_string(id,'QHAIL') - wrf%dom(id)%type_qi = get_type_ind_from_type_string(id,'QICE') - wrf%dom(id)%type_qs = get_type_ind_from_type_string(id,'QSNOW') - wrf%dom(id)%type_qgvol = get_type_ind_from_type_string(id,'QVGRAUPEL') - wrf%dom(id)%type_qhvol = get_type_ind_from_type_string(id,'QVHAIL') - wrf%dom(id)%type_qnice = get_type_ind_from_type_string(id,'QNICE') - wrf%dom(id)%type_qndrp = get_type_ind_from_type_string(id,'QNDRP') - wrf%dom(id)%type_qnsnow = get_type_ind_from_type_string(id,'QNSNOW') - wrf%dom(id)%type_qnrain = get_type_ind_from_type_string(id,'QNRAIN') - wrf%dom(id)%type_qngraupel = get_type_ind_from_type_string(id,'QNGRAUPEL') - wrf%dom(id)%type_qnhail = get_type_ind_from_type_string(id,'QNHAIL') - wrf%dom(id)%type_u10 = get_type_ind_from_type_string(id,'U10') - wrf%dom(id)%type_v10 = get_type_ind_from_type_string(id,'V10') - wrf%dom(id)%type_t2 = get_type_ind_from_type_string(id,'T2') - wrf%dom(id)%type_th2 = get_type_ind_from_type_string(id,'TH2') - wrf%dom(id)%type_q2 = get_type_ind_from_type_string(id,'Q2') - wrf%dom(id)%type_ps = get_type_ind_from_type_string(id,'PSFC') - wrf%dom(id)%type_mu = get_type_ind_from_type_string(id,'MU') - wrf%dom(id)%type_tsk = get_type_ind_from_type_string(id,'TSK') - wrf%dom(id)%type_2dflash = get_type_ind_from_type_string(id,'FLASH_RATE_2D') - wrf%dom(id)%type_tslb = get_type_ind_from_type_string(id,'TSLB') - wrf%dom(id)%type_smois = get_type_ind_from_type_string(id,'SMOIS') - wrf%dom(id)%type_sh2o = get_type_ind_from_type_string(id,'SH2O') - wrf%dom(id)%type_refl = get_type_ind_from_type_string(id,'REFL_10CM') - wrf%dom(id)%type_dref = get_type_ind_from_type_string(id,'DIFF_REFL_10CM') - wrf%dom(id)%type_spdp = get_type_ind_from_type_string(id,'SPEC_DIFF_10CM') - wrf%dom(id)%type_fall_spd = get_type_ind_from_type_string(id,'FALL_SPD_Z_WEIGHTED') - !wrf%dom(id)%type_fall_spd = get_type_ind_from_type_string(id,'VT_DBZ_WT') - wrf%dom(id)%type_hdiab = get_type_ind_from_type_string(id,'H_DIABATIC') - -enddo WRFDomains - -wrf%model_size = dart_index - 1 -write(errstring,*) ' wrf model size is ',wrf%model_size -call error_handler(E_MSG, 'static_init_model', errstring) - -end subroutine static_init_model - - -!####################################################################### - -function get_model_size() - -integer :: get_model_size - -get_model_size = wrf%model_size - -end function get_model_size - -!####################################################################### - -function get_number_domains() - -integer :: get_number_domains - -get_number_domains = num_domains - -end function get_number_domains - -!####################################################################### - -function get_wrf_static_data(dom_num) - -integer, intent(in) :: dom_num - -type(wrf_static_data_for_dart) :: get_wrf_static_data - -get_wrf_static_data = wrf%dom(dom_num) - -return -end function get_wrf_static_data - -!####################################################################### - -subroutine get_wrf_state_variables(state_var) - -character(len=129), intent(out) :: state_var(num_state_table_columns,max_state_variables) - -state_var = wrf_state_variables - -end subroutine get_wrf_state_variables - -!####################################################################### - -function get_model_time_step() -!------------------------------------------------------------------------ -! function get_model_time_step() -! -! Returns the time step of the model. In the long run should be replaced -! by a more general routine that returns details of a general time-stepping -! capability. -! -! toward that end ... we are now reading a namelist variable for the -! width of the assimilation time window. - -type(time_type) :: get_model_time_step -integer :: model_dt, assim_dt - -! We need to coordinate the desired assimilation window to be a -! multiple of the model time step (which has no precision past integer seconds). - -model_dt = nint(wrf%dom(1)%dt) - -! The integer arithmetic does its magic. -assim_dt = (assimilation_period_seconds / model_dt) * model_dt - -get_model_time_step = set_time(assim_dt) - -end function get_model_time_step - - -!####################################################################### - - -subroutine get_state_meta_data_distrib(state_ens_handle, index_in, location, var_type_out, id_out) - -! Given an integer index into the DART state vector structure, returns the -! associated location. This is not a function because the more general -! form of the call has a second intent(out) optional argument kind. -! Maybe a functional form should be added? - -! this version has an optional last argument that is never called by -! any of the dart code, which can return the wrf domain number. - -type(ensemble_type), intent(in) :: state_ens_handle -integer, intent(in) :: index_in -type(location_type), intent(out) :: location -integer, optional, intent(out) :: var_type_out, id_out - -integer :: var_type, dart_type -integer :: index, ip, jp, kp -integer :: nz, ny, nx -logical :: var_found -real(r8) :: lon, lat, lev -character(len=129) :: string1 - -integer :: i, id -logical, parameter :: debug = .false. - -if(debug) then - write(errstring,*)' index_in = ',index_in - call error_handler(E_MSG,'get_state_meta_data',errstring,' ',' ',' ') -endif - -! identity obs come in with a negative value - absolute -! index into the state vector. obs_def_mod code calls this -! with -1 * identity_index so it's always positive, but the -! code here in vert_convert() passes in the raw obs_kind -! so it could, indeed, be negative. -index = abs(index_in) - -! dump out a list of all domains and variable types -if(debug) then - do id=1,num_domains - do i=1, wrf%dom(id)%number_of_wrf_variables - write(errstring,*)' domain, var, var_type(i) = ',id,i,wrf%dom(id)%var_type(i) - call error_handler(E_MSG,'get_state_meta_data',errstring) - enddo - enddo -endif - -! loop through the wrf vars (U, V, PS, etc) in state vector, starting -! at domain 1. see if the start/end index range in the 1d state vector -! includes the requested index. if you get to the end of the list of vars -! and you haven't found it yet, bump up the domain number and start -! search over from the start of the wrf var list. -! if you get to the end of the domains and you haven't found the -! valid range, the index must be larger than the state vector -! which is a fatal error. - -var_found = .false. -i = 0 -id = 1 -do while (.not. var_found) - i = i + 1 - if(i .gt. wrf%dom(id)%number_of_wrf_variables) then - i = 1 - if (id < num_domains) then - id = id + 1 - else - write(string1,*)' size of state vector = ',wrf%model_size - write(errstring,*)' dart_index ',index_in, ' is out of range' - call error_handler(E_ERR,'get_state_meta_data', errstring, & - source, revision, revdate, text2=string1) - endif - endif - if( (index .ge. wrf%dom(id)%var_index(1,i) ) .and. & - (index .le. wrf%dom(id)%var_index(2,i) ) ) then - var_found = .true. - var_type = wrf%dom(id)%var_type(i) - dart_type = wrf%dom(id)%dart_kind(i) - index = index - wrf%dom(id)%var_index(1,i) + 1 - endif -end do - -! now find i,j,k location. -! index has been normalized such that it is relative to -! array starting at (1,1,1) - -nx = wrf%dom(id)%var_size(1,i) -ny = wrf%dom(id)%var_size(2,i) -nz = wrf%dom(id)%var_size(3,i) - -kp = 1 + (index-1)/(nx*ny) -jp = 1 + (index - (kp-1)*nx*ny - 1)/nx -ip = index - (kp-1)*nx*ny - (jp-1)*nx - -! at this point, (ip,jp,kp) refer to indices in the variable's own grid - -if(debug) write(*,*) ' ip, jp, kp for index ',ip,jp,kp,index -if(debug) write(*,*) ' Var type: ',var_type - -! first obtain lat/lon from (ip,jp) -call get_wrf_horizontal_location( ip, jp, var_type, id, lon, lat ) - -!HK Not doing this conversion for now in the distributed version. Doing it on demand in -! vert_convert - NOPE I think that this is really slow, because each step in the sequential -! obs do loop is limited by the slowest processor doing vertical conversion. It makes -! more sense for everyone to blast through all their pieces of state. Idea: overlap this -! with task 0 writing the diagnostic files. - -! now convert to desired vertical coordinate (defined in the namelist) -if (wrf%dom(id)%localization_coord == VERTISLEVEL) then - ! here we need level index of mass grid - if( (var_type == wrf%dom(id)%type_w ) .or. (var_type == wrf%dom(id)%type_gz) ) then - lev = real(kp) - 0.5_r8 - else - lev = real(kp) - endif -elseif (wrf%dom(id)%localization_coord == VERTISPRESSURE) then - ! directly convert to pressure - lev = model_pressure_distrib(ip, jp, kp, id, var_type, state_ens_handle) -elseif (wrf%dom(id)%localization_coord == VERTISHEIGHT) then - lev = model_height_distrib(ip, jp, kp, id, var_type, state_ens_handle) -elseif (wrf%dom(id)%localization_coord == VERTISSCALEHEIGHT) then - lev = -log(model_pressure_distrib(ip, jp, kp, id, var_type, state_ens_handle) / & - model_surface_pressure_distrib(ip, jp, id, var_type, state_ens_handle)) -endif - -if(debug) write(*,*) 'lon, lat, lev: ',lon, lat, lev - -! convert to DART location type -location = set_location(lon, lat, lev, wrf%dom(id)%localization_coord) - -! return DART variable kind if requested -if(present(var_type_out)) var_type_out = dart_type - -! return domain id if requested -if(present(id_out)) id_out = id - -end subroutine get_state_meta_data_distrib - -!-------------------------------------------------------------------- -!> Distributed version of model interpolate -!> obs_kind is called as location type in assim model model -!> Storing the mean copy level location as the observation location -!> to save recomputation of v_p in vert_convert in get_close_obs -! -! Should this code be simplified so there is not so much repetition? -! This is the main forward operator subroutine for WRF. -! Given an ob (its DART location and kind), the corresponding model -! value is computed at nearest i,j,k. Thus, first i,j,k is obtained -! from ob lon,lat,z and then the state value that corresponds to -! the ob kind is interpolated. -! -! No location conversions are carried out in this subroutine. See -! get_close_obs, where ob vertical location information is converted -! to the requested vertical coordinate type. -subroutine model_interpolate_distrib(state_ens_handle, location, obs_kind, istatus, expected_obs) - -! x: Full DART state vector relevant to what's being updated -! in the filter (mean or individual members). -! istatus: Returned 0 if everything is OK, >0 if error occured. -! 1 = missing domain id -! 2 = bad vertical level -! 3 = unsupported obs kind -! 10 = polar observation while restrict_polar namelist true -! 99 = unknown reason (reached end of routine with missing_r8 -! as obs_val) - -! modified 26 June 2006 to accomodate vortex attributes -! modified 13 December 2006 to accomodate changes for the mpi version -! modified 22 October 2007 to accomodate global WRF (3.0) -! modified 18 November 2008 to allow unknown kinds to return without stopping -! modified 5 February 2010 to add circulation calc for vortex obs - -! Helen Kershaw - Aim: to not require the whole state vector - -! arguments -type(location_type), intent(in) :: location -integer, intent(in) :: obs_kind -integer, intent(out) :: istatus(:) -!HK -type(ensemble_type), intent(in) :: state_ens_handle -real(r8), intent(out) :: expected_obs(:) -real(r8), allocatable :: v - -! local -logical, parameter :: debug = .false. -logical, parameter :: restrict_polar = .false. -logical, parameter :: use_old_vortex = .true. ! set to .false. to use circ code -real(r8), parameter :: drad = pi / 18.0_r8 -real(r8) :: xloc, yloc, xloc_u, yloc_v, xyz_loc(3) -integer :: i, i_u, j, j_v, k2 -real(r8) :: dx,dy,dxm,dym,dx_u,dxm_u,dy_v,dym_v -integer :: id -logical :: surf_var -real(r8), allocatable :: a1(:) !HK -real(r8), allocatable :: zloc(:) !HK -integer, allocatable :: k(:) !HK -real(r8), allocatable :: dz(:), dzm(:) !HK -real(r8), allocatable :: utrue(:),vtrue(:) !HK - -! from getCorners -integer, dimension(2) :: ll, lr, ul, ur, ll_v, lr_v, ul_v, ur_v -integer :: rc, ill, ilr, iul, iur, i1, i2 - -real(r8), allocatable :: fld(:,:) -real(r8), allocatable, dimension(:,:) :: v_h, v_p - -! local vars, used in finding sea-level pressure and vortex center -real(r8), allocatable, dimension(:) :: t1d, p1d, qv1d, z1d -real(r8), allocatable, dimension(:,:) :: vfld, pp, pd, uwnd, vwnd, vort -real(r8), allocatable, dimension(:) :: x1d, y1d, xx1d, yy1d -integer :: center_search_half_size, center_track_xmin, center_track_ymin, & - center_track_xmax, center_track_ymax, circ_half_size, & - circ_xmin, circ_xmax, circ_ymin, circ_ymax, xlen, ylen, & - xxlen, yylen, ii1, ii2, cxlen, cylen, imax, jmax -real(r8) :: clat, clon, cxloc, cyloc, vcrit, magwnd, maxwspd, circ, & - circ_half_length, asum, distgrid, dgi1, dgi2 - -! local vars, used in calculating density, pressure, height -real(r8), allocatable :: rho1(:) , rho2(:) , rho3(:), rho4(:) -real(r8), allocatable :: pres1(:), pres2(:), pres3(:), pres4(:), pres(:) -logical, allocatable :: is_lev0(:) !HK - -! local var for terrain elevation check for surface stations -real(r8) :: mod_sfc_elevation - - -!HK -real(r8), allocatable :: x_ill(:), x_iul(:), x_ilr(:), x_iur(:), ugrid(:), vgrid(:) -integer :: e, count, uk !< index varibles for loop -integer, allocatable :: uniquek(:), ksort(:) -real(r8), allocatable :: failedcopies(:) -integer :: ens_size - -ens_size = copies_in_window(state_ens_handle) -allocate(x_ill(ens_size), x_iul(ens_size), x_ilr(ens_size), x_iur(ens_size)) -allocate(fld(2,ens_size), a1(ens_size)) -allocate(zloc(ens_size), is_lev0(ens_size)) -allocate(k(ens_size), dz(ens_size), dzm(ens_size)) -allocate(ksort(ens_size)) -allocate(failedcopies(ens_size)) -allocate(ugrid(ens_size), vgrid(ens_size)) -allocate(pres1(ens_size), pres2(ens_size), pres3(ens_size), pres4(ens_size), pres(ens_size)) -allocate(rho1(ens_size), rho2(ens_size), rho3(ens_size), rho4(ens_size)) -allocate(utrue(ens_size), vtrue(ens_size)) - -id = 1 - -! HK printing out sizes of wrf_static_data_for_dart -!print*, '******** wrf_static_data_for_dart' -!print*, 'znu, dn, dnw, zs, znw ', size(wrf%dom(id)%znu), size(wrf%dom(id)%dn), size(wrf%dom(id)%dnw), size(wrf%dom(id)%zs), size(wrf%dom(id)%znw) -!print*, 'mub, hgt ', size(wrf%dom(id)%mub), size(wrf%dom(id)%hgt) -!print*, 'latitude, latitude_u, latitude_v ', size(wrf%dom(id)%latitude), size(wrf%dom(id)%latitude_u), size(wrf%dom(id)%latitude_v) -!print*, 'longitude, longitude_u, longitude_v ', size(wrf%dom(id)%longitude), size(wrf%dom(id)%longitude_u), size(wrf%dom(id)%longitude_v) -!print*, 'phb ', size(wrf%dom(id)%phb) - -!print*, 'var_index ', size(wrf%dom(id)%var_index) -!print*, 'var_size ', size(wrf%dom(id)%var_size) -!print*, 'var_type ', size(wrf%dom(id)%var_type) -!print*, 'var_index_list ', size(wrf%dom(id)%var_index_list) -!print*, 'var_update_list ', size(wrf%dom(id)%var_update_list) -!print*, 'dart_kind ', size(wrf%dom(id)%dart_kind) -!print*, 'land ', size(wrf%dom(id)%land) -!print*, 'lower_bound,upper_bound ', size(wrf%dom(id)%lower_bound), size(wrf%dom(id)%upper_bound) -!print*, 'clamp_or_fail ', size(wrf%dom(id)%clamp_or_fail) -!print*, 'description, units, stagger, coordinates ', size(wrf%dom(id)%description), size(wrf%dom(id)%units), size(wrf%dom(id)%stagger), size(wrf%dom(id)%coordinates) -!print*, 'dart_ind ', size(wrf%dom(id)%dart_ind) - - -! Initialize stuff -istatus(:) = 0 -fld(:,:) = missing_r8 -expected_obs(:) = missing_r8 !< array of obs_vals -failedcopies(:) = 1 - -! If identity observation (obs_kind < 0), then no need to interpolate -if ( obs_kind < 0 ) then - - ! identity observation -> -(obs_kind)=DART state vector index - ! obtain state value directly from index - - ! HK This is no longer true with a distributed state vector - !obs_val = x(-1*obs_kind) - call error_handler(E_ERR, 'model_interpolate', 'identity obs in model interpolate', source, revision, revdate) - -! Otherwise, we need to do interpolation -else - - ! Is this a valid kind to interpolate? Set up in the static_init_model code, - ! based on entries in wrf_state_vector namelist item. - if (.not. in_state_vector(obs_kind)) then - write(errstring, *) 'cannot interpolate ' // trim(get_name_for_quantity(obs_kind)) & - // ' with the current WRF arrays in state vector' - call error_handler(E_ERR, 'model_interpolate', errstring, & - source, revision, revdate) - endif - - ! Unravel location_type information - xyz_loc = get_location(location) - - !---------------------------------- - ! 0. Prelude to Interpolation - !---------------------------------- - - ! 0.a Horizontal stuff - - ! first obtain domain id, and mass points (i,j) -! JPH --- scm is only defined for d1 - if ( .not. scm ) then - call get_domain_info(xyz_loc(1),xyz_loc(2),id,xloc,yloc) - else - id = 1 - xloc = 1.0_r8 - yloc = 1.0_r8 - endif - - ! check that we obtained a valid domain id number - if (id==0) then - istatus = 1 - return - endif - - if ( debug ) then - write(*,*) 'retreiving obs kind ',obs_kind,' on domain ',id - endif - - !***************************************************************************** - ! Check polar-b.c. constraints -- if restrict_polar = .true., then we are not - ! processing observations poleward of the 1st or last mass grid points. - ! If we have tried to pass a polar observation, then exit with istatus = 10 - if ( wrf%dom(id)%polar .and. restrict_polar ) then - if ( yloc < 1.0_r8 .or. yloc >= real(wrf%dom(id)%sn,r8) ) then - - ! Perhaps write to dart_log.out? - write(errstring,*)'Obs cannot be polar with restrict_polar on: yloc = ',yloc - call error_handler(E_WARN,'model_interpolate', errstring, & - source, revision, revdate) - - istatus = 10 ! istatus 10, if it's not used, will mean the observation is too polar - print*, 'model_mod.f90 :: model_interpolate :: No polar observations! istatus = ', istatus - return - endif - endif - !***************************************************************************** - - ! print info if debugging - if(debug) then - i = xloc - j = yloc - print*,xyz_loc(2), xyz_loc(1), xloc,yloc - write(*,*) ' corners of lat ' - write(*,*) wrf%dom(id)%latitude(i,j),wrf%dom(id)%latitude(i+1,j), & - wrf%dom(id)%latitude(i,j+1), & - wrf%dom(id)%latitude(i+1,j+1) - write(*,*) ' corners of long ' - write(*,*) wrf%dom(id)%longitude(i,j),wrf%dom(id)%longitude(i+1,j), & - wrf%dom(id)%longitude(i,j+1), & - wrf%dom(id)%longitude(i+1,j+1) - endif - - ! get integer (west/south) grid point and distances to neighboring grid points - ! distances are used as weights to carry out horizontal interpolations - call toGrid(xloc,i,dx,dxm) - call toGrid(yloc,j,dy,dym) - - ! 0.b Vertical stuff - - if ( debug ) then - write(*,*) 'vert_is_pressure ',vert_is_pressure(location) - write(*,*) 'vert_is_height ',vert_is_height(location) - endif - - ! HK - ! Allocate both a vertical height and vertical pressure coordinate -- 0:bt x ens_size - allocate(v_h(0:wrf%dom(id)%bt, ens_size), v_p(0:wrf%dom(id)%bt, ens_size)) - - ! Set surf_var to .false. and then change in vert_is_surface section if necessary - surf_var = .false. - - ! Determine corresponding model level for obs location - ! This depends on the obs vertical coordinate - ! From this we get a meaningful z-direction real-valued index number - if(vert_is_level(location)) then - ! Ob is by model level - zloc = xyz_loc(3) - - elseif(vert_is_pressure(location)) then - ! Ob is by pressure: get corresponding mass level zloc from - ! computed column pressure profile - call get_model_pressure_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_p,state_ens_handle, ens_size) - - !print*, 'v_p distrib ', v_p - -! testing get_model_pressure_profile_distrib -! istatus = 0 -! expected_obs = -9 -! return - - ! get pressure vertical co-ordinate - call pres_to_zk_distrib(xyz_loc(3), v_p, wrf%dom(id)%bt, ens_size, zloc,is_lev0) - if(debug .and. obs_kind /= QTY_SURFACE_PRESSURE) & - print*,' obs is by pressure and zloc,lev0 =',zloc, is_lev0 - if(debug) print*,'model pressure profile' - if(debug) print*,v_p - - ! If location is above model surface but below the lowest sigma level, - ! the default is to reject it. But if the namelist value is true, then - ! accept the observation and later on extrapolate the values from levels - ! 1 and 2 downward. - - !HK ensemble loop, however do you reject the obs for all ensembles later anyway? - - do e = 1, ens_size - - if (is_lev0(e)) then - ! the pres_to_zk() routine has returned a valid zloc in case we - ! want to use it. the default is to reject the observation and so - ! we overwrite it with missing -- but, if the namelist value is set - ! to true, leave zloc alone. - if (.not. allow_obs_below_vol) zloc(e) = missing_r8 - if (debug .and. .not. allow_obs_below_vol) print*, 'setting zloc missing' - - ! else need to set a qc here? - endif - - enddo - - elseif(vert_is_height(location)) then - - ! Ob is by height: get corresponding mass level zloc from - ! computed column height profile - call get_model_height_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_h, state_ens_handle, ens_size) - ! get height vertical co-ordinate - do e = 1, ens_size ! HK should there be a height_to_zk_distrib? - call height_to_zk(xyz_loc(3), v_h(:, e), wrf%dom(id)%bt,zloc(e),is_lev0(e)) - if(debug) print*,' obs is by height and zloc,lev0 =',zloc(e), is_lev0(e) - if(debug) print*,'model height profile' - if(debug) print*,v_h - - ! If location is above model surface but below the lowest sigma level, - ! the default is to reject it. But if the namelist value is true, then - ! accept the observation and later on extrapolate the values from levels - ! 1 and 2 downward. - if (is_lev0(e)) then - ! the height_to_zk() routine has returned a valid zloc in case we - ! want to use it. the default is to reject the observation and so - ! we overwrite it with missing. but if the namelist value is set - ! to true, leave zloc alone. - if (.not. allow_obs_below_vol) zloc(e) = missing_r8 - if (debug .and. .not. allow_obs_below_vol) print*, 'setting zloc missing member ', e - ! else need to set a qc here? - endif - - enddo - - elseif(vert_is_surface(location)) then - zloc = 1.0_r8 - surf_var = .true. - if(debug) print*,' obs is at the surface = ', xyz_loc(3) - - ! if you want to have a distance check to see if the station height - ! is too far away from the model surface height, here is the place to - ! reject the observation. - - ! Elevation check function drawn from Ryan's wrf preprocessing code. - ! The elevation is now passed in instead of calling model_interpolate: - ! sfc_elev_max_diff - if < 0 routine is skipped. - ! mod_sfc_elevation - interpolated model surface height at the lowest - ! - model layer. - ! z_loc - the third array element of xyz_loc - ! the station elevation against the estimated model surface height at the - ! station location, the maximum difference in elevation allowed (m), and the - ! observation location. There is no check for whether the third element for - ! the xyz_loc array is anything other than station height in meters. The - ! function returns a logical where .true. means the obs station elevation - ! 'passed' the height check. Here, if a height check fails, set an istatus - ! of '1' and bail out of this routine. - if ( sfc_elev_max_diff >= 0 ) then - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners HGT for sfc rc = ', rc - - ! Interpolation for the HGT field -- HGT is NOT part of state vector x, but rather - ! in the associated domain meta data - mod_sfc_elevation = dym*( dxm*wrf%dom(id)%hgt(ll(1), ll(2)) + & - dx*wrf%dom(id)%hgt(lr(1), lr(2)) ) + & - dy*( dxm*wrf%dom(id)%hgt(ul(1), ul(2)) + & - dx*wrf%dom(id)%hgt(ur(1), ur(2)) ) - endif - if ( .not. height_diff_check(sfc_elev_max_diff,xyz_loc(3),mod_sfc_elevation) ) zloc = missing_r8 - endif - - elseif(vert_is_undef(location)) then - ! the zloc value should not be used since there is no actual vertical - ! location for this observation, but give zloc a valid value to avoid - ! the error checks below for missing_r8 - zloc = 0.0_r8 - if(debug) print*,' obs height is intentionally undefined' - - else - write(errstring,*) 'wrong option for which_vert ', & - nint(query_location(location,'which_vert')) - call error_handler(E_ERR,'model_interpolate', errstring, & - source, revision, revdate) - - endif - - - ! Deal with missing vertical coordinates -- return with istatus .ne. 0 - ! HK This is annoying. Back to earlier question of QC if one ensemble fails do we - ! discard all the obervations? - ! For now, to replicate the original code behaviour keep track of the failed copies - - ! ********* bail out early code ********** - !if(any(zloc == missing_r8)) then - ! print*, 'One of the ensembles has failed the vertical co ordinate rank', my_task_id() - ! expected_obs = missing_r8 !HK this is redundant? expected_obs is initialized to missing_r8 - ! istatus = 2 - ! deallocate(v_h, v_p) - ! return - !endif - ! ********* endof bail out early code ******** - - do e = 1, ens_size - if(zloc(e) == missing_r8) then - failedcopies(e) = missing_r8 - istatus(e) = 2 - endif - enddo - - ! Set a working integer k value -- if (int(zloc) < 1), then k = 1 - k = max(1,int(zloc)) !HK k is now ensemble size - - - ! Find the unique k values - ksort = sort(k) - - count = 1 - do e = 2, ens_size - if ( ksort(e) /= ksort(e-1) ) count = count + 1 - enddo - - allocate(uniquek(count)) - - uk = 1 - do e = 1, ens_size - if ( all(uniquek /= k(e)) ) then - uniquek(uk) = k(e) - uk = uk + 1 - endif - enddo - - ! The big horizontal interp loop below computes the data values in the level - ! below and above the actual location, and then does a separate vertical - ! interpolation (if the obs is not a 2d surface obs). The two values are - ! stored in fld(1:2). Set them to missing here, and if the code below cannot - ! compute a value, it can just drop out and not have to explicitly set it to - ! missing anymore. - fld(:,:) = missing_r8 !HK - - !---------------------------------- - ! 1. Horizontal Interpolation - !---------------------------------- - - ! This part is the forward operator -- compute desired model state value for given point. - - ! Strategy is to do the horizontal interpolation on two different levels in the - ! vertical, and then to do the vertical interpolation afterwards, since it depends on - ! what the vertical coordinate is - - ! Large if-structure to select on obs_kind of desired field.... - ! Table of Contents: - ! 1.a Horizontal Winds (U, V, U10, V10) - ! 1.b Sensible Temperature (T, T2) - ! 1.c Potential Temperature (Theta, TH2) - ! 1.d Density (Rho) - ! 1.e Vertical Wind (W) - ! 1.f Specific Humidity (SH, SH2) - ! 1.g Vapor Mixing Ratio (QV, Q2) - ! 1.h Rainwater Mixing Ratio (QR) - ! 1.i.1 Graupel Mixing Ratio (QG) - ! 1.i.2 Hail Mixing Ratio (QH) - ! 1.j Snow Mixing Ratio (QS) - ! 1.k Ice Mixing Ratio (QI) - ! 1.l Cloud Mixing Ratio (QC) - ! 1.m Droplet Number Concentration (QNDRP) - ! 1.n Ice Number Concentration (QNICE) - ! 1.o Snow Number Concentration (QNSNOW) - ! 1.p Rain Number Concentration (QNRAIN) - ! 1.q.1 Graupel Number Concentration (QNGRAUPEL) - ! 1.q.2 Hail Number Concentration (QNHAIL) - ! 1.r Previous time step condensational heating (H_DIABATIC) - ! 1.s Reflectivity weighted precip fall speed (FALL_SPD_Z_WEIGHTED) - ! 1.t Pressure (P) - ! 1.u Vortex Center Stuff from Yongsheng - ! 1.v.1 Radar Reflectivity (REFL_10CM) - ! 1.v.2 Differential Reflectivity (DIFF_REFL_10CM) - ! 1.v.3 Specific Differential Phase (SPEC_DIFF_10CM) - ! 1.w Geopotential Height (GZ) - ! 1.x Surface Elevation (HGT) - ! 1.y Surface Skin Temperature (TSK) - ! 1.z Land Mask (XLAND) - - ! NEWVAR: Currently you have to add a new case here to tell the code what - ! field inside the state vector you will be interpolating in. the eventual - ! plan is for there to be a default case which all simple interps fall into, - ! but for now we still have to add code. - - ! NOTE: the previous version of this code checked for surface observations with the syntax: - ! "if(.not. vert_is_surface(location) .or. .not. surf_var) then" - ! We identified this as redundant because surf_var is changed from .false. only by - ! the above code (section 0.b), which must be traced through before one can arrive - ! at the following forward operator code. Hence, we can remove the call to - ! vert_is_surface. - - if (obs_kind == QTY_RAINWATER_MIXING_RATIO .or. & - obs_kind == QTY_GRAUPEL_MIXING_RATIO .or. & - obs_kind == QTY_HAIL_MIXING_RATIO .or. & - obs_kind == QTY_SNOW_MIXING_RATIO .or. & - obs_kind == QTY_CLOUD_ICE .or. & - obs_kind == QTY_CLOUD_LIQUID_WATER .or. & - obs_kind == QTY_DROPLET_NUMBER_CONCENTR .or. & - obs_kind == QTY_ICE_NUMBER_CONCENTRATION .or. & - obs_kind == QTY_SNOW_NUMBER_CONCENTR .or. & - obs_kind == QTY_RAIN_NUMBER_CONCENTR .or. & - obs_kind == QTY_GRAUPEL_NUMBER_CONCENTR .or. & - obs_kind == QTY_HAIL_NUMBER_CONCENTR .or. & - obs_kind == QTY_CONDENSATIONAL_HEATING .or. & - obs_kind == QTY_POWER_WEIGHTED_FALL_SPEED .or. & - obs_kind == QTY_RADAR_REFLECTIVITY .or. & - obs_kind == QTY_DIFFERENTIAL_REFLECTIVITY .or. & - obs_kind == QTY_SPECIFIC_DIFFERENTIAL_PHASE ) then - - call simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_ens_handle) - - ! don't accept negative fld - if (obs_kind == QTY_RAINWATER_MIXING_RATIO .or. & - obs_kind == QTY_GRAUPEL_MIXING_RATIO .or. & - obs_kind == QTY_HAIL_MIXING_RATIO .or. & - obs_kind == QTY_SNOW_MIXING_RATIO .or. & - obs_kind == QTY_CLOUD_ICE .or. & - obs_kind == QTY_CLOUD_LIQUID_WATER .or. & - obs_kind == QTY_DROPLET_NUMBER_CONCENTR .or. & - obs_kind == QTY_ICE_NUMBER_CONCENTRATION .or. & - obs_kind == QTY_SNOW_NUMBER_CONCENTR .or. & - obs_kind == QTY_RAIN_NUMBER_CONCENTR .or. & - obs_kind == QTY_GRAUPEL_NUMBER_CONCENTR .or. & - obs_kind == QTY_HAIL_NUMBER_CONCENTR ) then - - fld = max(0.0_r8, fld) ! Don't accept negative - - endif - - !----------------------------------------------------- - ! 1.a Horizontal Winds (U, V, U10, V10) - - ! We need one case structure for both U & V because they comprise a vector which could need - ! transformation depending on the map projection (hence, the call to gridwind_to_truewind) - elseif( obs_kind == QTY_U_WIND_COMPONENT .or. obs_kind == QTY_V_WIND_COMPONENT) then ! U, V - - ! This is for 3D wind fields -- surface winds later - if(.not. surf_var) then - - if ( ( wrf%dom(id)%type_u >= 0 ) .and. ( wrf%dom(id)%type_v >= 0 ) ) then - - ! xloc and yloc are indices on mass-grid. If we are on a periodic longitude domain, - ! then xloc can range from [1 wes). This means that simply adding 0.5 to xloc has - ! the potential to render xloc_u out of the valid mass-grid index bounds (>wes). - ! To remedy this, we can either do periodicity check on xloc_u value, or we can - ! leave it to a subroutine or function to alter xloc to xloc_u if the observation - ! type requires it. - xloc_u = xloc + 0.5_r8 - yloc_v = yloc + 0.5_r8 - - ! Check periodicity if necessary -- but only subtract 'we' because the U-grid - ! cannot have an index < 1 (i.e., U(wes) = U(1) ). - if ( wrf%dom(id)%periodic_x .and. xloc_u > real(wrf%dom(id)%wes,r8) ) & - xloc_u = xloc_u - real(wrf%dom(id)%we,r8) - - ! Get South West gridpoint indices for xloc_u and yloc_v - call toGrid(xloc_u,i_u,dx_u,dxm_u) - call toGrid(yloc_v,j_v,dy_v,dym_v) - - do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i_u, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_u) .and. & - boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_v) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_u) .and. & - boundsCheck( j_v, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_v) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_u) ) then - - ! Need to get grid cell corners surrounding observation location -- with - ! periodicity, this could be non-consecutive (i.e., NOT necessarily i and i+1); - ! Furthermore, it could be different for the U-grid and V-grid. Remember, for - ! now, we are disallowing observations to be located poleward of the 1st and - ! last mass points. - - call getCorners(i_u, j, id, wrf%dom(id)%type_u, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners U rc = ', rc - - call getCorners(i, j_v, id, wrf%dom(id)%type_v, ll_v, ul_v, lr_v, ur_v, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners V rc = ', rc - - ! Now we want to get the corresponding DART state vector indices, and then - ! interpolate horizontally on TWO different vertical levels (so that we can - ! do the vertical interpolation properly later) - do k2 = 1, 2 - - ! Interpolation for the U field - ill = new_dart_ind(ll(1), ll(2), uniquek(uk)+k2-1, wrf%dom(id)%type_u, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk)+k2-1, wrf%dom(id)%type_u, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk)+k2-1, wrf%dom(id)%type_u, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk)+k2-1, wrf%dom(id)%type_u, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - ugrid = dym*( dxm_u*x_ill + dx_u*x_ilr ) + dy*( dxm_u*x_iul + dx_u*x_iur ) - - ! Interpolation for the V field - ill = new_dart_ind(ll_v(1), ll_v(2), uniquek(uk)+k2-1, wrf%dom(id)%type_v, id) - iul = new_dart_ind(ul_v(1), ul_v(2), uniquek(uk)+k2-1, wrf%dom(id)%type_v, id) - ilr = new_dart_ind(lr_v(1), lr_v(2), uniquek(uk)+k2-1, wrf%dom(id)%type_v, id) - iur = new_dart_ind(ur_v(1), ur_v(2), uniquek(uk)+k2-1, wrf%dom(id)%type_v, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - vgrid = dym_v*( dxm*x_ill + dx*x_ilr ) + dy_v*( dxm*x_iul + dx*x_iur ) - - do e = 1, ens_size - - if ( k(e) == uniquek(uk) ) then ! interpolate only if is the correct k - ! Certain map projections have wind on grid different than true wind (on map) - ! subroutine gridwind_to_truewind is in module_map_utils.f90 - call gridwind_to_truewind(xyz_loc(1), wrf%dom(id)%proj, ugrid(e), vgrid(e), & - utrue(e), vtrue(e)) - - ! Figure out which field was the actual desired observation and store that - ! field as one of the two elements of "fld" (the other element is the other - ! k-level) - if( obs_kind == QTY_U_WIND_COMPONENT) then - fld(k2, e) = utrue(e) - else ! must want v - fld(k2, e) = vtrue(e) - endif - endif - enddo - enddo - endif - - enddo - - endif - - - ! This is for surface wind fields -- NOTE: surface winds are on Mass grid - ! (therefore, TYPE_T), not U-grid & V-grid. - ! Also, because surface winds are at a given single vertical level, - ! only fld(1) will be filled. - else - - if ( ( wrf%dom(id)%type_u10 >= 0 ) .and. ( wrf%dom(id)%type_v10 >= 0 ) ) then - - ! JPH -- should test this for doubly periodic - ! JPH -- does not pass for SCM config, so just do it below - ! Check to make sure retrieved integer gridpoints are in valid range - if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners U10, V10 rc = ', rc - - ! Interpolation for the U10 field - ill = new_dart_ind(ll(1), ll(2), 1, wrf%dom(id)%type_u10, id) - iul = new_dart_ind(ul(1), ul(2), 1, wrf%dom(id)%type_u10, id) - ilr = new_dart_ind(lr(1), lr(2), 1, wrf%dom(id)%type_u10, id) - iur = new_dart_ind(ur(1), ur(2), 1, wrf%dom(id)%type_u10, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - ugrid = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - ! Interpolation for the V10 field - ill = new_dart_ind(ll(1), ll(2), 1, wrf%dom(id)%type_v10, id) - iul = new_dart_ind(ul(1), ul(2), 1, wrf%dom(id)%type_v10, id) - ilr = new_dart_ind(lr(1), lr(2), 1, wrf%dom(id)%type_v10, id) - iur = new_dart_ind(ur(1), ur(2), 1, wrf%dom(id)%type_v10, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - - vgrid = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - do e = 1, ens_size - call gridwind_to_truewind(xyz_loc(1), wrf%dom(id)%proj, ugrid(e), vgrid(e), & - utrue(e), vtrue(e)) - - ! U10 (U at 10 meters) - if( obs_kind == QTY_U_WIND_COMPONENT) then - fld(1, e) = utrue(e) - ! V10 (V at 10 meters) - else - fld(1, e) = vtrue(e) - endif - - enddo - - - endif - endif - endif - - !----------------------------------------------------- - ! 1.b Sensible Temperature (T, T2) - - elseif ( obs_kind == QTY_TEMPERATURE ) then - ! This is for 3D temperature field -- surface temps later - !print*, 'k ', k - - if(.not. surf_var) then - - if ( wrf%dom(id)%type_t >= 0 ) then - - do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners T rc = ', rc - - ! Interpolation for T field at level k - ill = new_dart_ind(ll(1), ll(2), uniquek(uk), wrf%dom(id)%type_t, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk), wrf%dom(id)%type_t, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk), wrf%dom(id)%type_t, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk), wrf%dom(id)%type_t, id) - - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - ! In terms of perturbation potential temperature - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk), id, state_ens_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk), id, state_ens_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk), id, state_ens_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk), id, state_ens_handle, ens_size) - - ! Pressure at location - pres = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - ! Full sensible temperature field - fld(1, e) = (ts0 + a1(e))*(pres(e)/ps0)**kappa - endif - enddo - - ! Interpolation for T field at level k+1 - ill = new_dart_ind(ll(1), ll(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - - ! In terms of perturbation potential temperature - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - - ! Pressure at location - pres = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - ! Full sensible temperature field - fld(2, e) = (ts0 + a1(e))*(pres(e)/ps0)**kappa - endif - enddo - - - endif - enddo - endif - - ! This is for surface temperature (T2) - else - - if ( wrf%dom(id)%type_t2 >= 0 ) then ! HK is there a better way to do this? - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_t2, dxm, dx, dy, dym, ens_size, state_ens_handle) - endif - endif - - !----------------------------------------------------- - ! 1.c Potential Temperature (Theta, TH2) - - ! Note: T is perturbation potential temperature (potential temperature - ts0) - ! TH2 is potential temperature at 2 m - elseif ( obs_kind == QTY_POTENTIAL_TEMPERATURE ) then - ! This is for 3D potential temperature field -- surface pot temps later - if(.not. surf_var) then - - if ( wrf%dom(id)%type_t >= 0 ) then - - do uk = 1, count - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners Theta rc = ', rc - - ! Interpolation for Theta field at level k - ill = new_dart_ind(ll(1), ll(2), uniquek(uk), wrf%dom(id)%type_t, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk), wrf%dom(id)%type_t, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk), wrf%dom(id)%type_t, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk), wrf%dom(id)%type_t, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(1, e) = ts0 + dym*( dxm*x_ill(e) + dx*x_ilr(e)) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - - ! Interpolation for Theta field at level k+1 - ill = new_dart_ind(ll(1), ll(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk)+1, wrf%dom(id)%type_t, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(2, e) = ts0 + dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - endif - enddo - endif - - ! This is for surface potential temperature (TH2) - else - - if ( wrf%dom(id)%type_th2 >= 0 ) then - - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_th2, dxm, dx, dy, dym, ens_size, state_ens_handle) - - endif - endif - - !----------------------------------------------------- - ! 1.d Density (Rho) - elseif (obs_kind == QTY_DENSITY) then - - do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners Rho rc = ', rc - - ! calculate full rho at corners of interp box - ! and interpolate to desired horizontal location - - ! Hmmm, it does not appear that Rho is part of the DART state vector, so there - ! is not a reference to wrf%dom(id)%dart_ind -- we'll have to go right from - ! the corner indices - - ! Interpolation for the Rho field at level k - rho1 = model_rho_t_distrib(ll(1), ll(2), uniquek(uk), id, state_ens_handle, ens_size) - rho2 = model_rho_t_distrib(lr(1), lr(2), uniquek(uk), id, state_ens_handle, ens_size) - rho3 = model_rho_t_distrib(ul(1), ul(2), uniquek(uk), id, state_ens_handle, ens_size) - rho4 = model_rho_t_distrib(ur(1), ur(2), uniquek(uk), id, state_ens_handle, ens_size) - - do e = 1, ens_size - if (k(e) == uniquek(uk) ) then - fld(1, e) = dym*( dxm*rho1(e) + dx*rho2(e) ) + dy*( dxm*rho3(e) + dx*rho4(e) ) - endif - enddo - - ! Interpolation for the Rho field at level k+1 - rho1 = model_rho_t_distrib(ll(1), ll(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - rho2 = model_rho_t_distrib(lr(1), lr(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - rho3 = model_rho_t_distrib(ul(1), ul(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - rho4 = model_rho_t_distrib(ur(1), ur(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - - do e = 1, ens_size - if (k(e) == uniquek(uk) ) then - fld(2, e) = dym*( dxm*rho1(e) + dx*rho2(e) ) + dy*( dxm*rho3(e) + dx*rho4(e) ) - endif - enddo - - endif - - enddo - - !----------------------------------------------------- - ! 1.e Vertical Wind (W) - - elseif ( obs_kind == QTY_VERTICAL_VELOCITY ) then - - ! Adjust zloc for staggered ZNW grid (or W-grid, as compared to ZNU or M-grid) - zloc = zloc + 0.5_r8 - - !>@todo what should you do with this? - k = max(1,int(zloc)) - - call simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_ens_handle ) - - !----------------------------------------------------- - ! 1.f Specific Humidity (SH, SH2) - ! Look at me - ! Convert water vapor mixing ratio to specific humidity: - else if( obs_kind == QTY_SPECIFIC_HUMIDITY ) then - - ! This is for 3D specific humidity -- surface spec humidity later - if(.not. surf_var) then - - ! First confirm that vapor mixing ratio is in the DART state vector - if ( wrf%dom(id)%type_qv >= 0 ) then - - UNIQUEK_LOOP: do uk = 1, count ! for the different ks - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) ! HK why is this type_t - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners SH rc = ', rc - - ! Interpolation for SH field at level k - ill = new_dart_ind(ll(1), ll(2), uniquek(uk), wrf%dom(id)%type_qv, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk), wrf%dom(id)%type_qv, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk), wrf%dom(id)%type_qv, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk), wrf%dom(id)%type_qv, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct k - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) ! think this can go outside the k loop - fld(1,e) = a1(e) /(1.0_r8 + a1(e)) - endif - enddo - - ! Interpolation for SH field at level k+1 - ill = new_dart_ind(ll(1), ll(2), uniquek(uk)+1, wrf%dom(id)%type_qv, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk)+1, wrf%dom(id)%type_qv, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk)+1, wrf%dom(id)%type_qv, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk)+1, wrf%dom(id)%type_qv, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - fld(2,e) = a1(e) /(1.0_r8 + a1(e)) - endif - enddo - endif - enddo UNIQUEK_LOOP - - endif - - ! This is for surface specific humidity (calculated from Q2) - else - - ! confirm that field is in the DART state vector - if ( wrf%dom(id)%type_q2 >= 0 ) then - ! Check to make sure retrieved integer gridpoints are in valid range - if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners SH2 rc = ', rc - - ! Interpolation for the SH2 field - ill = new_dart_ind(ll(1), ll(2), 1, wrf%dom(id)%type_q2, id) - iul = new_dart_ind(ul(1), ul(2), 1, wrf%dom(id)%type_q2, id) - ilr = new_dart_ind(lr(1), lr(2), 1, wrf%dom(id)%type_q2, id) - iur = new_dart_ind(ur(1), ur(2), 1, wrf%dom(id)%type_q2, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - - a1 = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - fld(1,:) = a1 /(1.0_r8 + a1) - - endif - endif - endif - - - !----------------------------------------------------- - ! 1.g Vapor Mixing Ratio (QV, Q2) - else if( obs_kind == QTY_VAPOR_MIXING_RATIO ) then - - ! This is for 3D vapor mixing ratio -- surface QV later - if(.not. surf_var) then - call simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_ens_handle ) - else ! This is for surface QV (Q2) - ! Confirm that right field is in the DART state vector - if ( wrf%dom(id)%type_q2 >= 0 ) then - !HK I am not sure what the type should be - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_q2, dxm, dx, dy, dym, ens_size, state_ens_handle) - endif - endif - - ! Don't accept negative water vapor amounts (?) - fld = max(0.0_r8, fld) - - !----------------------------------------------------- - ! 1.t Pressure (P) - else if( obs_kind == QTY_PRESSURE .or. obs_kind == QTY_SURFACE_PRESSURE ) then - ! This is for the 3D pressure field -- surface pressure later - if(.not. surf_var) then - - do uk = 1, count - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners P rc = ', rc - - ! Hmmm, it does not appear that P is part of the DART state vector, so there - ! is not a reference to wrf%dom(id)%dart_ind -- we'll have to go right from - ! the corner indices - - ! Interpolation for the P field at level k - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk), id, state_ens_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk), id, state_ens_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk), id, state_ens_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk), id, state_ens_handle, ens_size) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct k - fld(1, e) = dym*( dxm*pres1(e) + dx*pres2(e) ) + dy*( dxm*pres3(e) + dx*pres4(e) ) - endif - enddo - - - ! Interpolation for the P field at level k+1 - pres1 = model_pressure_t_distrib(ll(1), ll(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), uniquek(uk)+1, id, state_ens_handle, ens_size) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then ! interpolate only if it is the correct k - fld(2, e) = dym*( dxm*pres1(e) + dx*pres2(e) ) + dy*( dxm*pres3(e) + dx*pres4(e) ) - endif - enddo - - endif - enddo - - ! This is for surface pressure (PSFC) - else - - if ( wrf%dom(id)%type_ps >= 0 ) then - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners PS rc = ', rc - - ! Interpolation for the PS field - ill = new_dart_ind(ll(1), ll(2), 1, wrf%dom(id)%type_ps, id) - iul = new_dart_ind(ul(1), ul(2), 1, wrf%dom(id)%type_ps, id) - ilr = new_dart_ind(lr(1), lr(2), 1, wrf%dom(id)%type_ps, id) - iur = new_dart_ind(ur(1), ur(2), 1, wrf%dom(id)%type_ps, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - do e = 1, ens_size - ! I'm not quite sure where this comes from, but I will trust them on it.... - if ( x_ill(e) /= 0.0_r8 .and. x_ilr(e) /= 0.0_r8 .and. x_iul(e) /= 0.0_r8 .and. & - x_iur(e) /= 0.0_r8 ) then - - fld(1, e) = dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - - ! JPH special treatment for scm configuration, where PS is not defined - ! on the boundaries and the weights are already 1,0 - elseif ( wrf%dom(id)%scm ) then !HK is this redunant? What happens if you fail both conditions. - fld(1, e) = x_ill(e) - endif - enddo - endif - endif - endif - - - !----------------------------------------------------- - ! 1.u Vortex Center Stuff from Yongsheng - else if ( obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON .or. & - obs_kind == QTY_VORTEX_PMIN .or. obs_kind == QTY_VORTEX_WMAX ) then - if( my_task_id() == 0 ) print*, '*** vortex forward operator not tested' - - - -!***************************************************************************** -! END OF VERBATIM BIT - what does this mean? -!***************************************************************************** - - - !----------------------------------------------------- - ! 1.w Geopotential Height (GZ) - - ! GZ is on the ZNW grid (bottom_top_stagger), so its bottom-most level is defined to - ! be at eta = 1 (the surface). Thus, we have a 3D variable that contains a surface - ! variable; the same is true for W as well. If one wants to observe the surface value - ! of either of these variables, then one can simply operate on the full 3D field - ! (toGrid below should return dz ~ 0 and dzm ~ 1) - else if( obs_kind == QTY_GEOPOTENTIAL_HEIGHT ) then - if( my_task_id() == 0 ) print*, '*** geopotential height forward operator not tested' - - ! make sure vector includes the needed field - if ( wrf%dom(id)%type_gz >= 0 ) then - - ! Adjust zloc for staggered ZNW grid (or W-grid, as compared to ZNU or M-grid) - zloc = zloc + 0.5_r8 - k = max(1,int(zloc)) ! Only 1 value of k across the ensemble? - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_gz ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_gz ) .and. & - boundsCheck( k(1), .false., id, dim=3, type=wrf%dom(id)%type_gz ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_gz, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners GZ rc = ', rc - - ! Interpolation for GZ field at level k - ill = new_dart_ind(ll(1), ll(2), k(1), wrf%dom(id)%type_gz, id) - iul = new_dart_ind(ul(1), ul(2), k(1), wrf%dom(id)%type_gz, id) - ilr = new_dart_ind(lr(1), lr(2), k(1), wrf%dom(id)%type_gz, id) - iur = new_dart_ind(ur(1), ur(2), k(1), wrf%dom(id)%type_gz, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - - fld(1,:) = ( dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) + & - dym*( dxm*wrf%dom(id)%phb(ll(1), ll(2), k) + & - dx *wrf%dom(id)%phb(lr(1), lr(2), k) ) + & - dy *( dxm*wrf%dom(id)%phb(ul(1), ul(2), k) + & - dx *wrf%dom(id)%phb(ur(1), ur(2), k) ) ) / gravity - - ! Interpolation for GZ field at level k+1 - ill = new_dart_ind(ll(1), ll(2), k(1)+1, wrf%dom(id)%type_gz, id) - iul = new_dart_ind(ul(1), ul(2), k(1)+1, wrf%dom(id)%type_gz, id) - ilr = new_dart_ind(lr(1), lr(2), k(1)+1, wrf%dom(id)%type_gz, id) - iur = new_dart_ind(ur(1), ur(2), k(1)+1, wrf%dom(id)%type_gz, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - - fld(2, :) = ( dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) + & - dym*( dxm*wrf%dom(id)%phb(ll(1), ll(2), k(1)+1) + & - dx *wrf%dom(id)%phb(lr(1), lr(2), k(1)+1) ) + & - dy *( dxm*wrf%dom(id)%phb(ul(1), ul(2), k(1)+1) + & - dx *wrf%dom(id)%phb(ur(1), ur(2), k(1)+1) ) ) / gravity - - endif - endif - - !----------------------------------------------------- - ! 1.x Surface Elevation (HGT) - - ! Surface Elevation has been added by Ryan Torn to accommodate altimeter observations. - ! HGT is not in the dart_ind vector, so get it from wrf%dom(id)%hgt. - else if( obs_kind == QTY_SURFACE_ELEVATION ) then - - if ( debug ) print*,'Getting surface elevation' - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners HGT rc = ', rc - - ! Interpolation for the HGT field -- HGT is NOT part of state vector x, but rather - ! in the associated domain meta data - fld(1, :) = dym*( dxm*wrf%dom(id)%hgt(ll(1), ll(2)) + & - dx*wrf%dom(id)%hgt(lr(1), lr(2)) ) + & - dy*( dxm*wrf%dom(id)%hgt(ul(1), ul(2)) + & - dx*wrf%dom(id)%hgt(ur(1), ur(2)) ) - - endif - - - !----------------------------------------------------- - ! 1.y Surface Skin Temperature (TSK) - - else if( obs_kind == QTY_SKIN_TEMPERATURE ) then - ! make sure vector includes the needed field - if ( wrf%dom(id)%type_tsk >= 0 ) then - call surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf%dom(id)%type_tsk, dxm, dx, dy, dym, ens_size, state_ens_handle) - endif - - !----------------------------------------------------- - ! 1.z Land Mask (XLAND) - - ! Land Mask has been added to accommodate satellite observations. - ! XLAND is not in the dart_ind vector, so get it from wrf%dom(id)%land - else if( obs_kind == QTY_LANDMASK ) then - if( my_task_id() == 0 ) print*, '*** Land mask forward operator not tested' - - if ( debug ) print*,'Getting land mask' - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners XLAND rc = ', rc - - ! Interpolation for the XLAND field -- XLAND is NOT part of state vector x, but rather - ! in the associated domain meta data - fld(1, :) = dym*( dxm*real(wrf%dom(id)%land(ll(1), ll(2))) + & - dx*real(wrf%dom(id)%land(lr(1), lr(2))) ) + & - dy*( dxm*real(wrf%dom(id)%land(ul(1), ul(2))) + & - dx*real(wrf%dom(id)%land(ur(1), ur(2))) ) - - endif - - !----------------------------------------------------- - ! If obs_kind is not negative (for identity obs), or if it is not one of the above 15 - ! explicitly checked-for kinds, then set error istatus and missing_r8. - else - - expected_obs = missing_r8 - istatus = 3 - if (debug) print*, 'unrecognized obs KIND, value = ', obs_kind - deallocate(v_h, v_p) - return - - endif - - - !---------------------------------- - ! 2. Vertical Interpolation - !---------------------------------- - - ! Do vertical interpolation -- only for non-surface, non-identity obs. - - ! The previous section (1. Horizontal Interpolation) has produced a variable called - ! "fld", which nominally has two entries in it. 3D fields have hopefully produced - ! 2 non-zero entries, whereas surface fields only have filled the first entry. - ! If a full 3D field, then do vertical interpolation between sandwiched model levels - ! (k and k+1). - - ! Check to make sure that we did something sensible in the Horizontal Interpolation - ! section above. All valid obs_kinds will have changed fld(1,e). - - !HK I am unsure as to whether this should be done on the array expected_obs or one ensemble - ! member (e) at a time. - do e = 1, ens_size - - if ( fld(1,e) == missing_r8 ) then - - expected_obs(e) = missing_r8 - - else ! We purposefully changed fld(1,e), so continue onward - - ! If a surface variable, or a variable with no particular vertical location - ! (basically the entire column) then no need to do any vertical interpolation - if ( surf_var .or. vert_is_undef(location) ) then - - !obs_val = fld(1) - expected_obs(e) = fld(1,e) !HK - - ! If an interior variable, then we DO need to do vertical interpolation - else - - ! First make sure fld(2,:) is no longer a missing value - if ( fld(2,e) == missing_r8 ) then !HK should be any? - - expected_obs(e) = missing_r8 - - ! Do vertical interpolation -- at this point zloc is >= 1 unless - ! the namelist value allow_obs_below_vol is true, in which case - ! it is >= 0, and < 1 is a request to extrapolate. - else - - ! Get fractional distances between grid points - call toGrid(zloc(e), k(e), dz(e), dzm(e)) - if (debug) print*, 'zloc(e), k(e), dz(e), dzm(e) = ', zloc(e), k(e), dz(e), dzm(e) - if (debug) print*, 'fld(1,e), fld(2,e) = ', fld(1,e), fld(2,e) - - ! If you get here and zloc < 1.0, then k will be 0, and - ! we should extrapolate. fld(1,:) and fld(2,:) where computed - ! at levels 1 and 2. - - if (k(e) >= 1) then - ! Linearly interpolate between grid points - expected_obs(e) = dzm(e)*fld(1,e) + dz(e)*fld(2,e) - if (debug) print*, 'interpolated obs_val = ', expected_obs(e) - else - ! Extrapolate below first level. - expected_obs(e) = fld(1,e) - (fld(2,e)-fld(1,e))*dzm(e) - if (debug) print*, 'extrapolated obs_val = ', expected_obs(e) - endif - - endif - endif - endif - - ! Fill in failed copies - if ( failedcopies(e) == missing_r8 ) expected_obs(e) = missing_r8 - - enddo - -endif ! end of "if ( obs_kind < 0 )" - -! Now that we are done, check to see if a missing value somehow -! made it through without already having set an error return code. -do e = 1, ens_size - if ( expected_obs(e) == missing_r8 .and. istatus(e) == 0 ) then - istatus(e) = 99 - endif -enddo - -! Pring the observed value if in debug mode -if(debug) then - print*,'model_interpolate_distrib() return value for obs_kind ',obs_kind, ' = ',expected_obs -endif - -! Deallocate variables before exiting -deallocate(v_h, v_p) -deallocate(x_ill, x_iul, x_ilr, x_iur) -deallocate(failedcopies) -deallocate(uniquek) - -end subroutine model_interpolate_distrib - -!####################################################################### -!> This is used in the filter_assim. The vertical conversion is done using the -!> mean state. -!> I think at the moment you are over communicating -subroutine vert_convert_distrib(state_ens_handle, location, obs_kind, istatus) - -! This subroutine converts a given ob/state vertical coordinate to -! the vertical localization coordinate type requested through the -! model_mod namelist. -! -! Notes: (1) obs_kind is only necessary to check whether the ob -! is an identity ob. -! (2) This subroutine can convert both obs' and state points' -! vertical coordinates. Remember that state points get -! their DART location information from get_state_meta_data -! which is called by filter_assim during the assimilation -! process. -! (3) x is the relevant DART state vector for carrying out -! interpolations necessary for the vertical coordinate -! transformations. As the vertical coordinate is only used -! in distance computations, this is actually the "expected" -! vertical coordinate, so that computed distance is the -! "expected" distance. Thus, under normal circumstances, -! x that is supplied to vert_convert should be the -! ensemble mean. Nevertheless, the subroutine has the -! functionality to operate on any DART state vector that -! is supplied to it. - -type(ensemble_type), intent(in) :: state_ens_handle -type(location_type), intent(inout) :: location -integer, intent(in) :: obs_kind -integer, intent(out) :: istatus - -! changed zloc to zin and zout, since the point of this routine -! is to convert zloc from one value to another. ztype{in,out} -! are the vertical types as defined by the 3d sphere locations mod. -real(r8) :: xloc, yloc, zin, xyz_loc(3), zout -integer :: id, i, j, rc, ztypein, ztypeout -real(r8) :: dx,dy,dxm,dym, dz, dzm -integer, dimension(2) :: ll, lr, ul, ur - -real(r8), allocatable, dimension(:) :: v_h -real(r8), allocatable, dimension(:) :: v_p !< only need the mean value - -! local vars, used in calculating pressure and height -real(r8) :: pres1, pres2, pres3, pres4 -real(r8) :: presa, presb, psurf -real(r8) :: hgt1, hgt2, hgt3, hgt4, hgta, hgtb - -!HK -real(r8) :: zk -integer :: k -logical :: lev0 - -! assume failure. -istatus = 1 - -! first off, check if ob is identity ob. if so get_state_meta_data() will -! return location information already in the requested vertical type. -!>@todo This in not true anymore if you don't convert all the state variables -! to the localization coordinate in get_state_meta_data -if (obs_kind < 0) then - call get_state_meta_data_distrib(state_ens_handle, obs_kind,location) - istatus = 0 - return -endif - -! if the existing coord is already in the requested vertical units -! or if the vert is 'undef' which means no specifically defined -! vertical coordinate, return now. -ztypein = nint(query_location(location, 'which_vert')) -ztypeout = vert_localization_coord -if ((ztypein == ztypeout) .or. (ztypein == VERTISUNDEF)) then - istatus = 0 - return -endif - -! we do need to convert the vertical. start by -! extracting the location lat/lon/vert values. -xyz_loc = get_location(location) - -!if(my_task_id() == 0) write(10, *) xyz_loc - -! the routines below will use zin as the incoming vertical value -! and zout as the new outgoing one. start out assuming failure -! (zout = missing) and wait to be pleasantly surprised when it works. -zin = xyz_loc(3) -zout = missing_r8 - -! if the vertical is missing to start with, return it the same way -! with the requested type as out. -if (zin == missing_r8) then - location = set_location(xyz_loc(1),xyz_loc(2),missing_r8,ztypeout) - !print*, 'missing rank', my_task_id() - return -endif - -! first obtain domain id, and where we are in the grid (xloc,yloc) -if ( .not. scm ) then - call get_domain_info(xyz_loc(1),xyz_loc(2),id,xloc,yloc) -else - id = 1 - xloc = 1.0_r8 - yloc = 1.0_r8 -endif - -! cannot find domain info, return error. set location to missing value -! but using requested vertical coord. istatus already set above. -if (id==0) then - location = set_location(xyz_loc(1),xyz_loc(2),missing_r8,ztypeout) - !print*, 'can not find domain rank', my_task_id() - - return -endif - -! get integer (west/south) grid point and distances to neighboring grid points -! distances are used as weights to carry out horizontal interpolations -call toGrid(xloc,i,dx,dxm) -call toGrid(yloc,j,dy,dym) - -! Check that integer indices of Mass grid are in valid ranges for the given -! boundary conditions (i.e., periodicity). if not, bail here. -if ( .not. boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .or. & - .not. boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - location = set_location(xyz_loc(1),xyz_loc(2),missing_r8,ztypeout) - !print*, 'out of bounds rank', my_task_id() - - return -endif - -!HK Note the result is not bitwise -!if(my_task_id() == 0) then -! write(10, *) '------' -! write(10, *) 'xyz_loc ', xyz_loc -! location = set_location(xyz_loc(1),xyz_loc(2),xyz_loc(3),ztypeout) -! write(10, *) 'set_location ', location%lon, location%lat, location%vloc -! xyz_loc = get_location(location) -! write(10, *) 'get_location ', xyz_loc -! location = set_location(xyz_loc(1),xyz_loc(2),xyz_loc(3),ztypeout) -! write(10, *) 'set_location ', location%lon, location%lat, location%vloc -!endif - - -! Get indices of corners (i,i+1,j,j+1), which depend on periodicities. -! since the boundsCheck routines succeeded, this call should never fail -! so make it a fatal error if it does return an error code. -call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) -if ( rc /= 0 ) then - write(errstring,*) 'for i,j: ', i, j, ' getCorners rc = ', rc - call error_handler(E_ERR,'model_mod.f90::vert_convert', errstring, & - source, revision, revdate) -endif - -! at this point we have set: xloc, yloc, i, j, ll, ul, lr, ur, zin, id, -! dx, dxm, dy, dym already, and i, j have been checked to verify they -! are valid values for this grid. if you need k, dz, dzm below you still -! need to compute and validate them first. - -! Convert the incoming vertical type (ztypein) into the vertical -! localization coordinate given in the namelist (ztypeout). - -! convert from: -select case (ztypein) - -! ------------------------------------------------------- -! incoming vertical coordinate is 'model level number' -! ---------------------------------------------------- --- -case (VERTISLEVEL) - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - - ! get neighboring mass level indices & compute weights to zin - call toGrid(zin,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! compute pressure at all neighboring mass points and interpolate - presa = model_pressure_t_distrib(ll(1), ll(2), k ,id, state_ens_handle) - presb = model_pressure_t_distrib(ll(1), ll(2), k+1,id, state_ens_handle) - pres1 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(lr(1), lr(2), k ,id, state_ens_handle) - presb = model_pressure_t_distrib(lr(1), lr(2), k+1,id, state_ens_handle) - pres2 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ul(1), ul(2), k ,id, state_ens_handle) - presb = model_pressure_t_distrib(ul(1), ul(2), k+1,id, state_ens_handle) - pres3 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ur(1), ur(2), k ,id, state_ens_handle) - presb = model_pressure_t_distrib(ur(1), ur(2), k+1,id, state_ens_handle) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - - ! get neighboring mass level indices & compute weights to zin - ! need to add half a grid to get to staggered vertical coordinate - call toGrid(zin+0.5_r8,k,dz,dzm) - !print*, 'k', k, 'zin ', zin - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_gz)) then - ! print*, 'bounds check fail' - goto 100 - endif - - ! HK if this is a piece of state, I believe you don't need to the four corners, - ! the location is the lower left corner. - ! compute height at all neighboring mass points and interpolate - ! You have already converted the state in get_state_meta_data - hgta = model_height_w_distrib(ll(1), ll(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(ll(1), ll(2), k+1,id,state_ens_handle) - hgt1 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(lr(1), lr(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(lr(1), lr(2), k+1,id,state_ens_handle) - hgt2 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ul(1), ul(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(ul(1), ul(2), k+1,id,state_ens_handle) - hgt3 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ur(1), ur(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(ur(1), ur(2), k+1,id,state_ens_handle) - hgt4 = dzm*hgta + dz*hgtb - zout = dym*( dxm*hgt1 + dx*hgt2 ) + dy*( dxm*hgt3 + dx*hgt4 ) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - - ! get neighboring mass level indices & compute weights to zin - call toGrid(zin,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! pressure at height - presa = model_pressure_t_distrib(ll(1), ll(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ll(1), ll(2), k+1,id,state_ens_handle) - pres1 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(lr(1), lr(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(lr(1), lr(2), k+1,id,state_ens_handle) - pres2 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ul(1), ul(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ul(1), ul(2), k+1,id,state_ens_handle) - pres3 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ur(1), ur(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ur(1), ur(2), k+1,id,state_ens_handle) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - ! surface pressure - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_ens_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_ens_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_ens_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_ens_handle) - zout = -log(zout / (dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ))) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'model level number' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was model level.') - - - end select ! incoming vert was model level - -! ------------------------------------------------------- -! incoming vertical coordinate is 'pressure' in Pa -! ------------------------------------------------------- -case (VERTISPRESSURE) - - ! If obs is by pressure: get corresponding mass level zk, - ! then get neighboring mass level indices and compute weights - - ! get model pressure profile and - ! get pressure vertical co-ordinate in model level number - allocate(v_p(0:wrf%dom(id)%bt)) - !HK This has already been called in model interpolate - ! - not for observations that were not in the assimilate catagory - call get_model_pressure_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_p, state_ens_handle) - - !if (my_task_id() == 0) then - ! write(10, *) v_p - !endif - - call pres_to_zk(zin, v_p(:), wrf%dom(id)%bt, zk, lev0) - deallocate(v_p) - - !if (my_task_id() == 0) then - ! write(10, *) zk - !endif - - ! if you cannot get a model level out of the pressure profile, bail to end - if ( zk == missing_r8 ) goto 100 !HK I don't think zk = missing_r8 even if there is an error - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' in Pa - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - ! pres_to_zk() above converted pressure into a real number - ! of vertical model levels, including the fraction. - zout = zk - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' in Pa - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - ! adding 0.5 to get to the staggered vertical grid - ! because height is on staggered vertical grid - call toGrid(zk+0.5, k, dz, dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_gz)) goto 100 - - ! HK should check whether this is state or not. If it is state, there is no need to - ! do all four corners, it is the lower left corner. - - ! compute height at all neighboring mass points and interpolate - hgta = model_height_w_distrib(ll(1), ll(2), k ,id, state_ens_handle) - hgtb = model_height_w_distrib(ll(1), ll(2), k+1,id, state_ens_handle) - hgt1 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(lr(1), lr(2), k ,id, state_ens_handle) - hgtb = model_height_w_distrib(lr(1), lr(2), k+1,id, state_ens_handle) - hgt2 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ul(1), ul(2), k ,id, state_ens_handle) - hgtb = model_height_w_distrib(ul(1), ul(2), k+1,id, state_ens_handle) - hgt3 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ur(1), ur(2), k ,id, state_ens_handle) - hgtb = model_height_w_distrib(ur(1), ur(2), k+1,id, state_ens_handle) - hgt4 = dzm*hgta + dz*hgtb - zout = dym*( dxm*hgt1 + dx*hgt2 ) + dy*( dxm*hgt3 + dx*hgt4 ) - - !if (my_task_id() == 0) then - ! write(10, *) zout - !endif - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' in Pa - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - call toGrid(zk,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! compute surface pressure at all neighboring mass points and interpolate - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_ens_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_ens_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_ens_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_ens_handle) - zout = -log(zin / (dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ))) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'pressure' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was pressure.') - - - end select ! incoming vert was pressure - - -! ------------------------------------------------------- -! incoming vertical coordinate is 'height' in meters -! ------------------------------------------------------- -case (VERTISHEIGHT) - - ! If obs is by height: get corresponding mass level zk, - ! then get neighboring mass level indices and compute weights - - ! get model height profile and - ! get height vertical co-ordinate in model level number - allocate(v_h(0:wrf%dom(id)%bt)) - call get_model_height_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_h, state_ens_handle) - call height_to_zk(zin, v_h, wrf%dom(id)%bt,zk,lev0) - deallocate(v_h) - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - ! height_to_zk() above converted pressure into a real number - ! of vertical model levels, including the fraction. - zout = zk - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - call toGrid(zk,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! compute pressure at all neighboring mass points and interpolate - presa = model_pressure_t_distrib(ll(1), ll(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ll(1), ll(2), k+1,id,state_ens_handle) - pres1 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(lr(1), lr(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(lr(1), lr(2), k+1,id,state_ens_handle) - pres2 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ul(1), ul(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ul(1), ul(2), k+1,id,state_ens_handle) - pres3 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ur(1), ur(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ur(1), ur(2), k+1,id,state_ens_handle) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - call toGrid(zk,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_t)) goto 100 - - ! pressure at height - presa = model_pressure_t_distrib(ll(1), ll(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ll(1), ll(2), k+1,id,state_ens_handle) - pres1 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(lr(1), lr(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(lr(1), lr(2), k+1,id,state_ens_handle) - pres2 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ul(1), ul(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ul(1), ul(2), k+1,id,state_ens_handle) - pres3 = dzm*presa + dz*presb - presa = model_pressure_t_distrib(ur(1), ur(2), k ,id,state_ens_handle) - presb = model_pressure_t_distrib(ur(1), ur(2), k+1,id,state_ens_handle) - pres4 = dzm*presa + dz*presb - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - ! surface pressure - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_ens_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_ens_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_ens_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_ens_handle) - zout = -log(zout / (dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ))) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'height' in meters - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was height.') - - - end select ! incoming vert was height - - -! ------------------------------------------------------- -! incoming vertical coordinate is 'scale height' -! ------------------------------------------------------- -case (VERTISSCALEHEIGHT) - - ! If obs is by scale height: compute the surface pressure, - ! get corresponding mass level zk, then get neighboring mass - ! level indices and compute weights - - pres1 = model_pressure_s_distrib(ll(1), ll(2), id,state_ens_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id,state_ens_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id,state_ens_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id,state_ens_handle) - psurf = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - ! get model pressure profile and - ! get pressure vertical co-ordinate in model level number - allocate(v_p(0:wrf%dom(id)%bt)) - call get_model_pressure_profile_distrib(i,j,dx,dy,dxm,dym,wrf%dom(id)%bt,id,v_p, state_ens_handle) - call pres_to_zk(exp(-zin)*psurf, v_p, wrf%dom(id)%bt,zk,lev0) - deallocate(v_p) - - ! if you cannot get a model level out of the pressure profile, bail to end - if (zk == missing_r8) goto 100 - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - ! pres_to_zk() above converted scale height/pressure into - ! a real number of vertical model levels, including the fraction. - zout = zk - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - ! adding 0.5 to get to the staggered vertical grid - ! because height is on staggered vertical grid - call toGrid(zk+0.5,k,dz,dzm) - - ! Check that integer height index is in valid range. if not, bail to end - if(.not. boundsCheck(k, .false., id, dim=3, type=wrf%dom(id)%type_gz)) goto 100 - - ! compute height at all neighboring mass points and interpolate - hgta = model_height_w_distrib(ll(1), ll(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(ll(1), ll(2), k+1,id,state_ens_handle) - hgt1 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(lr(1), lr(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(lr(1), lr(2), k+1,id,state_ens_handle) - hgt2 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ul(1), ul(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(ul(1), ul(2), k+1,id,state_ens_handle) - hgt3 = dzm*hgta + dz*hgtb - hgta = model_height_w_distrib(ur(1), ur(2), k ,id,state_ens_handle) - hgtb = model_height_w_distrib(ur(1), ur(2), k+1,id,state_ens_handle) - hgt4 = dzm*hgta + dz*hgtb - zout = dym*( dxm*hgt1 + dx*hgt2 ) + dy*( dxm*hgt3 + dx*hgt4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - zout = exp(-zin)*psurf - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'scale height' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was scale height.') - - - end select ! incoming vert was scale height - -! ------------------------------------------------------- -! incoming vertical coordinate is 'surface' (assumes zin is height in meters) -! ------------------------------------------------------- -case(VERTISSURFACE) - - ! convert into: - select case (ztypeout) - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'model level' - ! ------------------------------------------------------- - case (VERTISLEVEL) - zout = 1.0_r8 - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'pressure' in Pa - ! ------------------------------------------------------- - case (VERTISPRESSURE) - - ! compute surface pressure at all neighboring mass points - pres1 = model_pressure_s_distrib(ll(1), ll(2), id, state_ens_handle) - pres2 = model_pressure_s_distrib(lr(1), lr(2), id, state_ens_handle) - pres3 = model_pressure_s_distrib(ul(1), ul(2), id, state_ens_handle) - pres4 = model_pressure_s_distrib(ur(1), ur(2), id, state_ens_handle) - zout = dym*( dxm*pres1 + dx*pres2 ) + dy*( dxm*pres3 + dx*pres4 ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'scale height' - ! ------------------------------------------------------- - case (VERTISSCALEHEIGHT) - zout = -log(1.0_r8) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate should be 'height' in meters - ! ------------------------------------------------------- - case (VERTISHEIGHT) - ! a surface ob is assumed to have height as vertical coordinate. - ! this code needs to be revised if this is not true - ! (in that case uncomment lines below to get terrain height - ! from model) - zout = zin - !! or: directly interpolate terrain height at neighboring mass points - !zout = dym*( dxm*wrf%dom(id)%hgt(i, j) + & - ! dx*wrf%dom(id)%hgt(i+1,j) ) + & - ! dy*( dxm*wrf%dom(id)%hgt(i, j+1) + & - ! dx*wrf%dom(id)%hgt(i+1,j+1) ) - - - ! ------------------------------------------------------- - ! incoming vertical coordinate is 'surface' - ! outgoing vertical coordinate is unrecognized - ! ------------------------------------------------------- - case default - write(errstring,*) 'Requested vertical coordinate not recognized: ', ztypeout - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate, & - text2='Incoming vertical coordinate was surface.') - - - end select ! incoming vert was surface - -! ------------------------------------------------------- -! incoming vertical coordinate has no case section -! ------------------------------------------------------- -case default - write(errstring,*) 'Incoming vertical coordinate type not recognized: ',ztypein - call error_handler(E_ERR,'vert_convert', errstring, & - source, revision, revdate) - -end select ! incoming z vertical type - - -! on error, come here. istatus was set to 1 and zout to missing_r8 -! so unless they have been reset to good values, things did not work. -100 continue - -! Returned location -location = set_location(xyz_loc(1),xyz_loc(2),zout,ztypeout) - -!if(my_task_id() == 0 ) print*, xyz_loc(1), xyz_loc(2), zout, ztypeout - -! Set successful return code only if zout has good value -if(zout /= missing_r8) istatus = 0 - -end subroutine vert_convert_distrib - -!####################################################################### - - -function get_wrf_index( i,j,k,var_type,id ) - -integer, intent(in) :: i,j,k,var_type,id - -integer :: get_wrf_index -integer :: in - -write(errstring,*)'function get_wrf_index should not be called -- still needs updating!' -call error_handler(E_ERR,'get_wrf_index', errstring, & - source, revision, revdate) - -do in = 1, wrf%dom(id)%number_of_wrf_variables - if(var_type == wrf%dom(id)%var_type(in) ) then - exit - endif -enddo - -! If one decides to use get_wrf_index, then the following test should be updated -! to take periodicity into account at the boundaries -- or should it? -if(i >= 1 .and. i <= wrf%dom(id)%var_size(1,in) .and. & - j >= 1 .and. j <= wrf%dom(id)%var_size(2,in) .and. & - k >= 1 .and. k <= wrf%dom(id)%var_size(3,in)) then - - get_wrf_index = wrf%dom(id)%dart_ind(i,j,k,var_type) - -!!$ get_wrf_index = wrf%dom(id)%var_index(1,in)-1 + & -!!$ i + wrf%dom(id)%var_size(1,in)*((j-1) + & -!!$ wrf%dom(id)%var_size(2,in)*(k-1)) - -else - - write(errstring,*)'Indices ',i,j,k,' exceed grid dimensions: ', & - wrf%dom(id)%var_size(1,in), & - wrf%dom(id)%var_size(2,in),wrf%dom(id)%var_size(3,in) - call error_handler(E_ERR,'get_wrf_index', errstring, & - source, revision, revdate) - -endif - -end function get_wrf_index - - -!*********************************************************************** - - -subroutine get_wrf_horizontal_location( i, j, var_type, id, long, lat ) - -integer, intent(in) :: i,j,var_type, id -real(r8), intent(out) :: long, lat - -! given i, j indices into the horizontal grid return the lat/long. -! if u or v staggering use the staggered grids, otherwise use the mass -! grid. this code has changed -- earlier versions only had the mass -! grid available and used it to compute cell midpoints and called them -! the staggered points. now that all three grids are being read, look -! up the point locations directly from the appropriate array. - -if (var_type == wrf%dom(id)%type_u) then - long = wrf%dom(id)%longitude_u(i,j) - lat = wrf%dom(id)%latitude_u(i,j) -elseif (var_type == wrf%dom(id)%type_v) then - long = wrf%dom(id)%longitude_v(i,j) - lat = wrf%dom(id)%latitude_v(i,j) -else - long = wrf%dom(id)%longitude(i,j) - lat = wrf%dom(id)%latitude(i,j) -endif - -do while (long < 0.0_r8) - long = long + 360.0_r8 -end do -do while (long > 360.0_r8) - long = long - 360.0_r8 -end do - -end subroutine get_wrf_horizontal_location - - - -!*********************************************************************** - - -function nc_write_model_atts( ncFileID ) result (ierr) -!----------------------------------------------------------------- -! Writes the model-specific attributes to a netCDF file -! A. Caya May 7 2003 -! T. Hoar Mar 8 2004 writes prognostic flavor - -logical, parameter :: write_precip = .false. - -integer, intent(in) :: ncFileID ! netCDF file identifier -integer :: ierr ! return value of function - -!----------------------------------------------------------------- - -integer :: nDimensions, nVariables, nAttributes, unlimitedDimID -integer :: StateVarDimID, StateVarID, TimeDimID - -integer, dimension(num_domains) :: weDimID, weStagDimID, snDimID, snStagDimID, & - btDimID, btStagDimID, slSDimID, tmp - -integer :: MemberDimID, DomDimID -integer :: DXVarID, DYVarID, TRUELAT1VarID, TRUELAT2VarID, STAND_LONVarID -integer :: CEN_LATVarID, CEN_LONVarID, MAP_PROJVarID -integer :: PERIODIC_XVarID, POLARVarID -integer :: metadataID, wrfStateID, wrfDimID, WRFStateVarID, WRFStateDimID - -integer, dimension(num_domains) :: DNVarID, ZNUVarID, DNWVarID, phbVarID, & - MubVarID, LonVarID, LatVarID, ilevVarID, XlandVarID, hgtVarID , LatuVarID, & - LatvVarID, LonuVarID, LonvVarID, ZNWVarID - -! currently unused, but if needed could be added back in. these fields -! only appear to be supported in certain projections, so the code should -! test to be sure they exist before trying to read them from the netcdf file. -!integer, dimension(num_domains) :: MapFacMVarID, MapFacUVarID, MapFacVVarID - -integer :: var_id -integer :: i, id - -character(len=129) :: title - -character(len=8) :: crdate ! needed by F90 DATE_AND_TIME intrinsic -character(len=10) :: crtime ! needed by F90 DATE_AND_TIME intrinsic -character(len=5) :: crzone ! needed by F90 DATE_AND_TIME intrinsic -integer, dimension(8) :: values ! needed by F90 DATE_AND_TIME intrinsic -character(len=NF90_MAX_NAME) :: str1 - -character (len=1) :: idom - -character(len=129), allocatable, dimension(:) :: textblock -integer :: nlines, linelen -integer :: linelenDimID, nlinesDimID, nmlVarID -integer :: ind, my_index -character(len=NF90_MAX_NAME) :: attname, varname -character(len=129) :: unitsval, descriptionval, coordinatesval, long_nameval, coordinate_char -integer, dimension(5) :: dimids_3D -integer, dimension(4) :: dimids_2D -logical :: debug = .false. - -!----------------------------------------------------------------- - -ierr = 0 ! assume normal termination - -!----------------------------------------------------------------- -! make sure ncFileID refers to an open netCDF file, -! and then put into define mode. -!----------------------------------------------------------------- - -call nc_check(nf90_get_att(ncFileID, NF90_GLOBAL, 'title', title), 'nc_write_model_atts','get_att') -call nc_check(nf90_Inquire(ncFileID, nDimensions, nVariables, nAttributes, unlimitedDimID), & - 'nc_write_model_atts','inquire') -call nc_check(nf90_Redef(ncFileID),'nc_write_model_atts','redef') - -!----------------------------------------------------------------- -! We need the dimension ID for the number of copies -!----------------------------------------------------------------- - -call nc_check(nf90_inq_dimid(ncid=ncFileID, name="NMLlinelen", dimid=linelenDimID), & - 'nc_write_model_atts','inq_dimid NMLlinelen') -call nc_check(nf90_inq_dimid(ncid=ncFileID, name="copy", dimid=MemberDimID), & - 'nc_write_model_atts','inq_dimid copy') -call nc_check(nf90_inq_dimid(ncid=ncFileID, name="time", dimid= TimeDimID), & - 'nc_write_model_atts','inq_dimid time') - -if ( TimeDimID /= unlimitedDimId ) then - write(errstring,*)'Time Dimension ID ',TimeDimID, & - ' must match Unlimited Dimension ID ',unlimitedDimID - call error_handler(E_ERR,'nc_write_model_atts', errstring, source, revision, revdate) -endif - -!----------------------------------------------------------------- -! Define the model size, state variable dimension ... whatever ... -!----------------------------------------------------------------- -call nc_check(nf90_def_dim(ncid=ncFileID, name="StateVariable", & - len=wrf%model_size, dimid = StateVarDimID), & - 'nc_write_model_atts','def_dim StateVariable') - -!----------------------------------------------------------------- -! Write Global Attributes -!----------------------------------------------------------------- -call DATE_AND_TIME(crdate,crtime,crzone,values) -write(str1,'(''YYYY MM DD HH MM SS = '',i4,5(1x,i2.2))') & - values(1), values(2), values(3), values(5), values(6), values(7) - -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "creation_date",str1), & - 'nc_write_model_atts','put_att creation_date') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model","WRF"), & - 'nc_write_model_atts','put_att model') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model_source",source), & - 'nc_write_model_atts','put_att model_source') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model_revision",revision), & - 'nc_write_model_atts','put_att model_revision') -call nc_check(nf90_put_att(ncFileID, NF90_GLOBAL, "model_revdate",revdate), & - 'nc_write_model_atts','put_att model_revdate') - -!----------------------------------------------------------------- -! how about namelist input? might be nice to save ... -! long lines are truncated when read into textblock -!----------------------------------------------------------------- - -call find_textfile_dims(wrf_nml_file, nlines, linelen) -if (nlines <= 0 .or. linelen <= 0) have_wrf_nml_file = .false. - -if (have_wrf_nml_file) then - allocate(textblock(nlines)) - textblock = '' - - call nc_check(nf90_def_dim(ncid=ncFileID, name="nlines", & - len = nlines, dimid = nlinesDimID), & - 'nc_write_model_atts', 'def_dim nlines ') -endif - -!----------------------------------------------------------------- -! Define the dimensions IDs -!----------------------------------------------------------------- - -call nc_check(nf90_def_dim(ncid=ncFileID, name="domain", & - len = num_domains, dimid = DomDimID), & - 'nc_write_model_atts','def_dim domain') - -do id=1,num_domains - write( idom , '(I1)') id - call nc_check(nf90_def_dim(ncid=ncFileID, name="west_east_d0"//idom, & - len = wrf%dom(id)%we, dimid = weDimID(id)), & - 'nc_write_model_atts','def_dim west_east_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="west_east_stag_d0"//idom, & - len = wrf%dom(id)%wes, dimid = weStagDimID(id)), & - 'nc_write_model_atts','def_dim west_east_stag_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="south_north_d0"//idom, & - len = wrf%dom(id)%sn, dimid = snDimID(id)), & - 'nc_write_model_atts','def_dim south_north_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="south_north_stag_d0"//idom, & - len = wrf%dom(id)%sns, dimid = snStagDimID(id)), & - 'nc_write_model_atts','def_dim south_north_stag_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="bottom_top_d0"//idom, & - len = wrf%dom(id)%bt, dimid = btDimID(id)), & - 'nc_write_model_atts','def_dim bottom_top_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="bottom_top_stag_d0"//idom, & - len = wrf%dom(id)%bts, dimid = btStagDimID(id)), & - 'nc_write_model_atts','def_dim bottom_top_stag_d0'//idom) - call nc_check(nf90_def_dim(ncid=ncFileID, name="soil_layers_stag_d0"//idom, & - len = wrf%dom(id)%sls, dimid = slSDimID(id)), & - 'nc_write_model_atts','def_dim soil_layers_stag_d0'//idom) -enddo - -!----------------------------------------------------------------- -! Create the (empty) Variables and the Attributes -!----------------------------------------------------------------- - -!----------------------------------------------------------------- -! Create the (empty) static variables and their attributes -! Commented block is from wrfinput -!----------------------------------------------------------------- - -if (have_wrf_nml_file) then - call nc_check(nf90_def_var(ncFileID,name="WRFnml", xtype=nf90_char, & - dimids = (/ linelenDimID, nlinesDimID /), varid=nmlVarID), & - 'nc_write_model_atts', 'def_var WRFnml') - call nc_check(nf90_put_att(ncFileID, nmlVarID, "long_name", & - "namelist.input contents"), 'nc_write_model_atts', 'put_att WRFnml') -endif - -call nc_check(nf90_def_var(ncFileID, name="DX", xtype=nf90_real, & - dimids= DomDimID, varid=DXVarID), & - 'nc_write_model_atts','def_var DX') -call nc_check(nf90_put_att(ncFileID, DXVarID, "long_name", "X HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DX long_name') -call nc_check(nf90_put_att(ncFileID, DXVarID, "description", "X HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DX description') -call nc_check(nf90_put_att(ncFileID, DXVarID, "units", "m"), & - 'nc_write_model_atts','put_att DX units') - -call nc_check(nf90_def_var(ncFileID, name="DY", xtype=nf90_real, & - dimids= DomDimID, varid=DYVarID), & - 'nc_write_model_atts','def_var DY') -call nc_check(nf90_put_att(ncFileID, DYVarID, "long_name", "Y HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DY long_name') -call nc_check(nf90_put_att(ncFileID, DYVarID, "description", "Y HORIZONTAL RESOLUTION"), & - 'nc_write_model_atts','put_att DY description') -call nc_check(nf90_put_att(ncFileID, DYVarID, "units", "m"), & - 'nc_write_model_atts','put_att DY units') - -call nc_check(nf90_def_var(ncFileID, name="TRUELAT1", xtype=nf90_real, & - dimids= DomDimID, varid=TRUELAT1VarID), & - 'nc_write_model_atts','def_var TRUELAT1') -call nc_check(nf90_put_att(ncFileID, TRUELAT1VarID, "long_name", & - "first standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT1 long_name') -call nc_check(nf90_put_att(ncFileID, TRUELAT1VarID, "description", & - "first standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT1 description') -call nc_check(nf90_put_att(ncFileID, TRUELAT1VarID, "units", & - "degrees, negative is south"), & - 'nc_write_model_atts','put_att TRUELAT1 units') - -call nc_check(nf90_def_var(ncFileID, name="TRUELAT2", xtype=nf90_real, & - dimids= DomDimID, varid=TRUELAT2VarID), & - 'nc_write_model_atts','def_var TRUELAT2') -call nc_check(nf90_put_att(ncFileID, TRUELAT2VarID, "long_name", & - "second standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT2 long_name') -call nc_check(nf90_put_att(ncFileID, TRUELAT2VarID, "description", & - "second standard parallel"), & - 'nc_write_model_atts','put_att TRUELAT2 description') -call nc_check(nf90_put_att(ncFileID, TRUELAT2VarID, "units", & - "degrees, negative is south"), & - 'nc_write_model_atts','put_att TRUELAT2 units') - -call nc_check(nf90_def_var(ncFileID, name="STAND_LON", xtype=nf90_real, & - dimids= DomDimID, varid=STAND_LONVarID), & - 'nc_write_model_atts','def_var STAND_LON') -call nc_check(nf90_put_att(ncFileID, STAND_LONVarID, "long_name", & - "standard longitude"), & - 'nc_write_model_atts','put_att STAND_LON long_name') -call nc_check(nf90_put_att(ncFileID, STAND_LONVarID, "description", & - "standard longitude"), & - 'nc_write_model_atts','put_att STAND_LON description') -call nc_check(nf90_put_att(ncFileID, STAND_LONVarID, "units", & - "degrees, negative is west"), & - 'nc_write_model_atts','put_att STAND_LON units') - -call nc_check(nf90_def_var(ncFileID, name="CEN_LAT", xtype=nf90_real, & - dimids= DomDimID, varid=CEN_LATVarID), & - 'nc_write_model_atts','def_var CEN_LAT') -call nc_check(nf90_put_att(ncFileID, CEN_LATVarID, "long_name", & - "center latitude"), & - 'nc_write_model_atts','put_att CEN_LAT long_name') -call nc_check(nf90_put_att(ncFileID, CEN_LATVarID, "description", & - "center latitude"), & - 'nc_write_model_atts','put_att CEN_LAT description') -call nc_check(nf90_put_att(ncFileID, CEN_LATVarID, "units", & - "degrees, negative is south"), & - 'nc_write_model_atts','put_att CEN_LAT units') - -call nc_check(nf90_def_var(ncFileID, name="CEN_LON", xtype=nf90_real, & - dimids= DomDimID, varid=CEN_LONVarID), & - 'nc_write_model_atts','def_var CEN_LON') -call nc_check(nf90_put_att(ncFileID, CEN_LONVarID, "long_name", & - "central longitude"), & - 'nc_write_model_atts','put_att CEN_LON long_name') -call nc_check(nf90_put_att(ncFileID, CEN_LONVarID, "description", & - "central longitude"), & - 'nc_write_model_atts','put_att CEN_LON description') -call nc_check(nf90_put_att(ncFileID, CEN_LONVarID, "units", & - "degrees, negative is west"), & - 'nc_write_model_atts','put_att CEN_LON units') - -call nc_check(nf90_def_var(ncFileID, name="MAP_PROJ", xtype=nf90_real, & - dimids= DomDimID, varid=MAP_PROJVarID), & - 'nc_write_model_atts','def_var MAP_PROJ') -call nc_check(nf90_put_att(ncFileID, MAP_PROJVarID, "long_name", & - "domain map projection"), & - 'nc_write_model_atts','put_att MAP_PROJ long_name') -call nc_check(nf90_put_att(ncFileID, MAP_PROJVarID, "description", & - "domain map projection"), & - 'nc_write_model_atts','put_att MAP_PROJ description') -call nc_check(nf90_put_att(ncFileID, MAP_PROJVarID, "units", & - "0=none, 1=Lambert, 2=polar, 3=Mercator, 5=Cylindrical, 6=Cassini"), & - 'nc_write_model_atts','put_att MAP_PROJ units') - -!nc -- we need to add in code here to report the domain values for the -! boundary condition flags periodic_x and polar. Since these are -! carried internally as logicals, they will first need to be -! converted back to integers. -call nc_check(nf90_def_var(ncFileID, name="PERIODIC_X", xtype=nf90_int, & - dimids= DomDimID, varid=PERIODIC_XVarID), & - 'nc_write_model_atts','def_var PERIODIC_X') -call nc_check(nf90_put_att(ncFileID, PERIODIC_XVarID, "long_name", & - "Longitudinal periodic b.c. flag"), & - 'nc_write_model_atts','put_att PERIODIC_X long_name') -call nc_check(nf90_put_att(ncFileID, PERIODIC_XVarID, "description", & - "Longitudinal periodic b.c. flag"), & - 'nc_write_model_atts','put_att PERIODIC_X desciption') -call nc_check(nf90_put_att(ncFileID, PERIODIC_XVarID, "units", & - "logical: 1 = .true., 0 = .false."), & - 'nc_write_model_atts','put_att PERIODIC_X units') - -call nc_check(nf90_def_var(ncFileID, name="POLAR", xtype=nf90_int, & - dimids= DomDimID, varid=POLARVarID), & - 'nc_write_model_atts','def_var POLAR') -call nc_check(nf90_put_att(ncFileID, POLARVarID, "long_name", & - "Polar periodic b.c. flag"), & - 'nc_write_model_atts','put_att POLAR long_name') -call nc_check(nf90_put_att(ncFileID, POLARVarID, "description", & - "Polar periodic b.c. flag"), & - 'nc_write_model_atts','put_att POLAR description') -call nc_check(nf90_put_att(ncFileID, POLARVarID, "units", & - "logical: 1 = .true., 0 = .false."), & - 'nc_write_model_atts','put_att POLAR units') - - - -do id=1,num_domains - write( idom , '(I1)') id - - call nc_check(nf90_def_var(ncFileID, name="DN_d0"//idom, xtype=nf90_real, & - dimids= btDimID(id), varid=DNVarID(id)), & - 'nc_write_model_atts','def_var DN_do'//idom) - call nc_check(nf90_put_att(ncFileID, DNVarID(id), "long_name", & - "dn values on half (mass) levels"), & - 'nc_write_model_atts','put_att DN_do'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, DNVarID(id), "description", & - "dn values on half (mass) levels"), & - 'nc_write_model_atts','put_att DN_do'//idom//' description') - call nc_check(nf90_put_att(ncFileID, DNVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att DN_do'//idom//' units') - - call nc_check(nf90_def_var(ncFileID, name="ZNU_d0"//idom, xtype=nf90_real, & - dimids= btDimID(id), varid=ZNUVarID(id)), & - 'nc_write_model_atts','def_var ZNU_d0'//idom) - call nc_check(nf90_put_att(ncFileID, ZNUVarID(id), "long_name", & - "eta values on half (mass) levels"), & - 'nc_write_model_atts','put_att ZNU_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, ZNUVarID(id), "description", & - "eta values on half (mass) levels"), & - 'nc_write_model_atts','put_att ZNU_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, ZNUVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att ZNU_d0'//idom//' units') - - call nc_check(nf90_def_var(ncFileID, name="ZNW_d0"//idom, xtype=nf90_real, & - dimids= btStagDimID(id), varid=ZNWVarID(id)), & - 'nc_write_model_atts','def_var ZNW_d0'//idom) - call nc_check(nf90_put_att(ncFileID, ZNWVarID(id), "long_name", & - "eta values on full (mass) levels"), & - 'nc_write_model_atts','put_att ZNW_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, ZNWVarID(id), "description", & - "eta values on full (mass) levels"), & - 'nc_write_model_atts','put_att ZNW_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, ZNWVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att ZNW_d0'//idom//' units') - - call nc_check(nf90_def_var(ncFileID, name="DNW_d0"//idom, xtype=nf90_real, & - dimids= btDimID(id), varid=DNWVarID(id)), & - 'nc_write_model_atts','def_var DNW_d0'//idom) - call nc_check(nf90_put_att(ncFileID, DNWVarID(id), "long_name", & - "dn values on full (w) levels"), & - 'nc_write_model_atts','def_var DNW_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, DNWVarID(id), "description", & - "dn values on full (w) levels"), & - 'nc_write_model_atts','def_var DNW_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, DNWVarID(id), "units", & - ""), & - 'nc_write_model_atts','def_var DNW_d0'//idom//' units') - -! -! float MUB(Time, south_north, west_east) ; -! MUB:FieldType = 104 ; -! MUB:MemoryOrder = "XY " ; -! MUB:stagger = "" ; - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - call nc_check(nf90_def_var(ncFileID, name="MUB_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snDimID(id) /), varid=MubVarID(id)), & - 'nc_write_model_atts','def_var MUB_d0'//idom) - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "long_name", & - "base state dry air mass in column"), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "description", & - "base state dry air mass in column"), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "units", "Pa"), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, MubVarID(id), "coordinates", & - trim(coordinate_char)), & - 'nc_write_model_atts','put_att MUB_d0'//idom//' coordinates') - -! Longitudes -! float XLONG(Time, south_north, west_east) ; -! XLONG:FieldType = 104 ; -! XLONG:MemoryOrder = "XY " ; -! XLONG:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLONG_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snDimID(id) /), varid=LonVarID(id)), & - 'nc_write_model_atts','def_var XLONG_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "long_name", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "units", "degrees_east"), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "valid_range", & - (/ -180.0_r8, 180.0_r8 /)), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "description", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_d0'//idom//' description') - -! float XLONG_U(Time, south_north, west_east_stag) ; -! XLONG:FieldType = 104 ; -! XLONG:MemoryOrder = "XY " ; -! XLONG:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLONG_U_d0"//idom, xtype=nf90_real, & - dimids= (/ weStagDimID(id), snDimID(id) /), varid=LonuVarID(id)), & - 'nc_write_model_atts','def_var XLONG_U_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LonVarID(id), "long_name", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LonuVarID(id), "units", "degrees_east"), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LonuVarID(id), "valid_range", & - (/ -180.0_r8, 180.0_r8 /)), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LonuVarID(id), "description", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_U_d0'//idom//' description') - -! float XLONG_V(Time, south_north_stag, west_east) ; -! XLONG:FieldType = 104 ; -! XLONG:MemoryOrder = "XY " ; -! XLONG:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLONG_V_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snStagDimID(id) /), varid=LonvVarID(id)), & - 'nc_write_model_atts','def_var XLONG_V_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "long_name", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "units", "degrees_east"), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "valid_range", & - (/ -180.0_r8, 180.0_r8 /)), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LonvVarID(id), "description", & - "LONGITUDE, WEST IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLONG_V_d0'//idom//' description') - -! Latitudes -! float XLAT(Time, south_north, west_east) ; -! XLAT:FieldType = 104 ; -! XLAT:MemoryOrder = "XY " ; -! XLAT:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLAT_d0"//idom, xtype=nf90_real, & - dimids=(/ weDimID(id), snDimID(id) /), varid=LatVarID(id)), & - 'nc_write_model_atts','def_var XLAT_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "long_name", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "units", "degrees_north"), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "valid_range", & - (/ -90.0_r8, 90.0_r8 /)), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LatVarID(id), "description", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_d0'//idom//' description') - -! float XLAT_U(Time, south_north, west_east_stag) ; -! XLAT_U:FieldType = 104 ; -! XLAT_U:MemoryOrder = "XY " ; -! XLAT_U:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLAT_U_d0"//idom, xtype=nf90_real, & - dimids=(/ weStagDimID(id), snDimID(id) /), varid=LatuVarID(id)), & - 'nc_write_model_atts','def_var XLAT_U_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "long_name", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "units", "degrees_north"), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "valid_range", & - (/ -90.0_r8, 90.0_r8 /)), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LatuVarID(id), "description", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_U_d0'//idom//' description') - -! float XLAT_V(Time, south_north_stag, west_east) ; -! XLAT_V:FieldType = 104 ; -! XLAT_V:MemoryOrder = "XY " ; -! XLAT_V:stagger = "" ; - call nc_check(nf90_def_var(ncFileID, name="XLAT_V_d0"//idom, xtype=nf90_real, & - dimids=(/ weDimID(id), snStagDimID(id) /), varid=LatvVarID(id)), & - 'nc_write_model_atts','def_var XLAT_V_d0'//idom) - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "long_name", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "units", "degrees_north"), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "valid_range", & - (/ -90.0_r8, 90.0_r8 /)), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, LatvVarID(id), "description", & - "LATITUDE, SOUTH IS NEGATIVE"), & - 'nc_write_model_atts','put_att XLAT_V_d0'//idom//' description') - -! grid levels - call nc_check(nf90_def_var(ncFileID, name="level_d0"//idom, xtype=nf90_short, & - dimids=btDimID(id), varid=ilevVarID(id)), & - 'nc_write_model_atts','def_var level_d0'//idom) - call nc_check(nf90_put_att(ncFileID, ilevVarID(id), "long_name", & - "level index"), & - 'nc_write_model_atts','put_att level_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, ilevVarID(id), "description", & - "level index"), & - 'nc_write_model_atts','put_att level_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, ilevVarID(id), "units", & - ""), & - 'nc_write_model_atts','put_att level_d0'//idom//' units') - -! Land Mask -! float XLAND(Time, south_north, west_east) ; -! XLAND:FieldType = 104 ; -! XLAND:MemoryOrder = "XY " ; -! XLAND:units = "NA" ; -! XLAND:stagger = "" ; - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - call nc_check(nf90_def_var(ncFileID, name="XLAND_d0"//idom, xtype=nf90_short, & - dimids= (/ weDimID(id), snDimID(id) /), varid=XlandVarID(id)), & - 'nc_write_model_atts','def_var XLAND_d0'//idom) - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "long_name", & - "LAND MASK (1 FOR LAND, 2 FOR WATER)"), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "units", " "), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "coordinates", & - trim(coordinate_char)), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' coordinates') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "valid_range", (/ 1, 2 /)), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' valid_range') - call nc_check(nf90_put_att(ncFileID, XlandVarID(id), "description", & - "LAND MASK (1 FOR LAND, 2 FOR WATER)"), & - 'nc_write_model_atts','put_att XLAND_d0'//idom//' description') - -! PHB -! float PHB(Time, bottom_top_stag, south_north, west_east) ; -! PHB:FieldType = 104 ; -! PHB:MemoryOrder = "XYZ" ; -! PHB:stagger = "Z" ; - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - call nc_check(nf90_def_var(ncFileID, name="PHB_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snDimID(id), btStagDimID(id) /), varid=phbVarId(id)), & - 'nc_write_model_atts','def_var PHB_d0'//idom) - call nc_check(nf90_put_att(ncFileID, phbVarId(id), "long_name", & - "base-state geopotential"), & - 'nc_write_model_atts','put_att PHB_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, phbVarId(id), "description", & - "base-state geopotential"), & - 'nc_write_model_atts','put_att PHB_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, phbVarId(id), "units", "m2/s2"), & - 'nc_write_model_atts','put_att PHB_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, phbVarId(id), "coordinates", & - trim(coordinate_char)), & - 'nc_write_model_atts','put_att PHB_d0'//idom//' coordinates') - call nc_check(nf90_put_att(ncFileID, phbVarId(id), "units_long_name", "m{2} s{-2}"), & - 'nc_write_model_atts','put_att PHB_d0'//idom//' units_long_name') - - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - call nc_check(nf90_def_var(ncFileID, name="HGT_d0"//idom, xtype=nf90_real, & - dimids= (/ weDimID(id), snDimID(id) /), varid=hgtVarId(id)), & - 'nc_write_model_atts','def_var HGT_d0'//idom) - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "long_name", "Terrain Height"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "description", "Terrain Height"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' description') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "units", "m"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "coordinates", & - trim(coordinate_char)), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' coordinates') - call nc_check(nf90_put_att(ncFileID, hgtVarId(id), "units_long_name", "meters"), & - 'nc_write_model_atts','put_att HGT_d0'//idom//' units_long_name') - - ! can't deal with precip yet - need to leave hard coded - - if ( trim(adjustl(title(1:2))) == 'pr' .and. write_precip ) then - - call nc_check(nf90_def_var(ncid=ncFileID, name="RAINC_d0"//idom, xtype=nf90_real, & - dimids = (/ weDimID(id), snDimID(id), MemberDimID, unlimitedDimID /), & - varid = var_id),'nc_write_model_atts','def_var RAINC_d0'//idom) - call nc_check(nf90_put_att(ncFileID, var_id, "units", "mm"), & - 'nc_write_model_atts','put_att RAINC_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, var_id, "long_name", & - "ACCUMULATED TOTAL CUMULUS PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINC_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, var_id, "description", & - "ACCUMULATED TOTAL CUMULUS PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINC_d0'//idom//' description') - - call nc_check(nf90_def_var(ncid=ncFileID, name="RAINNC_d0"//idom, xtype=nf90_real, & - dimids = (/ weDimID(id), snDimID(id), MemberDimID, unlimitedDimID /), & - varid = var_id),'nc_write_model_atts','def_var RAINNC_d0'//idom) - call nc_check(nf90_put_att(ncFileID, var_id, "units", "mm"), & - 'nc_write_model_atts','put_att RAINNC_d0'//idom//' units') - call nc_check(nf90_put_att(ncFileID, var_id, "long_name", & - "ACCUMULATED TOTAL GRID POINT PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINNC_d0'//idom//' long_name') - call nc_check(nf90_put_att(ncFileID, var_id, "description", & - "ACCUMULATED TOTAL GRID POINT PRECIPITATION"), & - 'nc_write_model_atts','put_att RAINNC_d0'//idom//' description') - - endif - -enddo - -if ( output_state_vector ) then - - !----------------------------------------------------------------- - ! Create attributes for the state vector - !----------------------------------------------------------------- - - call nc_check(nf90_inq_dimid(ncFileID, "metadatalength", metadataID), & - 'nc_write_model_atts','inq_dimid metadatalength') - - call nc_check(nf90_def_dim(ncid=ncFileID, name="WRFStateVariables", & - len = wrf%dom(1)%number_of_wrf_variables, dimid = wrfStateID), & - 'nc_write_model_atts','def_dim WRFStateVariables') - - call nc_check(nf90_def_dim(ncid=ncFileID, name="WRFVarDimension", & - len = 3, dimid = wrfDimID), & - 'nc_write_model_atts','def_dim WRFVarDimensionID') - - ! Define the state variable name variable - call nc_check(nf90_def_var(ncid=ncFileID,name="WRFStateVariables", xtype=nf90_char, & - dimids=(/ metadataID, wrfStateID /), varid=WRFStateVarID), & - 'nc_write_model_atts','def_var WRFStateVariables') - - call nc_check(nf90_put_att(ncFileID, WRFStateVarID, "long_name", & - "WRF State Variable Name"), & - 'nc_write_model_atts','put_att WRFStateVariables long_name') - - ! Define the WRF state variable dimension lengths - call nc_check(nf90_def_var(ncid=ncFileID,name="WRFStateDimensions", xtype=nf90_int, & - dimids=(/ wrfDimID, wrfStateID, DomDimID /), varid=WRFStateDimID), & - 'nc_write_model_atts','def_var WRFStateDimensions') - - call nc_check(nf90_put_att(ncFileID, WRFStateDimID, "long_name", & - "WRF State Variable Dimensions"), & - 'nc_write_model_atts','put_att WRFStateDimensions long_name') - - ! Define the actual state vector - - call nc_check(nf90_def_var(ncid=ncFileID, name="state", xtype=nf90_real, & - dimids = (/ StateVarDimID, MemberDimID, unlimitedDimID /), & - varid=StateVarID), & - 'nc_write_model_atts','def_var state') - call nc_check(nf90_put_att(ncFileID, StateVarID, "long_name", & - "model state or fcopy"), & - 'nc_write_model_atts','put_att state long_name') - - ! only define those that are present in the state vector - do id = 1,num_domains ! this makes sure we get them all - write( idom , '(I1)') id - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - ! units - attname = trim(wrf_state_variables(1,my_index))//'_units' - - ! if we didn't already write it, do it now - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - - unitsval = trim(wrf%dom(id)%units(ind)) - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - unitsval), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - ! description - attname = trim(wrf_state_variables(1,my_index))//'_description' - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - descriptionval = trim(wrf%dom(id)%description(ind)) - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - descriptionval), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - ! long_name - same as description - attname = trim(wrf_state_variables(1,my_index))//'_long_name' - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - long_nameval = trim(wrf%dom(id)%description(ind)) - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - long_nameval), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - ! coordinates - define the name of the (sometimes staggered) - ! coordinate variables to use to decode locations - attname = trim(wrf_state_variables(1,my_index))//'_coordinates' - if ( nf90_inquire_attribute(ncFileID,StateVarID,trim(attname)) & - /= NF90_NOERR ) then - coordinatesval = trim(wrf%dom(id)%coordinates(ind)) - if (coordinatesval(1:7) .eq. 'XLONG_U') then - coordinate_char = "XLONG_U_d0"//idom//" XLAT_U_d0"//idom - else if (coordinatesval(1:7) .eq. 'XLONG_V') then - coordinate_char = "XLONG_V_d0"//idom//" XLAT_V_d0"//idom - else - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - end if - call nc_check(nf90_put_att(ncFileID, StateVarId, trim(attname),& - trim(coordinate_char)), & - 'nc_write_model_atts','put_att state '//trim(attname)) - endif - - enddo - enddo - - ! Leave define mode so we can actually fill the variables. - - call nc_check(nf90_enddef(ncfileID),'nc_write_model_atts','enddef') - - do ind = 1,wrf%dom(1)%number_of_wrf_variables - my_index = wrf%dom(1)%var_index_list(ind) - call nc_check(nf90_put_var(ncFileID,WRFStateVarID,trim(wrf_state_variables(1,my_index)), & - start = (/ 1, ind /), count = (/ len_trim(wrf_state_variables(1,my_index)), 1 /)), & - 'nc_write_model_atts', 'put_var WRFStateVariables') - enddo - - do id = 1, num_domains - do ind = 1,wrf%dom(id)%number_of_wrf_variables - call nc_check(nf90_put_var(ncFileID,WRFStateDimID,wrf%dom(id)%var_size(:,ind), & - start = (/ 1, ind, id /), count = (/ 3, 1, 1 /)), & - 'nc_write_model_atts', 'put_var WRFStateDimensions') - enddo - enddo - -else ! physical arrays - -do id=1,num_domains - write( idom , '(I1)') id - - !---------------------------------------------------------------------------- - ! Create the (empty) Prognostic Variables and their attributes - !---------------------------------------------------------------------------- - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - - varname = trim(wrf_state_variables(1,my_index))//'_d0'//idom - - if ( debug ) write(*,*) 'Defining variable ',varname - - if ( wrf%dom(id)%var_size(3,my_index) > 1 ) then ! 3D variable - - dimids_3D(4:5) = (/MemberDimID,unlimitedDimID/) - - ! get first two dimensions based on stagger (could do this - ! differently) - select case (trim(wrf%dom(id)%stagger(ind))) - case ('Z') ! mass grid staggered vertical - dimids_3D(1:2)=(/weDimID(id),snDimId(id)/) - case ('X') ! U grid unstaggered vertical - dimids_3D(1:2)=(/weStagDimID(id),snDimId(id)/) - case ('Y') ! V grid unstaggered vertical - dimids_3D(1:2)=(/weDimID(id),snStagDimId(id)/) - case default - dimids_3D(1:2)=(/weDimID(id),snDimId(id)/) - end select - - ! vertical dimension can be stag, unstag, or staggered soil - ! need to use if/then/else instead of select because testing - ! is against variables - if ( wrf%dom(id)%var_size(3,my_index) == wrf%dom(id)%bts ) then - dimids_3D(3)=btStagDimID(id) - elseif ( wrf%dom(id)%var_size(3,my_index) == wrf%dom(id)%bt ) then - dimids_3D(3)=btDimID(id) - elseif ( wrf%dom(id)%var_size(3,my_index) == wrf%dom(id)%sls ) then - dimids_3D(3)=slSDimID(id) - else - write(errstring,*)'Could not determine dim_id for vertical dimension to output variable '//varname - call error_handler(E_ERR,'nc_write_model_atts',errstring,source, revision,revdate) - endif - - if ( debug ) write(*,*) '3D with dim ids ',dimids_3D - - call nc_check(nf90_def_var(ncid=ncFileID, name=varname, & - xtype=nf90_real,dimids=dimids_3D, varid=var_id), & - 'nc_write_model_atts','def_var '//varname) - - else ! must be 2D - - dimids_2D(3:4) = (/MemberDimID,unlimitedDimID/) - - select case (trim(wrf%dom(id)%stagger(ind))) - case ('Z') ! mass grid staggered vertical - dimids_2D(1:2)=(/weDimID(id),snDimId(id)/) - case ('X') ! U grid unstaggered vertical - dimids_2D(1:2)=(/weStagDimID(id),snDimId(id)/) - case ('Y') ! V grid unstaggered vertical - dimids_2D(1:2)=(/weDimID(id),snStagDimId(id)/) - case default - dimids_2D(1:2)=(/weDimID(id),snDimId(id)/) - end select - - if ( debug ) write(*,*) '2D with dim ids ',dimids_2D - - call nc_check(nf90_def_var(ncid=ncFileID, name=varname, & - xtype=nf90_real,dimids=dimids_2D, varid=var_id), & - 'nc_write_model_atts','def_var '//varname) - - endif ! 3D or 2D - - unitsval = trim(wrf%dom(id)%units(my_index)) - - call nc_check(nf90_put_att(ncFileID, var_id, "units", trim(unitsval)), & - 'nc_write_model_atts','put_att '//varname//' units') - - descriptionval = trim(wrf%dom(id)%description(my_index)) - - call nc_check(nf90_put_att(ncFileID, var_id, "description", trim(descriptionval)), & - 'nc_write_model_atts','put_att '//varname//' description') - - long_nameval = trim(wrf%dom(id)%description(my_index)) - - call nc_check(nf90_put_att(ncFileID, var_id, "long_name", trim(long_nameval)), & - 'nc_write_model_atts','put_att '//varname//' long_name') - - coordinatesval = trim(wrf%dom(id)%coordinates(my_index)) - if (coordinatesval(1:7) .eq. 'XLONG_U') then - coordinate_char = "XLONG_U_d0"//idom//" XLAT_U_d0"//idom - else if (coordinatesval(1:7) .eq. 'XLONG_V') then - coordinate_char = "XLONG_V_d0"//idom//" XLAT_V_d0"//idom - else - coordinate_char = "XLONG_d0"//idom//" XLAT_d0"//idom - end if - call nc_check(nf90_put_att(ncFileID, var_id, "coordinates", trim(coordinate_char)), & - 'nc_write_model_atts','put_att '//varname//' coordinates') - - - !------No info on long_name or units_long_name right now! -! call nc_check(nf90_put_att(ncFileID, var_id, "long_name", "x-wind component"), & -! 'nc_write_model_atts','put_att U_d0'//idom//' long_name') -! call nc_check(nf90_put_att(ncFileID, var_id, "units_long_name", "m s{-1}"), & -! 'nc_write_model_atts','put_att U_d0'//idom//' units_long_name') - - enddo ! variables - -enddo ! domains - -! Leave define mode so we can actually fill the variables. -call nc_check(nf90_enddef(ncfileID),'nc_write_model_atts','enddef') - -endif - -!----------------------------------------------------------------- -! Fill the variables we can -!----------------------------------------------------------------- - -if (have_wrf_nml_file) then - call file_to_text(wrf_nml_file, textblock) - call nc_check(nf90_put_var(ncFileID, nmlVarID, textblock ), & - 'nc_write_model_atts', 'put_var nmlVarID') - deallocate(textblock) -endif - -call nc_check(nf90_put_var(ncFileID, DXVarID, wrf%dom(1:num_domains)%dx), & - 'nc_write_model_atts','put_var dx') -call nc_check(nf90_put_var(ncFileID, DYVarID, wrf%dom(1:num_domains)%dy), & - 'nc_write_model_atts','put_var dy') -call nc_check(nf90_put_var(ncFileID, TRUELAT1VarID, wrf%dom(1:num_domains)%proj%truelat1), & - 'nc_write_model_atts','put_var truelat1') -call nc_check(nf90_put_var(ncFileID, TRUELAT2VarID, wrf%dom(1:num_domains)%proj%truelat2), & - 'nc_write_model_atts','put_var truelat2') -call nc_check(nf90_put_var(ncFileID, STAND_LONVarID, wrf%dom(1:num_domains)%proj%stdlon), & - 'nc_write_model_atts','put_var stdlon') -call nc_check(nf90_put_var(ncFileID, CEN_LATVarID, wrf%dom(1:num_domains)%cen_lat), & - 'nc_write_model_atts','put_var cen_lat') -call nc_check(nf90_put_var(ncFileID, CEN_LONVarID, wrf%dom(1:num_domains)%cen_lon), & - 'nc_write_model_atts','put_var cen_lon') -call nc_check(nf90_put_var(ncFileID, MAP_PROJVarID, wrf%dom(1:num_domains)%map_proj), & - 'nc_write_model_atts','put_var map_proj') - -!nc -- convert internally logical boundary condition variables into integers before filling -do id=1,num_domains - if ( wrf%dom(id)%periodic_x ) then - tmp(id) = 1 - else - tmp(id) = 0 - endif -end do -call nc_check(nf90_put_var(ncFileID, PERIODIC_XVarID, tmp(1:num_domains) ), & - 'nc_write_model_atts','put_var PERIODIC_XVarID') - -do id=1,num_domains - if ( wrf%dom(id)%polar ) then - tmp(id) = 1 - else - tmp(id) = 0 - endif -end do -call nc_check(nf90_put_var(ncFileID, POLARVarID, tmp(1:num_domains) ), & - 'nc_write_model_atts','put var POLARVarID') - - -do id=1,num_domains - -! defining grid levels - call nc_check(nf90_put_var(ncFileID, DNVarID(id), wrf%dom(id)%dn), & - 'nc_write_model_atts','put_var dn') - call nc_check(nf90_put_var(ncFileID, ZNUVarID(id), wrf%dom(id)%znu), & - 'nc_write_model_atts','put_var znu') - call nc_check(nf90_put_var(ncFileID, ZNWVarID(id), wrf%dom(id)%znw), & - 'nc_write_model_atts','put_var znw') - call nc_check(nf90_put_var(ncFileID, DNWVarID(id), wrf%dom(id)%dnw), & - 'nc_write_model_atts','put_var dnw') - -! defining horizontal - call nc_check(nf90_put_var(ncFileID, mubVarID(id), wrf%dom(id)%mub), & - 'nc_write_model_atts','put_var mub') - call nc_check(nf90_put_var(ncFileID, LonVarID(id), wrf%dom(id)%longitude), & - 'nc_write_model_atts','put_var longitude') - call nc_check(nf90_put_var(ncFileID, LonuVarID(id), wrf%dom(id)%longitude_u), & - 'nc_write_model_atts','put_var longitude_u') - call nc_check(nf90_put_var(ncFileID, LonvVarID(id), wrf%dom(id)%longitude_v), & - 'nc_write_model_atts','put_var longitude_v') - call nc_check(nf90_put_var(ncFileID, LatVarID(id), wrf%dom(id)%latitude), & - 'nc_write_model_atts','put_var latitude') - call nc_check(nf90_put_var(ncFileID, LatuVarID(id), wrf%dom(id)%latitude_u), & - 'nc_write_model_atts','put_var latitude_u') - call nc_check(nf90_put_var(ncFileID, LatvVarID(id), wrf%dom(id)%latitude_v), & - 'nc_write_model_atts','put_var latitude_v') - call nc_check(nf90_put_var(ncFileID, ilevVarID(id), (/ (i,i=1,wrf%dom(id)%bt) /)), & - 'nc_write_model_atts','put_var bt') - call nc_check(nf90_put_var(ncFileID, XlandVarID(id), wrf%dom(id)%land), & - 'nc_write_model_atts','put_var land') -! call nc_check(nf90_put_var(ncFileID, MapFacMVarID(id), wrf%dom(id)%mapfac_m), & -! 'nc_write_model_atts','put_var mapfac_m') -! call nc_check(nf90_put_var(ncFileID, MapFacUVarID(id), wrf%dom(id)%mapfac_u), & -! 'nc_write_model_atts','put_var mapfac_u') -! call nc_check(nf90_put_var(ncFileID, MapFacVVarID(id), wrf%dom(id)%mapfac_v), & -! 'nc_write_model_atts','put_var mapfac_v') - call nc_check(nf90_put_var(ncFileID, phbVarID(id), wrf%dom(id)%phb), & - 'nc_write_model_atts','put_var phb') - call nc_check(nf90_put_var(ncFileID, hgtVarID(id), wrf%dom(id)%hgt), & - 'nc_write_model_atts','put_var hgt') - -enddo - -!----------------------------------------------------------------- -! Flush the buffer and leave netCDF file open -!----------------------------------------------------------------- - -call nc_check(nf90_sync(ncFileID),'nc_write_model_atts','sync') - -end function nc_write_model_atts - - - -function nc_write_model_vars( ncFileID, statevec, copyindex, timeindex ) result (ierr) -!----------------------------------------------------------------- -! Writes the model-specific variables to a netCDF file -! TJH 25 June 2003 -! -! TJH 29 July 2003 -- for the moment, all errors are fatal, so the -! return code is always '0 == normal', since the fatal errors stop execution. - - -integer, intent(in) :: ncFileID ! netCDF file identifier -real(r8), dimension(:), intent(in) :: statevec -integer, intent(in) :: copyindex -integer, intent(in) :: timeindex -integer :: ierr ! return value of function - -!----------------------------------------------------------------- - -logical, parameter :: debug = .false. -integer :: nDimensions, nVariables, nAttributes, unlimitedDimID -integer :: StateVarID, VarID, id, ind, my_index -integer :: i,j -real(r8), allocatable, dimension(:,:) :: temp2d -real(r8), allocatable, dimension(:,:,:) :: temp3d -character(len=NF90_MAX_NAME) :: varname -character(len=1) :: idom -integer, dimension(2) :: dimsizes_2D -integer, dimension(3) :: dimsizes_3D - -ierr = 0 ! assume normal termination - -!----------------------------------------------------------------- -! make sure ncFileID refers to an open netCDF file, -! then get all the Variable ID's we need. -!----------------------------------------------------------------- - -call nc_check(nf90_Inquire(ncFileID, nDimensions, nVariables, nAttributes, unlimitedDimID), & - 'nc_write_model_vars','inquire') - -if ( output_state_vector ) then - - call nc_check(nf90_inq_varid(ncFileID, "state", StateVarID), & - 'nc_write_model_vars','inq_varid state') - call nc_check(nf90_put_var(ncFileID, StateVarID, statevec, & - start=(/ 1, copyindex, timeindex /)), & - 'nc_write_model_vars','put_var statevec') - -else - -j = 0 - -do id=1,num_domains - - write( idom , '(I1)') id - - !---------------------------------------------------------------------------- - ! Fill the variables - !---------------------------------------------------------------------------- - - do ind = 1,wrf%dom(id)%number_of_wrf_variables - - ! actual location in state variable table - my_index = wrf%dom(id)%var_index_list(ind) - - varname = trim(wrf_state_variables(1,my_index))//'_d0'//idom - - call nc_check(nf90_inq_varid(ncFileID, trim(varname), VarID), & - 'nc_write_model_vars','inq_varid '//trim(varname)) - - i = j + 1 - j = i + wrf%dom(id)%var_size(1,ind) * & - wrf%dom(id)%var_size(2,ind) * & - wrf%dom(id)%var_size(3,ind) - 1 - - if (debug) write(*,'(a10,'' = statevec('',i7,'':'',i7,'') with dims '',3(1x,i3))') & - trim(varname),i,j,wrf%dom(id)%var_size(1,ind),wrf%dom(id)%var_size(2,ind),wrf%dom(id)%var_size(3,ind) - - if ( wrf%dom(id)%var_size(3,ind) > 1 ) then - - dimsizes_3D = (/wrf%dom(id)%var_size(1,ind), & - wrf%dom(id)%var_size(2,ind), & - wrf%dom(id)%var_size(3,ind)/) - - allocate ( temp3d(dimsizes_3D(1),dimsizes_3D(2),dimsizes_3D(3)) ) - temp3d = reshape(statevec(i:j), (/ dimsizes_3D(1),dimsizes_3D(2),dimsizes_3D(3) /) ) - call nc_check(nf90_put_var( ncFileID, VarID, temp3d, & - start=(/ 1, 1, 1, copyindex, timeindex /) ), & - 'nc_write_model_vars','put_var '//trim(varname)) - deallocate(temp3d) - - else ! must be 2D - - dimsizes_2D = (/wrf%dom(id)%var_size(1,ind), & - wrf%dom(id)%var_size(2,ind)/) - - allocate ( temp2d(dimsizes_2D(1),dimsizes_2D(2)) ) - temp2d = reshape(statevec(i:j), (/ dimsizes_3D(1),dimsizes_3D(2) /) ) - call nc_check(nf90_put_var( ncFileID, VarID, temp2d, & - start=(/ 1, 1, copyindex, timeindex /) ), & - 'nc_write_model_vars','put_var '//trim(varname)) - deallocate(temp2d) - - - endif - - enddo ! variables - -enddo ! domains - -endif - -!----------------------------------------------------------------- -! Flush the buffer and leave netCDF file open -!----------------------------------------------------------------- - -call nc_check(nf90_sync(ncFileID), 'nc_write_model_vars','sync') - -end function nc_write_model_vars - -!------------------------------- - -! public stubs - -!********************************************** - -subroutine adv_1step(x, Time) - -! Does single time-step advance with vector state as -! input and output. - - real(r8), intent(inout) :: x(:) - -! Time is needed for more general models like this; need to add in to -! low-order models - type(time_type), intent(in) :: Time - -call error_handler(E_ERR,'adv_1step', & - 'WRF model cannot be called as a subroutine; async cannot = 0', & - source, revision, revdate) - - -end subroutine adv_1step - -!********************************************** - -subroutine end_model() -! nothing to do, which is fine. - -end subroutine end_model - -!********************************************** - -subroutine init_time(i_time) -! For now returns value of Time_init which is set in initialization routines. - - type(time_type), intent(out) :: i_time - -!Where should initial time come from here? -! WARNING: CURRENTLY SET TO 0 - i_time = set_time(0, 0) - -end subroutine init_time - -!********************************************** - -subroutine init_conditions(x) -! Reads in restart initial conditions and converts to vector - -! Following changed to intent(inout) for ifc compiler;should be like this - real(r8), intent(inout) :: x(:) - -msgstring2 = "cannot run with 'start_from_restart = .false.' " -msgstring3 = 'use ensemble_init in the WRF utils dir, or use wrf_to_dart' -call error_handler(E_ERR,'init_conditions', & - 'WARNING!! WRF model has no built-in default state', & - source, revision, revdate, & - text2=msgstring2, text3=msgstring3) - -end subroutine init_conditions - - - -!####################################################################### - -subroutine toGrid (x, j, dx, dxm) - -! Transfer obs. x to grid j and calculate its -! distance to grid j and j+1 - - real(r8), intent(in) :: x - real(r8), intent(out) :: dx, dxm - integer, intent(out) :: j - - j = int (x) - - dx = x - real (j) - - dxm= 1.0_r8 - dx - -end subroutine toGrid - -!####################################################################### - -subroutine toGrid_distrib(x, ens_size, j, dx, dxm) -!HK I don't think this is needed. -! Transfer obs. x to grid j and calculate its -! distance to grid j and j+1 - - integer, intent(in) :: ens_size - real(r8), intent(in) :: x(ens_size) - real(r8), intent(out) :: dx(ens_size), dxm(ens_size) - integer, intent(out) :: j(ens_size) - - j = int (x) - - dx = x - real (j) - - dxm= 1.0_r8 - dx - -end subroutine toGrid_distrib - -!####################################################################### - -subroutine pres_to_zk(pres, mdl_v, n3, zk, lev0) - -! Calculate the model level "zk" on half (mass) levels, -! corresponding to pressure "pres". - - integer, intent(in) :: n3 - real(r8), intent(in) :: pres - real(r8), intent(in) :: mdl_v(0:n3) - real(r8), intent(out) :: zk - logical, intent(out) :: lev0 - - integer :: k - - zk = missing_r8 - lev0 = .false. - - ! if out of range completely, return missing_r8 and lev0 false - if (pres > mdl_v(0) .or. pres < mdl_v(n3)) return - - ! if above surface but below lowest sigma level, return the - ! sigma value but set lev0 true - if(pres <= mdl_v(0) .and. pres > mdl_v(1)) then - lev0 = .true. - if (log_vert_interp) then - zk = (log(mdl_v(0)) - log(pres))/(log(mdl_v(0)) - log(mdl_v(1))) - else - zk = (mdl_v(0) - pres)/(mdl_v(0) - mdl_v(1)) - endif - return - endif - - ! find the 2 sigma levels the value is between and return that - ! as a real number, including the fraction between the levels. - do k = 1,n3-1 - if(pres <= mdl_v(k) .and. pres >= mdl_v(k+1)) then - if (log_vert_interp) then - zk = real(k) + (log(mdl_v(k)) - log(pres))/(log(mdl_v(k)) - log(mdl_v(k+1))) - else - zk = real(k) + (mdl_v(k) - pres)/(mdl_v(k) - mdl_v(k+1)) - endif - exit - endif - enddo - -end subroutine pres_to_zk - -!####################################################################### - -subroutine pres_to_zk_distrib(pres, mdl_v, n3, ens_size, zk, lev0) -! Calculate the model level "zk" on half (mass) levels, -! corresponding to pressure "pres". - - integer, intent(in) :: n3 - real(r8), intent(in) :: pres - integer, intent(in) :: ens_size - real(r8), intent(in) :: mdl_v(0:n3, ens_size) - real(r8), intent(out) :: zk(ens_size) - logical, intent(out) :: lev0(ens_size) - - integer :: k, e - - zk = missing_r8 - lev0 = .false. - - do e = 1, ens_size - - ! if out of range completely, return missing_r8 and lev0 false - if (pres > mdl_v(0,e) .or. pres < mdl_v(n3,e)) then - ! do nothing, keep zk(e) = missing_r8 and lev0(e) = false - - ! if above surface but below lowest sigma level, return the - ! sigma value but set lev0 true - elseif(pres <= mdl_v(0,e) .and. pres > mdl_v(1,e)) then - lev0(e) = .true. - if (log_vert_interp) then - zk(e) = (log(mdl_v(0,e)) - log(pres))/(log(mdl_v(0,e)) - log(mdl_v(1,e))) - else - zk(e) = (mdl_v(0,e) - pres)/(mdl_v(0,e) - mdl_v(1,e)) - endif - - else - - ! find the 2 sigma levels the value is between and return that - ! as a real number, including the fraction between the levels. - do k = 1, n3-1 - if(pres <= mdl_v(k,e) .and. pres >= mdl_v(k+1,e)) then - if (log_vert_interp) then - zk(e) = real(k) + (log(mdl_v(k,e)) - log(pres))/(log(mdl_v(k,e)) - log(mdl_v(k+1,e))) - else - zk(e) = real(k) + (mdl_v(k,e) - pres)/(mdl_v(k,e) - mdl_v(k+1,e)) - endif - exit - endif - enddo - - endif - - enddo - -end subroutine pres_to_zk_distrib - -!####################################################################### - -subroutine height_to_zk(obs_v, mdl_v, n3, zk, lev0) - -! Calculate the model level "zk" on half (mass) levels, -! corresponding to height "obs_v". - - real(r8), intent(in) :: obs_v - integer, intent(in) :: n3 - real(r8), intent(in) :: mdl_v(0:n3) - real(r8), intent(out) :: zk - logical, intent(out) :: lev0 - - integer :: k - - zk = missing_r8 - lev0 = .false. - - ! if out of range completely, return missing_r8 and lev0 false - if (obs_v < mdl_v(0) .or. obs_v > mdl_v(n3)) return - - ! if above surface but below lowest 3-d height level, return the - ! height value but set lev0 true - if(obs_v >= mdl_v(0) .and. obs_v < mdl_v(1)) then - lev0 = .true. - zk = (mdl_v(0) - obs_v)/(mdl_v(0) - mdl_v(1)) - return - endif - - ! find the 2 height levels the value is between and return that - ! as a real number, including the fraction between the levels. - do k = 1,n3-1 - if(obs_v >= mdl_v(k) .and. obs_v <= mdl_v(k+1)) then - zk = real(k) + (mdl_v(k) - obs_v)/(mdl_v(k) - mdl_v(k+1)) - exit - endif - enddo - -end subroutine height_to_zk - -!####################################################### - -subroutine get_model_pressure_profile_distrib_fwd(i,j,dx,dy,dxm,dym,n,id,v_p, state_ens_handle, ens_size) - -! Calculate the full model pressure profile on half (mass) levels, -! horizontally interpolated at the observation location. - -integer, intent(in) :: i,j,n,id -real(r8), intent(in) :: dx,dy,dxm,dym -integer, intent(in) :: ens_size -real(r8), intent(out) :: v_p(0:n, ens_size) -type(ensemble_type), intent(in) :: state_ens_handle -integer e !< for ensemble loop - -integer, dimension(2) :: ll, lr, ul, ur -integer :: ill,ilr,iul,iur,k, rc -real(r8), allocatable :: pres1(:), pres2(:), pres3(:), pres4(:) -logical :: debug = .false. - -!HK -real(r8), allocatable :: x_ill(:), x_ilr(:), x_iul(:), x_iur(:) - -allocate(pres1(ens_size), pres2(ens_size), pres3(ens_size), pres4(ens_size)) -allocate(x_ill(ens_size), x_ilr(ens_size), x_iul(ens_size), x_iur(ens_size)) - -if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: getCorners rc = ', rc - - - do k=1,n - pres1 = model_pressure_t_distrib(ll(1), ll(2), k,id,state_ens_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), k,id,state_ens_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), k,id,state_ens_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), k,id,state_ens_handle, ens_size) - - v_p(k, :) = interp_4pressure_distrib(pres1, pres2, pres3, pres4, dx, dxm, dy, dym, ens_size) - enddo - - - if (debug) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: n, v_p() ', n, v_p(1:n, :) - - if ( wrf%dom(id)%type_ps >= 0 ) then - - ill = new_dart_ind(ll(1), ll(2), 1, wrf%dom(id)%type_ps, id) - ilr = new_dart_ind(lr(1), lr(2), 1, wrf%dom(id)%type_ps, id) - iul = new_dart_ind(ul(1), ul(2), 1, wrf%dom(id)%type_ps, id) - iur = new_dart_ind(ur(1), ur(2), 1, wrf%dom(id)%type_ps, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - ! I'm not quite sure where this comes from, but I will trust them on it.... - ! Do you have to do this per ensemble? - !>@todo This is messy - do e = 1,ens_size - - if ( x_ill(e) /= 0.0_r8 .and. x_ilr(e) /= 0.0_r8 .and. x_iul(e) /= 0.0_r8 .and. & - x_iur(e) /= 0.0_r8 ) then - - v_p(0,e:e) = interp_4pressure_distrib(x_ill(e:e), x_ilr(e:e), x_iul(e:e), x_iur(e:e), dx, dxm, dy, dym, 1) - - else - - ! HK I think this is a bug, you are just going to grab the first copy - is this fixed? - ! in each iteration of the loop - call error_handler(E_WARN, 'model_mod.f90 check for correctness', 'Helen') - pres1 = model_pressure_t_distrib(ll(1), ll(2), 2,id,state_ens_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), 2,id,state_ens_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), 2,id,state_ens_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), 2,id,state_ens_handle, ens_size) - - v_p(0,e:e) = interp_4pressure_distrib(pres1(e:e), pres2(e:e), pres3(e:e), pres4(e:e), dx, dxm, dy, dym, 1, & - extrapolate=.true., edgep=v_p(1,e)) - - endif - - enddo - - else - - pres1 = model_pressure_t_distrib(ll(1), ll(2), 2,id,state_ens_handle, ens_size) - pres2 = model_pressure_t_distrib(lr(1), lr(2), 2,id,state_ens_handle, ens_size) - pres3 = model_pressure_t_distrib(ul(1), ul(2), 2,id,state_ens_handle, ens_size) - pres4 = model_pressure_t_distrib(ur(1), ur(2), 2,id,state_ens_handle, ens_size) - - v_p(0,:) = interp_4pressure_distrib(pres1, pres2, pres3, pres4, dx, dxm, dy, dym, ens_size, & - extrapolate=.true., edgep=v_p(1,:)) - - endif - - if (debug) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: v_p(0) ', v_p(0, :) -else - v_p(:,:) = missing_r8 - -endif - -deallocate(pres1, pres2, pres3, pres4, x_ill, x_ilr, x_iul, x_iur) - -end subroutine get_model_pressure_profile_distrib_fwd - -!####################################################### - -subroutine get_model_pressure_profile_distrib_mean(i,j,dx,dy,dxm,dym,n,id,v_p, state_ens_handle) - -! Calculate the full model pressure profile on half (mass) levels, -! horizontally interpolated at the observation location. - -integer, intent(in) :: i,j,n,id -real(r8), intent(in) :: dx,dy,dxm,dym -real(r8), intent(out) :: v_p(0:n) -type(ensemble_type), intent(in) :: state_ens_handle - -integer, dimension(2) :: ll, lr, ul, ur -integer :: ill,ilr,iul,iur,k, rc -real(r8) :: pres1, pres2, pres3, pres4 -logical :: debug = .false. - -!HK -real(r8) :: x_ill, x_ilr, x_iul, x_iur - -if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_t, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: getCorners rc = ', rc - - - do k=1,n - pres1 = model_pressure_t_distrib(ll(1), ll(2), k,id,state_ens_handle) - pres2 = model_pressure_t_distrib(lr(1), lr(2), k,id,state_ens_handle) - pres3 = model_pressure_t_distrib(ul(1), ul(2), k,id,state_ens_handle) - pres4 = model_pressure_t_distrib(ur(1), ur(2), k,id,state_ens_handle) - - v_p(k) = interp_4pressure(pres1, pres2, pres3, pres4, dx, dxm, dy, dym) - enddo - - - if (debug) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: n, v_p() ', n, v_p(1:n) - - if ( wrf%dom(id)%type_ps >= 0 ) then - - ill = new_dart_ind(ll(1), ll(2), 1, wrf%dom(id)%type_ps, id) - ilr = new_dart_ind(lr(1), lr(2), 1, wrf%dom(id)%type_ps, id) - iul = new_dart_ind(ul(1), ul(2), 1, wrf%dom(id)%type_ps, id) - iur = new_dart_ind(ur(1), ur(2), 1, wrf%dom(id)%type_ps, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - ! I'm not quite sure where this comes from, but I will trust them on it.... - ! Do you have to do this per ensemble? - - if ( x_ill /= 0.0_r8 .and. x_ilr /= 0.0_r8 .and. x_iul /= 0.0_r8 .and. & - x_iur /= 0.0_r8 ) then - - v_p(0) = interp_4pressure(x_ill, x_ilr, x_iul, x_iur, dx, dxm, dy, dym) - - else - - pres1 = model_pressure_t_distrib(ll(1), ll(2), 2,id,state_ens_handle) - pres2 = model_pressure_t_distrib(lr(1), lr(2), 2,id,state_ens_handle) - pres3 = model_pressure_t_distrib(ul(1), ul(2), 2,id,state_ens_handle) - pres4 = model_pressure_t_distrib(ur(1), ur(2), 2,id,state_ens_handle) - - v_p(0) = interp_4pressure(pres1, pres2, pres3, pres4, dx, dxm, dy, dym, & - extrapolate=.true., edgep=v_p(1)) - - endif - - else - - pres1 = model_pressure_t_distrib(ll(1), ll(2), 2,id,state_ens_handle) - pres2 = model_pressure_t_distrib(lr(1), lr(2), 2,id,state_ens_handle) - pres3 = model_pressure_t_distrib(ul(1), ul(2), 2,id,state_ens_handle) - pres4 = model_pressure_t_distrib(ur(1), ur(2), 2,id,state_ens_handle) - - v_p(0) = interp_4pressure(pres1, pres2, pres3, pres4, dx, dxm, dy, dym, & - extrapolate=.true., edgep=v_p(1)) - - endif - - if (debug) & - print*, 'model_mod.f90 :: get_model_pressure_profile :: v_p(0) ', v_p(0) -else - v_p(:) = missing_r8 - -endif - -end subroutine get_model_pressure_profile_distrib_mean - -!####################################################### -!> Only for the mean value. -!> Used in get_state_meta_data for the vertical conversion -function model_pressure_distrib(i, j, k, id, var_type, state_ens_handle) - -! Calculate the pressure at grid point (i,j,k), domain id. -! The grid is defined according to var_type. - -type(ensemble_type), intent(in) :: state_ens_handle -integer, intent(in) :: i,j,k,id,var_type -real(r8) :: model_pressure_distrib - -integer :: off -real(r8) :: pres1, pres2 - -model_pressure_distrib = missing_r8 - -! If W-grid (on ZNW levels), then we need to average in vertical, unless -! we are at the upper or lower boundary in which case we will extrapolate. -if( (var_type == wrf%dom(id)%type_w) .or. (var_type == wrf%dom(id)%type_gz) ) then - - if( k == 1 ) then - - pres1 = model_pressure_t_distrib(i, j, k, id, state_ens_handle) - pres2 = model_pressure_t_distrib(i, j, k+1,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true.) - - elseif( k == wrf%dom(id)%var_size(3,wrf%dom(id)%type_w) ) then - - pres1 = model_pressure_t_distrib(i,j,k-1,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i,j,k-2,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true.) - - else - - pres1 = model_pressure_t_distrib(i, j, k, id, state_ens_handle) - pres2 = model_pressure_t_distrib(i, j, k-1,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2) - - endif - -! If U-grid, then pressure is defined between U points, so average -- -! averaging depends on longitude periodicity -elseif( var_type == wrf%dom(id)%type_u ) then - - if( i == wrf%dom(id)%var_size(1,wrf%dom(id)%type_u) ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = model_pressure_t_distrib(i-1,j,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(1, j,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = model_pressure_t_distrib(i-1,j,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i-2,j,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( i == 1 ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = model_pressure_t_distrib(i, j,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(wrf%dom(id)%we,j,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = model_pressure_t_distrib(i, j,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i+1,j,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = model_pressure_t_distrib(i, j,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i-1,j,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -! If V-grid, then pressure is defined between V points, so average -- -! averaging depends on polar periodicity -elseif( var_type == wrf%dom(id)%type_v ) then - - if( j == wrf%dom(id)%var_size(2,wrf%dom(id)%type_v) ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The upper corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = model_pressure_t_distrib(off,j-1,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i ,j-1,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = model_pressure_t_distrib(i,j-1,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i,j-2,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( j == 1 ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The lower corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = model_pressure_t_distrib(off,j,k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i, j,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = model_pressure_t_distrib(i,j, k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i,j+1,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = model_pressure_t_distrib(i,j, k,id, state_ens_handle) - pres2 = model_pressure_t_distrib(i,j-1,k,id, state_ens_handle) - model_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -elseif( var_type == wrf%dom(id)%type_mu .or. var_type == wrf%dom(id)%type_tslb .or. & - var_type == wrf%dom(id)%type_ps .or. var_type == wrf%dom(id)%type_u10 .or. & - var_type == wrf%dom(id)%type_v10 .or. var_type == wrf%dom(id)%type_t2 .or. & - var_type == wrf%dom(id)%type_th2 .or. & - var_type == wrf%dom(id)%type_q2 .or. var_type == wrf%dom(id)%type_tsk .or. & - var_type == wrf%dom(id)%type_smois .or. var_type == wrf%dom(id)%type_sh2o) then - - model_pressure_distrib = model_pressure_s_distrib(i,j,id, state_ens_handle) - -else - - pres1 = model_pressure_t_distrib(i,j,k,id, state_ens_handle) - model_pressure_distrib = pres1 - -endif - -end function model_pressure_distrib - -!####################################################### - -function model_surface_pressure_distrib(i, j, id, var_type, state_ens_handle) - -! Calculate the surface pressure at grid point (i,j), domain id. -! The grid is defined according to var_type. - -type(ensemble_type), intent(in) :: state_ens_handle -integer, intent(in) :: i,j,id,var_type -real(r8) :: model_surface_pressure_distrib - -integer :: off -real(r8) :: pres1, pres2 - -model_surface_pressure_distrib = missing_r8 - - -! If U-grid, then pressure is defined between U points, so average -- -! averaging depends on longitude periodicity -if( var_type == wrf%dom(id)%type_u ) then - - if( i == wrf%dom(id)%var_size(1,wrf%dom(id)%type_u) ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = model_pressure_s_distrib(i-1,j,id, state_ens_handle) - pres2 = model_pressure_s_distrib(1, j,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = model_pressure_s_distrib(i-1,j,id, state_ens_handle) - pres2 = model_pressure_s_distrib(i-2,j,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( i == 1 ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at seam in longitude, take first and last M-grid points - pres1 = model_pressure_s_distrib(i, j,id, state_ens_handle) - pres2 = model_pressure_s_distrib(wrf%dom(id)%we,j,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - else - - ! If not periodic, then try extrapolating - pres1 = model_pressure_s_distrib(i, j,id, state_ens_handle) - pres2 = model_pressure_s_distrib(i+1,j,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = model_pressure_s_distrib(i, j,id, state_ens_handle) - pres2 = model_pressure_s_distrib(i-1,j,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -! If V-grid, then pressure is defined between V points, so average -- -! averaging depends on polar periodicity -elseif( var_type == wrf%dom(id)%type_v ) then - - if( j == wrf%dom(id)%var_size(2,wrf%dom(id)%type_v) ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The upper corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = model_pressure_s_distrib(off,j-1,id, state_ens_handle) - pres2 = model_pressure_s_distrib(i ,j-1,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = model_pressure_s_distrib(i,j-1,id, state_ens_handle) - pres2 = model_pressure_s_distrib(i,j-2,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - elseif( j == 1 ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The lower corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - pres1 = model_pressure_s_distrib(off,j,id, state_ens_handle) - pres2 = model_pressure_s_distrib(i, j,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - ! If not periodic, then try extrapolating - else - - pres1 = model_pressure_s_distrib(i,j, id, state_ens_handle) - pres2 = model_pressure_s_distrib(i,j+1,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, extrapolate=.true., vertical=.false.) - - endif - - else - - pres1 = model_pressure_s_distrib(i,j, id, state_ens_handle) - pres2 = model_pressure_s_distrib(i,j-1,id, state_ens_handle) - model_surface_pressure_distrib = interp_pressure(pres1, pres2, vertical=.false.) - - endif - -else - - model_surface_pressure_distrib = model_pressure_s_distrib(i,j,id, state_ens_handle) - -endif - -end function model_surface_pressure_distrib - -!####################################################### - -function model_pressure_t_distrib_fwd(i,j,k,id,state_ens_handle, ens_size) - -! Calculate total pressure on mass point (half (mass) levels, T-point). - -integer, intent(in) :: ens_size -integer, intent(in) :: i,j,k,id -real(r8) :: model_pressure_t_distrib_fwd(ens_size) -!HK -type(ensemble_type), intent(in) :: state_ens_handle - -real (kind=r8), PARAMETER :: rd_over_rv = gas_constant / gas_constant_v -real (kind=r8), PARAMETER :: cpovcv = 1.4_r8 ! cp / (cp - gas_constant) - -integer :: iqv !< I think this is i for index -integer :: it !< change to array -real(r8) :: qvf1(ens_size),rho(ens_size), x_iqv(ens_size), x_it(ens_size) - -model_pressure_t_distrib_fwd = missing_r8 - -! Adapted the code from WRF module_big_step_utilities_em.F ---- -! subroutine calc_p_rho_phi Y.-R. Guo (10/20/2004) - -! Simplification: alb*mub = (phb(i,j,k+1) - phb(i,j,k))/dnw(k) - -if (wrf%dom(id)%type_qv < 0 .or. wrf%dom(id)%type_t < 0) then - call error_handler(E_ERR, 'model_pressure_t:', & - 'BOTH QVAPOR and T must be in state vector to compute total pressure', & - source, revision, revdate) -endif - -iqv = new_dart_ind(i,j,k,wrf%dom(id)%type_qv, id) -it = new_dart_ind(i,j,k,wrf%dom(id)%type_t, id) - -call get_state(x_iqv, iqv, state_ens_handle) -call get_state(x_it, it, state_ens_handle) - -qvf1(:) = 1.0_r8 + x_iqv(:) / rd_over_rv -!print*, 'qvf1 ', qvf1 - -rho(:) = model_rho_t_distrib(i,j,k,id,state_ens_handle, ens_size) -!print*, 'rho ', rho - -! .. total pressure: -model_pressure_t_distrib_fwd = ps0 * ( (gas_constant*(ts0+x_it)*qvf1) / & - (ps0/rho) )**cpovcv - -end function model_pressure_t_distrib_fwd - -!####################################################### - -function model_pressure_t_distrib_mean(i,j,k,id,state_ens_handle) - -! Calculate total pressure on mass point (half (mass) levels, T-point). - -integer, intent(in) :: i,j,k,id -real(r8) :: model_pressure_t_distrib_mean -type(ensemble_type), intent(in) :: state_ens_handle - -real (kind=r8), PARAMETER :: rd_over_rv = gas_constant / gas_constant_v -real (kind=r8), PARAMETER :: cpovcv = 1.4_r8 ! cp / (cp - gas_constant) - -integer :: iqv !< I think this is i for index -integer :: it -real(r8) :: qvf1, rho, x_iqv, x_it - -model_pressure_t_distrib_mean = missing_r8 - -! Adapted the code from WRF module_big_step_utilities_em.F ---- -! subroutine calc_p_rho_phi Y.-R. Guo (10/20/2004) - -! Simplification: alb*mub = (phb(i,j,k+1) - phb(i,j,k))/dnw(k) - -if (wrf%dom(id)%type_qv < 0 .or. wrf%dom(id)%type_t < 0) then - call error_handler(E_ERR, 'model_pressure_t:', & - 'BOTH QVAPOR and T must be in state vector to compute total pressure', & - source, revision, revdate) -endif - -iqv = new_dart_ind(i,j,k,wrf%dom(id)%type_qv, id) -it = new_dart_ind(i,j,k,wrf%dom(id)%type_t, id) - -call get_state(x_iqv, iqv, state_ens_handle) -call get_state(x_it, it, state_ens_handle) - -qvf1 = 1.0_r8 + x_iqv / rd_over_rv - -rho = model_rho_t_distrib(i,j,k,id,state_ens_handle) - -! .. total pressure: -model_pressure_t_distrib_mean = ps0 * ( (gas_constant*(ts0+x_it)*qvf1) / & - (ps0/rho) )**cpovcv - -end function model_pressure_t_distrib_mean - -!####################################################### - -function model_pressure_s_distrib(i, j, id, state_ens_handle) - -! compute pressure at surface at mass point - -integer, intent(in) :: i,j,id -type(ensemble_type), intent(in) :: state_ens_handle - -real(r8) :: model_pressure_s_distrib - -integer :: ips, imu -real(r8) :: x_imu, x_ips - - -! make sure one of these is good. -if ( wrf%dom(id)%type_mu < 0 .and. wrf%dom(id)%type_ps < 0 ) then - call error_handler(E_ERR, 'model_pressure_s:', & - 'One of MU or PSFC must be in state vector to compute surface pressure', & - source, revision, revdate) -endif - -if ( wrf%dom(id)%type_ps >= 0 ) then - ips = new_dart_ind(i,j,1,wrf%dom(id)%type_ps, id) - call get_state(x_ips, ips, state_ens_handle) - model_pressure_s_distrib = x_ips - -else - imu = new_dart_ind(i,j,1,wrf%dom(id)%type_mu, id) - call get_state(x_imu, imu, state_ens_handle) - model_pressure_s_distrib = wrf%dom(id)%p_top + wrf%dom(id)%mub(i,j) + x_imu - -endif - -end function model_pressure_s_distrib - -!####################################################### - -function interp_pressure(p1, p2, extrapolate, vertical) - -! interpolate halfway between pressures 1 and 2 in log units. -! if extrapolate is true, extrapolate where 1 is the edge and -! 2 is the inner value, going 1/2 grid cell out. - -real(r8), intent(in) :: p1, p2 -logical, intent(in), optional :: extrapolate -logical, intent(in), optional :: vertical -real(r8) :: interp_pressure - -logical :: do_interp -logical :: is_vert -real(r8) :: intermediate - -! default is to do interpolation; only extrapolate if the optional -! arg is specified and if it is true. -do_interp = .true. -if (present(extrapolate)) then - if (extrapolate) do_interp = .false. -endif - -! if vert is specified and is false, check log_horz_interpM instead -! of log_vert_interp to decide log vs linear interpolation for the -! Midpoint value. default is to do vertical interpolation. -is_vert = .true. -if (present(vertical)) then - is_vert = vertical -endif - -! once we like the results, remove the log_vert_interp test. -if (do_interp) then - if (( is_vert .and. log_vert_interp ) .or. & - (.not. is_vert .and. log_horz_interpM)) then - interp_pressure = exp((log(p1) + log(p2))/2.0_r8) - else - interp_pressure = (p1 + p2)/2.0_r8 - endif -else - if (( is_vert .and. log_vert_interp ) .or. & - (.not. is_vert .and. log_horz_interpM)) then - intermediate = (3.0_r8*log(p1) - log(p2))/2.0_r8 - if (intermediate <= 0.0_r8) then - interp_pressure = p1 - else - interp_pressure = exp(intermediate) - endif - else - interp_pressure = (3.0_r8*p1 - p2)/2.0_r8 - endif -endif - -end function interp_pressure - -!####################################################### - -function interp_4pressure_distrib(p1, p2, p3, p4, dx, dxm, dy, dym, ens_size, extrapolate, edgep) - -! given 4 corners of a quad, where the p1, p2, p3 and p4 points are -! respectively: lower left, lower right, upper left, upper right -! and dx is the distance in x, dxm is 1.0-dx, dy is distance in y -! and dym is 1.0-dy, interpolate the pressure while converted to log. -! if extrapolate is true, extrapolate where edgep is the edge pressure -! and the 4 points and dx/dy give the location of the inner point. - -integer, intent(in) :: ens_size -real(r8), intent(in) :: p1(ens_size), p2(ens_size), p3(ens_size), p4(ens_size) -real(r8), intent(in) :: dx, dxm, dy, dym -logical, intent(in), optional :: extrapolate -real(r8), intent(in), optional :: edgep(ens_size) -real(r8) :: interp_4pressure_distrib(ens_size) - -logical :: do_interp -real(r8) :: intermediate(ens_size) -real(r8) :: l1(ens_size), l2(ens_size), l3(ens_size), l4(ens_size) - -!HK -integer :: i - -! default is to do interpolation; only extrapolate if the optional -! arg is specified and if it is true. for extrapolation 'edgep' is -! required; it is unused for interpolation. -do_interp = .true. -if (present(extrapolate)) then - if (extrapolate) do_interp = .false. -endif - -if (.not. do_interp .and. .not. present(edgep)) then - call error_handler(E_ERR, 'interp_4pressure:', & - 'edgep must be specified for extrapolation. internal error.', & - source, revision, revdate) -endif - -if (log_horz_interpQ) then - l1 = log(p1) - l2 = log(p2) - l3 = log(p3) - l4 = log(p4) -endif - - -! once we like the results, remove the log_horz_interpQ test. -if (do_interp) then - if (log_horz_interpQ) then - interp_4pressure_distrib = exp(dym*( dxm*l1 + dx*l2 ) + dy*( dxm*l3 + dx*l4 )) - else - interp_4pressure_distrib = dym*( dxm*p1 + dx*p2 ) + dy*( dxm*p3 + dx*p4 ) - endif -else - if (log_horz_interpQ) then - intermediate = (3.0_r8*log(edgep) - & - dym*( dxm*l1 + dx*l2 ) - dy*( dxm*l3 + dx*l4 ))/2.0_r8 - - do i = 1, size(intermediate) - if (intermediate(i) <= 0.0_r8) then - !>@todo is this correct? - interp_4pressure_distrib(i) = edgep(i) - else - interp_4pressure_distrib(i) = exp(intermediate(i)) - endif - enddo - else - interp_4pressure_distrib = (3.0_r8*edgep - & - dym*( dxm*p1 + dx*p2 ) - dy*( dxm*p3 + dx*p4 ))/2.0_r8 - endif -endif - -end function interp_4pressure_distrib - -!####################################################### -!> Interpolate function -function interp_4pressure(p1, p2, p3, p4, dx, dxm, dy, dym, extrapolate, edgep) - -! given 4 corners of a quad, where the p1, p2, p3 and p4 points are -! respectively: lower left, lower right, upper left, upper right -! and dx is the distance in x, dxm is 1.0-dx, dy is distance in y -! and dym is 1.0-dy, interpolate the pressure while converted to log. -! if extrapolate is true, extrapolate where edgep is the edge pressure -! and the 4 points and dx/dy give the location of the inner point. - -real(r8), intent(in) :: p1, p2, p3, p4 -real(r8), intent(in) :: dx, dxm, dy, dym -logical, intent(in), optional :: extrapolate -real(r8), intent(in), optional :: edgep -real(r8) :: interp_4pressure - -logical :: do_interp -real(r8) :: intermediate -real(r8) :: l1, l2, l3, l4 - -! default is to do interpolation; only extrapolate if the optional -! arg is specified and if it is true. for extrapolation 'edgep' is -! required; it is unused for interpolation. -do_interp = .true. -if (present(extrapolate)) then - if (extrapolate) do_interp = .false. -endif - -if (.not. do_interp .and. .not. present(edgep)) then - call error_handler(E_ERR, 'interp_4pressure:', & - 'edgep must be specified for extrapolation. internal error.', & - source, revision, revdate) -endif - -if (log_horz_interpQ) then - l1 = log(p1) - l2 = log(p2) - l3 = log(p3) - l4 = log(p4) -endif - - -! once we like the results, remove the log_horz_interpQ test. -if (do_interp) then - if (log_horz_interpQ) then - interp_4pressure = exp(dym*( dxm*l1 + dx*l2 ) + dy*( dxm*l3 + dx*l4 )) - else - interp_4pressure = dym*( dxm*p1 + dx*p2 ) + dy*( dxm*p3 + dx*p4 ) - endif -else - if (log_horz_interpQ) then - intermediate = (3.0_r8*log(edgep) - & - dym*( dxm*l1 + dx*l2 ) - dy*( dxm*l3 + dx*l4 ))/2.0_r8 - if (intermediate <= 0.0_r8) then - interp_4pressure = edgep - else - interp_4pressure = exp(intermediate) - endif - else - interp_4pressure = (3.0_r8*edgep - & - dym*( dxm*p1 + dx*p2 ) - dy*( dxm*p3 + dx*p4 ))/2.0_r8 - endif -endif - -end function interp_4pressure - -!####################################################### - -function model_rho_t_distrib_fwd(i,j,k,id,state_ens_handle, ens_size) - -! Calculate the total density on mass point (half (mass) levels, T-point). - -integer, intent(in) :: ens_size -integer, intent(in) :: i,j,k,id -type(ensemble_type), intent(in) :: state_ens_handle -real(r8) :: model_rho_t_distrib_fwd(ens_size) - -integer :: imu,iph,iphp1 -real(r8) :: ph_e(ens_size), x_imu(ens_size), x_iph(ens_size), x_iphp1(ens_size) - -model_rho_t_distrib_fwd(:) = missing_r8 - -! Adapted the code from WRF module_big_step_utilities_em.F ---- -! subroutine calc_p_rho_phi Y.-R. Guo (10/20/2004) - -! Simplification: alb*mub = (phb(i,j,k+1) - phb(i,j,k))/dnw(k) - -if (wrf%dom(id)%type_mu < 0 .or. wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'model_rho_t:', & - 'BOTH MU and PH must be in state vector to compute total density', & - source, revision, revdate) -endif - -imu = new_dart_ind(i,j,1, wrf%dom(id)%type_mu, id) -iph = new_dart_ind(i,j,k, wrf%dom(id)%type_gz, id) -iphp1 = new_dart_ind(i,j,k+1,wrf%dom(id)%type_gz, id) - -call get_state(x_imu, imu, state_ens_handle) -call get_state(x_iph, iph, state_ens_handle) -call get_state(x_iphp1, iphp1, state_ens_handle) - -ph_e = ( (x_iphp1 + wrf%dom(id)%phb(i,j,k+1)) & - - (x_iph + wrf%dom(id)%phb(i,j,k )) ) / wrf%dom(id)%dnw(k) - -! now calculate rho = - mu / dphi/deta - -model_rho_t_distrib_fwd(:) = - (wrf%dom(id)%mub(i,j)+x_imu) / ph_e - -end function model_rho_t_distrib_fwd - -!####################################################### - -function model_rho_t_distrib_mean(i,j,k,id,state_ens_handle) - -! Calculate the total density on mass point (half (mass) levels, T-point). - -integer, intent(in) :: i,j,k,id -type(ensemble_type), intent(in) :: state_ens_handle -real(r8) :: model_rho_t_distrib_mean - -integer :: imu,iph,iphp1 -real(r8) :: ph_e, x_imu, x_iph, x_iphp1 - -model_rho_t_distrib_mean = missing_r8 - -! Adapted the code from WRF module_big_step_utilities_em.F ---- -! subroutine calc_p_rho_phi Y.-R. Guo (10/20/2004) - -! Simplification: alb*mub = (phb(i,j,k+1) - phb(i,j,k))/dnw(k) - -if (wrf%dom(id)%type_mu < 0 .or. wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'model_rho_t:', & - 'BOTH MU and PH must be in state vector to compute total density', & - source, revision, revdate) -endif - -imu = new_dart_ind(i,j,1, wrf%dom(id)%type_mu, id) -iph = new_dart_ind(i,j,k, wrf%dom(id)%type_gz, id) -iphp1 = new_dart_ind(i,j,k+1,wrf%dom(id)%type_gz, id) - -call get_state(x_imu, imu, state_ens_handle) -call get_state(x_iph, iph, state_ens_handle) -call get_state(x_iphp1, iphp1, state_ens_handle) - -ph_e = ( (x_iphp1 + wrf%dom(id)%phb(i,j,k+1)) & - - (x_iph + wrf%dom(id)%phb(i,j,k )) ) / wrf%dom(id)%dnw(k) - -! now calculate rho = - mu / dphi/deta - -model_rho_t_distrib_mean = - (wrf%dom(id)%mub(i,j)+x_imu) / ph_e - -end function model_rho_t_distrib_mean - -!####################################################### - -subroutine get_model_height_profile_distrib_fwd(i,j,dx,dy,dxm,dym,n,id,v_h, state_ens_handle, ens_size) - -! Calculate the model height profile on half (mass) levels, -! horizontally interpolated at the observation location. -! This routine used to compute geopotential heights; it now -! computes geometric heights. - -integer, intent(in) :: i,j,n,id -real(r8), intent(in) :: dx,dy,dxm,dym -integer, intent(in) :: ens_size -real(r8), intent(out) :: v_h(0:n, ens_size) -type(ensemble_type), intent(in) :: state_ens_handle -integer e !< for ensemble loop - -real(r8) :: fll(n+1, ens_size), geop(ens_size), lat(ens_size) -integer :: ill,iul,ilr,iur,k, rc -integer, dimension(2) :: ll, lr, ul, ur -logical :: debug = .false. - -real(r8), allocatable :: x_ill(:), x_ilr(:), x_iul(:), x_iur(:) - -allocate(x_ill(ens_size), x_ilr(ens_size), x_iul(ens_size), x_iur(ens_size)) - -if (wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'get_model_height_profile:', & - 'PH must be in state vector to compute height profile', & - source, revision, revdate) -endif - -if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_gz ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_gz ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_gz, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: get_model_height_profile :: getCorners rc = ', rc - - do k = 1, wrf%dom(id)%var_size(3,wrf%dom(id)%type_gz) - - ill = new_dart_ind(ll(1), ll(2), k, wrf%dom(id)%type_gz, id) - iul = new_dart_ind(ul(1), ul(2), k, wrf%dom(id)%type_gz, id) - ilr = new_dart_ind(lr(1), lr(2), k, wrf%dom(id)%type_gz, id) - iur = new_dart_ind(ur(1), ur(2), k, wrf%dom(id)%type_gz, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - geop(:) = ( dym*( dxm*( wrf%dom(id)%phb(ll(1),ll(2),k) + x_ill ) + & - dx*( wrf%dom(id)%phb(lr(1),lr(2),k) + x_ilr ) ) + & - dy*( dxm*( wrf%dom(id)%phb(ul(1),ul(2),k) + x_iul ) + & - dx*( wrf%dom(id)%phb(ur(1),ur(2),k) + x_iur ) ) )/gravity - - lat(:) = ( wrf%dom(id)%latitude(ll(1),ll(2)) + & - wrf%dom(id)%latitude(lr(1),lr(2)) + & - wrf%dom(id)%latitude(ul(1),ul(2)) + & - wrf%dom(id)%latitude(ur(1),ur(2)) ) / 4.0_r8 - - do e = 1, ens_size - fll(k, e) = compute_geometric_height(geop(e), lat(e)) - enddo - - end do - - do k=1,n - v_h(k, :) = 0.5_r8*(fll(k, :) + fll(k+1, :) ) - end do - - v_h(0, :) = dym*( dxm*wrf%dom(id)%hgt(ll(1), ll(2)) + & - dx*wrf%dom(id)%hgt(lr(1), lr(2)) ) + & - dy*( dxm*wrf%dom(id)%hgt(ul(1), ul(2)) + & - dx*wrf%dom(id)%hgt(ur(1), ur(2)) ) - - if (debug) & - print*, 'model_mod.f90 :: get_model_height_profile :: n, v_h() ', n, v_h(1:n, :) - - if (debug) & - print*, 'model_mod.f90 :: get_model_height_profile :: v_h(0) ', v_h(0, :) - -! If the boundsCheck functions return an unsatisfactory integer index, then set -! fld as missing data -else - - print*,'Not able the get height_profile' - print*,i,j,dx,dy,dxm,dym,n,id,wrf%dom(id)%var_size(1,wrf%dom(id)%type_gz), & - wrf%dom(id)%var_size(2,wrf%dom(id)%type_gz) - - v_h(:, :) = missing_r8 - -endif - -end subroutine get_model_height_profile_distrib_fwd - -!####################################################### - -subroutine get_model_height_profile_distrib_mean(i,j,dx,dy,dxm,dym,n,id,v_h, state_ens_handle) - -! Calculate the model height profile on half (mass) levels, -! horizontally interpolated at the observation location. -! This routine used to compute geopotential heights; it now -! computes geometric heights. - -integer, intent(in) :: i,j,n,id -real(r8), intent(in) :: dx,dy,dxm,dym -real(r8), intent(out) :: v_h(0:n) -type(ensemble_type), intent(in) :: state_ens_handle -integer e !< for ensemble loop - -real(r8) :: fll(n+1), geop, lat -integer :: ill,iul,ilr,iur,k, rc -integer, dimension(2) :: ll, lr, ul, ur -logical :: debug = .false. -real(r8) :: x_ill, x_ilr, x_iul, x_iur - - -if (wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'get_model_height_profile:', & - 'PH must be in state vector to compute height profile', & - source, revision, revdate) -endif - -if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_gz ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_gz ) ) then - - call getCorners(i, j, id, wrf%dom(id)%type_gz, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: get_model_height_profile :: getCorners rc = ', rc - - do k = 1, wrf%dom(id)%var_size(3,wrf%dom(id)%type_gz) - - ill = new_dart_ind(ll(1), ll(2), k, wrf%dom(id)%type_gz, id) - iul = new_dart_ind(ul(1), ul(2), k, wrf%dom(id)%type_gz, id) - ilr = new_dart_ind(lr(1), lr(2), k, wrf%dom(id)%type_gz, id) - iur = new_dart_ind(ur(1), ur(2), k, wrf%dom(id)%type_gz, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - geop = ( dym*( dxm*( wrf%dom(id)%phb(ll(1),ll(2),k) + x_ill ) + & - dx*( wrf%dom(id)%phb(lr(1),lr(2),k) + x_ilr ) ) + & - dy*( dxm*( wrf%dom(id)%phb(ul(1),ul(2),k) + x_iul ) + & - dx*( wrf%dom(id)%phb(ur(1),ur(2),k) + x_iur ) ) )/gravity - - lat = ( wrf%dom(id)%latitude(ll(1),ll(2)) + & - wrf%dom(id)%latitude(lr(1),lr(2)) + & - wrf%dom(id)%latitude(ul(1),ul(2)) + & - wrf%dom(id)%latitude(ur(1),ur(2)) ) / 4.0_r8 - - fll(k) = compute_geometric_height(geop, lat) - - end do - - do k=1,n - v_h(k) = 0.5_r8*(fll(k) + fll(k+1) ) - end do - - v_h(0) = dym*( dxm*wrf%dom(id)%hgt(ll(1), ll(2)) + & - dx*wrf%dom(id)%hgt(lr(1), lr(2)) ) + & - dy*( dxm*wrf%dom(id)%hgt(ul(1), ul(2)) + & - dx*wrf%dom(id)%hgt(ur(1), ur(2)) ) - - if (debug) & - print*, 'model_mod.f90 :: get_model_height_profile :: n, v_h() ', n, v_h(1:n) - - if (debug) & - print*, 'model_mod.f90 :: get_model_height_profile :: v_h(0) ', v_h(0) - -! If the boundsCheck functions return an unsatisfactory integer index, then set -! fld as missing data -else - - print*,'Not able the get height_profile' - print*,i,j,dx,dy,dxm,dym,n,id,wrf%dom(id)%var_size(1,wrf%dom(id)%type_gz), & - wrf%dom(id)%var_size(2,wrf%dom(id)%type_gz) - - v_h(:) = missing_r8 - -endif - -end subroutine get_model_height_profile_distrib_mean - - -!####################################################### - -function model_height_distrib(i,j,k,id,var_type, state_ens_handle) - -! This routine used to compute geopotential heights; it now -! computes geometric heights. - -type(ensemble_type), intent(in) :: state_ens_handle -integer, intent(in) :: i,j,k,id,var_type -real(r8) :: model_height_distrib - -integer :: i1, i2, i3, i4, off -real(r8) :: x_i1, x_i2, x_i3, x_i4 -real(r8) :: geop, lat - -model_height_distrib = missing_r8 - -if (wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'model_height:', & - 'PH must be in state vector to compute total pressure', & - source, revision, revdate) -endif - -! If W-grid (on ZNW levels), then we are fine because it is native to GZ -if( (var_type == wrf%dom(id)%type_w) .or. (var_type == wrf%dom(id)%type_gz) ) then - - i1 = new_dart_ind(i,j,k,wrf%dom(id)%type_gz, id) - call get_state(x_i1, i1, state_ens_handle) - - geop = (wrf%dom(id)%phb(i,j,k)+x_i1)/gravity - model_height_distrib = compute_geometric_height(geop, wrf%dom(id)%latitude(i, j)) - -! If U-grid, then height is defined between U points, both in horizontal -! and in vertical, so average -- averaging depends on longitude periodicity -elseif( var_type == wrf%dom(id)%type_u ) then - - if( i == wrf%dom(id)%var_size(1,wrf%dom(id)%type_u) ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at the seam in longitude, so take first and last mass points - i1 = new_dart_ind(i-1,j,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i-1,j,k+1,wrf%dom(id)%type_gz, id) - i3 = new_dart_ind(1, j,k ,wrf%dom(id)%type_gz, id) - i4 = new_dart_ind(1, j,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i3, state_ens_handle) - call get_state(x_i4, i4, state_ens_handle) - - - geop = ( (wrf%dom(id)%phb(i-1,j,k ) + x_i1) & - +(wrf%dom(id)%phb(i-1,j,k+1) + x_i2) & - +(wrf%dom(id)%phb(1 ,j,k ) + x_i3) & - +(wrf%dom(id)%phb(1 ,j,k+1) + x_i4) )/(4.0_r8*gravity) - - lat = ( wrf%dom(id)%latitude(i-1,j) & - +wrf%dom(id)%latitude(i-1,j) & - +wrf%dom(id)%latitude(1 ,j) & - +wrf%dom(id)%latitude(1 ,j) ) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = new_dart_ind(i-1,j,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i-1,j,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i1 -1, state_ens_handle) - call get_state(x_i4, i2 -1, state_ens_handle) - - - geop = ( 3.0_r8*(wrf%dom(id)%phb(i-1,j,k )+x_i1) & - +3.0_r8*(wrf%dom(id)%phb(i-1,j,k+1)+x_i2) & - -(wrf%dom(id)%phb(i-2,j,k )+x_i3) & - -(wrf%dom(id)%phb(i-2,j,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i-1,j) & - +3.0_r8*wrf%dom(id)%latitude(i-1,j) & - -wrf%dom(id)%latitude(i-2,j) & - -wrf%dom(id)%latitude(i-2,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - elseif( i == 1 ) then - - ! Check to see if periodic in longitude - if ( wrf%dom(id)%periodic_x ) then - - ! We are at the seam in longitude, so take first and last mass points - off = wrf%dom(id)%we - i1 = new_dart_ind(i ,j,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i ,j,k+1,wrf%dom(id)%type_gz, id) - i3 = new_dart_ind(off,j,k ,wrf%dom(id)%type_gz, id) - i4 = new_dart_ind(off,j,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i3, state_ens_handle) - call get_state(x_i4, i4, state_ens_handle) - - geop = ( (wrf%dom(id)%phb(i ,j,k ) + x_i1) & - +(wrf%dom(id)%phb(i ,j,k+1) + x_i2) & - +(wrf%dom(id)%phb(off,j,k ) + x_i3) & - +(wrf%dom(id)%phb(off,j,k+1) + x_i4) )/(4.0_r8*gravity) - - lat = ( wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(off,j) & - +wrf%dom(id)%latitude(off,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = new_dart_ind(i,j,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i,j,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i1 +1, state_ens_handle) - call get_state(x_i4, i2 +1, state_ens_handle) - - - geop = ( 3.0_r8*(wrf%dom(id)%phb(i ,j,k )+x_i1) & - +3.0_r8*(wrf%dom(id)%phb(i ,j,k+1)+x_i2) & - -(wrf%dom(id)%phb(i+1,j,k )+x_i3) & - -(wrf%dom(id)%phb(i+1,j,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i ,j) & - +3.0_r8*wrf%dom(id)%latitude(i ,j) & - -wrf%dom(id)%latitude(i+1,j) & - -wrf%dom(id)%latitude(i+1,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - else - - i1 = new_dart_ind(i,j,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i,j,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i1 -1, state_ens_handle) - call get_state(x_i4, i2 -1, state_ens_handle) - - - geop = ( (wrf%dom(id)%phb(i ,j,k )+x_i1) & - +(wrf%dom(id)%phb(i ,j,k+1)+x_i2) & - +(wrf%dom(id)%phb(i-1,j,k )+x_i3) & - +(wrf%dom(id)%phb(i-1,j,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i-1,j) & - +wrf%dom(id)%latitude(i-1,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - -! If V-grid, then pressure is defined between V points, both in horizontal -! and in vertical, so average -- averaging depends on polar periodicity -elseif( var_type == wrf%dom(id)%type_v ) then - - if( j == wrf%dom(id)%var_size(2,wrf%dom(id)%type_v) ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The upper corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - i1 = new_dart_ind(off,j-1,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(off,j-1,k+1,wrf%dom(id)%type_gz, id) - i3 = new_dart_ind(i ,j-1,k ,wrf%dom(id)%type_gz, id) - i4 = new_dart_ind(i ,j-1,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i3, state_ens_handle) - call get_state(x_i4, i4, state_ens_handle) - - geop = ( (wrf%dom(id)%phb(off,j-1,k )+x_i1) & - +(wrf%dom(id)%phb(off,j-1,k+1)+x_i2) & - +(wrf%dom(id)%phb(i ,j-1,k )+x_i3) & - +(wrf%dom(id)%phb(i ,j-1,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( wrf%dom(id)%latitude(off,j-1) & - +wrf%dom(id)%latitude(off,j-1) & - +wrf%dom(id)%latitude(i ,j-1) & - +wrf%dom(id)%latitude(i ,j-1)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = new_dart_ind(i,j-1,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i,j-1,k+1,wrf%dom(id)%type_gz, id) - i3 = new_dart_ind(i,j-2,k ,wrf%dom(id)%type_gz, id) - i4 = new_dart_ind(i,j-2,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i3, state_ens_handle) - call get_state(x_i4, i4, state_ens_handle) - - geop = ( 3.0_r8*(wrf%dom(id)%phb(i,j-1,k )+x_i1) & - +3.0_r8*(wrf%dom(id)%phb(i,j-1,k+1)+x_i2) & - -(wrf%dom(id)%phb(i,j-2,k )+x_i3) & - -(wrf%dom(id)%phb(i,j-2,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i,j-1) & - +3.0_r8*wrf%dom(id)%latitude(i,j-1) & - -wrf%dom(id)%latitude(i,j-2) & - -wrf%dom(id)%latitude(i,j-2)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - elseif( j == 1 ) then - - ! Check to see if periodic in latitude (polar) - if ( wrf%dom(id)%polar ) then - - ! The lower corner is 180 degrees of longitude away - off = i + wrf%dom(id)%we/2 - if ( off > wrf%dom(id)%we ) off = off - wrf%dom(id)%we - - i1 = new_dart_ind(off,j,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(off,j,k+1,wrf%dom(id)%type_gz, id) - i3 = new_dart_ind(i ,j,k ,wrf%dom(id)%type_gz, id) - i4 = new_dart_ind(i ,j,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i3, state_ens_handle) - call get_state(x_i4, i4, state_ens_handle) - - geop = ( (wrf%dom(id)%phb(off,j,k )+x_i1) & - +(wrf%dom(id)%phb(off,j,k+1)+x_i2) & - +(wrf%dom(id)%phb(i ,j,k )+x_i3) & - +(wrf%dom(id)%phb(i ,j,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( wrf%dom(id)%latitude(off,j) & - +wrf%dom(id)%latitude(off,j) & - +wrf%dom(id)%latitude(i ,j) & - +wrf%dom(id)%latitude(i ,j)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - else - - ! If not periodic, then try extrapolating - i1 = new_dart_ind(i,j ,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i,j ,k+1,wrf%dom(id)%type_gz, id) - i3 = new_dart_ind(i,j+1,k ,wrf%dom(id)%type_gz, id) - i4 = new_dart_ind(i,j+1,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i3, state_ens_handle) - call get_state(x_i4, i4, state_ens_handle) - - geop = ( 3.0_r8*(wrf%dom(id)%phb(i,j ,k )+x_i1) & - +3.0_r8*(wrf%dom(id)%phb(i,j ,k+1)+x_i2) & - -(wrf%dom(id)%phb(i,j+1,k )+x_i3) & - -(wrf%dom(id)%phb(i,j+1,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( 3.0_r8*wrf%dom(id)%latitude(i,j ) & - +3.0_r8*wrf%dom(id)%latitude(i,j ) & - -wrf%dom(id)%latitude(i,j+1) & - -wrf%dom(id)%latitude(i,j+1)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - - else - - i1 = new_dart_ind(i,j ,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i,j ,k+1,wrf%dom(id)%type_gz, id) - i3 = new_dart_ind(i,j-1,k ,wrf%dom(id)%type_gz, id) - i4 = new_dart_ind(i,j-1,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - call get_state(x_i3, i3, state_ens_handle) - call get_state(x_i4, i4, state_ens_handle) - - geop = ( (wrf%dom(id)%phb(i,j ,k )+x_i1) & - +(wrf%dom(id)%phb(i,j ,k+1)+x_i2) & - +(wrf%dom(id)%phb(i,j-1,k )+x_i3) & - +(wrf%dom(id)%phb(i,j-1,k+1)+x_i4) )/(4.0_r8*gravity) - - lat = ( wrf%dom(id)%latitude(i,j ) & - +wrf%dom(id)%latitude(i,j ) & - +wrf%dom(id)%latitude(i,j-1) & - +wrf%dom(id)%latitude(i,j-1)) / 4.0_r8 - - model_height_distrib = compute_geometric_height(geop, lat) - - endif - -elseif( var_type == wrf%dom(id)%type_mu .or. & - var_type == wrf%dom(id)%type_ps .or. & - var_type == wrf%dom(id)%type_tsk) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) - -elseif( var_type == wrf%dom(id)%type_tslb .or. & - var_type == wrf%dom(id)%type_smois .or. & - var_type == wrf%dom(id)%type_sh2o ) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) - wrf%dom(id)%zs(k) - -elseif( var_type == wrf%dom(id)%type_u10 .or. & - var_type == wrf%dom(id)%type_v10 ) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) + 10.0_r8 - -elseif( var_type == wrf%dom(id)%type_t2 .or. & - var_type == wrf%dom(id)%type_th2 .or. & - var_type == wrf%dom(id)%type_q2 ) then - - model_height_distrib = wrf%dom(id)%hgt(i,j) + 2.0_r8 - -else - - i1 = new_dart_ind(i,j,k ,wrf%dom(id)%type_gz, id) - i2 = new_dart_ind(i,j,k+1,wrf%dom(id)%type_gz, id) - - call get_state(x_i1, i1, state_ens_handle) - call get_state(x_i2, i2, state_ens_handle) - - geop = ( (wrf%dom(id)%phb(i,j,k )+x_i1) & - +(wrf%dom(id)%phb(i,j,k+1)+x_i2) )/(2.0_r8*gravity) - - lat = wrf%dom(id)%latitude(i,j) - - model_height_distrib = compute_geometric_height(geop, lat) - -endif - -end function model_height_distrib - -!####################################################### - -!> Distributed version of model_height_w -!> Only one value, the mean, is used because model_height_w_distrib -!> is only used in the vertical conversion -function model_height_w_distrib(i, j, k, id, state_ens_handle) - -! return total height at staggered vertical coordinate -! and horizontal mass coordinates - -type(ensemble_type), intent(in) :: state_ens_handle -integer, intent(in) :: i,j,k,id -real(r8) :: x_i1 -real(r8) :: model_height_w_distrib - -integer :: i1 -real(r8) :: geop - -if (wrf%dom(id)%type_gz < 0) then - call error_handler(E_ERR, 'model_height_w:', & - 'PH must be in state vector to compute staggered model height', & - source, revision, revdate) -endif - -i1 = new_dart_ind(i,j,k,wrf%dom(id)%type_gz, id) - -call get_state(x_i1, i1, state_ens_handle) - -geop = (wrf%dom(id)%phb(i,j,k) + x_i1)/gravity -model_height_w_distrib = compute_geometric_height(geop, wrf%dom(id)%latitude(i, j)) - -end function model_height_w_distrib - -!####################################################### - - -subroutine pert_model_state(state, pert_state, interf_provided) - -! Perturbs a single model state for generating initial ensembles. -! WARNING - this routine is not a substitute for a good set -! of real initial condition files. Intended as a last resort, -! this routine should be used to start a long free-run model -! advance to spin-up a set of internally consistent states with -! their own structure before assimilating a set of obserations. - -real(r8), intent(in) :: state(:) -real(r8), intent(out) :: pert_state(:) -logical, intent(out) :: interf_provided - -real(r8) :: pert_amount = 0.005 ! 0.5% - -real(r8) :: pert_ampl, range -real(r8) :: minv, maxv, temp -type(random_seq_type) :: random_seq -integer :: id, i, j, s, e -integer, save :: counter = 0 - -! generally you do not want to perturb a single state -! to begin an experiment - unless you make minor perturbations -! and then run the model free for long enough that differences -! develop which contain actual structure. if you comment -! out the next 4 lines, the subsequent code is a pert routine which -! can be used to add minor perturbations which can be spun up. -! note that as written, if all values in a field are identical -! (i.e. 0.0) this routine will not change those values, since -! it won't make a new value outside the original min/max of that -! variable in the state vector. - -call error_handler(E_ERR,'pert_model_state', & - 'WRF model cannot be started from a single vector', & - source, revision, revdate, & - text2='see comments in wrf/model_mod.f90::pert_model_state()') - -! NOT REACHED unless preceeding 4 lines commented out - -! start of pert code -interf_provided = .true. - -! the first time through get the task id (0:N-1) and set a unique seed -! per task. this should reproduce from run to run if you keep the number -! of MPI tasks the same. it WILL NOT reproduce if the number of tasks changes. -! if this routine could at some point get the global ensemble member number -! as an argument, that would be unique and the right thing to use as a seed. -! -! the line below only executes the first time since counter gets incremented -! after the first use and the value is saved between calls. it is trying to -! generate a unique base number, and then just increments by 1 each subsequent -! time it is called (which only happens if there are multiple ensemble -! members/task). it is assuming there are no more than 1000 ensembles/task, -! which seems safe given the current sizes of state vecs and hardware memory. - -if (counter == 0) counter = ((my_task_id()+1) * 1000) - -call init_random_seq(random_seq, counter) -counter = counter + 1 - -! do the perturbation per domain, per variable type -do id=1, num_domains - do i=1, wrf%dom(id)%number_of_wrf_variables - ! starting and ending indices in the linear state vect - s = wrf%dom(id)%var_index(1, i) - e = wrf%dom(id)%var_index(2, i) - ! original min/max data values of each type - minv = minval(state(s:e)) - maxv = maxval(state(s:e)) - !! Option 1: - !! make the perturbation amplitude N% of the total - !! range of this variable. values could vary a lot - !! over some of the types, like pressure - !range = maxv - minv - !pert_ampl = pert_amount * range - do j=s, e - ! once you change pert_state, state is changed as well - ! since they are the same storage as called from filter. - ! you have to save it if you want to use it again. - temp = state(j) ! original value - ! Option 2: perturb each value individually - !! make the perturbation amplitude N% of this value - pert_ampl = pert_amount * temp - pert_state(j) = random_gaussian(random_seq, state(j), pert_ampl) - ! keep it from exceeding the original range - pert_state(j) = max(minv, pert_state(j)) - pert_state(j) = min(maxv, pert_state(j)) - enddo - enddo -enddo - - -end subroutine pert_model_state - -!####################################################### -! !WARNING:: at the moment, this code is *not* called -! !so there is no requirement to have a wrf namelist in -! !the current directory. the only thing it was extracting -! !was the dt, and that exists in the wrf input netcdf file -! !and is now read from there. -! -! subroutine read_dt_from_wrf_nml() -! -! real(r8) :: dt -! -! integer :: time_step, time_step_fract_num, time_step_fract_den -! integer :: max_dom, feedback, smooth_option -! integer, dimension(3) :: s_we, e_we, s_sn, e_sn, s_vert, e_vert -! integer, dimension(3) :: dx, dy, ztop, grid_id, parent_id -! integer, dimension(3) :: i_parent_start, j_parent_start, parent_grid_ratio -! integer, dimension(3) :: parent_time_step_ratio -! integer :: io, iunit, id -! integer :: num_metgrid_levels, p_top_requested, nproc_x, nproc_y -! -! !nc -- we added "num_metgrid_levels" to the domains nml to make all well with the -! ! namelist.input file belonging to global WRF, -! ! also "p_top_requested" in domains nml -! ! also "nproc_x" & "nproc_y" -! !nc -- we notice that "ztop" is unused in code -- perhaps get rid of later? -! namelist /domains/ time_step, time_step_fract_num, time_step_fract_den -! namelist /domains/ max_dom -! namelist /domains/ s_we, e_we, s_sn, e_sn, s_vert, e_vert -! namelist /domains/ dx, dy, ztop, grid_id, parent_id -! namelist /domains/ i_parent_start, j_parent_start, parent_grid_ratio -! namelist /domains/ parent_time_step_ratio -! namelist /domains/ feedback, smooth_option -! namelist /domains/ num_metgrid_levels, p_top_requested, nproc_x, nproc_y -! -! ! Begin by reading the namelist input -! call find_namelist_in_file("namelist.input", "domains", iunit) -! read(iunit, nml = domains, iostat = io) -! call check_namelist_read(iunit, io, "domains") -! -! ! Record the namelist values used for the run ... -! if (do_nml_file()) write(nmlfileunit, nml=domains) -! if (do_nml_term()) write( * , nml=domains) -! -! if (max_dom /= num_domains) then -! -! write(*,*) 'max_dom in namelist.input = ',max_dom -! write(*,*) 'num_domains in input.nml = ',num_domains -! call error_handler(E_ERR,'read_dt_from_wrf_nml', & -! 'Make them consistent.', source, revision,revdate) -! -! endif -! -! if (time_step_fract_den /= 0) then -! dt = real(time_step) + real(time_step_fract_num) / real(time_step_fract_den) -! else -! dt = real(time_step) -! endif -! -! do id=1,num_domains -! wrf%dom(id)%dt = dt / real(parent_time_step_ratio(id)) -! enddo -! -! end subroutine read_dt_from_wrf_nml - - - -subroutine compute_seaprs ( nz, z, t, p , q , & - sea_level_pressure, debug) -!------------------------------------------------------------------------- -! compute_seaprs Estimate sea level pressure. -! -! This routines has been taken "as is" from wrf_user_fortran_util_0.f -! -! This routine assumes -! index order is (i,j,k) -! wrf staggering -! units: pressure (Pa), temperature(K), height (m), mixing ratio (kg kg{-1}) -! availability of 3d p, t, and qv; 2d terrain; 1d half-level zeta string -! output units of SLP are Pa, but you should divide that by 100 for the -! weather weenies. -! virtual effects are included -! -! Dave -! -! cys: change to 1d -! TJH: verified intent() qualifiers, declaration syntax, uses error_handler - - IMPLICIT NONE - INTEGER, intent(in) :: nz - REAL(r8), intent(in) :: z(nz), p(nz), q(nz) - REAL(r8), intent(inout) :: t(nz) - REAL(r8), intent(out) :: sea_level_pressure - LOGICAL, intent(in) :: debug - - INTEGER :: level - REAL(r8) :: t_surf, t_sea_level - -! Some required physical constants: - - REAL(r8) :: R, G, GAMMA - PARAMETER (R=287.04_r8, G=9.81_r8, GAMMA=0.0065_r8) - -! Specific constants for assumptions made in this routine: - - REAL(r8) :: TC, PCONST - PARAMETER (TC=273.16_r8 + 17.5_r8, PCONST = 10000.0_r8) - - LOGICAL :: ridiculous_mm5_test - PARAMETER (ridiculous_mm5_test = .TRUE.) -! PARAMETER (ridiculous_mm5_test = .false.) - -! Local variables: - - INTEGER :: k - INTEGER :: klo, khi - - REAL(r8) :: plo, phi, tlo, thi, zlo, zhi - REAL(r8) :: p_at_pconst, t_at_pconst, z_at_pconst - REAL(r8) :: z_half_lowest - - REAL(r8), PARAMETER :: cp = 7.0_r8*R/2.0_r8 - REAL(r8), PARAMETER :: rcp = R/cp - REAL(r8), PARAMETER :: p1000mb = 100000.0_r8 - - LOGICAL :: l1 , l2 , l3, found - -! Find least zeta level that is PCONST Pa above the surface. We later use this -! level to extrapolate a surface pressure and temperature, which is supposed -! to reduce the effect of the diurnal heating cycle in the pressure field. - - t = t*(p/p1000mb)**rcp - - level = -1 - - k = 1 - found = .false. - do while( (.not. found) .and. (k.le.nz)) - IF ( p(k) .LT. p(1)-PCONST ) THEN - level = k - found = .true. - END IF - k = k+1 - END DO - - IF ( level .EQ. -1 ) THEN - PRINT '(A,I4,A)','Troubles finding level ', & - NINT(PCONST)/100,' above ground.' - print*, 'p=',p - print*, 't=',t - print*, 'z=',z - print*, 'q=',q - write(errstring,*)'Error_in_finding_100_hPa_up' - call error_handler(E_ERR,'compute_seaprs',errstring,' ',' ',' ') - END IF - - -! Get temperature PCONST Pa above surface. Use this to extrapolate -! the temperature at the surface and down to sea level. - - klo = MAX ( level - 1 , 1 ) - khi = MIN ( klo + 1 , nz - 1 ) - - IF ( klo .EQ. khi ) THEN - PRINT '(A)','Trapping levels are weird.' - PRINT '(A,I3,A,I3,A)','klo = ',klo,', khi = ',khi, & - ': and they should not be equal.' - write(errstring,*)'Error_trapping_levels' - call error_handler(E_ERR,'compute_seaprs',errstring,' ',' ',' ') - END IF - - plo = p(klo) - phi = p(khi) - tlo = t(klo)*(1. + 0.608 * q(klo) ) - thi = t(khi)*(1. + 0.608 * q(khi) ) -! zlo = zetahalf(klo)/ztop*(ztop-terrain(i,j))+terrain(i,j) -! zhi = zetahalf(khi)/ztop*(ztop-terrain(i,j))+terrain(i,j) - zlo = z(klo) - zhi = z(khi) - - p_at_pconst = p(1) - pconst - t_at_pconst = thi-(thi-tlo)*LOG(p_at_pconst/phi)*LOG(plo/phi) - z_at_pconst = zhi-(zhi-zlo)*LOG(p_at_pconst/phi)*LOG(plo/phi) - - t_surf = t_at_pconst*(p(1)/p_at_pconst)**(gamma*R/g) - t_sea_level = t_at_pconst+gamma*z_at_pconst - - -! If we follow a traditional computation, there is a correction to the sea level -! temperature if both the surface and sea level temnperatures are *too* hot. - - IF ( ridiculous_mm5_test ) THEN - l1 = t_sea_level .LT. TC - l2 = t_surf .LE. TC - l3 = .NOT. l1 - IF ( l2 .AND. l3 ) THEN - t_sea_level = TC - ELSE - t_sea_level = TC - 0.005*(t_surf-TC)**2 - END IF - END IF - -! The grand finale: ta da! - -! z_half_lowest=zetahalf(1)/ztop*(ztop-terrain(i,j))+terrain(i,j) - z_half_lowest=z(1) - sea_level_pressure = p(1) * & - EXP((2.*g*z_half_lowest)/ & - (R*(t_sea_level+t_surf))) - -! sea_level_pressure(i,j) = sea_level_pressure(i,j)*0.01 - - if (debug) then - print *,'slp=',sea_level_pressure - endif -! print *,'t=',t(10:15,10:15,1),t(10:15,2,1),t(10:15,3,1) -! print *,'z=',z(10:15,1,1),z(10:15,2,1),z(10:15,3,1) -! print *,'p=',p(10:15,1,1),p(10:15,2,1),p(10:15,3,1) -! print *,'slp=',sea_level_pressure(10:15,10:15), & -! sea_level_pressure(10:15,10:15),sea_level_pressure(20,10:15) - -end subroutine compute_seaprs - - - -SUBROUTINE splin2(x1a,x2a,ya,y2a,m,n,x1,x2,y) - INTEGER m,n,NN - REAL(r8), intent(in) :: x1,x2,x1a(m),x2a(n),y2a(m,n),ya(m,n) - real(r8), intent(out) :: y - PARAMETER (NN=200) -! USES spline,splint - INTEGER j,k - REAL(r8) y2tmp(NN),ytmp(NN),yytmp(NN) - do 12 j=1,m - do 11 k=1,n - ytmp(k)=ya(j,k) - y2tmp(k)=y2a(j,k) -11 continue - call splint(x2a,ytmp,y2tmp,n,x2,yytmp(j)) -12 continue - call spline(x1a,yytmp,m,1.e30_r8,1.e30_r8,y2tmp) - call splint(x1a,yytmp,y2tmp,m,x1,y) - return -END subroutine splin2 - -SUBROUTINE splie2(x1a,x2a,ya,m,n,y2a) - INTEGER m,n,NN - REAL(r8), intent(in) :: x1a(m),x2a(n),ya(m,n) - REAL(r8), intent(out) :: y2a(m,n) - PARAMETER (NN=200) -! USES spline - INTEGER j,k - REAL(r8) y2tmp(NN),ytmp(NN) - do 13 j=1,m - do 11 k=1,n - ytmp(k)=ya(j,k) -11 continue - call spline(x2a,ytmp,n,1.e30_r8,1.e30_r8,y2tmp) - do 12 k=1,n - y2a(j,k)=y2tmp(k) -12 continue -13 continue - return -END subroutine splie2 - -SUBROUTINE spline(x,y,n,yp1,ypn,y2) - INTEGER n,NMAX - REAL(r8), intent(in) :: yp1,ypn,x(n),y(n) - REAL(r8), intent(out) :: y2(n) - PARAMETER (NMAX=800) - INTEGER i,k - REAL(r8) p,qn,sig,un,u(NMAX) - if (yp1.gt..99e30) then - y2(1)=0. - u(1)=0. - else - y2(1)=-0.5 - u(1)=(3./(x(2)-x(1)))*((y(2)-y(1))/(x(2)-x(1))-yp1) - endif - do 11 i=2,n-1 - sig=(x(i)-x(i-1))/(x(i+1)-x(i-1)) - p=sig*y2(i-1)+2. - y2(i)=(sig-1.)/p - u(i)=(6.*((y(i+1)-y(i))/(x(i+ & - 1)-x(i))-(y(i)-y(i-1))/(x(i)-x(i-1)))/(x(i+1)-x(i-1))-sig* & - u(i-1))/p -11 continue - if (ypn.gt..99e30) then - qn=0. - un=0. - else - qn=0.5 - un=(3./(x(n)-x(n-1)))*(ypn-(y(n)-y(n-1))/(x(n)-x(n-1))) - endif - y2(n)=(un-qn*u(n-1))/(qn*y2(n-1)+1.) - do 12 k=n-1,1,-1 - y2(k)=y2(k)*y2(k+1)+u(k) -12 continue - return -END subroutine spline - - -SUBROUTINE splint(xa,ya,y2a,n,x,y) - INTEGER n - REAL(r8),intent(in) :: x,xa(n),y2a(n),ya(n) - REAL(r8),intent(out) :: y - INTEGER k,khi,klo - REAL(r8) a,b,h - klo=1 - khi=n -1 if (khi-klo.gt.1) then - k=(khi+klo)/2 - if(xa(k).gt.x)then - khi=k - else - klo=k - endif - goto 1 - endif - h=xa(khi)-xa(klo) - if (h.eq.0.) then - write(*,*) 'bad xa input in splint' - stop - endif - a=(xa(khi)-x)/h - b=(x-xa(klo))/h - y=a*ya(klo)+b*ya(khi)+((a**3-a)*y2a(klo)+(b**3-b)*y2a(khi))*(h**2)/6. - return -END subroutine splint - - -!####################################################################### - - -subroutine ens_mean_for_model(filter_ens_mean) - -! Not used in low-order models -! Stores provided ensemble mean within the module for later use - -real(r8), intent(in) :: filter_ens_mean(:) - -call error_handler(E_ERR, 'ens_mean_for_model', 'not allowed in distributed version') - -end subroutine ens_mean_for_model - - -!####################################################################### - -subroutine get_domain_info(obslon,obslat,id,iloc,jloc) - -real(r8), intent(in) :: obslon, obslat -integer, intent(out) :: id -real(r8), intent(out) :: iloc, jloc - -logical :: dom_found - -! given arbitrary lat and lon values, returns closest domain id and -! horizontal mass point grid points (xloc,yloc) - -dom_found = .false. - -id = num_domains -do while (.not. dom_found) - - ! Checking for exact equality on real variable types is generally a bad idea. - - if( (wrf%dom(id)%proj%hemi == 1.0_r8 .and. obslat < -90.0_r8) .or. & - (wrf%dom(id)%proj%hemi == -1.0_r8 .and. obslat > 90.0_r8) .or. & - (wrf%dom(id)%proj%code == PROJ_MERC .and. abs(obslat) > 90.0_r8) ) then - - ! catch latitudes that are out of range - ignore them but print out a warning. - write(errstring, *) 'obs with latitude out of range: ', obslat - call error_handler(E_MSG, 'model_mod', errstring) - - else - call latlon_to_ij(wrf%dom(id)%proj,min(max(obslat,-89.9999999_r8),89.9999999_r8),obslon,iloc,jloc) - - ! Array bound checking depends on whether periodic or not -- these are - ! real-valued indices here, so we cannot use boundsCheck :( - - if ( wrf%dom(id)%periodic_x .and. .not. wrf%dom(id)%periodic_y ) then - if ( wrf%dom(id)%polar ) then - ! Periodic X & M_grid ==> [1 we+1) - ! Periodic Y & M_grid ==> [0.5 sn+0.5] - if ( iloc >= 1.0_r8 .and. iloc < real(wrf%dom(id)%we,r8)+1.0_r8 .and. & - jloc >= 0.5_r8 .and. jloc <= real(wrf%dom(id)%sn,r8)+0.5_r8 ) & - dom_found = .true. - else - ! Periodic X & M_grid ==> [1 we+1) - ! NOT Periodic Y & M_grid ==> [1 sn] - if ( iloc >= 1.0_r8 .and. iloc < real(wrf%dom(id)%we,r8)+1.0_r8 .and. & - jloc >= 1.0_r8 .and. jloc <= real(wrf%dom(id)%sn,r8) ) & - dom_found = .true. - endif - - elseif ( wrf%dom(id)%periodic_x .and. wrf%dom(id)%periodic_y ) then - ! Periodic X & M_grid ==> [1 we+1) - ! Periodic Y & M_grid ==> [1 sn+1] - if ( iloc >= 1.0_r8 .and. iloc < real(wrf%dom(id)%we,r8)+1.0_r8 .and. & - jloc >= 1.0_r8 .and. jloc <= real(wrf%dom(id)%sn,r8)+1.0_r8 ) & - dom_found = .true. - - else - if ( wrf%dom(id)%polar ) then - ! NOT Periodic X & M_grid ==> [1 we] - ! Periodic Y & M_grid ==> [0.5 sn+0.5] - if ( iloc >= 1.0_r8 .and. iloc <= real(wrf%dom(id)%we,r8) .and. & - jloc >= 0.5_r8 .and. jloc <= real(wrf%dom(id)%sn,r8)+0.5_r8 ) & - dom_found = .true. - else - ! NOT Periodic X & M_grid ==> [1 we] - ! NOT Periodic Y & M_grid ==> [1 sn] - if ( iloc >= 1.0_r8 .and. iloc <= real(wrf%dom(id)%we,r8) .and. & - jloc >= 1.0_r8 .and. jloc <= real(wrf%dom(id)%sn,r8) ) & - dom_found = .true. - endif - endif - - endif - - if (.not. dom_found) then - id = id - 1 - if (id == 0) return - endif - -end do - -end subroutine get_domain_info - -!####################################################################### -!> Distributed version of get_close_obs -subroutine get_close_obs_distrib(gc, base_obs_loc, base_obs_kind, obs_loc, & - obs_kind, num_close, close_ind, dist, state_ens_handle) - -! Given a DART ob (referred to as "base") and a set of obs priors or state variables -! (obs_loc, obs_kind), returns the subset of close ones to the "base" ob, their -! indices, and their distances to the "base" ob... - -! For vertical distance computations, general philosophy is to convert all vertical -! coordinates to a common coordinate. This coordinate type is defined in the namelist -! with the variable "vert_localization_coord". - -! Vertical conversion is carried out by the subroutine vert_convert. - -! Note that both base_obs_loc and obs_loc are intent(inout), meaning that these -! locations are possibly modified here and returned as such to the calling routine. -! The calling routine is always filter_assim and these arrays are local arrays -! within filter_assim. In other words, these modifications will only matter within -! filter_assim, but will not propagate backwards to filter. - -!HK -type(ensemble_type), intent(inout) :: state_ens_handle -type(get_close_type), intent(in) :: gc -type(location_type), intent(inout) :: base_obs_loc, obs_loc(:) -integer, intent(in) :: base_obs_kind, obs_kind(:) -integer, intent(out) :: num_close, close_ind(:) -real(r8), intent(out) :: dist(:) - -integer :: t_ind, istatus1, istatus2, k -integer :: base_which, local_obs_which -real(r8), dimension(3) :: base_array, local_obs_array -type(location_type) :: local_obs_loc - - -! Initialize variables to missing status -num_close = 0 -close_ind = -99 -dist = 1.0e9 - -istatus1 = 0 -istatus2 = 0 - -! Convert base_obs vertical coordinate to requested vertical coordinate if necessary - -base_array = get_location(base_obs_loc) -base_which = nint(query_location(base_obs_loc)) - -if (.not. horiz_dist_only) then - if (base_which /= wrf%dom(1)%localization_coord) then - !print*, 'base_which ', base_which, 'loc coord ', wrf%dom(1)%localization_coord - call vert_convert_distrib(state_ens_handle, base_obs_loc, base_obs_kind, istatus1) - !call error_handler(E_ERR, 'you should not call this ', 'get_close_obs_distrib') - elseif (base_array(3) == missing_r8) then - istatus1 = 1 - endif -endif - -if (istatus1 == 0) then - - ! Get all the potentially close obs but no dist (optional argument dist(:) is not present) - ! This way, we are decreasing the number of distance computations that will follow. - ! This is a horizontal-distance operation and we don't need to have the relevant vertical - ! coordinate information yet (for obs_loc). - call loc_get_close_obs(gc, base_obs_loc, base_obs_kind, obs_loc, obs_kind, & - num_close, close_ind) - - ! Loop over potentially close subset of obs priors or state variables - do k = 1, num_close - - t_ind = close_ind(k) - local_obs_loc = obs_loc(t_ind) - local_obs_which = nint(query_location(local_obs_loc)) - - ! Convert local_obs vertical coordinate to requested vertical coordinate if necessary. - ! This should only be necessary for obs priors, as state location information already - ! contains the correct vertical coordinate (filter_assim's call to get_state_meta_data). - if (.not. horiz_dist_only) then - if (local_obs_which /= wrf%dom(1)%localization_coord) then - call vert_convert_distrib(state_ens_handle, local_obs_loc, obs_kind(t_ind), istatus2) - ! Store the "new" location into the original full local array - obs_loc(t_ind) = local_obs_loc !HK Overwritting the location - else - istatus2 = 0 - endif - endif - - ! Compute distance - set distance to a very large value if vert coordinate is missing - ! or vert_convert returned error (istatus2=1) - local_obs_array = get_location(local_obs_loc) - if (((.not. horiz_dist_only).and.(local_obs_array(3) == missing_r8)).or.(istatus2 == 1)) then - dist(k) = 1.0e9 - else - dist(k) = get_dist(base_obs_loc, local_obs_loc, base_obs_kind, obs_kind(t_ind)) - endif - - !print*, 'k ', k, 'rank ', my_task_id() - - end do -endif - -end subroutine get_close_obs_distrib - -!####################################################################### -!nc -- additional function from Greg Lawson & Nancy Collins -! -! logical function boundsCheck determines whether real-valued location indices are -! within a sensible range based on the assumed (un)staggered grid and based on -! whether the domain is assumed to be periodic in a given direction. - -function boundsCheck ( ind, periodic, id, dim, type ) - - integer, intent(in) :: ind, id, dim, type - logical, intent(in) :: periodic - - logical :: boundsCheck -! logical, parameter :: restrict_polar = .true. - logical, parameter :: restrict_polar = .false. - - ! Consider cases in REAL-VALUED indexing: - ! - ! I. Longitude -- x-direction - ! A. PERIODIC (period_x = .true.) - ! - ! Consider Mass-grid (& V-grid) longitude grid with 4 west-east gridpoints - ! Values :: [ -135 -45 45 135 ] .. {225} - ! Indices :: [ 1 2 3 4 ] .. {1,5} - ! Complementary U-grid - ! Values :: [ -180 -90 0 90 180 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 5) ---> [1 we+1) - ! ---> [-135 225) - ! 2. U-grid ---> [1 5) ---> [1 wes) - ! ---> [-180 180) - ! [Note that above "allowable values" reflect that one should be able to have - ! an observation anywhere on a given longitude circle -- the information - ! exists in order to successfully interpolate to anywhere over [0 360).] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & U-grid. Hence, two examples: - ! a. If there is an observation location at -165 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 4.667 - ! * An observation of TYPE_U (on the U-grid) would have ind = 1.167 - ! b. If there is an observation location at 0 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 2.5 - ! * An observation of TYPE_U (on the U-grid) would have ind = 3.0 - ! - ! B. NOT periodic (period_x = .false.) - ! - ! Consider Mass-grid (& V-grid) longitude grid with 4 west-east gridpoints - ! Values :: [ 95 105 115 125 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary U-grid - ! Values :: [ 90 100 110 120 130 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 4] ---> [1 we] - ! ---> [95 125] - ! 2. U-grid ---> [1.5 4.5] ---> [1.5 we+0.5] - ! ---> [95 125] - ! [Note that above "allowable values" reflect that one should only be able to - ! have an observation within the M-grid, since that is the only way to - ! guarantee that the necessary information exists in order to successfully - ! interpolate to a specified location.] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & U-grid. Hence, two examples: - ! a. If there is an observation location at 96 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 1.1 - ! * An observation of TYPE_U (on the U-grid) would have ind = 1.6 - ! b. If there is an observation location at 124 longitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 3.9 - ! * An observation of TYPE_U (on the U-grid) would have ind = 4.4 - ! - ! II. Latitude -- y-direction - ! A. PERIODIC (polar = .true.) - ! - ! Consider Mass-grid (& U-Grid) latitude grid with 4 south-north gridpoints - ! Values :: [ -67.5 -22.5 22.5 67.5 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary V-grid - ! Values :: [ -90 -45 0 45 90 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [0.5 4.5] ---> [0.5 sn+0.5] - ! ---> [-90 90] - ! 2. U-grid ---> [1 5] ---> [1 sns] - ! ---> [-90 90] - ! [Note that above "allowable values" reflect that one should be able to have - ! an observation anywhere along a give latitude circle -- the information - ! exists in order to successfully interpolate to anywhere over [-90 90]; - ! however, in latitude this poses a special challenge since the seams join - ! two separate columns of data over the pole, as opposed to in longitude - ! where the seam wraps back on a single row of data.] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & V-grid. Hence, two examples: - ! a. If there is an observation location at -75 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 0.833 - ! * An observation of TYPE_V (on the V-grid) would have ind = 1.333 - ! b. If there is an observation location at 0 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 2.5 - ! * An observation of TYPE_V (on the V-grid) would have ind = 3.0 - ! - ! B. NOT periodic (polar = .false.) - ! - ! Consider Mass-grid (& U-Grid) latitude grid with 4 south-north gridpoints - ! Values :: [ 10 20 30 40 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary V-grid - ! Values :: [ 5 15 25 35 45 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 4] ---> [1 sn] - ! ---> [10 40] - ! 2. U-grid ---> [1.5 4.5] ---> [1.5 sn+0.5] - ! ---> [10 40] - ! [Note that above "allowable values" reflect that one should only be able to - ! have an observation within the M-grid, since that is the only way to - ! guarantee that the necessary information exists in order to successfully - ! interpolate to a specified location.] - ! - ! It is up to the routine calling "boundsCheck" to have handled the 0.5 offset - ! in indices between the M-grid & V-grid. Hence, two examples: - ! a. If there is an observation location at 11 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 1.1 - ! * An observation of TYPE_V (on the V-grid) would have ind = 1.6 - ! b. If there is an observation location at 25 latitude, then: - ! * An observation of TYPE_T (on the M-grid) would have ind = 2.5 - ! * An observation of TYPE_V (on the V-grid) would have ind = 3.0 - ! - ! III. Vertical -- z-direction (periodicity not an issue) - ! - ! Consider Mass vertical grid with 4 bottom-top gridpoints - ! Values :: [ 0.875 0.625 0.375 0.125 ] - ! Indices :: [ 1 2 3 4 ] - ! Complementary W-grid - ! Values :: [ 1 0.75 0.50 0.25 0 ] - ! Indices :: [ 1 2 3 4 5 ] - ! - ! What are the allowable values for a real-valued index on each of these grids? - ! 1. M-grid ---> [1 4] ---> [1 bt] - ! ---> [0.875 0.125] - ! 2. W-grid ---> [1.5 4.5] ---> [1.5 bt+0.5] - ! ---> [0.875 0.125] - ! - ! [Note that above "allowable values" reflect that one should only be able to - ! have an observation within the M-grid, since that is the only way to - ! guarantee that the necessary information exists in order to successfully - ! interpolate to a specified location.] - ! - - ! Summary of Allowable REAL-VALUED Index Values ==> INTEGER Index Values - ! - ! In longitude (x) direction - ! Periodic & M_grid ==> [1 we+1) ==> [1 wes) - ! Periodic & U_grid ==> [1 wes) ==> [1 wes) - ! NOT Periodic & M_grid ==> [1 we] ==> [1 we) - ! NOT Periodic & U_grid ==> [1.5 we+0.5] ==> [1 wes) - ! In latitude (y) direction - ! Periodic & M_grid ==> [0.5 sn+0.5] ==> [0 sns) *though in practice, [1 sn)* - ! Periodic & V_grid ==> [1 sns] ==> [1 sns) *though allowable range, [1.5 sn+.5]* - ! NOT Periodic & M_grid ==> [1 sn] ==> [1 sn) - ! NOT Periodic & V_grid ==> [1.5 sn+0.5] ==> [1 sns) - ! In vertical (z) direction - ! M_grid ==> [1 bt] ==> [1 bt) - ! W_grid ==> [1.5 bt+0.5] ==> [1 bts) - - - ! Assume boundsCheck is false unless we can prove otherwise - boundsCheck = .false. - - ! First check direction (dimension) - ! Longitude (x-direction) has dim == 1 - if ( dim == 1 ) then - - ! Next check periodicity - if ( periodic ) then - - ! If periodic in longitude, then no need to check staggering because both - ! M and U grids allow integer indices from [1 wes) - if ( ind >= 1 .and. ind < wrf%dom(id)%wes ) boundsCheck = .true. - - else - - ! If NOT periodic in longitude, then we need to check staggering because - ! M and U grids allow different index ranges - - ! Check staggering by comparing var_size(dim,type) to the staggered dimension - ! for dim == 1 stored in wrf%dom(id) - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%wes ) then - ! U-grid allows integer range of [1 wes) - if ( ind >= 1 .and. ind < wrf%dom(id)%wes ) boundsCheck = .true. - else - ! M & V-grid allow [1 we) - if ( ind >= 1 .and. ind < wrf%dom(id)%we ) boundsCheck = .true. - endif - - endif - - ! Latitude (y-direction) has dim == 2 - elseif ( dim == 2 ) then - - ! Next check periodicity - if ( periodic ) then - - ! We need to check staggering because M and V grids allow different indices - -!*** NOTE: For now are disallowing observation locations that occur poleward of the -! first and last M-grid gridpoints. This means that this function will -! return false for polar observations. This need not be the case because -! the information should be available for proper interpolation across the -! poles, but it will require more clever thinking. Hopefully this can -! be added in later. - - ! Check staggering by comparing var_size(dim,type) to the staggered dimension - ! for dim == 2 stored in wrf%dom(id) - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%sns ) then - ! V-grid allows integer range [1 sns) - if ( ind >= 1 .and. ind < wrf%dom(id)%sns ) boundsCheck = .true. - else - ! For now we will set a logical flag to more restrictively check the array - ! bounds under our no-polar-obs assumptions - if ( restrict_polar ) then - ! M & U-grid allow integer range [1 sn) in practice (though properly, [0 sns) ) - if ( ind >= 1 .and. ind < wrf%dom(id)%sn ) boundsCheck = .true. - else - ! M & U-grid allow integer range [0 sns) in unrestricted circumstances - if ( ind >= 0 .and. ind < wrf%dom(id)%sns ) boundsCheck = .true. - endif - endif - - else - - ! We need to check staggering because M and V grids allow different indices - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%sns ) then - ! V-grid allows [1 sns) - if ( ind >= 1 .and. ind < wrf%dom(id)%sns ) boundsCheck = .true. - else - ! M & U-grid allow [1 sn) - if ( ind >= 1 .and. ind < wrf%dom(id)%sn ) boundsCheck = .true. - endif - - endif - - elseif ( dim == 3 ) then - - ! No periodicity to worry about in the vertical! However, we still need to check - ! staggering because the ZNU and ZNW grids allow different index ranges - if ( wrf%dom(id)%var_size(dim,type) == wrf%dom(id)%bts ) then - ! W vertical grid allows [1 bts) - if ( ind >= 1 .and. ind < wrf%dom(id)%bts ) boundsCheck = .true. - else - ! M vertical grid allows [1 bt) - if ( ind >= 1 .and. ind < wrf%dom(id)%bt ) boundsCheck = .true. - endif - - else - - print*, 'model_mod.f90 :: function boundsCheck :: dim must equal 1, 2, or 3!' - - endif - - -end function boundsCheck - -!####################################################################### -! get_orners takes in an i and j index, information about domain and grid staggering, -! and then returns the four cornering gridpoints' 2-element integer indices. -subroutine getCorners(i, j, id, type, ll, ul, lr, ur, rc) - - integer, intent(in) :: i, j, id, type - integer, dimension(2), intent(out) :: ll, ul, lr, ur - integer, intent(out) :: rc - -! logical, parameter :: restrict_polar = .true. - logical, parameter :: restrict_polar = .false. - - ! set return code to 0, and change this if necessary - rc = 0 - - !---------------- - ! LOWER LEFT - !---------------- - - ! i and j are the lower left (ll) corner already - ! - ! NOTE :: once we allow for polar periodicity, the incoming j index could actually - ! be 0, which would imply a ll(2) value of 1, with a ll(1) value 180 degrees - ! of longitude away from the incoming i index! But we have not included - ! this possibility yet. - - ! As of 22 Oct 2007, this option is not allowed! - ! Note that j = 0 can only happen if we are on the M (or U) wrt to latitude - if ( wrf%dom(id)%polar .and. j == 0 .and. .not. restrict_polar ) then - - ! j = 0 should be mapped to j = 1 (ll is on other side of globe) - ll(2) = 1 - - ! Need to map i index 180 degrees away - ll(1) = i + wrf%dom(id)%we/2 - - ! Check validity of bounds & adjust by periodicity if necessary - if ( ll(1) > wrf%dom(id)%we ) ll(1) = ll(1) - wrf%dom(id)%we - - ! We shouldn't be able to get this return code if restrict_polar = .true. -! rc = 1 -! print*, 'model_mod.f90 :: getCorners :: Tried to do polar bc -- rc = ', rc - - else - - ll(1) = i - ll(2) = j - - endif - - - !---------------- - ! LOWER RIGHT - !---------------- - - ! Most of the time, the lower right (lr) corner will simply be (i+1,j), but we need to check - ! Summary of x-direction corners: - ! Periodic & M_grid has ind = [1 wes) - ! ind = [1 we) ==> ind_p_1 = ind + 1 - ! ind = [we wes) ==> ind_p_1 = 1 - ! Periodic & U_grid has ind = [1 wes) - ! ind = [1 we) ==> ind_p_1 = ind + 1 - ! ind = [we wes) ==> ind_p_1 = wes ( keep in mind that U(1) = U(wes) if periodic ) - ! NOT Periodic & M_grid has ind = [1 we) - ! ind = [1 we-1) ==> ind_p_1 = ind + 1 - ! ind = [we-1 we) ==> ind_p_1 = we - ! NOT Periodic & U_grid has ind = [1 wes) - ! ind = [1 we) ==> ind_p_1 = ind + 1 - ! ind = [we wes) ==> ind_p_1 = wes - - if ( wrf%dom(id)%periodic_x ) then - - ! Check to see what grid we have, M vs. U - if ( wrf%dom(id)%var_size(1,type) == wrf%dom(id)%wes ) then - ! U-grid is always i+1 -- do this in reference to already adjusted ll points - lr(1) = ll(1) + 1 - lr(2) = ll(2) - else - ! M-grid is i+1 except if we <= ind < wes, in which case it's 1 - if ( i < wrf%dom(id)%we ) then - lr(1) = ll(1) + 1 - else - lr(1) = 1 - endif - lr(2) = ll(2) - endif - - else - - ! Regardless of grid, NOT Periodic always has i+1 - lr(1) = ll(1) + 1 - lr(2) = ll(2) - - endif - - - !---------------- - ! UPPER LEFT - !---------------- - -!*** NOTE: For now are disallowing observation locations that occur poleward of the -! first and last M-grid gridpoints. This need not be the case because -! the information should be available for proper interpolation across the -! poles, but it will require more clever thinking. Hopefully this can -! be added in later. - - ! Most of the time, the upper left (ul) corner will simply be (i,j+1), but we need to check - ! Summary of y-direction corners: - ! Periodic & M_grid has ind = [0 sns) *though in practice, [1 sn)* - ! ind = [1 sn-1) ==> ind_p_1 = ind + 1 - ! ind = [sn-1 sn) ==> ind_p_1 = sn - ! Periodic & V_grid has ind = [1 sns) - ! ind = [1 sn) ==> ind_p_1 = ind + 1 - ! ind = [sn sns) ==> ind_p_1 = sns - ! NOT Periodic & M_grid has ind = [1 sn) - ! ind = [1 sn-1) ==> ind_p_1 = ind + 1 - ! ind = [sn-1 sn) ==> ind_p_1 = sn - ! NOT Periodic & V_grid has ind = [1 sns) - ! ind = [1 sn) ==> ind_p_1 = ind + 1 - ! ind = [sn sns) ==> ind_p_1 = sns - ! - ! Hence, with our current polar obs restrictions, all four possible cases DO map into - ! ul = (i,j+1). But this will not always be the case. - - if ( wrf%dom(id)%polar ) then - - ! Check to see what grid we have, M vs. V - if ( wrf%dom(id)%var_size(2,type) == wrf%dom(id)%sns ) then - ! V-grid is always j+1, even if we allow for full [1 sns) range - ul(1) = ll(1) - ul(2) = ll(2) + 1 - else - ! M-grid changes depending on polar restriction - if ( restrict_polar ) then - ! If restricted, then we can simply add 1 - ul(1) = ll(1) - ul(2) = ll(2) + 1 - else - ! If not restricted, then we can potentially wrap over the north pole, which - ! means that ul(2) is set to sn and ul(1) is shifted by 180 deg. - - if ( j == wrf%dom(id)%sn ) then - ! j > sn should be mapped to j = sn (ul is on other side of globe) - ul(2) = wrf%dom(id)%sn - - ! Need to map i index 180 degrees away - ul(1) = ll(1) + wrf%dom(id)%we/2 - - ! Check validity of bounds & adjust by periodicity if necessary - if ( ul(1) > wrf%dom(id)%we ) ul(1) = ul(1) - wrf%dom(id)%we - - ! We shouldn't be able to get this return code if restrict_polar = .true. -! rc = 1 -! print*, 'model_mod.f90 :: getCorners :: Tried to do polar bc -- rc = ', rc - - elseif ( j == 0 ) then - ! In this case, we have place ll on the other side of the globe, so we - ! cannot reference ul to ll - ul(1) = i - ul(2) = 1 - - else - ! We can confidently set to j+1 - ul(1) = ll(1) - ul(2) = ll(2) + 1 - endif - - endif - endif - - elseif ( wrf%dom(id)%periodic_y ) then - - ! Check to see what grid we have, M vs. U - if ( wrf%dom(id)%var_size(2,type) == wrf%dom(id)%sns ) then - ! V-grid is always j+1 -- do this in reference to already adjusted ll points - ul(1) = ll(1) - ul(2) = ll(2)+1 - else - ! M-grid is j+1 except if we <= ind < wes, in which case it's 1 - if ( j < wrf%dom(id)%sn ) then - ul(2) = ll(2) + 1 - else - ul(2) = 1 - endif - ul(1) = ll(1) - endif - - else - - ! Regardless of grid, NOT Periodic always has j+1 - ul(1) = ll(1) - ul(2) = ll(2) + 1 - - endif - - - !---------------- - ! UPPER RIGHT - !---------------- - -!*** NOTE: For now are disallowing observation locations that occur poleward of the -! first and last M-grid gridpoints. This need not be the case because -! the information should be available for proper interpolation across the -! poles, but it will require more clever thinking. Hopefully this can -! be added in later. - - ! Most of the time, the upper right (ur) corner will simply be (i+1,j+1), but we need to check - ! In fact, we can largely get away with ur = (lr(1),ul(2)). Where this will NOT work is - ! where we have had to re-map the i index to the other side of the globe (180 deg) due to - ! the polar boundary condition. There are no situations where ur(2) will not be equal to - ! ul(2). - - ur(2) = ul(2) - - ! Need to check if ur(1) .ne. lr(1) - if ( wrf%dom(id)%polar .and. .not. restrict_polar ) then - - ! Only if j == 0 or j == sn - if ( j == 0 .or. j == wrf%dom(id)%sn) then - ! j == 0 means that ll(1) = i + 180 deg, so we cannot use lr(1) -- hence, we will - ! add 1 to ul(1), unless doing so spans the longitude seam point. - ! j == sn means that ul(1) = i + 180 deg. Here we cannot use lr(1) either because - ! it will be half a domain away from ul(1)+1. Be careful of longitude seam point. - - ! Here we need to check longitude periodicity and the type of grid - if ( wrf%dom(id)%periodic_x ) then - - ! Check to see what grid we have, M vs. U - if ( wrf%dom(id)%var_size(1,type) == wrf%dom(id)%wes ) then - ! U-grid is always i+1 -- do this in reference to already adjusted ll points - ur(1) = ul(1) + 1 - else - ! M-grid is i+1 except if we <= ind < wes, in which case it's 1 - if ( ul(1) < wrf%dom(id)%we ) then - ur(1) = ul(1) + 1 - else - ur(1) = 1 - endif - endif - - else - - ! Regardless of grid, NOT Periodic always has i+1 - ur(1) = ul(1) + 1 - - endif - - ! If not a special j value, then we are set for the ur(1) = lr(1) - else - - ur(1) = lr(1) - - endif - - ! If not an unrestricted polar periodic domain, then we have nothing to worry about - else - - ur(1) = lr(1) - - endif - -end subroutine getCorners - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine read_wrf_dimensions(ncid,bt,bts,sn,sns,we,wes,sls) - -! ncid: input, file handl -! id: input, domain id - -integer, intent(in) :: ncid -integer, intent(out) :: bt,bts,sn,sns,we,wes,sls -logical, parameter :: debug = .false. -integer :: var_id -character (len=NF90_MAX_NAME) :: name - -! get wrf grid dimensions - - call nc_check( nf90_inq_dimid(ncid, "bottom_top", var_id), & - 'static_init_model','inq_dimid bottom_top') - call nc_check( nf90_inquire_dimension(ncid, var_id, name, bt), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "bottom_top_stag", var_id), & - 'static_init_model','inq_dimid bottom_top_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, bts), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "south_north", var_id), & - 'static_init_model','inq_dimid south_north') - call nc_check( nf90_inquire_dimension(ncid, var_id, name, sn), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "south_north_stag", var_id), & - 'static_init_model','inq_dimid south_north_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, sns), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "west_east", var_id), & - 'static_init_model','inq_dimid west_east') - call nc_check( nf90_inquire_dimension(ncid, var_id, name, we), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "west_east_stag", var_id), & - 'static_init_model','inq_dimid west_east_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, wes), & - 'static_init_model','inquire_dimension '//trim(name)) - - call nc_check( nf90_inq_dimid(ncid, "soil_layers_stag", var_id), & - 'static_init_model','inq_dimid soil_layers_stag') ! reuse var_id, no harm - call nc_check( nf90_inquire_dimension(ncid, var_id, name, sls), & - 'static_init_model','inquire_dimension '//trim(name)) - - if(debug) then - write(*,*) ' dimensions bt, sn, we are ',bt, & - sn, we - write(*,*) ' staggered bt, sn, we are ',bts, & - sns,wes - endif - - RETURN - -end subroutine read_wrf_dimensions - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine read_wrf_file_attributes(ncid,id) - -! ncid: input, file handl -! id: input, domain id - -integer, intent(in) :: ncid, id -logical, parameter :: debug = .false. - -! get meta data and static data we need - - call nc_check( nf90_get_att(ncid, nf90_global, 'DX', wrf%dom(id)%dx), & - 'static_init_model', 'get_att DX') - call nc_check( nf90_get_att(ncid, nf90_global, 'DY', wrf%dom(id)%dy), & - 'static_init_model', 'get_att DY') - call nc_check( nf90_get_att(ncid, nf90_global, 'DT', wrf%dom(id)%dt), & - 'static_init_model', 'get_att DT') - write(errstring, *) 'dt from wrfinput_d0X file is: ', wrf%dom(id)%dt - call error_handler(E_MSG, ' ', errstring) - if(debug) write(*,*) ' dx, dy, dt are ',wrf%dom(id)%dx, & - wrf%dom(id)%dy, wrf%dom(id)%dt - - call nc_check( nf90_get_att(ncid, nf90_global, 'MAP_PROJ', wrf%dom(id)%map_proj), & - 'static_init_model', 'get_att MAP_PROJ') - if(debug) write(*,*) ' map_proj is ',wrf%dom(id)%map_proj - - call nc_check( nf90_get_att(ncid, nf90_global, 'CEN_LAT', wrf%dom(id)%cen_lat), & - 'static_init_model', 'get_att CEN_LAT') - if(debug) write(*,*) ' cen_lat is ',wrf%dom(id)%cen_lat - - call nc_check( nf90_get_att(ncid, nf90_global, 'CEN_LON', wrf%dom(id)%cen_lon), & - 'static_init_model', 'get_att CEN_LON') - if(debug) write(*,*) ' cen_lon is ',wrf%dom(id)%cen_lon - - call nc_check( nf90_get_att(ncid, nf90_global, 'TRUELAT1', truelat1), & - 'static_init_model', 'get_att TRUELAT1') - if(debug) write(*,*) ' truelat1 is ',truelat1 - - call nc_check( nf90_get_att(ncid, nf90_global, 'TRUELAT2', truelat2), & - 'static_init_model', 'get_att TRUELAT2') - if(debug) write(*,*) ' truelat2 is ',truelat2 - - call nc_check( nf90_get_att(ncid, nf90_global, 'STAND_LON', stdlon), & - 'static_init_model', 'get_att STAND_LON') - if(debug) write(*,*) ' stdlon is ',stdlon - - RETURN - -end subroutine read_wrf_file_attributes - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine assign_boundary_conditions(id) - -! id: input, domain id - -integer, intent(in) :: id -logical, parameter :: debug = .false. - -!nc -- fill in the boundary conditions (periodic_x and polar) here. This will -! need to be changed once these are taken from the NetCDF input instead -! of the model namelist -! NOTE :: because NetCDF cannot handle logicals, these boundary conditions -! are likely to be read in as integers. The agreed upon strategy is to -! test whether the integers are equal to 0 (for .false.) or 1 (for .true.) -! and set the wrf%dom(id)% values to logicals to be used internally within -! model_mod.f90. -! -! Jeff Anderson points out that not everyone will convert to wrf3.0 and this -! global attribute convention may not be backward-compatible. So we should -! test for existence of attributes and have defaults (from model_mod -! namelist) ready if they do not exist. Note that defaults are currently -! true (as of 24 Oct 2007), but once the attributes arrive, the defaults -! should be false. - if ( id == 1 ) then - wrf%dom(id)%periodic_x = periodic_x - wrf%dom(id)%periodic_y = periodic_y - wrf%dom(id)%polar = polar - wrf%dom(id)%scm = scm - else - wrf%dom(id)%periodic_x = .false. - wrf%dom(id)%periodic_y = .false. - wrf%dom(id)%polar = .false. - wrf%dom(id)%scm = .false. - endif - if(debug) write(*,*) ' periodic_x ',wrf%dom(id)%periodic_x - if(debug) write(*,*) ' periodic_y ',wrf%dom(id)%periodic_y - if(debug) write(*,*) ' polar ',wrf%dom(id)%polar - if(debug) write(*,*) ' scm ',wrf%dom(id)%scm - -end subroutine assign_boundary_conditions - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine read_wrf_static_data(ncid,id) - -! ncid: input, file handle -! id: input, domain id - -integer, intent(in) :: ncid, id -logical, parameter :: debug = .false. -integer :: var_id - - call nc_check( nf90_inq_varid(ncid, "P_TOP", var_id), & - 'read_wrf_static_data','inq_varid P_TOP') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%p_top), & - 'read_wrf_static_data','get_var P_TOP') - -! get 1D (z) static data defining grid levels - - allocate(wrf%dom(id)%dn(1:wrf%dom(id)%bt)) - call nc_check( nf90_inq_varid(ncid, "DN", var_id), & - 'read_wrf_static_data','inq_varid DN') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%dn), & - 'read_wrf_static_data','get_var DN') - if(debug) write(*,*) ' dn ',wrf%dom(id)%dn - - allocate(wrf%dom(id)%znu(1:wrf%dom(id)%bt)) - call nc_check( nf90_inq_varid(ncid, "ZNU", var_id), & - 'read_wrf_static_data','inq_varid ZNU') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%znu), & - 'read_wrf_static_data','get_var ZNU') - if(debug) write(*,*) ' znu is ',wrf%dom(id)%znu - - allocate(wrf%dom(id)%znw(1:wrf%dom(id)%bts)) - call nc_check( nf90_inq_varid(ncid, "ZNW", var_id), & - 'read_wrf_static_data','inq_varid ZNW') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%znw), & - 'read_wrf_static_data','get_var ZNW') - if(debug) write(*,*) ' znw is ',wrf%dom(id)%znw - - allocate(wrf%dom(id)%dnw(1:wrf%dom(id)%bt)) - call nc_check( nf90_inq_varid(ncid, "DNW", var_id), & - 'read_wrf_static_data','inq_varid DNW') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%dnw), & - 'read_wrf_static_data','get_var DNW') - if(debug) write(*,*) ' dnw is ',wrf%dom(id)%dnw - - allocate(wrf%dom(id)%zs(1:wrf%dom(id)%sls)) - call nc_check( nf90_inq_varid(ncid, "ZS", var_id), & - 'read_wrf_static_data','inq_varid ZS') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%zs), & - 'read_wrf_static_data','get_var ZS') - -! get 2D (x,y) base state for mu, latitude, longitude - - allocate(wrf%dom(id)%mub(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "MUB", var_id), & - 'read_wrf_static_data','inq_varid MUB') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%mub), & - 'read_wrf_static_data','get_var MUB') - if(debug) then - write(*,*) ' corners of mub ' - write(*,*) wrf%dom(id)%mub(1,1),wrf%dom(id)%mub(wrf%dom(id)%we,1), & - wrf%dom(id)%mub(1,wrf%dom(id)%sn),wrf%dom(id)%mub(wrf%dom(id)%we, & - wrf%dom(id)%sn) - endif - - allocate(wrf%dom(id)%longitude(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLONG", var_id), & - 'read_wrf_static_data','inq_varid XLONG') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%longitude), & - 'read_wrf_static_data','get_var XLONG') - - allocate(wrf%dom(id)%longitude_u(1:wrf%dom(id)%wes,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLONG_U", var_id), & - 'read_wrf_static_data','inq_varid XLONG_U') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%longitude_u), & - 'read_wrf_static_data','get_var XLONG_U') - - allocate(wrf%dom(id)%longitude_v(1:wrf%dom(id)%we,1:wrf%dom(id)%sns)) - call nc_check( nf90_inq_varid(ncid, "XLONG_V", var_id), & - 'read_wrf_static_data','inq_varid XLONG_V') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%longitude_v), & - 'read_wrf_static_data','get_var XLONG_V') - - allocate(wrf%dom(id)%latitude(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLAT", var_id), & - 'read_wrf_static_data','inq_varid XLAT') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%latitude), & - 'read_wrf_static_data','get_var XLAT') - - allocate(wrf%dom(id)%latitude_u(1:wrf%dom(id)%wes,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLAT_U", var_id), & - 'read_wrf_static_data','inq_varid XLAT_U') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%latitude_u), & - 'read_wrf_static_data','get_var XLAT_U') - - allocate(wrf%dom(id)%latitude_v(1:wrf%dom(id)%we,1:wrf%dom(id)%sns)) - call nc_check( nf90_inq_varid(ncid, "XLAT_V", var_id), & - 'read_wrf_static_data','inq_varid XLAT_V') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%latitude_v), & - 'read_wrf_static_data','get_var XLAT_V') - - allocate(wrf%dom(id)%land(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "XLAND", var_id), & - 'read_wrf_static_data','inq_varid XLAND') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%land), & - 'read_wrf_static_data','get_var XLAND') - if(debug) then - write(*,*) ' corners of land ' - write(*,*) wrf%dom(id)%land(1,1),wrf%dom(id)%land(wrf%dom(id)%we,1), & - wrf%dom(id)%land(1,wrf%dom(id)%sn),wrf%dom(id)%land(wrf%dom(id)%we, & - wrf%dom(id)%sn) - endif - - if(debug) then - write(*,*) ' corners of lat ' - write(*,*) wrf%dom(id)%latitude(1,1),wrf%dom(id)%latitude(wrf%dom(id)%we,1), & - wrf%dom(id)%latitude(1,wrf%dom(id)%sn), & - wrf%dom(id)%latitude(wrf%dom(id)%we,wrf%dom(id)%sn) - write(*,*) ' corners of long ' - write(*,*) wrf%dom(id)%longitude(1,1),wrf%dom(id)%longitude(wrf%dom(id)%we,1), & - wrf%dom(id)%longitude(1,wrf%dom(id)%sn), & - wrf%dom(id)%longitude(wrf%dom(id)%we,wrf%dom(id)%sn) - endif - - allocate(wrf%dom(id)%hgt(1:wrf%dom(id)%we,1:wrf%dom(id)%sn)) - call nc_check( nf90_inq_varid(ncid, "HGT", var_id), & - 'read_wrf_static_data','inq_varid HGT') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%hgt), & - 'read_wrf_static_data','get_var HGT') - -! get 3D base state geopotential - - allocate(wrf%dom(id)%phb(1:wrf%dom(id)%we,1:wrf%dom(id)%sn,1:wrf%dom(id)%bts)) - call nc_check( nf90_inq_varid(ncid, "PHB", var_id), & - 'read_wrf_static_data','inq_varid PHB') - call nc_check( nf90_get_var(ncid, var_id, wrf%dom(id)%phb), & - 'read_wrf_static_data','get_var PHB') - if(debug) then - write(*,*) ' corners of phb ' - write(*,*) wrf%dom(id)%phb(1,1,1),wrf%dom(id)%phb(wrf%dom(id)%we,1,1), & - wrf%dom(id)%phb(1,wrf%dom(id)%sn,1),wrf%dom(id)%phb(wrf%dom(id)%we, & - wrf%dom(id)%sn,1) - write(*,*) wrf%dom(id)%phb(1,1,wrf%dom(id)%bts), & - wrf%dom(id)%phb(wrf%dom(id)%we,1,wrf%dom(id)%bts), & - wrf%dom(id)%phb(1,wrf%dom(id)%sn,wrf%dom(id)%bts), & - wrf%dom(id)%phb(wrf%dom(id)%we,wrf%dom(id)%sn,wrf%dom(id)%bts) - endif - -end subroutine read_wrf_static_data - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine setup_map_projection(id) - -! id: input, domain id - -integer, intent(in) :: id -logical, parameter :: debug = .false. - -integer :: proj_code -real(r8) :: latinc,loninc - -! Initializes the map projection structure to missing values - - call map_init(wrf%dom(id)%proj) - -! Populate the map projection structure - -!nc -- added in case structures for CASSINI and CYL -!nc -- global wrfinput_d01 has truelat1 = 1.e20, so we need to change it where needed -!nc -- for PROJ_LATLON stdlon and truelat1 have different meanings -- -!nc -- stdlon --> loninc and truelat1 --> latinc -!JPH --- this latinc/loninc calculations are only valid for global domains - - if ( wrf%dom(id)%scm ) then -! JPH -- set to zero which should cause the map utils to return NaNs if called - latinc = 0.0_r8 - loninc = 0.0_r8 - else - latinc = 180.0_r8/wrf%dom(id)%sn - loninc = 360.0_r8/wrf%dom(id)%we - endif - - if(wrf%dom(id)%map_proj == map_latlon) then - truelat1 = latinc - stdlon = loninc - proj_code = PROJ_LATLON - elseif(wrf%dom(id)%map_proj == map_lambert) then - proj_code = PROJ_LC - elseif(wrf%dom(id)%map_proj == map_polar_stereo) then - proj_code = PROJ_PS - elseif(wrf%dom(id)%map_proj == map_mercator) then - proj_code = PROJ_MERC - elseif(wrf%dom(id)%map_proj == map_cyl) then - proj_code = PROJ_CYL - elseif(wrf%dom(id)%map_proj == map_cassini) then - proj_code = PROJ_CASSINI - else - call error_handler(E_ERR,'static_init_model', & - 'Map projection no supported.', source, revision, revdate) - endif - -!nc -- specified inputs to hopefully handle ALL map projections -- hopefully map_set will -! just ignore the inputs it doesn't need for its map projection of interest (?) -! -! NOTE:: We are NOT yet supporting the Gaussian grid or the Rotated Lat/Lon, so we -! are going to skip the entries: nlon, nlat, ixdim, jydim, stagger, phi, lambda -! -! + Gaussian grid uses nlat & nlon -! + Rotated Lat/Lon uses ixdim, jydim, stagger, phi, & lambda -! - call map_set( proj_code=proj_code, & - proj=wrf%dom(id)%proj, & - lat1=wrf%dom(id)%latitude(1,1), & - lon1=wrf%dom(id)%longitude(1,1), & - lat0=90.0_r8, & - lon0=0.0_r8, & - knowni=1.0_r8, & - knownj=1.0_r8, & - dx=wrf%dom(id)%dx, & - latinc=latinc, & - loninc=loninc, & - stdlon=stdlon, & - truelat1=truelat1, & - truelat2=truelat2 ) - -end subroutine setup_map_projection - -!-------------------------------------------- -!-------------------------------------------- - -subroutine fill_default_state_table(default_table) - -character(len=129), intent(out) :: default_table(num_state_table_columns,max_state_variables) - -integer :: row - -default_table = 'NULL' -row = 0 - -! fill default state variable table here. -row = row+1 -default_table(:,row) = (/ 'U ', & - 'QTY_U_WIND_COMPONENT', & - 'TYPE_U ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'V ', & - 'QTY_V_WIND_COMPONENT', & - 'TYPE_V ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'W ', & - 'QTY_VERTICAL_VELOCITY', & - 'TYPE_W ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'PH ', & - 'QTY_GEOPOTENTIAL_HEIGHT', & - 'TYPE_GZ ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'T ', & - 'QTY_POTENTIAL_TEMPERATURE', & - 'TYPE_T ', & - 'UPDATE ', & - '999 ' /) -row = row+1 -default_table(:,row) = (/ 'MU ', & - 'QTY_PRESSURE', & - 'TYPE_MU ', & - 'UPDATE ', & - '999 ' /) - -row = row+1 -default_table(:,row) = (/ 'QVAPOR ', & - 'QTY_VAPOR_MIXING_RATIO', & - 'TYPE_QV ', & - 'UPDATE ', & - '999 ' /) - -return - -end subroutine fill_default_state_table - -!-------------------------------------------- -!-------------------------------------------- - -subroutine fill_dart_kinds_table(wrf_state_variables, in_state_vector) - -! for each row in the kinds table, tick them off in an array -! of all possible kinds. then do some simple error checks for -! kinds we know have interactions -- like both wind vectors are -! required to convert directions from projection to lat/lon, etc - -character(len=*), intent(in) :: wrf_state_variables(:, :) -logical, intent(inout) :: in_state_vector(:) - -integer :: row, i, nextkind - -in_state_vector = .false. - -row = size(wrf_state_variables, 2) - -! NEWVAR: for a simple new variable you do not need to do anything here. -! NEWVAR: but if a new kind has interactions - like if you have an obs -! NEWVAR: of this kind, you actually interpolate in multiple fields in -! NEWVAR: the state vector (e.g. wind needs both U and V), then you -! NEWVAR: might need to add some code here. -! NEWVAR: see each of part1, part 2, and part 3 below. - -! part 1: mark off all the kinds that the user specifies, plus the -! kinds that are related and can be interpolated from the given kind. - -do i = 1, row - - ! end of the list? - if (wrf_state_variables(2, i) == 'NULL') exit - - nextkind = get_index_for_quantity(trim(wrf_state_variables(2, i))) - select case(nextkind) - - ! wrf stores potential temperature (temperature perturbations around a - ! threshold) but we can interpolate sensible temperature from it - case (QTY_POTENTIAL_TEMPERATURE) - in_state_vector(QTY_TEMPERATURE) = .true. - in_state_vector(QTY_POTENTIAL_TEMPERATURE) = .true. - - ! we use vapor mixing ratio to compute specific humidity - case (QTY_VAPOR_MIXING_RATIO) - in_state_vector(QTY_VAPOR_MIXING_RATIO) = .true. - in_state_vector(QTY_SPECIFIC_HUMIDITY) = .true. - - ! unrecognized kind string in namelist. - ! 0 is actually QTY_STATE_VARIABLE and not supported here. - case (-1, 0) - write(errstring, *) 'unrecognized KIND string: ' // trim(wrf_state_variables(2, i)) - call error_handler(E_ERR, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) - - ! everything else, set it to be supported - case default - in_state_vector(nextkind) = .true. - - end select - - ! NOTE: PSFC can be labeled either QTY_PRESSURE or QTY_SURFACE_PRESSURE - ! in the namelist, but make sure however it is labeled that for now we - ! allow surface pressure interpolation. this may go away once we work out - ! QTY_FOO vs QTY_SURFACE_FOO - are they fundamentally different things - ! or should the decision be made based on a QTY_FOO and the vertical - ! location type -- if it is VERTISSURFACE, then you do the 2d calc in the - ! surface field, otherwise you do the full-up 3d interpolation. - if ( wrf_state_variables(1, i) == 'PSFC' ) then - in_state_vector(QTY_SURFACE_PRESSURE) = .true. - endif - -enddo - - -! part 2: if you specified one kind but the interpolation is going to -! require another, make sure the combinations make sense. i.e. if you -! have U wind, you also have to have V wind, etc. - -do i = 1, size(in_state_vector) - - select case(i) - - ! the vortex center computations require wind speeds and phb? - case (QTY_VORTEX_LAT, QTY_VORTEX_LON, QTY_VORTEX_PMIN, QTY_VORTEX_WMAX) - if ((.not. in_state_vector(QTY_U_WIND_COMPONENT)) .or. & - (.not. in_state_vector(QTY_V_WIND_COMPONENT)) .or. & - (.not. in_state_vector(QTY_TEMPERATURE)) .or. & - (.not. in_state_vector(QTY_VAPOR_MIXING_RATIO)) .or. & - (.not. in_state_vector(QTY_PRESSURE))) then - write(errstring, *) 'VORTEX kinds will require U,V,T,QVAPOR,MU in state vector' - ! FIXME: not fatal error, just informative at this point. - call error_handler(E_MSG, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) - endif - - ! if you have one wind component you have to have both - case (QTY_U_WIND_COMPONENT, QTY_V_WIND_COMPONENT) - if (in_state_vector(QTY_U_WIND_COMPONENT) .neqv. in_state_vector(QTY_V_WIND_COMPONENT)) then - write(errstring, *) 'WIND kinds will require both U,V in state vector' - ! FIXME: not fatal error, just informative at this point. - call error_handler(E_MSG, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) - endif - - ! by default anything else is fine - - end select -enddo - - -! part 3: fields you just have to have, always, and other exceptions -! and things that break the rules. -if (.not. in_state_vector(QTY_GEOPOTENTIAL_HEIGHT)) then - write(errstring, *) 'PH is always a required field' - call error_handler(E_ERR, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) -endif - -! FIXME: is this true? or is pressure always required, and surface -! pressure required only if you have any of the surface obs? -if ((.not. in_state_vector(QTY_PRESSURE)) .and. & - (.not. in_state_vector(QTY_SURFACE_PRESSURE))) then - write(errstring, *) 'One of MU or PSFC is a required field' - call error_handler(E_ERR, 'fill_dart_kinds_table', errstring, & - source, revision, revdate) -endif - -! surface elevation is read in outside the state vector mechanism, -! directly from the wrfinput template file, and does not vary from -! one ensemble member to another. -in_state_vector(QTY_SURFACE_ELEVATION) = .true. - -! there is no field that directly maps to the vortex measurements. -! if you have all the fields it needs, allow them. -if (in_state_vector(QTY_U_WIND_COMPONENT) .and. & - in_state_vector(QTY_V_WIND_COMPONENT) .and. & - in_state_vector(QTY_TEMPERATURE) .and. & - in_state_vector(QTY_VAPOR_MIXING_RATIO) .and. & - in_state_vector(QTY_PRESSURE)) then ! ok to add vortex types - in_state_vector(QTY_VORTEX_LAT) = .true. - in_state_vector(QTY_VORTEX_LON) = .true. - in_state_vector(QTY_VORTEX_PMIN) = .true. - in_state_vector(QTY_VORTEX_WMAX) = .true. -endif - -! if you have geopotential height and pressure, you can compute -! a density value. -if (in_state_vector(QTY_GEOPOTENTIAL_HEIGHT) .and. & - in_state_vector(QTY_PRESSURE) ) in_state_vector(QTY_DENSITY) = .true. - - -! allow reflectivity to be asked for, because the obs_def has an alternative -! way to interpolate it even if it is not in the state vector. if this -! is not allowed it will error exit instead of returning with an invalid istatus -! to indicate to the caller that the interpolation failed. -! ditto for power weighted fall speed. -in_state_vector(QTY_RADAR_REFLECTIVITY) = .true. -in_state_vector(QTY_POWER_WEIGHTED_FALL_SPEED) = .true. - -! FIXME: i was going to suggest nuking this routine all together because it makes -! the default behavior be to exit with an error when requesting to interpolate an -! unknown kind, instead of simply returning with a failed interpolation and letting -! the calling code handle it. but it does turn out to be amazingly useful when -! trying to interpolate something you do expect to be there, so now i'm unsure what -! to suggest here. nsc 30jul09 - -end subroutine fill_dart_kinds_table - -!-------------------------------------------- -!-------------------------------------------- - -integer function get_number_of_wrf_variables(id, state_table, var_element_list, var_update_list) - -integer, intent(in) :: id -character(len=*), intent(in) :: state_table(num_state_table_columns,max_state_variables) -integer, intent(out), optional :: var_element_list(max_state_variables) -logical, intent(out), optional :: var_update_list(max_state_variables) - -integer :: ivar, num_vars -logical :: debug = .false. - -if ( present(var_element_list) ) var_element_list = -1 - -ivar = 1 -num_vars = 0 -do while ( trim(state_table(5,ivar)) /= 'NULL' ) - - if ( variable_is_on_domain(state_table(5,ivar),id) ) then - num_vars = num_vars + 1 - if ( present(var_element_list) ) var_element_list(num_vars) = ivar - - if (present(var_update_list)) then - if (state_table(4,ivar) == 'NO_COPY_BACK') then - var_update_list(num_vars) = .false. - else - var_update_list(num_vars) = .true. - endif - endif - - endif - - ivar = ivar + 1 - -enddo ! ivar - -if ( debug ) then - print*,'In function get_number_of_wrf_variables' - print*,'Found ',num_vars,' state variables for domain id ',id -endif - -get_number_of_wrf_variables = num_vars - -return - -end function get_number_of_wrf_variables - -!-------------------------------------------- -!-------------------------------------------- - -subroutine set_variable_bound_defaults(nbounds,lb,ub,instructions) - - integer, intent(in) :: nbounds - real(r8), dimension(nbounds), intent(out) :: lb, ub - character(len=10), dimension(nbounds), intent(out) :: instructions - - lb(:) = missing_r8 - ub(:) = missing_r8 - instructions(:) = 'NULL' - - return - -end subroutine set_variable_bound_defaults - -!-------------------------------------------- -!-------------------------------------------- - -subroutine get_variable_bounds(bounds_table,wrf_var_name,lb,ub,instructions) - -! matches WRF variable name in bounds table to input name, and assigns -! the bounds and instructions if they exist - - character(len=*), intent(in) :: bounds_table(num_bounds_table_columns,max_state_variables) - character(len=*), intent(in) :: wrf_var_name - real(r8), intent(out) :: lb,ub - character(len=10),intent(out) :: instructions - - character(len=50) :: wrf_varname_trim, bounds_varname_trim - character(len=50) :: bound_trim - integer :: ivar - logical :: debug = .false. - - wrf_varname_trim = '' - wrf_varname_trim = trim(wrf_var_name) - - ivar = 1 - do while ( trim(bounds_table(1,ivar)) /= 'NULL' ) - - bounds_varname_trim = trim(bounds_table(1,ivar)) - - if ( bounds_varname_trim == wrf_varname_trim ) then - - bound_trim = trim(bounds_table(2,ivar)) - if ( bound_trim /= 'NULL' ) then - read(bound_trim,'(d16.8)') lb - else - lb = missing_r8 - endif - - bound_trim = trim(bounds_table(3,ivar)) - if ( bound_trim /= 'NULL' ) then - read(bound_trim,'(d16.8)') ub - else - ub = missing_r8 - endif - - ! instructions are required - instructions = trim(bounds_table(4,ivar)) - - if ( instructions == 'NULL' ) then - call error_handler(E_ERR,'get_variable_bounds','instructions for bounds on '//wrf_varname_trim//' are required',& - source,revision,revdate) - endif - - endif - - ivar = ivar + 1 - - enddo !ivar - - if ( debug ) then - write(*,*) 'In get_variable_bounds assigned ',wrf_varname_trim - write(*,*) ' bounds ',lb,ub,instructions - endif - - return - -end subroutine get_variable_bounds - -!-------------------------------------------- -!-------------------------------------------- - -logical function variable_is_on_domain(domain_id_string, id) - -integer, intent(in) :: id -character(len=*), intent(in) :: domain_id_string - -integer :: domain_int, i - -variable_is_on_domain = .false. - - ! if '999' then counts all domains - if ( trim(domain_id_string) == '999' ) then - variable_is_on_domain = .true. - else - i = 1 - do while ( domain_id_string(i:i) /= ' ' ) - read(domain_id_string(i:i),'(i1)') domain_int - if ( domain_int == id ) variable_is_on_domain = .true. - i = i+1 - enddo - endif - -return - -end function variable_is_on_domain - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine get_variable_size_from_file(ncid,id,wrf_var_name,bt,bts,sn,sns, & - we,wes,stagger,var_size) - -!NOTE: only supports 2D and 3D variables (ignoring time dimension) - -! ncid: input, file handle -! id: input, domain index - -integer, intent(in) :: ncid, id -integer, intent(in) :: bt, bts, sn, sns, we, wes -character(len=*), intent(in) :: wrf_var_name -integer, intent(out) :: var_size(3) -character(len=129),intent(out) :: stagger - -logical, parameter :: debug = .false. -integer :: var_id, ndims, dimids(10) -integer :: idim - - stagger = '' - -! get variable ID - call nc_check( nf90_inq_varid(ncid, trim(wrf_var_name), var_id), & - 'get_variable_size_from_file', & - 'inq_varid '//wrf_var_name) - -! get number of dimensions and dimension IDs - call nc_check( nf90_inquire_variable(ncid, var_id,ndims=ndims, & - dimids=dimids), & - 'get_variable_size_from_file', & - 'inquire_variable '//wrf_var_name) - -! get dimension length, ignoring first dimension (time) - do idim = 1,ndims-1 - call nc_check( nf90_inquire_dimension(ncid, dimids(idim), & - len=var_size(idim)), & - 'get_variable_size_from_file', & - 'inquire_dimension '//wrf_var_name) - enddo - -! if a 2D variable fill the vertical dimension with 1 - if ( ndims < 4 ) var_size(ndims) = 1 - - if ( debug ) then - print*,'In get_variable_size_from_file got variable size ',var_size - endif - - -! get variable attribute stagger - call nc_check( nf90_get_att(ncid, var_id, 'stagger', stagger), & - 'get_variable_size_from_file', & - 'get_att '//wrf_var_name//' '//stagger) - - if ( debug ) then - print*,'In get_variable_size_from_file got stagger ',trim(stagger),' for variable ',trim(wrf_var_name) - endif - -return - -end subroutine get_variable_size_from_file - -!-------------------------------------------------------------------- -!-------------------------------------------------------------------- - -subroutine get_variable_metadata_from_file(ncid,id,wrf_var_name,description, & - coordinates,units) - -! ncid: input, file handle -! id: input, domain index - -integer, intent(in) :: ncid, id -character(len=*), intent(in) :: wrf_var_name -character(len=129), intent(out) :: description, coordinates, units - -logical, parameter :: debug = .false. -integer :: var_id - - call nc_check( nf90_inq_varid(ncid, trim(wrf_var_name), var_id), & - 'get_variable_metadata_from_file', & - 'inq_varid '//wrf_var_name) - - description = '' - call nc_check( nf90_get_att(ncid, var_id, 'description', description), & - 'get_variable_metadata_from_file', & - 'get_att '//wrf_var_name//' '//description) - - coordinates = '' - call nc_check( nf90_get_att(ncid, var_id, 'coordinates', coordinates), & - 'get_variable_metadata_from_file', & - 'get_att '//wrf_var_name//' '//coordinates) - - units = '' - call nc_check( nf90_get_att(ncid, var_id, 'units', units), & - 'get_variable_metadata_from_file', & - 'get_att '//wrf_var_name//' '//units) - -return - -end subroutine get_variable_metadata_from_file - -!-------------------------------------------- -!-------------------------------------------- - -integer function get_type_ind_from_type_string(id, wrf_varname) - -! simply loop through the state variable table to get the index of the -! type for this domain. returns -1 if not found - - integer, intent(in) :: id - character(len=*), intent(in) :: wrf_varname - - integer :: ivar, my_index - logical :: debug = .false. - character(len=50) :: wrf_varname_trim, wrf_state_var_trim - - get_type_ind_from_type_string = -1 - - do ivar = 1,wrf%dom(id)%number_of_wrf_variables - - my_index = wrf%dom(id)%var_index_list(ivar) - - wrf_state_var_trim = trim(wrf_state_variables(1,my_index)) - wrf_varname_trim = trim(wrf_varname) - - if ( wrf_state_var_trim == wrf_varname_trim ) then - - get_type_ind_from_type_string = ivar - - endif - - enddo ! ivar - - if ( debug ) write(*,*) 'get_type_from_ind ',trim(wrf_varname),' ',get_type_ind_from_type_string - - return - -end function get_type_ind_from_type_string - -!------------------------------------------------------------- - -subroutine trans_2Dto1D( a1d, a2d, nx, ny ) - -integer, intent(in) :: nx,ny -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a2d(nx,ny) - -!--- - -integer :: i,j,m - -i=size(a2d,1) -j=size(a2d,2) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - m < nx*ny) then - write(errstring,*)'nx, ny, not compatible ',i,j,nx,ny - call error_handler(E_ERR,'trans_2d',errstring,source,revision,revdate) -endif - -do j=1,ny - do i=1,nx - a1d(i + nx*(j-1)) = a2d(i,j) - enddo -enddo - -end subroutine trans_2Dto1D - -!------------------------------------------------------------- - -subroutine trans_3Dto1D( a1d, a3d, nx, ny, nz ) - -integer, intent(in) :: nx,ny,nz -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a3d(:,:,:) - -!--- - -integer :: i,j,k,m - -i=size(a3d,1) -j=size(a3d,2) -k=size(a3d,3) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - k /= nz .or. & - m < nx*ny*nz) then - write(errstring,*)'nx, ny, nz, not compatible ',i,j,k,nx,ny,nz,m - call error_handler(E_ERR,'trans_3d',errstring,source,revision,revdate) -endif - -do k=1,nz - do j=1,ny - do i=1,nx - a1d(i + nx*(j-1) + nx*ny*(k-1) ) = a3d(i,j,k) - enddo - enddo -enddo - -end subroutine trans_3Dto1D - -!------------------------------------------------------------- - -subroutine trans_1Dto2D( a1d, a2d, nx, ny ) - -integer, intent(in) :: nx,ny -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a2d(nx,ny) - -!--- - -integer :: i,j,m - -i=size(a2d,1) -j=size(a2d,2) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - m < nx*ny) then - write(errstring,*)'nx, ny, not compatible ',i,j,nx,ny - call error_handler(E_ERR,'trans_2d',errstring,source,revision,revdate) -endif - -do j=1,ny - do i=1,nx - a2d(i,j) = a1d(i + nx*(j-1)) - enddo -enddo - -end subroutine trans_1Dto2D - -!------------------------------------------------------------- - -subroutine trans_1Dto3D( a1d, a3d, nx, ny, nz ) - -integer, intent(in) :: nx,ny,nz -real(r8), intent(inout) :: a1d(:) -real(r8), intent(inout) :: a3d(:,:,:) - -!--- - -integer :: i,j,k,m - -i=size(a3d,1) -j=size(a3d,2) -k=size(a3d,3) -m=size(a1d) - -if ( i /= nx .or. & - j /= ny .or. & - k /= nz .or. & - m < nx*ny*nz) then - write(errstring,*)'nx, ny, nz, not compatible ',i,j,k,nx,ny,nz,m - call error_handler(E_ERR,'trans_3d',errstring,source,revision,revdate) -endif - -do k=1,nz - do j=1,ny - do i=1,nx - a3d(i,j,k) = a1d(i + nx*(j-1) + nx*ny*(k-1) ) - enddo - enddo -enddo - -end subroutine trans_1Dto3D - -!---------------------------------------------------------------------- - -subroutine get_wrf_date (tstring, year, month, day, hour, minute, second) - -!-------------------------------------------------------- -! Returns integers taken from tstring -! It is assumed that the tstring char array is as YYYY-MM-DD_hh:mm:ss - -integer, intent(out) :: year, month, day, hour, minute, second -character(len=19), intent(in) :: tstring - -read(tstring( 1: 4),'(i4)') year -read(tstring( 6: 7),'(i2)') month -read(tstring( 9:10),'(i2)') day -read(tstring(12:13),'(i2)') hour -read(tstring(15:16),'(i2)') minute -read(tstring(18:19),'(i2)') second - -return - -end subroutine get_wrf_date - -!---------------------------------------------------------------------- - -subroutine set_wrf_date (tstring, year, month, day, hour, minute, second) - -!-------------------------------------------------------- -! Returns integers taken from tstring -! It is assumed that the tstring char array is as YYYY-MM-DD_hh:mm:ss - -integer, intent(in) :: year, month, day, hour, minute, second -character(len=19), intent(out) :: tstring - -character(len=4) :: ch_year -character(len=2) :: ch_month, ch_day, ch_hour, ch_minute, ch_second - -write(ch_year,'(i4)') year -write(ch_month,'(i2)') month -if (ch_month(1:1) == " ") ch_month(1:1) = "0" -write(ch_day,'(i2)') day -if (ch_day(1:1) == " ") ch_day(1:1) = "0" -write(ch_hour,'(i2)') hour -if (ch_hour(1:1) == " ") ch_hour(1:1) = "0" -write(ch_minute,'(i2)') minute -if (ch_minute(1:1) == " ") ch_minute(1:1) = "0" -write(ch_second,'(i2)') second -if (ch_second(1:1) == " ") ch_second(1:1) = "0" -tstring(1:4) = ch_year -tstring(5:5) = "-" -tstring(6:7) = ch_month -tstring(8:8) = "-" -tstring(9:10) = ch_day -tstring(11:11) = "_" -tstring(12:13) = ch_hour -tstring(14:14) = ":" -tstring(15:16) = ch_minute -tstring(17:17) = ":" -tstring(18:19) = ch_second - -end subroutine set_wrf_date - - -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -! -! height_diff_check - function that determines whether a pair of heights -! in meters are closer than the given tolerance. -! returns .TRUE. if closer or equal to limit -! -! max_diff_meters - maximum difference between 2 elevations (m) -! height1 - first height (m) -! height2 - second height (m) -! -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -function height_diff_check(max_diff_meters, height1, height2) - real(r8), intent(in) :: max_diff_meters, height1, height2 - logical :: height_diff_check - -height_diff_check = .true. - -if ( abs(height1 - height2) > max_diff_meters ) height_diff_check = .false. - -end function height_diff_check - -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -! -! compute_geometric_ht - subroutine the converts geopotential height -! into geometric height. -! -! geopot -- input real value geopotential height -! lat -- latitude of input (longitude not needed) -! -! output is real value geometric height -! -!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -function compute_geometric_height(geopot, lat) - real(r8), intent(in) :: geopot - real(r8), intent(in) :: lat - real(r8) :: compute_geometric_height - - -! -----------------------------------------------------------------------*/ - real(digits12) :: pi2, latr - real(digits12) :: semi_major_axis, semi_minor_axis, grav_polar, grav_equator - real(digits12) :: earth_omega, grav_constant, flattening, somigliana - real(digits12) :: grav_ratio, sin2, termg, termr, grav, eccentricity - -! Parameters below from WGS-84 model software inside GPS receivers. - parameter(semi_major_axis = 6378.1370d3) ! (m) - parameter(semi_minor_axis = 6356.7523142d3) ! (m) - parameter(grav_polar = 9.8321849378) ! (m/s2) - parameter(grav_equator = 9.7803253359) ! (m/s2) - parameter(earth_omega = 7.292115d-5) ! (rad/s) - parameter(grav_constant = 3.986004418d14) ! (m3/s2) - parameter(grav = 9.80665d0) ! (m/s2) WMO std g at 45 deg lat - parameter(eccentricity = 0.081819d0) ! unitless - parameter(pi2 = 3.14159265358979d0/180.d0) - -! Derived geophysical constants - parameter(flattening = (semi_major_axis-semi_minor_axis) / semi_major_axis) - - parameter(somigliana = (semi_minor_axis/semi_major_axis)*(grav_polar/grav_equator)-1.d0) - - parameter(grav_ratio = (earth_omega*earth_omega * & - semi_major_axis*semi_major_axis * semi_minor_axis)/grav_constant) - - - latr = lat * (pi2) ! in radians - sin2 = sin(latr) * sin(latr) - termg = grav_equator * ( (1.d0+somigliana*sin2) / & - sqrt(1.d0-eccentricity*eccentricity*sin2) ) - termr = semi_major_axis / (1.d0 + flattening + grav_ratio - 2.d0*flattening*sin2) - - ! geometric height conversion. in the special case that you're trying to - ! reproduce an old result and want to use the original geopotential height - ! instead of converting it, set 'use_geopotential_height' to .true. at the - ! top of this file. Otherwise the normal conversion to geometric height is - ! what you want here; we agreed that height was going to be in terms of - ! geometric height when we did the conversions. - - if (use_geopotential_height) then - compute_geometric_height = geopot - else - compute_geometric_height = (termr*geopot) / ( (termg/grav) * termr - geopot ) - endif - -end function compute_geometric_height - -!-------------------------------------------------------------------------- -!> Vorcity forward operator. -subroutine vorticity_forward_operator(use_old_vortex, fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_ens_handle) - -logical, intent(in) :: use_old_vortex -integer, intent(in) :: ens_size -type(ensemble_type), intent(in) :: state_ens_handle -type(wrf_dom), intent(in) :: wrf -integer, intent(in) :: id -integer, intent(in) :: i,j -integer, intent(in) :: uniquek(:) -integer, intent(in) :: k(ens_size) -integer, intent(in) :: obs_kind -real(r8), intent(in) :: dxm, dx, dy, dym -real(r8), intent(out) :: fld(2, ens_size) - -real(r8), allocatable, dimension(:) :: t1d, p1d, qv1d, z1d -real(r8), allocatable, dimension(:,:) :: vfld, pp, pd, uwnd, vwnd, vort -real(r8), allocatable, dimension(:) :: x1d, y1d, xx1d, yy1d -integer :: center_search_half_size, center_track_xmin, center_track_ymin, & - center_track_xmax, center_track_ymax, circ_half_size, & - circ_xmin, circ_xmax, circ_ymin, circ_ymax, xlen, ylen, & - xxlen, yylen, ii1, ii2, cxlen, cylen, imax, jmax -real(r8) :: clat, clon, cxloc, cyloc, vcrit, magwnd, maxwspd, circ, & - circ_half_length, asum, distgrid, dgi1, dgi2 - -integer :: i1, i2 -integer :: k2 - -real(r8) :: ugrid, vgrid ! ens_size? - -logical :: debug = .false. - -logical :: is_lev0(ens_size) -real(r8) :: zloc(ens_size) -integer :: uk - -!! define a search box bounded by center_track_*** -center_search_half_size = nint(center_search_half_length/wrf%dom(id)%dx) -if ( wrf%dom(id)%periodic_x ) then - center_track_xmin = i-center_search_half_size - center_track_xmax = i+center_search_half_size -else - center_track_xmin = max(1,i-center_search_half_size) - center_track_xmax = min(wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu),i+center_search_half_size) -endif -if ( wrf%dom(id)%periodic_y ) then - center_track_ymin = j-center_search_half_size - center_track_ymax = j+center_search_half_size -else - center_track_ymin = max(1,j-center_search_half_size) - center_track_ymax = min(wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu),j+center_search_half_size) -endif -if ( center_track_xmin<1 .or. center_track_xmax>wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) .or. & - center_track_ymin<1 .or. center_track_ymax>wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) .or. & - center_track_xmin >= center_track_xmax .or. center_track_ymin >= center_track_ymax) then - print*,'i,j,center_search_half_length,center_track_xmin(max),center_track_ymin(max)' - print*,i,j,center_search_half_length,center_track_xmin,center_track_xmax,center_track_ymin,center_track_ymax - write(errstring,*)'Wrong setup in center_track_nml' - call error_handler(E_ERR,'model_interpolate', errstring, source, revision, revdate) - -endif - - -UNIQUEK_LOOP: do uk = 1, size(uniquek) - - ! Check to make sure retrieved integer gridpoints are in valid range - if (.not. ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf%dom(id)%type_t ) .and. & - boundsCheck( k(uk), .false., id, dim=3, type=wrf%dom(id)%type_t ) )) then - - !Do something? - !return - - else - - if ( obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON .or. & - obs_kind == QTY_VORTEX_PMIN ) then - - !! define spline interpolation box dimensions - xlen = center_track_xmax - center_track_xmin + 1 - ylen = center_track_ymax - center_track_ymin + 1 - xxlen = (center_track_xmax - center_track_xmin)*center_spline_grid_scale + 1 - yylen = (center_track_ymax - center_track_ymin)*center_spline_grid_scale + 1 - - allocate(x1d(xlen), y1d(ylen)) ; allocate(xx1d(xxlen), yy1d(yylen)) - allocate(pd(xlen,ylen)) ; allocate(pp(xxlen,yylen)) - allocate(vfld(xlen,ylen)) - - do i1 = 1,xlen - x1d(i1) = (i1-1)+center_track_xmin - enddo - do i2 = 1,ylen - y1d(i2) = (i2-1)+center_track_ymin - enddo - do ii1 = 1,xxlen - xx1d(ii1) = center_track_xmin+real(ii1-1,r8)*1.0_r8/real(center_spline_grid_scale,r8) - enddo - do ii2 = 1,yylen - yy1d(ii2) = center_track_ymin+real(ii2-1,r8)*1.0_r8/real(center_spline_grid_scale,r8) - enddo - - endif - - if ( (obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON) .and. (.not. use_old_vortex) ) then - - call vorticity_one() - - else if ( obs_kind == QTY_VORTEX_PMIN .or. (use_old_vortex .and. & - (obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON)) ) then - - call vorticity_two() - - else if ( obs_kind == QTY_VORTEX_WMAX ) then ! Maximum wind speed - - call vorticity_three() - - endif ! if test on obs_kind - - endif - -enddo UNIQUEK_LOOP - -end subroutine vorticity_forward_operator - -!-------------------------------------------------------------------------- -!> vorticity one - need a better name than this -!if ( (obs_kind == QTY_VORTEX_LAT .or. obs_kind == QTY_VORTEX_LON) .and. (.not. use_old_vortex) ) then -subroutine vorticity_one() - -! determine window that one would need wind components, thus circulation -circ_half_size = nint(circulation_radius/wrf%dom(id)%dx) -circ_half_length = circulation_radius / wrf%dom(id)%dx - -if ( wrf%dom(id)%periodic_x ) then - circ_xmin = i-center_search_half_size-circ_half_size - circ_xmax = i+center_search_half_size+circ_half_size -else - circ_xmin = max(1,i-center_search_half_size-circ_half_size) - circ_xmax = min(wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu),i+center_search_half_size+circ_half_size) -endif - -if ( wrf%dom(id)%periodic_y ) then - circ_ymin = j-center_search_half_size-circ_half_size - circ_ymax = j+center_search_half_size+circ_half_size -else - circ_ymin = max(1,j-center_search_half_size-circ_half_size) - circ_ymax = min(wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu),j+center_search_half_size+circ_half_size) -endif - -cxlen = circ_xmax-circ_xmin+1 -cylen = circ_ymax-circ_ymin+1 -allocate(uwnd(cxlen+2,cylen+2)) -allocate(vwnd(cxlen+2,cylen+2)) -allocate(z1d(0:wrf%dom(id)%bt)) - -do i1 = circ_xmin-1, circ_xmax+1 - - ii1 = i1 - if ( wrf%dom(id)%periodic_x ) then - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = i1 - wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = i1 + wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - endif - else - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = 1 - endif - endif - - do i2 = circ_ymin-1, circ_ymax+1 - - ii2 = i2 - if ( wrf%dom(id)%periodic_y ) then - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - elseif ( i2 < 1 ) then - ii2 = i2 + wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - endif - else - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - elseif ( i2 < 1 ) then - ii2 = 1 - endif - endif - - ! calculate the wind components at the desired pressure level - do k2 = 1, wrf%dom(id)%var_size(3,wrf%dom(id)%type_t) - z1d(k2, :) = model_pressure_t(i1,i2,k2,id, state_ens_handle, ens_size) - enddo - z1d(0) = z1d(1) - call pres_to_zk_distrib(circulation_pres_level, z1d, wrf%dom(id)%bt, ens_size, zloc, is_lev0) - k2 = floor(zloc) ; dxm = mod(zloc,1.0_r8) ; dx = 1.0_r8 - dxm - - if ( zloc >= 1.0_r8 ) then ! vertically interpolate - - ugrid = (dx * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2, wrf%dom(id)%type_u)) + & - x(wrf%dom(id)%dart_ind(ii1+1,ii2, k2, wrf%dom(id)%type_u))) + & - dxm * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2+1,wrf%dom(id)%type_u)) + & - x(wrf%dom(id)%dart_ind(ii1+1,ii2, k2+1,wrf%dom(id)%type_u)))) * 0.5_r8 - - vgrid = (dx * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2, wrf%dom(id)%type_v)) + & - x(wrf%dom(id)%dart_ind(ii1, ii2+1,k2, wrf%dom(id)%type_v))) + & - dxm * (x(wrf%dom(id)%dart_ind(ii1, ii2, k2+1,wrf%dom(id)%type_v)) + & - x(wrf%dom(id)%dart_ind(ii1, ii2+1,k2+1,wrf%dom(id)%type_v)))) * 0.5_r8 - - else ! pressure level below ground. Take model level 1 winds - - ugrid = (x(wrf%dom(id)%dart_ind(ii1, ii2, 1,wrf%dom(id)%type_u)) + & - x(wrf%dom(id)%dart_ind(ii1+1,ii2, 1,wrf%dom(id)%type_u))) * 0.5_r8 - vgrid = (x(wrf%dom(id)%dart_ind(ii1, ii2, 1,wrf%dom(id)%type_v)) + & - x(wrf%dom(id)%dart_ind(ii1, ii2+1,1,wrf%dom(id)%type_v))) * 0.5_r8 - - endif - uwnd(i1-circ_xmin+2,i2-circ_ymin+2) = ugrid - vwnd(i1-circ_xmin+2,i2-circ_ymin+2) = vgrid - - enddo -enddo - -allocate(vort(cxlen,cylen)) -do i1 = circ_xmin, circ_xmax - - dgi1 = 2.0_r8 - if ( .not. wrf%dom(id)%periodic_x ) then - if ( i1 == 1 ) then - dgi1 = 1.0_r8 - elseif ( i1 == wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - dgi1 = 1.0_r8 - endif - endif - - do i2 = circ_ymin, circ_ymax - - dgi2 = 2.0_r8 - if ( .not. wrf%dom(id)%periodic_x ) then - if ( i2 == 1 ) then - dgi2 = 1.0_r8 - elseif ( i2 == wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - dgi2 = 1.0_r8 - endif - endif - - ii1 = i1-circ_xmin+1 - ii2 = i2-circ_ymin+1 - - ! compute the vorticity for each point needed to compute circulation - vort(ii1,ii2) = (vwnd(ii1+2,ii2+1) - vwnd(ii1+1,ii2+1)) / (wrf%dom(id)%dx * dgi2) + & - (vwnd(ii1+1,ii2+1) - vwnd(ii1 ,ii2+1)) / (wrf%dom(id)%dx * dgi2) - & - (uwnd(ii1+1,ii2+2) - uwnd(ii1+1,ii2+1)) / (wrf%dom(id)%dx * dgi1) - & - (uwnd(ii1+1,ii2+1) - uwnd(ii1+1,ii2 )) / (wrf%dom(id)%dx * dgi1) - - enddo -enddo - -! loop over all grid points in search area, compute average vorticity -! (circulation) within a certain distance of that grid point -do i1 = center_track_xmin, center_track_xmax - do i2 = center_track_ymin, center_track_ymax - - asum = 0.0_r8 ; circ = 0.0_r8 - do ii1 = max(circ_xmin,i1-circ_half_size), min(circ_xmax,i1+circ_half_size) - do ii2 = max(circ_ymin,i2-circ_half_size), min(circ_ymax,i2+circ_half_size) - - distgrid = sqrt(real(ii1-i1,r8) ** 2 + real(ii2-i2,r8) ** 2) * wrf%dom(id)%dx - if ( distgrid <= circulation_radius ) then - - asum = asum + 1.0_r8 - circ = circ + vort(ii1-circ_xmin+1,ii2-circ_ymin+1) - - endif - - enddo - enddo - - vfld(i1-center_track_xmin+1,i2-center_track_ymin+1) = circ / asum - - enddo -enddo - -! find maximum in circulation through spline interpolation -call splie2(x1d,y1d,vfld,xlen,ylen,pd) - -vcrit = -1.0e20_r8 -cxloc = -1 -cyloc = -1 -do ii1 = 1, xxlen - do ii2 = 1, yylen - call splin2(x1d,y1d,vfld,pd,xlen,ylen,xx1d(ii1),yy1d(ii2),pp(ii1,ii2)) - if (vcrit < pp(ii1,ii2)) then - vcrit = pp(ii1,ii2) - cxloc = xx1d(ii1) - cyloc = yy1d(ii2) - endif - enddo -enddo - -! forward operator fails if maximum is at edge of search area -if ( cxloc-xx1d(1) < 1.0_r8 .or. xx1d(xxlen)-cxloc < 1.0_r8 .or. & - cyloc-yy1d(1) < 1.0_r8 .or. yy1d(yylen)-cyloc < 1.0_r8 ) then - - fld(:) = missing_r8 - -else - - call ij_to_latlon(wrf%dom(id)%proj, cxloc, cyloc, clat, clon) - - if ( obs_kind == QTY_VORTEX_LAT ) then - fld(1) = clat - else - fld(1) = clon - endif - -endif - -deallocate(uwnd, vwnd, vort, z1d) -deallocate(vfld, pd, pp, x1d, y1d, xx1d, yy1d) - -end subroutine vorticity_one - -!-------------------------------------------------------------------------- -!> vorticity_two -subroutine vorticity_two() - - allocate(p1d(wrf%dom(id)%bt), t1d(wrf%dom(id)%bt)) - allocate(qv1d(wrf%dom(id)%bt), z1d(wrf%dom(id)%bt)) - - ! compute SLP for each grid point within the search area - do i1 = center_track_xmin, center_track_xmax - - ii1 = i1 - if ( wrf%dom(id)%periodic_x ) then - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = i1 - wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = i1 + wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - endif - endif - - do i2 = center_track_ymin, center_track_ymax - - ii2 = i2 - if ( wrf%dom(id)%periodic_y ) then - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - elseif ( i2 < 1 ) then - ii2 = i2 + wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - endif - endif - - do k2 = 1,wrf%dom(id)%var_size(3,wrf%dom(id)%type_t) - - p1d(k2) = model_pressure_t(ii1,ii2,k2,id,x) - t1d(k2) = x(wrf%dom(id)%dart_ind(ii1,ii2,k2,wrf%dom(id)%type_t)) + ts0 - qv1d(k2)= x(wrf%dom(id)%dart_ind(ii1,ii2,k2,wrf%dom(id)%type_qv)) - z1d(k2) = (x(wrf%dom(id)%dart_ind(ii1,ii2,k2, wrf%dom(id)%type_gz))+ & - x(wrf%dom(id)%dart_ind(ii1,ii2,k2+1,wrf%dom(id)%type_gz))+ & - wrf%dom(id)%phb(ii1,ii2,k2)+wrf%dom(id)%phb(ii1,ii2,k2+1))*0.5_r8/gravity - - enddo - call compute_seaprs(wrf%dom(id)%bt, z1d, t1d, p1d, qv1d, & - vfld(i1-center_track_xmin+1,i2-center_track_ymin+1), debug) - enddo - - enddo - - ! find minimum in MSLP through spline interpolation - call splie2(x1d,y1d,vfld,xlen,ylen,pd) - - vcrit = 1.0e20_r8 - do ii1=1,xxlen - do ii2=1,yylen - call splin2(x1d,y1d,vfld,pd,xlen,ylen,xx1d(ii1),yy1d(ii2),pp(ii1,ii2)) - if ( vcrit > pp(ii1,ii2)) then - vcrit = pp(ii1,ii2) - cxloc = xx1d(ii1) - cyloc = yy1d(ii2) - endif - enddo - enddo - - ! forward operator fails if maximum is at edge of search area - if ( cxloc-xx1d(1) < 1.0_r8 .or. xx1d(xxlen)-cxloc < 1.0_r8 .or. & - cyloc-yy1d(1) < 1.0_r8 .or. yy1d(yylen)-cyloc < 1.0_r8 ) then - - fld(:) = missing_r8 - - else - - call ij_to_latlon(wrf%dom(id)%proj, cxloc, cyloc, clat, clon) - - if ( obs_kind == QTY_VORTEX_PMIN ) then - fld(1) = vcrit - else if ( obs_kind == QTY_VORTEX_LAT ) then - fld(1) = clat - else - fld(1) = clon - endif - - endif - - deallocate(p1d, t1d, qv1d, z1d) - deallocate(vfld, pd, pp, x1d, y1d, xx1d, yy1d) - -end subroutine vorticity_two - -!-------------------------------------------------------------------------- -!> vorticity_three -subroutine vorticity_three() - - maxwspd = 0.0_r8 - do i1 = center_track_xmin, center_track_xmax - - ii1 = i1 - if ( wrf%dom(id)%periodic_x ) then - if ( i1 > wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) ) then - ii1 = i1 - wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - elseif ( i1 < 1 ) then - ii1 = i1 + wrf%dom(id)%var_size(1,wrf%dom(id)%type_mu) - endif - endif - - do i2 = center_track_ymin, center_track_ymax - - ii2 = i2 - if ( wrf%dom(id)%periodic_y ) then - if ( i2 > wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) ) then - ii2 = i2 - wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - elseif ( i2 < 1 ) then - ii2 = i2 + wrf%dom(id)%var_size(2,wrf%dom(id)%type_mu) - endif - endif - - if ( ( wrf%dom(id)%type_u10 >= 0 ) .and. ( wrf%dom(id)%type_v10 >= 0 ) ) then - ugrid = x(wrf%dom(id)%dart_ind(ii1,ii2,1,wrf%dom(id)%type_u10)) - vgrid = x(wrf%dom(id)%dart_ind(ii1,ii2,1,wrf%dom(id)%type_v10)) - else - ugrid = 0.5_r8*(x(wrf%dom(id)%dart_ind(ii1, ii2,1,wrf%dom(id)%type_u)) + & - x(wrf%dom(id)%dart_ind(ii1+1,ii2,1,wrf%dom(id)%type_u))) - vgrid = 0.5_r8*(x(wrf%dom(id)%dart_ind(ii1,ii2, 1,wrf%dom(id)%type_v)) + & - x(wrf%dom(id)%dart_ind(ii1,ii2+1,1,wrf%dom(id)%type_v))) - endif - - magwnd = sqrt(ugrid * ugrid + vgrid * vgrid) - if ( magwnd > maxwspd ) then - imax = i1 - jmax = i2 - maxwspd = magwnd - endif - - enddo - enddo - - ! forward operator fails if maximum is at edge of search area - if ( imax == center_track_xmin .or. jmax == center_track_ymin .or. & - imax == center_track_xmax .or. jmax == center_track_ymax ) then - fld(:) = missing_r8 - else - fld(1) = maxwspd - endif - -end subroutine vorticity_three - -!-------------------------------------------------------------------------- -!> Perform interpolation across the ensemble -subroutine simple_interp_distrib(fld, wrf, id, i, j, k, obs_kind, dxm, dx, dy, dym, uniquek, ens_size, state_ens_handle) - -integer, intent(in) :: ens_size -type(ensemble_type), intent(in) :: state_ens_handle -type(wrf_dom), intent(in) :: wrf -integer, intent(in) :: id -integer, intent(in) :: i,j -integer, intent(in) :: uniquek(:) -integer, intent(in) :: k(ens_size) -integer, intent(in) :: obs_kind -real(r8), intent(in) :: dxm, dx, dy, dym -real(r8), intent(out) :: fld(2, ens_size) - -integer :: ill, iul, ilr, iur, rc -integer, dimension(2) :: ll, ul, lr, ur -integer :: uk, e -logical :: in_state -integer :: wrf_type - -real(r8), dimension(ens_size) ::x_ill, x_iul, x_ilr, x_iur - -! Confirm that the obs kind is in the DART state vector and return the wrf_type -!>@todo should boundsCheck always be temperatue type? This is what it is in the original code -call obs_kind_in_state_vector(in_state, wrf_type, obs_kind, id) - -if ( in_state ) then - - ! An observation could be on different levels for each ensemble member. - ! But, you don't want to do ens_size*communication, so just do it for the levels you need. - UNIQUEK_LOOP: do uk = 1, size(uniquek) - - ! Check to make sure retrieved integer gridpoints are in valid range - if ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf_type ) .and.& - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf_type ) .and. & - boundsCheck( uniquek(uk), .false., id, dim=3, type=wrf_type ) ) then - - call getCorners(i, j, id, wrf_type, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners QNSNOW rc = ', rc - - ! Interpolation for QNSNOW field at level k - ill = new_dart_ind(ll(1), ll(2), uniquek(uk), wrf_type, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk), wrf_type, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk), wrf_type, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk), wrf_type, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(1,e) = dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - - ! Interpolation for QNSNOW field at level k+1 - ill = new_dart_ind(ll(1), ll(2), uniquek(uk)+1, wrf_type, id) - iul = new_dart_ind(ul(1), ul(2), uniquek(uk)+1, wrf_type, id) - ilr = new_dart_ind(lr(1), lr(2), uniquek(uk)+1, wrf_type, id) - iur = new_dart_ind(ur(1), ur(2), uniquek(uk)+1, wrf_type, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - - do e = 1, ens_size - if ( k(e) == uniquek(uk) ) then - fld(2, e) = dym*( dxm*x_ill(e) + dx*x_ilr(e) ) + dy*( dxm*x_iul(e) + dx*x_iur(e) ) - endif - enddo - - endif - - enddo UNIQUEK_LOOP - -else ! not in state - - call error_handler(E_ERR, 'simple_interp_distrib', 'obs_kind is not in state vector', source, revision, revdate) - -endif - -end subroutine simple_interp_distrib - -!------------------------------------------------------------------------ -!> interpolation for surface fields -subroutine surface_interp_distrib(fld, wrf, id, i, j, obs_kind, wrf_surf_type, dxm, dx, dy, dym, ens_size, state_ens_handle) - -integer, intent(in) :: ens_size -type(ensemble_type), intent(in) :: state_ens_handle -type(wrf_dom), intent(in) :: wrf -integer, intent(in) :: id -integer, intent(in) :: obs_kind -integer, intent(in) :: wrf_surf_type !< this has to be passed in is beccause obs_kind does not give a unique wrf_type ( could check obs_kind then if surf var?) -integer, intent(in) :: i,j -real(r8), intent(in) :: dxm, dx, dy, dym -real(r8), intent(out) :: fld(2, ens_size) - -integer :: ill, iul, ilr, iur, rc -integer, dimension(2) :: ll, ul, lr, ur - -logical :: in_state -integer :: wrf_type - -real(r8), dimension(ens_size) ::x_ill, x_iul, x_ilr, x_iur - -! Find the wrf_type from the obs kind -! check for in state is performed before surface_interp_distrib is called -!>@todo should boundsCheck always be temperatue type? This is what it is in the original code -call obs_kind_in_state_vector(in_state, wrf_type, obs_kind, id) - -! Check to make sure retrieved integer gridpoints are in valid range -if ( ( boundsCheck( i, wrf%dom(id)%periodic_x, id, dim=1, type=wrf_type ) .and. & - boundsCheck( j, wrf%dom(id)%polar, id, dim=2, type=wrf_type ) ) & - .or. wrf%dom(id)%scm ) then - - call getCorners(i, j, id, wrf_type, ll, ul, lr, ur, rc ) - if ( rc .ne. 0 ) & - print*, 'model_mod.f90 :: model_interpolate :: getCorners T2 rc = ', rc - - ! Interpolation for the T2 field - ill = new_dart_ind(ll(1), ll(2), 1, wrf_surf_type, id) - iul = new_dart_ind(ul(1), ul(2), 1, wrf_surf_type, id) - ilr = new_dart_ind(lr(1), lr(2), 1, wrf_surf_type, id) - iur = new_dart_ind(ur(1), ur(2), 1, wrf_surf_type, id) - - call get_state(x_ill, ill, state_ens_handle) - call get_state(x_iul, iul, state_ens_handle) - call get_state(x_iur, iur, state_ens_handle) - call get_state(x_ilr, ilr, state_ens_handle) - - fld(1, :) = dym*( dxm*x_ill + dx*x_ilr ) + dy*( dxm*x_iul + dx*x_iur ) - -endif - -end subroutine surface_interp_distrib - -!-------------------------------------------------------------------------- -!> test if an obs kind is in the state vector and set wrf_type -subroutine obs_kind_in_state_vector(part_of_state_vector, wrf_type, obs_kind, id) - -logical, intent(out) :: part_of_state_vector -integer, intent(out) :: wrf_type !< WRF -integer, intent(in) :: obs_kind !< DART -integer, intent(in) :: id - - -part_of_state_vector = .false. ! assume not in state vector - - -if ( ( obs_kind == QTY_VERTICAL_VELOCITY) .and. ( wrf%dom(id)%type_w >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_w -else if( ( obs_kind == QTY_RAINWATER_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qr >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qr -else if( ( obs_kind == QTY_GRAUPEL_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qg >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qg -else if( ( obs_kind == QTY_HAIL_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qh >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qh -else if( ( obs_kind == QTY_SNOW_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qs >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qs -else if( ( obs_kind == QTY_CLOUD_ICE ) .and. ( wrf%dom(id)%type_qi >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qi -else if( ( obs_kind == QTY_CLOUD_LIQUID_WATER ) .and. ( wrf%dom(id)%type_qc >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qc -else if( ( obs_kind == QTY_DROPLET_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qndrp >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qndrp -else if( ( obs_kind == QTY_ICE_NUMBER_CONCENTRATION ) .and. ( wrf%dom(id)%type_qnice >= 0 ) )then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnice -else if( ( obs_kind == QTY_SNOW_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qnsnow >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnsnow -else if( ( obs_kind == QTY_RAIN_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qnrain >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnrain -else if( ( obs_kind == QTY_GRAUPEL_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qngraupel >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qngraupel -else if( ( obs_kind == QTY_HAIL_NUMBER_CONCENTR ) .and. ( wrf%dom(id)%type_qnhail >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qnhail -else if( ( obs_kind == QTY_CONDENSATIONAL_HEATING ) .and. ( wrf%dom(id)%type_hdiab >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_hdiab -else if( ( obs_kind == QTY_POWER_WEIGHTED_FALL_SPEED ) .and. ( wrf%dom(id)%type_fall_spd >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_fall_spd -else if( ( obs_kind == QTY_RADAR_REFLECTIVITY ) .and. ( wrf%dom(id)%type_refl >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_refl -else if( ( obs_kind == QTY_DIFFERENTIAL_REFLECTIVITY ) .and. ( wrf%dom(id)%type_dref >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_dref -else if( ( obs_kind == QTY_SPECIFIC_DIFFERENTIAL_PHASE ) .and. ( wrf%dom(id)%type_spdp >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_spdp -else if ( ( obs_kind == QTY_VAPOR_MIXING_RATIO ) .and. ( wrf%dom(id)%type_qv >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_qv -else if ( ( obs_kind == QTY_TEMPERATURE ) .and. ( wrf%dom(id)%type_t >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_t -else if ( ( obs_kind == QTY_POTENTIAL_TEMPERATURE ) .and. ( wrf%dom(id)%type_t >= 0 ) ) then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_t -else if ( ( obs_kind == QTY_SKIN_TEMPERATURE ) .and. ( wrf%dom(id)%type_tsk >= 0 ) )then - part_of_state_vector = .true. - wrf_type = wrf%dom(id)%type_tsk -else - print*, 'kind', obs_kind - call error_handler(E_ERR, 'obs_kind_in_state_vector', 'not in state vector', source, revision, revdate) -endif - -end subroutine obs_kind_in_state_vector - -!-------------------------------------------------------------------- -!> Aim: to replace the dart_ind array (which can be larger than the state vector) -!> with a function. -function new_dart_ind(i, j, k, ind, domain) - -integer :: new_dart_ind -integer :: i, j, k, ind, domain ! info for the state element: x,y,z,type, domain -integer :: Ni, Nj, Nk, sum_below, extra, types_below -integer :: id ! domain loop type -integer :: ivar ! variable loop index - -! Find sum of all variable types below this one -sum_below = 0 - -! I think you could do these two loops once at the start of the module and store -! the results in a look up table - -! sum up domains below -do id = 1, domain -1 - do ivar = 1, wrf%dom(id)%number_of_wrf_variables - sum_below = sum_below + wrf%dom(id)%var_size(1, ivar) * & - wrf%dom(id)%var_size(2, ivar) * & - wrf%dom(id)%var_size(3, ivar) - enddo -enddo - -! sum up variables below -do types_below = 1, ind - 1 - sum_below = sum_below + wrf%dom(domain)%var_size(1, types_below) * & - wrf%dom(domain)%var_size(2, types_below) * & - wrf%dom(domain)%var_size(3, types_below) -enddo - -Ni = wrf%dom(domain)%var_size(1, ind) -Nj = wrf%dom(domain)%var_size(2, ind) -Nk = wrf%dom(domain)%var_size(3, ind) - -extra = Ni * Nj * (k - 1) + Ni * (j - 1) + i - -new_dart_ind = sum_below + extra - -end function new_dart_ind - -!-------------------------------------------------------------------- -!> pass the vertical localization coordinate to assim_tools_mod -function query_vert_localization_coord() - -integer :: query_vert_localization_coord - -query_vert_localization_coord = vert_localization_coord - -end function query_vert_localization_coord - - -!-------------------------------------------------------------------- -!> pass number of variables in the state out to filter -subroutine variables_domains(num_variables_in_state, num_doms) - -integer, intent(out) :: num_variables_in_state -integer, intent(out) :: num_doms !< number of domains - -!>@todo massive assumption same variables in each domain -num_variables_in_state = wrf%dom(1)%number_of_wrf_variables - -num_doms = num_domains - -end subroutine variables_domains - -!-------------------------------------------------------------------- -!> pass variable list to filter -function fill_variable_list(num_variables_in_state) - -integer :: num_variables_in_state -character(len=256) :: fill_variable_list(num_variables_in_state) - -fill_variable_list = wrf_state_variables(1,1:num_variables_in_state) - -end function fill_variable_list - -!-------------------------------------------------------------------- -!> construct info filename for get_state_variable_info -function info_file_name(domain) - -integer, intent(in) :: domain -character(len=256) :: info_file_name - -write(info_file_name, '(A, i2.2, A)') 'wrfinput_d', domain - -end function info_file_name - -!-------------------------------------------------------------------- -!> construct restart file name for reading -!> model time for CESM format? -function construct_file_name(stub, domain, copy) - -character(len=512), intent(in) :: stub -integer, intent(in) :: domain -integer, intent(in) :: copy -character(len=1024) :: construct_file_name - -!write(construct_file_name, '(A, i2.2, A, i2.2, A)') TRIM(stub), domain, '.', copy, '.nc' -if (copy < 10) then - write(construct_file_name, '(A, i1.1, A, i2.2)') TRIM(stub), copy, '/wrfinput_d', domain -else - write(construct_file_name, '(A, i2.2, A, i2.2)') TRIM(stub), copy, '/wrfinput_d', domain -endif - -end function construct_file_name - -!-------------------------------------------------------------------- -!> read the time from the input file -!> stolen from wrf_to_dart.f90 -function get_model_time(filename) - -character(len=1024) :: filename -integer :: year, month, day, hour, minute, second -integer :: ret !< netcdf return code -integer :: ndims, dimids(2), ivtype, ncid, var_id -character(len=80) :: varname -character(len=19) :: timestring -integer :: i, idims(2) - -type(time_type) :: get_model_time - - -call nc_check( nf90_open(filename, NF90_NOWRITE, ncid), & - 'opening', filename ) - -call nc_check( nf90_inq_varid(ncid, "Times", var_id), 'wrf_to_dart', & - 'inq_varid Times' ) -call nc_check( nf90_inquire_variable(ncid, var_id, varname, xtype=ivtype, & - ndims=ndims, dimids=dimids), 'wrf_to_dart', & - 'inquire_variable Times' ) - -do i=1,ndims ! isnt this just 1? - call nc_check( nf90_inquire_dimension(ncid, dimids(i), & - len=idims(i)),'wrf_to_dart','inquire_dimensions Times' ) -enddo - -call nc_check( nf90_get_var(ncid, var_id, timestring, & - start = (/ 1, idims(2) /)), 'wrf_to_dart','get_var Times' ) - -call get_wrf_date(timestring, year, month, day, hour, minute, second) -get_model_time = set_date(year, month, day, hour, minute, second) - - -call nc_check( nf90_close(ncid) , 'closing', filename) - -end function get_model_time -!-------------------------------------------------------------------- - -end module model_mod - -!> @} - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ From 971fed9dd696179ae4768645b1028190212f1e13 Mon Sep 17 00:00:00 2001 From: Helen Kershaw Date: Tue, 24 Aug 2021 14:29:57 -0600 Subject: [PATCH 04/16] remove cam-old Does not run with Manhattan. 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models/cam-old/work/quickbuild.csh diff --git a/models/cam-old/cam_to_dart.f90 b/models/cam-old/cam_to_dart.f90 deleted file mode 100644 index 8da071ec6a..0000000000 --- a/models/cam-old/cam_to_dart.f90 +++ /dev/null @@ -1,101 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program cam_to_dart - -!---------------------------------------------------------------------- -! purpose: interface between CAM and DART -! -! method: Read CAM 'initial' file (netCDF format) for model state and time. -! Reform fields into a DART state vector. -! Write out state vector in "proprietary" format for DART -! -! author: Tim Hoar 4/4/2011 -! -!---------------------------------------------------------------------- - -use types_mod, only : r8 -use utilities_mod, only : initialize_utilities, finalize_utilities, & - check_namelist_read, find_namelist_in_file, & - nmlfileunit, do_nml_file, do_nml_term -use model_mod, only : model_type, init_model_instance, & - end_model_instance, & - prog_var_to_vector, read_cam_init -use assim_model_mod, only : static_init_assim_model, get_model_size - -use state_vector_io_mod, only : open_restart_write, awrite_state_restart, & - close_restart -use time_manager_mod, only : time_type - -implicit none - -! version controlled file description for error handling, do not edit -character(len=256), parameter :: source = & - "$URL$" -character(len=32 ), parameter :: revision = "$Revision$" -character(len=128), parameter :: revdate = "$Date$" - - -!----------------------------------------------------------------------- -! namelist parameters with default values. -!----------------------------------------------------------------------- - -character (len = 128) :: cam_to_dart_input_file = 'caminput.nc' -character (len = 128) :: cam_to_dart_output_file = 'dart_ics' - -namelist /cam_to_dart_nml/ cam_to_dart_input_file, cam_to_dart_output_file - -! allocatable storage to read in a native format for cam state -real(r8), allocatable :: statevector(:) -type(model_type) :: var -type(time_type) :: model_time -integer :: iunit, x_size, io - -call initialize_utilities('cam_to_dart') - -! Read the namelist entry -call find_namelist_in_file("input.nml", "cam_to_dart_nml", iunit) -read(iunit, nml = cam_to_dart_nml, iostat = io) -call check_namelist_read(iunit, io, "cam_to_dart_nml") - -! Record the namelist values -if (do_nml_file()) write(nmlfileunit, nml=cam_to_dart_nml) -if (do_nml_term()) write( * , nml=cam_to_dart_nml) - -! Static init assim model sets the output file format (binary/ascii) -! and calls static_init_model -call static_init_assim_model() - -! Allocate the local state vector -x_size = get_model_size() -allocate(statevector(x_size)) - -! Allocate the instance of the cam model type for storage -! I'll just point to the space I need, not; -call init_model_instance(var) - -! Read the file cam state fragments into var; -! transform fields into state vector for DART - -call read_cam_init(cam_to_dart_input_file, var, model_time) - -call prog_var_to_vector(var, statevector) -call end_model_instance(var) - -! write out state vector in "proprietary" format -iunit = open_restart_write(cam_to_dart_output_file) -call awrite_state_restart(model_time, statevector, iunit) -call close_restart(iunit) - -call finalize_utilities('cam_to_dart') - -end program cam_to_dart - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/models/cam-old/cam_to_dart.nml b/models/cam-old/cam_to_dart.nml deleted file mode 100644 index 5a0f991976..0000000000 --- a/models/cam-old/cam_to_dart.nml +++ /dev/null @@ -1,5 +0,0 @@ - -&cam_to_dart_nml - cam_to_dart_input_file = 'caminput.nc', - cam_to_dart_output_file = 'dart_ics', - / diff --git a/models/cam-old/cam_to_dart.rst b/models/cam-old/cam_to_dart.rst deleted file mode 100644 index 2045c1bee0..0000000000 --- a/models/cam-old/cam_to_dart.rst +++ /dev/null @@ -1,94 +0,0 @@ -PROGRAM ``cam_to_dart`` -======================= - -.. attention:: - - ``cam-old`` works with versions of DART *before* Manhattan (9.x.x) and has yet to be updated. If you are interested in - using ``cam-old`` with more recent versions of DART, contact DAReS staff to assess the feasibility of an update. - Until that time, you should consider this documentation as out-of-date. - - -Overview --------- - -| ``cam_to_dart`` is the program that reads a CAM restart file (usually ``caminput.nc``) and creates a single DART - output/restart file (e.g. ``perfect_ics, filter_ics, ...`` ). If you have multiple input files, you will need to - rename the output files as you create them. -| The list of variables extracted from the CAM netCDF file and conveyed to DART is controlled by the set of - ``input.nml`` ``&model_nml:state_names_*`` variables. The ``date`` and ``datesec`` variables in the CAM netcdf file - are used to specify the valid time of the state vector. The time may be changed with the - :doc:`../../assimilation_code/programs/restart_file_tool/restart_file_tool` if desired. -| Some CAM restart files are from climatological runs and have a valid time that predates the use of the Gregorian - calendar. In such instances, the year component of the original date is changed to be a valid Gregorian year (by - adding 1601). A warning is issued to the screen and to the logfile. Please use the - :doc:`../../assimilation_code/programs/restart_file_tool/restart_file_tool` to change this time. -| Conditions required for successful execution of ``cam_to_dart``: - -- a valid ``input.nml`` namelist file for DART -- a CAM 'phis' netCDF file [default: ``cam_phis.nc``] -- a CAM restart file [default: ``caminput.nc``]. - -Since this program is called repeatedly for every ensemble member, we have found it convenient to link the CAM restart -files to the default input filename (``caminput.nc``). The default DART output filename is ``dart_ics`` - this may be -moved or linked as necessary. - -Namelist --------- - -This namelist is read from the file ``input.nml``. Namelists start with an ampersand '&' and terminate with a slash '/'. -Character strings that contain a '/' must be enclosed in quotes to prevent them from prematurely terminating the -namelist. - -:: - - &cam_to_dart_nml - cam_to_dart_input_file = 'caminput.nc', - cam_to_dart_output_file = 'dart_ics', - / - -| - -.. container:: - - +-------------------------+--------------------+---------------------------------------------------------------------+ - | Item | Type | Description | - +=========================+====================+=====================================================================+ - | cam_to_dart_input_file | character(len=128) | The name of the DART file containing the CAM state. | - +-------------------------+--------------------+---------------------------------------------------------------------+ - | cam_to_dart_output_file | character(len=128) | The name of the DART file containing the model state derived from | - | | | the CAM restart file. | - +-------------------------+--------------------+---------------------------------------------------------------------+ - -| - -Modules used ------------- - -:: - - assim_model_mod.f90 - types_mod.f90 - threed_sphere/location_mod.f90 - model_mod.f90 - null_mpi_utilities_mod.f90 - obs_kind_mod.f90 - random_seq_mod.f90 - time_manager_mod.f90 - utilities_mod.f90 - -Files read ----------- - -- DART namelist file; ``input.nml`` -- CAM restart file; ``caminput.nc`` -- CAM "phis" file specified in ``&model_nml::cam_phis`` (normally ``cam_phis.nc``) - -Files written -------------- - -- DART initial conditions/restart file; e.g. ``dart_ics`` - -References ----------- - -none diff --git a/models/cam-old/column_rand.f90 b/models/cam-old/column_rand.f90 deleted file mode 100644 index 163cd0bb34..0000000000 --- a/models/cam-old/column_rand.f90 +++ /dev/null @@ -1,161 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program column_rand - -! Allows creation of input file for generating a set of randomly located -! observation stations with full column of obs for CAM model. - -use types_mod, only : r8, PI -use utilities_mod, only : get_unit, initialize_utilities, finalize_utilities -use random_seq_mod, only : random_seq_type, init_random_seq, random_uniform - -implicit none - -! version controlled file description for error handling, do not edit -character(len=256), parameter :: source = & - "$URL$" -character(len=32 ), parameter :: revision = "$Revision$" -character(len=128), parameter :: revdate = "$Date$" - -integer, allocatable :: levels(:) -integer :: level, num_cols, num_levs, i, iunit -real(r8) :: lat, lon, t_err_var, uv_err_var, ps_err_var, q_err_var -type(random_seq_type) :: r - -! Initialize the utilities -call initialize_utilities('Column_rand') - -! Initialize the random sequence -call init_random_seq(r) - -! Open an output file and write header info -iunit = get_unit() -open(unit = iunit, file = 'cam_column_rand.out') - -write(*, *) 'input the number of columns per set' -read(*, *) num_cols - -write(*, *) 'input the number of model levels in column' -read(*, *) num_levs - -allocate(levels(num_levs)) -do i = 1, num_levs - write(*, *) 'Input vertical level ', i - read(*, *) levels(i) -end do - -! Output the total number of obs in set; Q is being observed, too -write(*, *) 'total num is ', num_cols * (num_levs * 4 + 1) -write(iunit, *) num_cols * (num_levs * 4 + 1) - -! No copies or qc -write(iunit, *) 0 -write(iunit, *) 0 - -! First get error variance for surface pressure -write(*, *) 'Input error VARIANCE for surface pressure obs' -read(*, *) ps_err_var - -! Get error variance for t, and u and v -write(*, *) 'Input error VARIANCE for T obs' -read(*, *) t_err_var -write(*, *) 'Input error VARIANCE for U and V obs' -read(*, *) uv_err_var -write(*, *) 'Input error VARIANCE for Q obs' -read(*, *) q_err_var - - -! Loop through each column -do i = 1, num_cols - - ! Get a random lon lat location for this column - ! Longitude is random from 0 to 360 - lon = random_uniform(r) * 360.0_r8 - - ! Latitude must be area weighted - lat = asin(random_uniform(r) * 2.0_r8 - 1.0_r8) - - ! Now convert from radians to degrees latitude - lat = lat * 360.0_r8 / (2.0_r8 * PI) - - ! Do ps ob - write(iunit, *) 0 - ! Kind for surface pressure is 3 - write(iunit, *) 3 - write(iunit, *) 1 - ! Level is -1 for ps - write(iunit, *) -1 - write(iunit, *) lon - write(iunit, *) lat - write(iunit, *) 0, 0 - write(iunit, *) ps_err_var - - ! Loop through each observation in the column - do level = 1, num_levs - - ! Write out the t observation - write(iunit, *) 0 - ! Kind for t is 4 - write(iunit, *) 4 - write(iunit, *) 1 - write(iunit, *) levels(level) - write(iunit, *) lon - write(iunit, *) lat - write(iunit, *) 0, 0 - write(iunit, *) t_err_var - - - ! Write out the u observation - write(iunit, *) 0 - ! Kind for u is 1 - write(iunit, *) 1 - write(iunit, *) 1 - write(iunit, *) levels(level) - write(iunit, *) lon - write(iunit, *) lat - write(iunit, *) 0, 0 - write(iunit, *) uv_err_var - - - ! Write out the v observation - write(iunit, *) 0 - ! Kind for v is 2 - write(iunit, *) 2 - write(iunit, *) 1 - write(iunit, *) levels(level) - write(iunit, *) lon - write(iunit, *) lat - write(iunit, *) 0, 0 - write(iunit, *) uv_err_var - - - ! Write out the q observation - write(iunit, *) 0 - ! Kind for q is 5 - write(iunit, *) 5 - write(iunit, *) 1 - write(iunit, *) levels(level) - write(iunit, *) lon - write(iunit, *) lat - write(iunit, *) 0, 0 - write(iunit, *) q_err_var - - end do -end do - -write(iunit, *) 'set_def.out' - -! Shut down the utilities cleanly -call finalize_utilities('column_rand') - -end program column_rand - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/models/cam-old/dart_to_cam.f90 b/models/cam-old/dart_to_cam.f90 deleted file mode 100644 index 0af4342b29..0000000000 --- a/models/cam-old/dart_to_cam.f90 +++ /dev/null @@ -1,127 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program dart_to_cam - -!---------------------------------------------------------------------- -! purpose: interface between CAM and DART -! -! method: Read DART state vector ("proprietary" format), including time(s). -! Reform state vector back into CAM fields. -! Replace those fields on the CAM initial file with the new values, -! preserving all other information on the file. -! Write a 'times' file containing the time information needed to -! update the CAM namelist start/stop times. -! -!---------------------------------------------------------------------- - -use types_mod, only : r8 -use utilities_mod, only : open_file, close_file, & - initialize_utilities, finalize_utilities, & - logfileunit, nmlfileunit, do_nml_file, do_nml_term, & - check_namelist_read, find_namelist_in_file -use model_mod, only : model_type, init_model_instance, write_cam_init, & - vector_to_prog_var, static_init_model, get_model_size, & - write_cam_times, end_model_instance -use state_vector_io_mod, only : aread_state_restart, open_restart_read, close_restart -use time_manager_mod, only : time_type, print_time, print_date - -implicit none - -! version controlled file description for error handling, do not edit -character(len=256), parameter :: source = & - "$URL$" -character(len=32 ), parameter :: revision = "$Revision$" -character(len=128), parameter :: revdate = "$Date$" - -!------------------------------------------------------------------ -! The namelist variables -!------------------------------------------------------------------ - -character (len = 128) :: dart_to_cam_input_file = 'dart_ics' -character (len = 128) :: dart_to_cam_output_file = 'caminput.nc' -logical :: advance_time_present = .true. - -namelist /dart_to_cam_nml/ dart_to_cam_input_file, & - dart_to_cam_output_file, & - advance_time_present - -!---------------------------------------------------------------------- - -type(model_type) :: var -type(time_type) :: model_time, adv_to_time -real(r8), allocatable :: statevector(:) -integer :: file_unit, vecsize, iunit, io - -!----------------------------------------------------------------------------- -! start of program -!----------------------------------------------------------------------------- - -call initialize_utilities('dart_to_cam') - -! Read the namelist to get the input filename and whether -! a simple restart/ic file or a model advance file. -call find_namelist_in_file("input.nml", "dart_to_cam_nml", iunit) -read(iunit, nml = dart_to_cam_nml, iostat = io) -call check_namelist_read(iunit, io, "dart_to_cam_nml") - -! Record the namelist values used for the run -if (do_nml_file()) write(nmlfileunit, nml=dart_to_cam_nml) -if (do_nml_term()) write( * , nml=dart_to_cam_nml) - -! initialize model -call static_init_model() - -vecsize = get_model_size() -allocate(statevector(vecsize)) - -! Allocate the instance of the cam model type for storage -call init_model_instance(var) - -! Get file for DART vector input -file_unit = open_restart_read(dart_to_cam_input_file) -! read in model time and state vector from DART -if (advance_time_present) then - call aread_state_restart(model_time, statevector, file_unit, adv_to_time) -else - call aread_state_restart(model_time, statevector, file_unit) -endif -call close_restart(file_unit) - -! decompose vector back into CAM fields -call vector_to_prog_var (statevector, var) -deallocate (statevector) - -! write fields to the netCDF initial file. -call write_cam_init(dart_to_cam_output_file, model_time, var) -call end_model_instance(var) - -! write cam times to a separate 'times' support file. used to -! update cam namelist start/stop times. -if (advance_time_present) call write_cam_times(model_time, adv_to_time) - -call print_date( model_time,'dart_to_cam:CAM model date') -call print_time( model_time,'dart_to_cam:DART model time') -call print_date( model_time,'dart_to_cam:CAM model date',logfileunit) -call print_time( model_time,'dart_to_cam:DART model time',logfileunit) - -if ( advance_time_present ) then -call print_time(adv_to_time,'dart_to_cam:advance_to time') -call print_date(adv_to_time,'dart_to_cam:advance_to date') -call print_time(adv_to_time,'dart_to_cam:advance_to time',logfileunit) -call print_date(adv_to_time,'dart_to_cam:advance_to date',logfileunit) -endif - - -call finalize_utilities('dart_to_cam') - -end program dart_to_cam - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/models/cam-old/dart_to_cam.nml b/models/cam-old/dart_to_cam.nml deleted file mode 100644 index c0a073b574..0000000000 --- a/models/cam-old/dart_to_cam.nml +++ /dev/null @@ -1,5 +0,0 @@ -&dart_to_cam_nml - dart_to_cam_input_file = 'dart_ics', - dart_to_cam_output_file = 'caminput.nc', - advance_time_present = .true., - / diff --git a/models/cam-old/dart_to_cam.rst b/models/cam-old/dart_to_cam.rst deleted file mode 100644 index 4794b952e7..0000000000 --- a/models/cam-old/dart_to_cam.rst +++ /dev/null @@ -1,106 +0,0 @@ -PROGRAM ``dart_to_cam`` -======================= - -.. attention:: - - ``cam-old`` works with versions of DART *before* Manhattan (9.x.x) and has yet to be updated. If you are interested in - using ``cam-old`` with more recent versions of DART, contact DAReS staff to assess the feasibility of an update. - Until that time, you should consider this documentation as out-of-date. - - -Overview --------- - -``dart_to_cam`` is the program that reads a DART restart or model advance file (e.g. -``perfect_ics, filter_ics, assim_model_state_id ...`` ). and overwrites the part of the CAM data in a single CAM restart -file (usually ``caminput.nc``) which is in the DART state vector. If you have multiple input files, you will need to -rename the output files as you create them. - -The list of variables extracted from the DART state vector and exported to the CAM netCDF file is controlled by the set -of ``input.nml`` ``&model_nml:state_names_*`` variables. - -If the input is a model advance file, containing 2 timestamps (the current model time and a future time the model should -run until), this program also creates a separate file named ``times`` that contains three lines: the advance-to time, -the current model time, and the number of hours to advance. These will need to be extracted and inserted in a CAM -namelist to indicate to CAM how long to run. - -This program also updates the ``date`` and ``datesec`` variables in the CAM netcdf file. Generally these are identical -times since the assimilation doesn't change the time of the data, but in case the original file had a different time -that was overwritten in the state vector, it will update the time for consistency. - -Conditions required for successful execution of ``dart_to_cam``: - -- a valid ``input.nml`` namelist file for DART -- a CAM 'phis' netCDF file [default: ``cam_phis.nc``] -- a DART restart file [default: ``dart_ics``] (read) -- a CAM restart file [default: ``caminput.nc``] (read and written) - -Since this program is called repeatedly for every ensemble member, we have found it convenient to link the DART input -and CAM restart files to the default filenames ``dart_ics`` and ``caminput.nc``). The output files may be moved or -relinked as necessary. - -Namelist --------- - -This namelist is read from the file ``input.nml``. Namelists start with an ampersand '&' and terminate with a slash '/'. -Character strings that contain a '/' must be enclosed in quotes to prevent them from prematurely terminating the -namelist. - -:: - - &dart_to_cam_nml - dart_to_cam_input_file = 'dart_ics', - dart_to_cam_output_file = 'caminput.nc', - advance_time_present = .true., - / - -| - -.. container:: - - +-------------------------+--------------------+---------------------------------------------------------------------+ - | Item | Type | Description | - +=========================+====================+=====================================================================+ - | dart_to_cam_input_file | character(len=128) | The name of the DART restart file containing the CAM state. | - +-------------------------+--------------------+---------------------------------------------------------------------+ - | dart_to_cam_output_file | character(len=128) | The name of the CAM restart netcdf file. | - +-------------------------+--------------------+---------------------------------------------------------------------+ - | advance_time_present | logical | Set to .false. for DART initial condition and restart files. Use | - | | | the .true. setting for the files written by filter during a model | - | | | advance. | - +-------------------------+--------------------+---------------------------------------------------------------------+ - -| - -Modules used ------------- - -:: - - assim_model_mod.f90 - types_mod.f90 - threed_sphere/location_mod.f90 - model_mod.f90 - null_mpi_utilities_mod.f90 - obs_kind_mod.f90 - random_seq_mod.f90 - time_manager_mod.f90 - utilities_mod.f90 - -Files read ----------- - -- DART namelist file; ``input.nml`` -- DART initial conditions/restart file; e.g. ``dart_ics`` (read) -- CAM restart file; ``caminput.nc`` (read and written) -- CAM "phis" file specified in ``&model_nml::cam_phis`` (normally ``cam_phis.nc``) - -Files written -------------- - -- CAM restart file; ``caminput.nc`` (read and written) - -References ----------- - -none diff --git a/models/cam-old/deprecated/README b/models/cam-old/deprecated/README deleted file mode 100644 index 5edfe15693..0000000000 --- a/models/cam-old/deprecated/README +++ /dev/null @@ -1,70 +0,0 @@ -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download -# -# DART $Id$ - -The files in this directory are deprecated. - -Thu Apr 17 15:16:29 MDT 2014 -I am removing these files from the deprecated directory. -These are the last versions, should you need to get them again. - - 6886 6524 thoar assimilate.Fzagar.csh - 6886 6524 thoar assimilate.hopper.csh - 6886 6524 thoar assimilate.ned.csh - 6886 6524 thoar assimilate.zagar.csh - 6886 6524 thoar auto_diagPOP2hpss_LSF.csh - 6886 6541 thoar CESM_setup_startup.csh - 6886 6524 thoar CESM_setup_startup_hopper.csh - 6886 6524 thoar CESM_setup_zagar.csh - 6886 6524 thoar CESM_setup_zagar_pmo.csh - 6886 6524 thoar clm.cpl7.template - 6886 6524 thoar clm_FillValue_vars.csh - 6886 6524 thoar input.normal.nml - 6886 6524 thoar input.pert0.nml - 6886 6524 thoar job_mpi_blueice_fragment.csh - 6886 6256 thoar mkmf_trans_date_to_dart - 6886 6256 thoar mkmf_trans_pv_sv - 6886 6256 thoar mkmf_trans_pv_sv_pert0 - 6886 6256 thoar mkmf_trans_pv_sv_time0 - 6886 6256 thoar mkmf_trans_sv_pv - 6886 6256 thoar mkmf_trans_time - 6886 4941 thoar path_names_trans_date_to_dart - 6886 6256 thoar path_names_trans_pv_sv - 6886 6256 thoar path_names_trans_pv_sv_pert0 - 6886 6256 thoar path_names_trans_pv_sv_time0 - 6886 6256 thoar path_names_trans_sv_pv - 6886 6256 thoar path_names_trans_time - 6886 6524 thoar pmo.ned.csh - 6886 6524 thoar setup_cesm_case.hopper.csh - 6886 6524 thoar setup_cesm_case.ned.csh - 6886 6524 thoar setup_cesm_case.pmo.csh - 6886 6256 thoar trans_date_to_dart.f90 - 6886 6340 nancy trans_date_to_dart.html - 6886 6256 thoar trans_pv_sv.f90 - 6886 6256 thoar trans_pv_sv_pert0.f90 - 6886 6256 thoar trans_pv_sv_time0.f90 - 6886 6340 nancy trans_pv_sv_time0.html - 6886 6256 thoar trans_sv_pv.f90 - 6886 6256 thoar trans_time.f90 - -The mkmf_trans_* and path_names_trans_* files are for building -the original translation tools between DART initial condition/ -restart file format and CAM netCDF file format, and also translating -time between DART and CAM formats. All are completely superceeded -by the following 3 tools: - -dart_to_cam -cam_to_dart -advance_time - -If you need to continue to use the original tools, -copy the source files into DART/models/cam, copy the mkmf_* -and path_names_* files into ../work, and run ./quickbuild.csh -from the work directory. - -# -# $URL$ -# $Revision$ -# $Date$ diff --git a/models/cam-old/deprecated/advance_model.csh b/models/cam-old/deprecated/advance_model.csh deleted file mode 100755 index 394ebd3927..0000000000 --- a/models/cam-old/deprecated/advance_model.csh +++ /dev/null @@ -1,208 +0,0 @@ -#!/bin/csh -# -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download -# -# Script to advance one ensemble member one filter "time step" -# when the model advance is executed as a separate process. -# Called by the filter executable (for async=2 or 4) -# Calls run-cam.csh, the CAM execution script. -# Calls translation program to translate time and model state. -# Runs on one of the compute nodes allotted to the filter executable. -# -# Arguments are -# arg#1 the process number of caller -# arg#2 the number of state copies belonging to that process -# arg#3 the name of the filter_control_file for that process - -set process = $1 -set num_states = $2 -set control_file = $3 - -# Get unique name for temporary working directory for this process's stuff -set temp_dir = 'advance_temp'${process} - -# args to previous version of this script -set myname = $0 -set CENTRALDIR = `pwd` - -# Create a clean temporary directory and go there -\rm -rf $temp_dir -mkdir -p $temp_dir -cd $temp_dir - -# People have the craziest aliases. These prevent the obsessive-compulsive -# from causing themselves no end of angst. -if ( ! $?REMOVE ) then - set REMOVE = 'rm -rf' -endif -if ( ! $?COPY ) then - set COPY = 'cp -fp' -endif -if ( ! $?MOVE ) then - set MOVE = 'mv -f' -endif -if ( ! $?LINK ) then - set LINK = 'ln -fs' -endif - -echo "advance_model.csh args = $1 $2 $3" > cam_out_temp -echo "CENTRALDIR is ${CENTRALDIR}" >> cam_out_temp -echo "temp_dir is $temp_dir" >> cam_out_temp - -# Get information about this experiment from file "casemodel", -# created by the main controlling script (job.csh) - -# set $case = the case and -# $model = the directory name (in the CAM source tree) -# where CAM executable will be found. -# set locations of the CAM and CLM input files -set case = `head -1 ${CENTRALDIR}/casemodel | tail -1` -set model = `head -2 ${CENTRALDIR}/casemodel | tail -1` -set cam_init = `head -3 ${CENTRALDIR}/casemodel | tail -1` -set clm_init = `head -4 ${CENTRALDIR}/casemodel | tail -1` -set list = `head -5 ${CENTRALDIR}/casemodel | tail -1` -set ice_init = $list[1] - -# output diagnostic information to the same file as the CAM list-directed output -echo "case $case model $model" >> cam_out_temp -echo "cam init is $cam_init" >> cam_out_temp -echo "clm init is $clm_init" >> cam_out_temp -echo "ice init is $ice_init" >> cam_out_temp - -# Loop through each ensemble this task is responsible for advancing. -set ensemble_number_line = 1 -set input_file_line = 2 -set output_file_line = 3 -set state_copy = 1 -while($state_copy <= $num_states) - - # loop through the control file, extracting lines in groups of 3. - set ensemble_number = `head -$ensemble_number_line ../$control_file | tail -1` - set input_file = `head -$input_file_line ../$control_file | tail -1` - set output_file = `head -$output_file_line ../$control_file | tail -1` - - # the previous script used element instead of ensemble_number. make them - # the same for now. - set element = $ensemble_number - echo "starting ${myname} for ens member $element at "`date` >> cam_out_temp - - # get model state initial conditions for this ensemble member - ${LINK} ${CENTRALDIR}/$input_file dart_restart - - # get filter namelists for use by cam - ${COPY} ${CENTRALDIR}/input.nml input.nml - - # this just creates a file that helps you figure out which member is - # being advanced in this directory. FYI only, you don't need it. - echo $element >! element - cp element element$element - - echo "ls $temp_dir for element $element" >> cam_out_temp - ls -lRt >> cam_out_temp - - # Need a base CAM initial file into which to copy state vector from filter. - # c[al]minput_$element also carry along CAM/CLM fields which are not updated - # by the filter (not part of the filter model state). - # Look for c[al]minput.nc resulting from the previous advance of this ensemble - # member from within the same day/obs_seq.out time span (in CENTRALDIR) - # When starting an experiment which has no spun up set of members, you - # should have already copied the single CAM initial file (e.g. campinput_0.nc) - # from the CAM build directory into the CENTRALDIR and made N copies of it. - - if (-e ${CENTRALDIR}/caminput_${element}.nc) then - ${COPY} ${CENTRALDIR}/caminput_${element}.nc caminput.nc - echo "caminput comes from ${CENTRALDIR}/caminput_${element}.nc" >> cam_out_temp - else - echo ERROR - need $CENTRALDIR/caminput_${element}.nc to exist - echo ERROR - need $CENTRALDIR/caminput_${element}.nc to exist >> cam_out_temp - exit -${element} - endif - - if ( -e ${CENTRALDIR}/clminput_${element}.nc) then - ${COPY} ${CENTRALDIR}/clminput_${element}.nc clminput.nc - else - echo ERROR - need $CENTRALDIR/clminput_${element}.nc to exist - echo ERROR - need $CENTRALDIR/clminput_${element}.nc to exist >> cam_out_temp - exit -${element} - endif - - if ( -e ${CENTRALDIR}/iceinput_${element}.nc) then - ${COPY} ${CENTRALDIR}/iceinput_${element}.nc iceinput.nc - else - echo ERROR - need $CENTRALDIR/iceinput_${element}.nc to exist - echo ERROR - need $CENTRALDIR/iceinput_${element}.nc to exist >> cam_out_temp - exit -${element} - # or if no ice restart file available; start it with ice_in = 'default' - # via existence of iceinput in run-cam.csh - endif - - # topography infomation - ${LINK} ${CENTRALDIR}/cam_phis.nc . - - # translate DART state vector into a CAM caminput.nc file, and create an - # ascii 'times' file, which will be used to set the namelist for cam to tell - # it how far to advance the model. - if (-e dart_restart && -e ${CENTRALDIR}/dart_to_cam) then - echo ' ' >> cam_out_temp - echo 'advance_model: executing dart_to_cam '`date` >> cam_out_temp - ${CENTRALDIR}/dart_to_cam >> cam_out_temp - ls -lt >> cam_out_temp - ${COPY} times ${CENTRALDIR} - else - echo "ERROR: either dart_restart file for $element or dart_to_cam not available" >> cam_out_temp - exit -${element} - endif - - # advance cam - echo executing: ${CENTRALDIR}/run-cam.csh ${case}-$element $model ${CENTRALDIR} >> cam_out_temp - ${CENTRALDIR}/run-cam.csh ${case}-$element $model ${CENTRALDIR} >>& cam_out_temp - - grep 'END OF MODEL RUN' cam_out_temp > /dev/null - if ($status == 0) then - # Extract the new state vector information from the new caminput.nc and - # put it in '$output_file' (time followed by state) - echo ' ' >> cam_out_temp - echo 'Executing cam_to_dart' >> cam_out_temp - ${CENTRALDIR}/cam_to_dart >> cam_out_temp - - # Move updated state vector and new CAM/CLM initial files back to experiment - # directory for use by filter and the next advance. - - ${MOVE} dart_ics ${CENTRALDIR}/$output_file - ${MOVE} namelist ${CENTRALDIR} - ${MOVE} caminput.nc ${CENTRALDIR}/caminput_${element}.nc - ${MOVE} clminput.nc ${CENTRALDIR}/clminput_${element}.nc - ${MOVE} iceinput.nc ${CENTRALDIR}/iceinput_${element}.nc - - echo "finished ${myname} for ens member $element at "`date` >> cam_out_temp - ${COPY} cam_out_temp ${CENTRALDIR}/H${hour}/cam_out_temp$element - ${MOVE} cam_out_temp ${CENTRALDIR}/cam_out_temp$element - else - echo "WARNING - CAM $element stopped abnormally" - echo "WARNING - CAM $element stopped abnormally" >> cam_out_temp - echo "=========================================" >> cam_out_temp - exit -${element} - endif -end - - # if this process needs to advance more than one model, read the next set of - # filenames and ensemble number at the top of this loop. - - @ state_copy++ - @ ensemble_number_line = $ensemble_number_line + 3 - @ input_file_line = $input_file_line + 3 - @ output_file_line = $output_file_line + 3 -end - -cd ${CENTRALDIR} - -${REMOVE} $temp_dir/* - -# Remove the filter_control file to signal completion -\rm -rf $control_file - -exit 0 - - diff --git a/models/cam-old/deprecated/advance_model.retry.csh b/models/cam-old/deprecated/advance_model.retry.csh deleted file mode 100755 index e9ada23db3..0000000000 --- a/models/cam-old/deprecated/advance_model.retry.csh +++ /dev/null @@ -1,279 +0,0 @@ -#!/bin/csh -# -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download -# -#---------------------------------------------------------------------- -# advance_model.csh -# -# Script to advance one ensemble member one filter "time step" -# when the model advance is executed as a separate process. -# Called by the filter executable (for async=2 or 4) -# Calls run-cam.csh, the CAM execution script. -# Calls 3 translation routines to translate time and model state. -# Runs on one of the compute nodes allotted to the filter executable. -# -# Arguments are -# arg#1 the process number of caller -# arg#2 the number of state copies belonging to that process -# arg#3 the name of the filter_control_file for that process - -#---------------------------------------------------------------------- - -set process = $1 -set num_states = $2 -set control_file = $3 - -set retry_max = 2 - -# Get unique name for temporary working directory for this process's stuff -set temp_dir = 'advance_temp'${process} - -# args to previous version of this script -set myname = $0 -set CENTRALDIR = `pwd` - -# Create a clean temporary directory and go there -\rm -rf $temp_dir -mkdir -p $temp_dir -cd $temp_dir - -# People have the craziest aliases. These prevent the obsessive-compulsive -# from causing themselves no end of angst. -if ( ! $?REMOVE ) then - set REMOVE = 'rm -rf' -endif -if ( ! $?COPY ) then - set COPY = 'cp -fp' -endif -if ( ! $?MOVE ) then - set MOVE = 'mv -f' -endif -if ( ! $?LINK ) then - set LINK = 'ln -fs' -endif - -echo "advance_model.csh args = $1 $2 $3" > cam_out_temp -echo "CENTRALDIR is ${CENTRALDIR}" >> cam_out_temp -echo "temp_dir is $temp_dir" >> cam_out_temp - -# Get information about this experiment from file "casemodel", -# created by the main controlling script (job.csh) - -# set $case = the case and -# $model = the directory name (in the CAM source tree) -# where CAM executable will be found. -# set locations of the CAM and CLM input files -set case = `head -1 ${CENTRALDIR}/casemodel | tail -1` -set model = `head -2 ${CENTRALDIR}/casemodel | tail -1` -set cam_init = `head -3 ${CENTRALDIR}/casemodel | tail -1` -set clm_init = `head -4 ${CENTRALDIR}/casemodel | tail -1` -set list = `head -5 ${CENTRALDIR}/casemodel | tail -1` -set ice_init = $list[1] - -# output diagnostic information to the same file as the CAM list-directed output -echo "case $case model $model" >> cam_out_temp -echo "cam init is $cam_init" >> cam_out_temp -echo "clm init is $clm_init" >> cam_out_temp -echo "ice init is $ice_init" >> cam_out_temp - -# Loop through each ensemble this task is responsible for advancing. -set ensemble_number_line = 1 -set input_file_line = 2 -set output_file_line = 3 -set state_copy = 1 -while($state_copy <= $num_states) - - # loop through the control file, extracting lines in groups of 3. - set ensemble_number = `head -$ensemble_number_line ../$control_file | tail -1` - set input_file = `head -$input_file_line ../$control_file | tail -1` - set output_file = `head -$output_file_line ../$control_file | tail -1` - - # the previous script used element instead of ensemble_number. make them - # the same for now. - set element = $ensemble_number - touch cam_out_temp - echo "starting ${myname} for ens member $element at "`date` >> cam_out_temp - - # get model state initial conditions for this ensemble member - ${LINK} ${CENTRALDIR}/$input_file dart_restart - - # get filter namelists for use by cam - ${COPY} ${CENTRALDIR}/input.nml input.nml - - # this just creates a file that helps you figure out which member is - # being advanced in this directory. FYI only, you don't need it. - echo $element >! element - cp element element$element - - echo "ls $temp_dir for element $element" >> cam_out_temp - ls -lRt >> cam_out_temp - - # Need a base CAM initial file into which to copy state vector from filter. - # c[al]minput_$element also carry along CAM/CLM fields which are not updated - # by the filter (not part of the filter model state). - # First look for c[al]minput.nc resulting from the previous advance of this ensemble - # member from within the same day/obs_seq.out time span (in CENTRALDIR) - # Failing that, look for the results of the last advance of this ensemble member - # of the previous obs_seq.out (i.e. in CENTRALDIR/exp_name/day/CAM) - # Failing that (when starting an experiment which has no spun up set of members) - # get a copy of a single CAM initial file (usually from somewhere independent - # of this experiment, i.e. /scratch/.../New_state/T42_GWD/CAM/caminput_0.nc) - - if (-e ${CENTRALDIR}/caminput_${element}.nc) then - ${COPY} ${CENTRALDIR}/caminput_${element}.nc caminput.nc - echo "CENTRALDIR caminput comes from ${CENTRALDIR}/caminput_${element}.nc" >> cam_out_temp - else if (-e ${cam_init}${element}.nc) then - ${COPY} ${cam_init}${element}.nc caminput.nc - echo "cam_init caminput comes from ${cam_init}${element}.nc" >> cam_out_temp - else - ${COPY} ${cam_init}0.nc caminput.nc - echo "DEFAULT caminput comes from ${cam_init}0.nc" >> cam_out_temp - endif - - if ( -e ${CENTRALDIR}/clminput_${element}.nc) then - ${COPY} ${CENTRALDIR}/clminput_${element}.nc clminput.nc - else if (-e ${clm_init}${element}.nc) then - ${COPY} ${clm_init}${element}.nc clminput.nc - else - ${COPY} ${clm_init}0.nc clminput.nc - endif - -# Pond restart files have unchangable names like -# FV_2deg-noleap-O2-Dev20-5-10.cice.r.volpn.2003-06-01-21600 -# Get iceinput and the related restart files for meltpond and aero -# Early versions of CAM3.6 (<3.56.59) -# if ( -e ${CENTRALDIR}/iceinput_${element}.tar) then -# tar -x -f ${CENTRALDIR}/iceinput_${element}.tar -# else if (-e ${ice_init}${element}.tar) then -# tar -x -f ${ice_init}${element}.tar - if ( -e ${CENTRALDIR}/iceinput_${element}.nc) then - ${COPY} ${CENTRALDIR}/iceinput_${element}.nc iceinput.nc - else if (-e ${ice_init}${element}.nc) then - ${COPY} ${ice_init}${element}.nc iceinput.nc - else - # no ice restart file available; start it with ice_in = 'default' via existence of iceinput - # in run-cam.csh - endif - - ${LINK} ${CENTRALDIR}/cam_phis.nc . - - # Create 'times' file for CAM from DART times in assim_model_state_ic# - # This info is passed to CAM through the creation of its namelist(s) - # Also extract state variables from assim_model_state_ic# into the caminput.nc file - if (-e dart_restart && -e ${CENTRALDIR}/dart_to_cam) then - echo ' ' >> cam_out_temp - echo 'Executing dart_to_cam' >> cam_out_temp - ${CENTRALDIR}/dart_to_cam >> cam_out_temp - ls -ltR >> cam_out_temp - ${COPY} times ${CENTRALDIR} - else - echo "ERROR: either ic file $element or dart_to_cam not available for dart_to_cam" >> cam_out_temp - exit 1 - endif - - # advance cam - # echo executing: ${model:h}/run-cam.csh ${case}-$element $model ${CENTRALDIR} >> cam_out_temp - set retry = 0 - while ($retry < $retry_max) - echo executing: ${CENTRALDIR}/run-cam.csh ${case}-$element $model ${CENTRALDIR} \ - >> cam_out_temp - ${CENTRALDIR}/run-cam.csh ${case}-$element $model ${CENTRALDIR} >>& cam_out_temp -# kdr added 12/28/2010 - set retry_status = $status - if ($retry_status == 0) then - grep 'END OF MODEL RUN' cam_out_temp > /dev/null - set retry_status = $status - else - echo 'run-cam.csh DIED with status' >> cam_out_temp - exit $retry_status - endif -# grep 'END OF MODEL RUN' cam_out_temp > /dev/null -# if ($status == 0) then - if ($retry_status == 0) then -# kdr end - set retry = $retry_max - # Extract the new state vector information from the new caminput.nc and - # put it in temp_ud (time followed by state) - echo ' ' >> cam_out_temp - echo 'Executing cam_to_dart' >> cam_out_temp - ${CENTRALDIR}/cam_to_dart >> cam_out_temp - - # Save CLM and CAM files for storage of analyses in CAM initial file format (analyses2initial) - # get the forecast time, which is the time of this CLM initial file - set seconds = (`head -1 times`) - if ($seconds[2] == 0) then - set hour = 24 - else - @ hour = $seconds[2] / 3600 - endif - if ($hour < 10) set hour = 0$hour - # Directory for storing CAM initial files until they can be averaged during - # archiving for analyses - if (! -d ${CENTRALDIR}/H${hour}) mkdir ${CENTRALDIR}/H${hour} - # Move updated state vector and new CAM/CLM initial files back to experiment - # directory for use by filter and the next advance. - # Store them in H## directories for later generation of ensemble average CAM and CLM - # initial file after all members are done. - ${MOVE} temp_ud ${CENTRALDIR}/$output_file - ${MOVE} namelist ${CENTRALDIR} - ${MOVE} caminput.nc ${CENTRALDIR}/H${hour}/caminput_${element}.nc - ${MOVE} clminput.nc ${CENTRALDIR}/H${hour}/clminput_${element}.nc - ${MOVE} iceinput.nc ${CENTRALDIR}/H${hour}/iceinput_${element}.nc - # Earlier versions ${MOVE} iceinput.tar ${CENTRALDIR}/H${hour}/iceinput_${element}.tar - - # link the new initial files into the CENTRAL directory where filter will find them. - ${LINK} ${CENTRALDIR}/H${hour}/caminput_${element}.nc \ - ${CENTRALDIR}/caminput_${element}.nc - ${LINK} ${CENTRALDIR}/H${hour}/clminput_${element}.nc \ - ${CENTRALDIR}/clminput_${element}.nc - ${LINK} ${CENTRALDIR}/H${hour}/iceinput_${element}.nc \ - ${CENTRALDIR}/iceinput_${element}.nc - # Earlier versions - # ${LINK} ${CENTRALDIR}/H${hour}/iceinput_${element}.tar \ - # ${CENTRALDIR}/iceinput_${element}.tar - - echo "finished ${myname} for ens member $element at "`date` >> cam_out_temp - ${COPY} cam_out_temp ${CENTRALDIR}/H${hour}/cam_out_temp$element - ${MOVE} cam_out_temp ${CENTRALDIR}/cam_out_temp$element - else - @ retry++ - if ($retry < $retry_max) then -# Add section to make CAM write out something every time step during this retry. -# Could be added to casemodel, but be careful of how run-cam.csh uses the number of -# lines in casemodel. - echo "WARNING - CAM $element stopped abnormally; will be retried" - echo "WARNING - CAM $element stopped abnormally; will be retried" >> cam_out_temp - echo "===========================================================" >> cam_out_temp - else - set DEADDIR = ${temp_dir}_dead - echo "WARNING - CAM $element stopped abnormally; see $DEADDIR" - echo "WARNING - CAM $element stopped abnormally; see $DEADDIR" >> cam_out_temp - ${COPY} cam_out_temp ${CENTRALDIR}/cam_out_temp${element}_died - mkdir $DEADDIR - ${MOVE} * $DEADDIR - exit -${element} - endif - endif - end - - # if this process needs to advance more than one model, read the next set of - # filenames and ensemble number at the top of this loop. - - @ state_copy++ - @ ensemble_number_line = $ensemble_number_line + 3 - @ input_file_line = $input_file_line + 3 - @ output_file_line = $output_file_line + 3 -end - -cd ${CENTRALDIR} - -${REMOVE} $temp_dir/* - -# Remove the filter_control file to signal completion -\rm -rf $control_file - -exit 0 - - diff --git a/models/cam-old/deprecated/check_model.csh b/models/cam-old/deprecated/check_model.csh deleted file mode 100755 index 1fd8b3cb7c..0000000000 --- a/models/cam-old/deprecated/check_model.csh +++ /dev/null @@ -1,21 +0,0 @@ -#!/bin/csh -# -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download - -if ($#argv < 1) then - echo "usage; check_cam num_ens_members" - exit -endif - -set n = 1 -while ($n <= $1) - tail -40 cam_out_temp$n | grep 'END OF MODEL RUN' > /dev/null - if ($status != 0) echo cam_out_temp$n finished abnormally - @ n++ -end - -exit 0 - - diff --git a/models/cam-old/deprecated/do_id_set_def_stdin.f90 b/models/cam-old/deprecated/do_id_set_def_stdin.f90 deleted file mode 100644 index 8c4a2fa636..0000000000 --- a/models/cam-old/deprecated/do_id_set_def_stdin.f90 +++ /dev/null @@ -1,35 +0,0 @@ -! DART software - Copyright UCAR. This open source software is provided -! by UCAR, "as is", without charge, subject to all terms of use at -! http://www.image.ucar.edu/DAReS/DART/DART_download -! -! $Id$ - -program main - -implicit none - -! version controlled file description for error handling, do not edit -character(len=256), parameter :: source = & - "$URL$" -character(len=32 ), parameter :: revision = "$Revision$" -character(len=128), parameter :: revdate = "$Date$" - -integer :: i -integer, parameter :: interval = 105 - -write(*, *) 'set_def.out' -write(*, *) 1 -write(*, *) 13440 / interval - -do i = 1, 13440, interval - write(*, *) 100.0 - write(*, *) i -end do - -end program main - -! -! $URL$ -! $Id$ -! $Revision$ -! $Date$ diff --git a/models/cam-old/deprecated/id_obs_stdin b/models/cam-old/deprecated/id_obs_stdin deleted file mode 100644 index 2bcdf71e55..0000000000 --- a/models/cam-old/deprecated/id_obs_stdin +++ /dev/null @@ -1,259 +0,0 @@ - set_def.out - 1 - 128 - 100.0000 - 1 - 100.0000 - 106 - 100.0000 - 211 - 100.0000 - 316 - 100.0000 - 421 - 100.0000 - 526 - 100.0000 - 631 - 100.0000 - 736 - 100.0000 - 841 - 100.0000 - 946 - 100.0000 - 1051 - 100.0000 - 1156 - 100.0000 - 1261 - 100.0000 - 1366 - 100.0000 - 1471 - 100.0000 - 1576 - 100.0000 - 1681 - 100.0000 - 1786 - 100.0000 - 1891 - 100.0000 - 1996 - 100.0000 - 2101 - 100.0000 - 2206 - 100.0000 - 2311 - 100.0000 - 2416 - 100.0000 - 2521 - 100.0000 - 2626 - 100.0000 - 2731 - 100.0000 - 2836 - 100.0000 - 2941 - 100.0000 - 3046 - 100.0000 - 3151 - 100.0000 - 3256 - 100.0000 - 3361 - 100.0000 - 3466 - 100.0000 - 3571 - 100.0000 - 3676 - 100.0000 - 3781 - 100.0000 - 3886 - 100.0000 - 3991 - 100.0000 - 4096 - 100.0000 - 4201 - 100.0000 - 4306 - 100.0000 - 4411 - 100.0000 - 4516 - 100.0000 - 4621 - 100.0000 - 4726 - 100.0000 - 4831 - 100.0000 - 4936 - 100.0000 - 5041 - 100.0000 - 5146 - 100.0000 - 5251 - 100.0000 - 5356 - 100.0000 - 5461 - 100.0000 - 5566 - 100.0000 - 5671 - 100.0000 - 5776 - 100.0000 - 5881 - 100.0000 - 5986 - 100.0000 - 6091 - 100.0000 - 6196 - 100.0000 - 6301 - 100.0000 - 6406 - 100.0000 - 6511 - 100.0000 - 6616 - 100.0000 - 6721 - 100.0000 - 6826 - 100.0000 - 6931 - 100.0000 - 7036 - 100.0000 - 7141 - 100.0000 - 7246 - 100.0000 - 7351 - 100.0000 - 7456 - 100.0000 - 7561 - 100.0000 - 7666 - 100.0000 - 7771 - 100.0000 - 7876 - 100.0000 - 7981 - 100.0000 - 8086 - 100.0000 - 8191 - 100.0000 - 8296 - 100.0000 - 8401 - 100.0000 - 8506 - 100.0000 - 8611 - 100.0000 - 8716 - 100.0000 - 8821 - 100.0000 - 8926 - 100.0000 - 9031 - 100.0000 - 9136 - 100.0000 - 9241 - 100.0000 - 9346 - 100.0000 - 9451 - 100.0000 - 9556 - 100.0000 - 9661 - 100.0000 - 9766 - 100.0000 - 9871 - 100.0000 - 9976 - 100.0000 - 10081 - 100.0000 - 10186 - 100.0000 - 10291 - 100.0000 - 10396 - 100.0000 - 10501 - 100.0000 - 10606 - 100.0000 - 10711 - 100.0000 - 10816 - 100.0000 - 10921 - 100.0000 - 11026 - 100.0000 - 11131 - 100.0000 - 11236 - 100.0000 - 11341 - 100.0000 - 11446 - 100.0000 - 11551 - 100.0000 - 11656 - 100.0000 - 11761 - 100.0000 - 11866 - 100.0000 - 11971 - 100.0000 - 12076 - 100.0000 - 12181 - 100.0000 - 12286 - 100.0000 - 12391 - 100.0000 - 12496 - 100.0000 - 12601 - 100.0000 - 12706 - 100.0000 - 12811 - 100.0000 - 12916 - 100.0000 - 13021 - 100.0000 - 13126 - 100.0000 - 13231 - 100.0000 - 13336 diff --git a/models/cam-old/deprecated/input.nml.diag.template b/models/cam-old/deprecated/input.nml.diag.template deleted file mode 100644 index a024a90e97..0000000000 --- a/models/cam-old/deprecated/input.nml.diag.template +++ /dev/null @@ -1,115 +0,0 @@ -# The times in the namelist for the obs_diag program are vectors -# that follow the following sequence: -# year month day hour minute second -# max_num_bins can be used to specify a fixed number of bins, -# in which case last_bin_center should be safely in the future. -# -# Acceptable latitudes range from [-90, 90] -# Acceptable longitudes range from [ 0, 360] - -&obs_diag_nml - obs_sequence_name = 'obs_NNNN/obs_seq.final', - first_bin_center = 20YY,MM, 1, 6, 0, 0 , - last_bin_center = 20YY,MM,DD, 0, 0, 0 , - bin_separation = 0, 0, 0, 6, 0, 0 , - bin_width = 0, 0, 0, 6, 0, 0 , - time_to_skip = 0, 0, 0, 0, 0, 0 , - max_num_bins = 1000, - trusted_obs = 'null', - Nregions = 4, - lonlim1 = 0.0, 0.0, 0.0, 235.0, - lonlim2 = 360.0, 360.0, 360.0, 295.0, - latlim1 = 20.0, -80.0, -20.0, 25.0, - latlim2 = 80.0, -20.0, 20.0, 55.0, - reg_names = 'Northern Hemisphere', 'Southern Hemisphere', 'Tropics', 'North America', - print_mismatched_locs = .false., - create_rank_histogram = .true., - outliers_in_histogram = .true., - use_zero_error_obs = .false., - verbose = .false. - / - -&ensemble_manager_nml - single_restart_file_in = .false., - single_restart_file_out = .false., - perturbation_amplitude = 0.2 / - -&assim_tools_nml - filter_kind = 1, - cutoff = 0.2, - sort_obs_inc = .false., - spread_restoration = .false., - sampling_error_correction = .false., - print_every_nth_obs = 10000, - adaptive_localization_threshold = -1/ - -&cov_cutoff_nml - select_localization = 1 / - -®_factor_nml - select_regression = 1, - input_reg_file = "time_mean_reg" - save_reg_diagnostics = .false., - reg_diagnostics_file = 'reg_diagnostics' / - -&obs_sequence_nml - write_binary_obs_sequence = .true. / - -&obs_kind_nml - / - -&assim_model_nml - write_binary_restart_files = .true. / - -&model_nml - output_state_vector = .false., - model_version = '4.0.1', - model_config_file = 'caminput.nc', - state_num_0d = 0, - state_num_1d = 0, - state_num_2d = 1, - state_num_3d = 6, - state_names_2d = 'PS ' - state_names_3d = 'T ','US ','VS ','Q ','CLDLIQ ','CLDICE ' - which_vert_1d = 0, - which_vert_2d = -1, - which_vert_3d = 6*1, - pert_names = ' ', - pert_sd = -888888.0d0, - pert_base_vals = -888888.0d0, - highest_obs_pressure_mb = 100.0, - highest_state_pressure_mb = 150.0, - max_obs_lat_degree = 89.0, - Time_step_seconds = 21600, - Time_step_days = 0 / - -! pert_sd < 0. signals pert_base_vals to be used as the values -! for each ensemble member (for 1 field), instead of the value for each field. -! DART special value -888888.0d0 can be used for that. -! pert_sd > 0 allows each point of the pert_names fields of each ens member -! to be randomly perturbed with a standard deviation of pert_sd. -! Used by filter's call to pert_model_state. -! pert_base_vals /= -888888.0d0 means that the values of the fields in pert_names -! sill be reset to the values in pert_base_vals. - - -&location_nml - horiz_dist_only = .false., - vert_normalization_pressure = 100000.0, - vert_normalization_height = 10000.0, - vert_normalization_level = 20.0, - approximate_distance = .true., - nlon = 141, - nlat = 72, - output_box_info = .false. / - -&utilities_nml - TERMLEVEL = 1, - logfilename = 'dart_log.out' / - -&mpi_utilities_nml - / - - -&obs_def_gps_nml - / diff --git a/models/cam-old/deprecated/job.simple.csh b/models/cam-old/deprecated/job.simple.csh deleted file mode 100755 index f1e869cdd7..0000000000 --- a/models/cam-old/deprecated/job.simple.csh +++ /dev/null @@ -1,367 +0,0 @@ -#!/bin/csh -# -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download -# -# Top level script to run a single assimilation experiment. -# -# Unlike the more complex job.csh, this script only processes a single -# observation file. Still fairly complex; requires a raft of -# data files and most of them are in hardcoded locations. -# -# You need to know which of several batch systems you are using. The most -# common one is LSF. PBS is also common. (POE is another but is -# not supported directly by this script. It is not recommended that you have a -# parallel cluster without a batch system (it schedules which nodes are assigned -# to which processes) but it is possible to run that way -- you have to do -# more work to get the information about which nodes are involved to the -# parallel tasks -- but anyway, there is a section below that uses ssh and no -# batch. -# -# How to submit this job: -# 1. Look at the #BSUB or #PBS sections below and adjust any of the parameters -# on your cluster. Queue names are very system specific; some systems -# require wall-clock limits; some require an explicit charge code. -# 2. Submit this script to the queue: -# LSF: bsub < job.simple.csh -# PBS: qsub job.simple.csh -# NONE: job.simple.csh -# -# The script moves the necessary files to the current directory and then -# starts 'filter' as a parallel job on all nodes; each of these tasks will -# call some a separate model_advance.csh when necessary. -# -# The central directory is where the scripts reside and where script and -# program I/O are expected to happen. -# -# -#============================================================================= -# This block of directives constitutes the preamble for the LSF queuing system -# LSF is used on the IBM Linux cluster 'lightning' -# LSF is used on the IMAGe Linux cluster 'coral' -# LSF is used on the IBM 'bluevista' -# The queues on lightning and bluevista are supposed to be similar. -# -# the normal way to submit to the queue is: bsub < job.simple.csh -# -# an explanation of the most common directives follows: -# -J Job name -# -o STDOUT filename -# -e STDERR filename -# -P account -# -q queue cheapest == [standby, economy, (regular,debug), premium] == $$$$ -# -n number of processors (really) -# -W hr:mn max wallclock time (required on some systems) -##============================================================================= -#BSUB -J DARTCAM -#BSUB -o DARTCAM.%J.log -#BSUB -q regular -#BSUB -n 1 -# -# -##============================================================================= -## This block of directives constitutes the preamble for the PBS queuing system -## PBS is used on the CGD Linux cluster 'bangkok' -## PBS is used on the CGD Linux cluster 'calgary' -## -## the normal way to submit to the queue is: qsub job.simple.csh -## -## an explanation of the most common directives follows: -## -N Job name -## -r n Declare job non-rerunable -## -e filename for standard error -## -o filename for standard out -## -q Queue name (small, medium, long, verylong) -## -l nodes=xx:ppn=2 requests BOTH processors on the node. On both bangkok -## and calgary, there is no way to 'share' the processors -## on the node with another job, so you might as well use -## them both. (ppn == Processors Per Node) -##============================================================================= -#PBS -N DARTCAM -#PBS -r n -#PBS -e DARTCAM.err -#PBS -o DARTCAM.log -#PBS -q medium -#PBS -l nodes=2:ppn=2 - -# A common strategy for the beginning is to check for the existence of -# some variables that get set by the different queuing mechanisms. -# This way, we know which queuing mechanism we are working with, -# and can set 'queue-independent' variables for use for the remainder -# of the script. - -if ($?LS_SUBCWD) then - - # LSF has a list of processors already in a variable (LSB_HOSTS) - - set CENTRALDIR = $LS_SUBCWD - set JOBNAME = $LSB_JOBNAME - alias submit 'mpirun.lsf \!*' - -else if ($?PBS_O_WORKDIR) then - - # PBS has a list of processors in a file whose name is (PBS_NODEFILE) - - set CENTRALDIR = $PBS_O_WORKDIR - set JOBNAME = $PBS_JOBNAME - alias submit 'mpirun \!*' - -else if ($?OCOTILLO_NODEFILE) then - - # no queueing system. Many possible options here. If you need a list - # of processors in a file, this makes one. For example ... - # setenv NODEFILE my_favorite_processors.list - # echo "node1" > $NODEFILE - # echo "node5" >> $NODEFILE - # echo "node7" >> $NODEFILE - # echo "node3" >> $NODEFILE - - set CENTRALDIR = `pwd` - set JOBNAME = DARTCAM - # i think this is what we want, but csh will not let you do multiline - # executions; this argues for using ksh (line 2 below)... (and maybe - # it needs a cd as well?) - #alias submit 'foreach i ($OCOTILLO_NODEFILE) ; ssh $i csh \!* ; end' - #alias submit='for i in $OCOTILLO_NODEFILE ; do ssh $i (cd $CENTRALDIR; csh $*) ; done' - alias submit 'csh \!*' - -else - - # interactive - - set CENTRALDIR = `pwd` - set JOBNAME = DARTCAM - alias submit 'csh \!*' - -endif - -set myname = $0 # this is the name of this script -set nonomatch # suppress "rm" warnings if wildcard does not match anything - -# Set the experiment name. - -set experiment = CAM1X - -cd ${CENTRALDIR} - -# The FORCE options are not optional. -# The VERBOSE options are useful for debugging though -# some systems don't like the -v option to any of the following -set OSTYPE = `uname -s` -switch ( ${OSTYPE} ) - case IRIX64: - setenv REMOVE 'rm -rf' - setenv COPY 'cp -p' - setenv MOVE 'mv -f' - breaksw - case AIX: - setenv REMOVE 'rm -rf' - setenv COPY 'cp -p' - setenv MOVE 'mv -f' - breaksw - default: - setenv REMOVE 'rm -rvf' - setenv COPY 'cp -vp' - setenv MOVE 'mv -fv' - breaksw -endsw - -echo " " -echo "Running $JOBNAME on host "`hostname` -echo "Initialized at "`date` -echo "CENTRALDIR is "`pwd` - -#----------------------------------------------------------------------------- -# Set variables containing various directory names where we will GET things -#----------------------------------------------------------------------------- - -# where the DART executables are: -set DARTDIR = /home/${user}/DART - -# where the CAM executables are, and what the case build name was: -set CAMsrc = /home/${user}/Cam3/cam3.1/models/atm/cam/bld -set CAMcase = T21-O2 - -# where the CAM data files are: -set CAMdata = /home/${user}/CAMDATA - -# where a precomputed set of initial condition files are -set DARTics = /home/${user}/CAM_init/T21x80/03-01-01/DART_lunes -set CAMics = /home/${user}/CAM_init/T21x80/03-01-01/CAM/caminput_ -set CLMics = /home/${user}/CAM_init/T21x80/03-01-01/CLM/clminput_ - -#----------------------------------------------------------------------------- -# Get the DARTCAM executables and scripts -#----------------------------------------------------------------------------- - -set DARTCAMDIR = ${DARTDIR}/models/cam -${COPY} ${DARTCAMDIR}/work/filter . -${COPY} ${DARTCAMDIR}/work/dart_to_cam . -${COPY} ${DARTCAMDIR}/work/cam_to_dart . -${COPY} ${DARTCAMDIR}/shell_scripts/advance_model.csh . -${COPY} ${DARTCAMDIR}/shell_scripts/run-pc.csh . - -${COPY} ${CAMdata}/input.nml . -${COPY} ${CAMdata}/obs_seq.out . - -#----------------------------------------------------------------------------- -# Determine the number of ensemble members from input.nml, -# It may exist in more than one place - we will use the first instance. -# Parse out the filter_nml string and use the next hunk of lines. -# ditto for the advance command -#----------------------------------------------------------------------------- - -set ENSEMBLESTRING = `grep -A 42 filter_nml input.nml | grep ens_size` -set ADVANCESTRING = `grep -A 42 filter_nml input.nml | grep adv_ens_command` -set ensemble_size = `echo $ENSEMBLESTRING[3] | sed -e "s#,##"` -set ADV_CMD = `echo $ADVANCESTRING[3] | sed -e 's#,##' -e 's#"##g'` - -set num_ens = $ensemble_size - - -echo "There are ${num_ens} ensemble members." - -#----------------------------------------------------------------------------- -# Get the necessary data files -- this is the hard part. -# This script does not involve 'cold starting' CAM, nor spinning up DART. -# The DARTics directory has one initial conditions file for -# each ensemble member. We need one for each ... -# The input.nml has a restart_in_file_name of 'filter_ic_old' -# which must match the filename here. -# Because that same namelist has 'single_restart_file_in' as .false., -# the restart_in_file_name gets an ensemble member number appended to it. -#----------------------------------------------------------------------------- - -# This copies the initial conditions for the correct number -# of ensemble members, and renames them with the ensemble number -# as a file extension. in all cases the extension is 4 digits, -# so use 'printf' to add leading 0s to the ensemble number. - - -set n = 1 -while($n <= ${num_ens}) - set from = `printf "%s.%04d" ${DARTics}/filter_ic $n` - set to = `printf "%s.%04d" filter_ic_old $n` - echo copying $from to $to - ${COPY} $from $to - @ n++ -end - -${COPY} ${CAMdata}/namelistin . -${COPY} ${CAMdata}/caminput.nc . -${COPY} ${CAMdata}/clminput.nc . - -#----------------------------------------------------------------------------- -# T21 -# The CAMsrc directory is MORE than just the location of the executable. -# There are more support widgets expected in the directory tree. -#----------------------------------------------------------------------------- - -set CAMexe = ${CAMsrc}/${CAMcase} - - -#----------------------------------------------------------------------------- -# Ensure the (output) experiment directory exists -# All the CAM-related files will get put in ${experiment}/CAM -# All the CLM-related files will get put in ${experiment}/CLM -# All the DART-related files will get put in ${experiment}/DART -#----------------------------------------------------------------------------- - -if (-d ${experiment}) then - echo "${experiment} already exists" -else - echo "Making run-time directory ${experiment} ..." - mkdir -p ${experiment} -endif -mkdir -p ${experiment}/{CLM,CAM,DART} - -#----------------------------------------------------------------------------- -# This is where I should check to make sure all the required files exist. -#----------------------------------------------------------------------------- - -if (! -e namelistin ) then - echo "ERROR ... need a namelistin file." - exit 99 -endif - -#----------------------------------------------------------------------------- -# get name of file containing PHIS from the CAM namelist. This will be used by -# static_init_model to read in the PHIS field, which is used for height obs. -#----------------------------------------------------------------------------- - grep bnd_topo namelistin >! topo_file - set STRING = "1,$ s#'##g" - set ensstring = `sed -e "$STRING" topo_file` - set topo_name = $ensstring[3] - cp $topo_name cam_phis.nc - -#----------------------------------------------------------------------------- -# Some information about CAM must be made available to advance_model.csh -# 'casemodel' is required (by advance_model.csh) to be in the Central directory -#----------------------------------------------------------------------------- - -echo "${experiment} ${CAMexe} ${CAMics} ${CLMics}" >! casemodel - -#----------------------------------------------------------------------------- -# Runs filter which integrates the results of model advances (async=2). -# -# A 20 member ensemble @ T21 can take anywhere between 10-30 minutes. -#----------------------------------------------------------------------------- - -submit filter - -#----------------------------------------------------------------------------- -#----------------------------------------------------------------------------- - -echo "Finished at "`date` - -#----------------------------------------------------------------------------- -# Move the output to storage after filter completes. -# At this point, all the restart,diagnostic files are in the CENTRALDIR -# and need to be moved to the 'experiment permanent' directory. -# We have had problems with some, but not all, files being moved -# correctly, so we are adding bulletproofing to check to ensure the filesystem -# has completed writing the files, etc. Sometimes we get here before -# all the files have finished being written. -#----------------------------------------------------------------------------- - -echo "Listing contents of CENTRALDIR before archiving" -ls -l - - -${MOVE} cam_out_temp[1-9]* ${experiment}/CAM -${MOVE} caminput_[1-9]*.nc ${experiment}/CAM - -${MOVE} clminput_[1-9]*.nc ${experiment}/CLM - -${MOVE} filter_ic_old* ${experiment}/DART -${MOVE} filter_ic_new* ${experiment}/DART -${MOVE} assim_model_state_ud[1-9]* ${experiment}/DART -${MOVE} assim_model_state_ic[1-9]* ${experiment}/DART -#${MOVE} inflate_ic_new ${experiment}/DART - -${MOVE} analysis.nc ${experiment}/DART -${MOVE} preassim.nc ${experiment}/DART -${MOVE} obs_seq.final ${experiment}/DART -${MOVE} dart_log.out ${experiment}/DART - -${COPY} namelistin ${experiment} -${MOVE} namelist ${experiment} -${MOVE} casemodel ${experiment} - -# Good style dictates that you save the scripts so you can see what worked. - -${COPY} input.nml ${experiment} -${COPY} *.csh ${experiment} -${COPY} $myname ${experiment} - -# CAM leaves a bunch of remnants in your $HOME directory. -# I have not figured out how to use them ... so I clean up. - -${REMOVE} ~/lnd.*.rpointer topog_file.nc - -ls -lrt - -exit 0 - - diff --git a/models/cam-old/deprecated/monthlydiags.csh b/models/cam-old/deprecated/monthlydiags.csh deleted file mode 100755 index 0f88aed83d..0000000000 --- a/models/cam-old/deprecated/monthlydiags.csh +++ /dev/null @@ -1,98 +0,0 @@ -#!/bin/csh -# -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download -# -# run obs_diag on the mainframe a month at a time. produce -# obs_diag_output.nc file(s) which need to be copied to -# another machine which has matlab to finish generating -# the plots that are normally looked at. - -# this program is expected to be started from the directory -# which has a collection of obs_NNNN dirs, each of which -# contains an obs_seq.final file. - -#----------------------------------------------------------------------------- -# run in share queue on bluefire. -#----------------------------------------------------------------------------- - -#BSUB -J obs_diags -#BSUB -o obs_diags.%J.log -#BSUB -P xxxxxxxx -#BSUB -q share -#BSUB -W 2:00 -#BSUB -n 1 - - -# general settings - check these to see they are appropriate - -# where to find the DART obs_diag program, and a template for -# the input.nml file. obs_diag should exist in this dir. -set DIAG = $DART/models/cam/work - - -# time period our standard experiments have covered. if you -# move to another time, you'll need to regenerate an appropriate -# table. you can set the start and stop month number below. -# remember month 1 is the spinup month, so generally our -# experiments run from month 2 to 13. - -set startmon = 2 -set endmon = 3 - - -# tables intended to ease the calendar computations. -# these are very date specific and MUST be changed for any other -# time spans that are run. day 1 = aug 1, 2006 -# should go one month past last day of simulation -set months = (aug06 sep06 oct06 nov06 dec06 jan07 feb07 mar07 \ - apr07 may07 jun07 jul07 aug07 sep07) - -# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 -set startd = ( 1 32 62 93 123 154 185 213 244 274 305 335 366 397) -set endd = (31 61 92 122 153 184 212 243 273 304 334 365 396 426) -set year = (06 06 06 06 06 07 07 07 07 07 07 07 07 07) -set calmon = ( 8 9 10 11 12 1 2 3 4 5 6 7 8 9) -set daysmon = (31 30 31 30 31 31 28 31 30 31 30 31 31 30) - -@ monnum = $startmon -while ( $monnum <= $endmon ) - set diagname = $months[$monnum] - - # assume there is an input.nml.diag.template here with the right - # patterns for sed to search and replace below. - # should error check for this file here. - - set sdy = `printf '%04d' $startd[$monnum]` - set smn = $calmon[$monnum] - set mdy = $daysmon[$monnum] - set syr = $year[$monnum] - - sed -e "s/NNNN/$sdy/" \ - -e "s/YY/$syr/" \ - -e "s/MM/$smn/" \ - -e "s/DD/$mdy/" \ - input.nml.diag.template >! input.nml - - echo updated input.nml here - - echo running obs_diag here - $DIAG/obs_diag >&! obs_diag.out - - set nextdir = ${startd[$monnum]}-${endd[$monnum]}s0_def_reg - - mkdir -p $nextdir - - touch $diagname - mv -f $diagname obs_diag_output.nc input.nml obs_diag.out \ - LargeInnov.txt $nextdir - - @ monnum++ -end - -echo all done - -exit 0 - - diff --git a/models/cam-old/deprecated/run-cam.csh b/models/cam-old/deprecated/run-cam.csh deleted file mode 100755 index 09b40335db..0000000000 --- a/models/cam-old/deprecated/run-cam.csh +++ /dev/null @@ -1,320 +0,0 @@ -#!/bin/csh -# -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download -# -# code added to specify an arbitrary forecast duration -# using [START,STOP]_[YMD,TOD] passed as a -namelist argument - -#----------------------------------------------------------------------- -## PC-linux -##------------ - -if ($#argv == 0) then - echo 'Usage: run-cam.csh CASE MODEL CENTRALDIR, ' - echo ' where ' - echo ' CASE and MODEL are set in job_mpi.csh or file casemodel' - echo ' and CENTRALDIR is passed from advance_model.csh' - exit -endif - -## Do our best to get sufficient stack memory -limit stacksize unlimited - -## Default namelist settings: -## $case is the case identifier for this run. It will be placed in the namelist. - -set case = $1 -echo case = $case - -## ROOT OF CAM DISTRIBUTION -# Directory which contains the CAM configuration to be used -# (resolution, optimization, etc); has files cam and config_cache.xml -# Should be full pathname, passed from advance_model.csh -# ie /gpfs/lightning/raeder/Cam3/cam3_0_7_brnchT_assim01/models/atm/cam/bld/T85-O1 -set camroot = $2 -echo camroot = $camroot - -# work directory where filter is run -set CENTRALDIR = $3 -echo CENTRALDIR = $CENTRALDIR - -# ROOT OF CAM DATA DISTRIBUTION - needs to be customized unless running at NCAR. -# Contains the initial and boundary data for the CAM distribution. -# (the root directory contains the subdirectories "atm" and "lnd") -# Up through 3.5.30 this does not need to be set if build-namelist gets an input namelist -# argument (namelistin), which has full pathnames of all the CAM input files. -# After 3.5.30 it seems that the variable needs to be set, regardless of namelistin. -setenv CSMDATA /fis/cgd/cseg/csm/inputdata - -## $wrkdir is a working directory where the model will be built and run. -# DART; it's the temp directory in which advance_model runs. -set wrkdir = `pwd` -## $cfgdir is the directory containing the CAM configuration scripts. -# and subdirectories with various configurations of this CAM version -set cfgdir = $camroot:h - -echo wrkdir = $wrkdir -echo cfgdir = $cfgdir - -# obtain cam executable and matching config_cache.xml -if ( ! -x cam ) then - if ( -x $camroot/cam ) then -# camroot is in the call, so don't need to copy every time -# cp $camroot/cam . - cp $camroot/config_cache.xml . - echo 'use cam and config_cache.xml from ' $camroot - else - echo 'cam is not found; must be pre-built and stored in ' $camroot - exit 3 - endif -else - echo 'cam exists in ' $wrkdir -endif - -set times = `cat $wrkdir/times` -echo run-cam times $times -# Can't use \ in the middle of a string and then add more to the string later. -# The \ doesn't appear in the string, but the "" become unbalanced. -set namelist_string = "&camexp START_YMD=$times[3] START_TOD=$times[4] " -set namelist_string = "$namelist_string STOP_YMD=$times[1] STOP_TOD=$times[2] NHTFRQ=$times[5] " - -#-------------------------------------------------------------- -## Create the namelist -# Extract the relevant line from input.nml -grep model_version input.nml >! ensstring.$$ - -# Replace the ,s with nothings and put the words into a list -set STRING = "1,$ s#,##g" -set ensstring = `sed -e "$STRING" ensstring.$$` -# Output the cam version to a file -echo $ensstring[3] >! ensstring.$$ - -# Replace the 's with nothings and put the words into a list -set STRING = "1,$ s#'##g" -set ensstring = `sed -e "$STRING" ensstring.$$` -# Output the cam version to a file -echo $ensstring >! ensstring.$$ - -echo "build-namelist for cam $ensstring ..." - -# replace the .s with spaces and put the resulting numbers into a string -set STRING = "1,$ s#\.# #g" -set ensstring = `sed -e "$STRING" ensstring.$$` - -set cam_version = multi-namelist -set verbosity = '' -set dir_arg = '-d' -set cice_nl = '' -if ($ensstring[1] == 3) then - if ($ensstring[2] < 5) then - set cam_version = single-namelist - set verbosity = 2 - echo 'version < 3.5' - else if ($ensstring[2] == 5 && $#ensstring == 3 ) then - if ($ensstring[3] < 30) then - set verbosity = 2 - echo 'version < 3.5.30' - endif - echo 'version 3.5.x' - endif -endif -if (($ensstring[1] == 3 && $ensstring[2] > 6) || $ensstring[1] >= 4 ) then - echo 'version > 3.6.0' - set dir_arg = '-dir' - # Get info from job_mpi.csh/casemodel about the cice_nl for build-namelist for CAM 3.6.26 - # /ptmp/raeder/Cam3.6/Dev20-1/obs_0029/ICE/iceinput_ 1949 2007 ......./sst_file.nc - # NO! advance_model puts the ice IC file in a generic name for this script to find - set list = `head -5 ${CENTRALDIR}/casemodel | tail -1` - if (-e iceinput.nc) then - set ice_ic = iceinput.nc - else - set ice_ic = default - endif -# Single line if-test isn't bomb proof in csh; the resultant is evaluated before the conditional. -# If resultant has a non-existent entity, an error results, even though it it won't try to use it. - - set str_yr_first = $list[2] - set str_yr_last = $list[3] - set sst = $list[4] - set cice_nl = "&ice stream_year_first=$str_yr_first stream_year_last=$str_yr_last" - set cice_nl = "$cice_nl model_year_align=$str_yr_first ice_ic='$ice_ic'" - set cice_nl = "$cice_nl stream_domfilename='$sst' stream_fldfilename='$sst' /" -# Fix the default value, which derails the gregorian calendar in ice_calendar.F90 - set cice_nl = "$cice_nl &setup_nml year_init=1 /" - echo $cice_nl - endif - if (($ensstring[1] == 3 && $ensstring[2] == 5 && $ensstring[3] >= 30) || \ - ($ensstring[1] == 3 && $ensstring[2] >= 6) || \ - ($ensstring[1] >= 4 )) then - if (! $?CSMDATA ) then - echo "run-cam.csh; CSMDATA must be defined in here for > Cam3.5.30" - exit 2 - endif - if (! -d $CSMDATA ) then - echo "run-cam.csh; CSMDATA = $CSMDATA must exist for > Cam3.5.30" - exit 2 - endif - endif -endif -if ($ensstring[1] <= 2) then - set cam_version = single-namelist - set verbosity = 2 -endif -rm ensstring.$$ - - -echo "multi namelist? $cam_version" - -#-------------------------------------------------------------- -ls -lt - -# Figure out the 2D CAM domain decomposition for this number of processors. -# lat/height and lon/lat decomp pairs npr_yz == (npr_y, npr_z, nprxy_x, nprxy_y) -# must satisfy nprocs = npr_y*npr_z = nprxy_x*nprxy_y, -# and helps to have npr_y = nprxy_y and npr_z = nprxy_x -# job_mpi.csh has calculated num_procs to make nprocs be correct, -# and the helpful condition is satisfied in the namelist below. -set length_casemodel = `wc -l ${CENTRALDIR}/casemodel` -if ($length_casemodel[1] == 8) then - set list = `head -8 ${CENTRALDIR}/casemodel | tail -1` - set num_procs = $list[1] - set lev_blocks = $list[2] - set lat_blocks = $list[3] - set lon_blocks = $lev_blocks - set namelist_string = \ - "$namelist_string npr_yz=$lat_blocks, $lev_blocks, $lon_blocks, $lat_blocks /" -else - set namelist_string = "$namelist_string /" -endif - - -if ($cam_version == 'single-namelist') then - $cfgdir/build-namelist -v 2 \ - -case ${camroot:t}-$case \ - -runtype initial \ - -o $wrkdir/namelist \ - -infile $CENTRALDIR/namelistin \ - -namelist "$namelist_string" \ - || echo "build-namelist failed" && exit 1 -else if ($cam_version == 'multi-namelist') then - # This builds all the *_in namelists CAM3.5 needs - - if ("$cice_nl" == '') then - $cfgdir/build-namelist -v $verbosity \ - -case ${camroot:t}-$case \ - -runtype startup \ - $dir_arg $wrkdir \ - -infile $CENTRALDIR/namelistin \ - -namelist "$namelist_string" \ - || echo "build-namelist failed" && exit 1 - else - $cfgdir/build-namelist -v $verbosity \ - -case ${camroot:t}-$case \ - -runtype startup \ - $dir_arg $wrkdir \ - -infile $CENTRALDIR/namelistin \ - -cice_nl "$cice_nl" \ - -namelist "$namelist_string" \ - || echo "build-namelist failed" && exit 1 - # For advance_model to copy back to CENTRALDIR for archiving; won't be used here. - cat *_in >! namelist - endif -endif - -echo "finished build-namelist ..." - -# Run CAM -# run_command is how *filter* is run, and may not be how CAM is run. -set parallel_cam = `head -6 ${CENTRALDIR}/casemodel | tail -1` -if ($parallel_cam[1] == 'true') then - # async=4; filter is parallel and CAM is too - set run_command = `head -7 ${CENTRALDIR}/casemodel | tail -1` -else - # async=2; filter is run parallel, but CAM is not - set run_command = ' ' -endif -echo "running CAM in $wrkdir" - -if ($cam_version == 'single-namelist') then - echo "with command; eval $run_command "'"$camroot/cam < namelist"'" -# $run_command $camroot/cam < namelist -# Bluefire - eval $run_command "$camroot/cam < namelist" -else if ($cam_version == 'multi-namelist') then - # CAM 3.5 explicitly opens the files? No redirect needed? - echo "with command $run_command $camroot/cam (no < namelist)" - eval $run_command $camroot/cam -endif -ls -lt - -# Iter; these are in the run directory; -echo ' ' -ls -l *\.[hir]* - -# in DART caminput, clminput and maybe iceinput and a history file -# need to be saved for each "element" of the ensemble -ls *cam2\.i\.* -if ($status == 0) then - mv *cam2\.i\.* caminput.nc -else - echo "No cam2.i. (caminput.nc) file available from CAM; ABORTING" - exit 4 -endif - -if ($cam_version == 'single-namelist') then - ls *clm2\.i\.* - if ($status == 0) then - mv *clm2\.i\.*\.nc clminput.nc - else - echo "No clm2.i. (caminput.nc) file available from CAM; ABORTING" - exit 5 - endif -else - ls *clm2\.r\.*\.nc - if ($status == 0) then - mv *clm2\.r\.*\.nc clminput.nc - else - echo "No clm2.r. (clminput.nc) file available from CAM; ABORTING" - exit 5 - endif - - ls *\.cice\.r\.*.nc - if ($status == 0) then - mv *\.cice\.r\.*.nc iceinput.nc - else - echo "No cice.r. (iceinput.nc) file available from CAM; ABORTING" - exit 6 - endif - - -# -# move only the youngest h0 file - set hist = `ls -t *\.h0\.*` - echo hist is $hist - if ($#hist > 0) then - mv $hist[1] hide_hist - endif -endif - -# mv *\.h0\.* ${CENTRALDIR}/cam_phis_good.nc -# ls *\.[hir]*\.[0-9]* -# Remove 'leftovers' ONLY if CAM completed correctly (old ice initials need to -# hang around for the retry) -grep 'END OF MODEL RUN' cam_out_temp > /dev/null -if ($status == 0) then - rm *\.[hir]*\.[0-9]* - echo "removed .[hir]." -endif -# restore file to meaningful name advance_model expects -if (-e hide_hist ) then - mv hide_hist $hist[1] -endif - -echo ' ' -ls -l ${wrkdir}/*input* ${wrkdir}/*\.h* - -exit 0 - - diff --git a/models/cam-old/deprecated/run-pc.csh b/models/cam-old/deprecated/run-pc.csh deleted file mode 100755 index 0240636a7a..0000000000 --- a/models/cam-old/deprecated/run-pc.csh +++ /dev/null @@ -1,136 +0,0 @@ -#!/bin/tcsh -f -# -# DART software - Copyright UCAR. This open source software is provided -# by UCAR, "as is", without charge, subject to all terms of use at -# http://www.image.ucar.edu/DAReS/DART/DART_download -# -# #td means code added to specify an arbitrary forecast duration -# using [START,STOP]_[YMD,TOD] passed as a -namelist argument - -#----------------------------------------------------------------------- -## PC-linux -##------------ - -if ($#argv == 0) then - echo 'Usage: run-pc.csh CASE MODEL PBS_O_WORKDIR, ' - echo ' where the namelist is in ~raeder/CamPathRel/Caminput/CASE/namelist' - echo ' CASE and MODEL are set in setup_advance_model.csh' - echo ' and PBS_O_WORKDIR is passed from advance_ens.csh' - exit -endif - -## nthreads is the number of Open-MP threads. On the PC we assume a -## pure shared-memory configuration which assigns 1 thread per processor. -set nthreads = 2 - -## Do our best to get sufficient stack memory -limit stacksize unlimited - -## ROOT OF CAM DISTRIBUTION - probably needs to be customized. -## Contains the source code for the CAM distribution. -## (the root directory contains the subdirectory "models") -# original; set camroot = /fs/cgd/data0/$LOGNAME/cam2.0.1 -# PWD should be tempdir#, which resides under cam_test, along with cam2.0.1 -set camroot = $2 -# set camroot = $model - -## Default namelist settings: -## $case is the case identifier for this run. It will be placed in the namelist. -## $runtype is the run type: initial, restart, or branch. -# initialize the datasets for iteration by starting with standard CAM data -# in namelist ~raeder/CamPathRel/Caminput/${case}/namelist0, -# (but use case = $case, no '0'; must move namelists around) - -set case = $1 -set runtype = initial -set relocate = ' ' -# set relocate = "-fflags '-Kpic -Bstatic'" - -# work directory (on anchorage, not nodes) where filter is run -set PBS_O_WORKDIR = $3 - -## ROOT OF CAM DATA DISTRIBUTION - needs to be customized unless running at NCAR. -## Contains the initial and boundary data for the CAM distribution. -## (the root directory contains the subdirectories "atm" and "lnd") -# Orig setenv CSMDATA /fs/cgd/csm/inputdata -# in DART caminput and clminput need to be saved for each "element" of -# the ensemble, but only when running them in parallel. -# For now keep caminput.nc and clminput.nc in 'cam_test'. -# not 'cam_test/tempdir#' - -setenv CSMDATA $PWD - -## $wrkdir is a working directory where the model will be built and run. -## $blddir is the directory where model will be compiled. -## $rundir is the directory where the model will be run. -## $cfgdir is the directory containing the CAM configuration scripts. -set wrkdir = $PWD -set blddir = $wrkdir/bld -set rundir = $wrkdir -set cfgdir = $camroot/models/atm/cam/bld - -## Ensure that run and build directories exist -# mkdir -p $rundir || echo "cannot create $rundir" && exit 1 -mkdir -p $blddir || echo "cannot create $blddir" && exit 1 - -## If an executable doesn't exist, build one. -if ( ! -x $blddir/cam ) then - if ( -x $PBS_O_WORKDIR/$cfgdir/cam ) then - cp $PBS_O_WORKDIR/$cfgdir/cam $blddir/cam - cp $PBS_O_WORKDIR/$cfgdir/config_cache.xml $blddir/config_cache.xml - else -# This will be somewhat inefficient when running on multiple nodes; -# recompiling for each element of the first batch -# But this will be fixed (most of the time) when iteration 0 run of cam -# is installed in advance_init.csh(?) - cd $blddir || echo "cd $blddir failed" && exit 1 - echo 'configuring CAM for case '$case -# config_cache_defaults is hard-wired into configure; -defaults doesn't change it -# so copy the defaults file I want into the name configure needs - $PBS_O_WORKDIR/$cfgdir/configure -nosmp $relocate \ - || echo "configure failed" && exit 1 - echo "building CAM in $blddir ..." - rm -f Depends - gmake -j2 >&! MAKE.out || echo "CAM build failed: see $blddir/MAKE.out" && exit 1 - echo "finished gmake of bld ..." -# store compiled cam for other "elements" in cam_async.csh - cp $blddir/cam $PBS_O_WORKDIR/$cfgdir/cam - cp $blddir/config_cache.xml $PBS_O_WORKDIR/$cfgdir/config_cache.xml - endif -endif - -#td added -namelist argument -## read in start and end dates and times -set times = `cat $PBS_O_WORKDIR/times` -echo $times - -## Create the namelist -cd $blddir || echo "cd $blddir failed" && exit 1 -echo "build-namelist ..." -$PBS_O_WORKDIR/$cfgdir/build-namelist -v 2 -case $case -runtype $runtype \ - -o $rundir/namelist -infile $PBS_O_WORKDIR/namelistin \ - -namelist "&camexp START_YMD=$times[3] START_TOD=$times[4] \ - STOP_YMD=$times[1] STOP_TOD=$times[2] NHTFRQ=$times[5] /" \ - || echo "build-namelist failed" && exit 1 -cp $rundir/namelist /scratch/cluster/raeder/SPMD/namelisttemp -#td added -namelist argument -# before move to CamPathRel; -infile ../../$cfgdir/$case.nl \ -# my namelist input -echo "finished build-namelist ..." - -## Run CAM -cd $rundir || echo "cd $rundir failed" && exit 1 -echo "running CAM in $rundir" -env OMP_NUM_TASKS=$nthreads MPSTKZ="128M" $blddir/cam < namelist || echo "CAM run failed" && exit 1 - -# Iter; these are in the run directory; -ls -l *.i.* -mv *cam2.i.* $wrkdir/caminput.nc -mv *clm2.i.* $wrkdir/clminput.nc -rm *.h* *.r* -echo ' ' -ls -l *input* - -exit 0 - - diff --git a/models/cam-old/doc/CESM_2013_Homme_DA.pdf b/models/cam-old/doc/CESM_2013_Homme_DA.pdf deleted file mode 100644 index 36b9d99c40909d82957c75b6f1814a2546d8d856..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3702690 zcma%i1yo$m(&ymr?t{C#ySuwHxO=eR!DVoFcXvo|3+@)&B{&3fho?Kp+zHVaqE2ViotPUva8;MxPrcA9GC*jM=d8${*4#m z7yY&L2CGdtzrS=RDI4HKM0OW8PibT1bvcV3VyFJ)J~w&_xot(Pj}|o|EILAEVl=iy z0T-pit9ZO8c%Og&o^{FaycbLgQH>pV`Cj7h6(l`oqf3(aCwiDbflOJRa|2mk;{02x zpPb{@(v93@5{;YXqt1>E4jSY>DH0t>FCMUim(-ItwSQWR4XuhP@y#ebOlDIs3xr3M 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