""" GridStat: Cloud Fractions Using GFS and MERRA2 Data =================================================== model_applications/clouds/GridStat_fcstGFS_obsMERRA2_lowAndTotalCloudFrac.conf """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # This use case captures various statistical measures of two model comparisons # for low and total cloud fraction with different neighborhood and probability # settings for internal model metrics and to aid in future model updates. ############################################################################## # Version Added # ------------- # # METplus version 5.1 ############################################################################## # Datasets # -------- # # **Forecast:** Global Forecast System (GFS) # # **Observations:** Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) # # **Grid:** GPP 17km masking region # # **Climatology:** None # # **Location:** All of the input data required for this use case can be # found in a sample data tarball. Each use case category will have # one or more sample data tarballs. It is only necessary to download # the tarball with the use case’s dataset and not the entire collection # of sample data. Click here to access the METplus releases page and download sample data # for the appropriate release: https://github.com/dtcenter/METplus/releases # This tarball should be unpacked into the directory that you will # set the value of INPUT_BASE. See :ref:`running-metplus` section for more information. ############################################################################## # METplus Components # ------------------ # # GridStat is the only MET tool called in this use case. # This use case utilizes Python Embedding, which is called using the PYTHON_NUMPY keyword # in the observation input template settings. The Python script is passed an input file, # model name, variable field name being analyzed, the initialization and valid times, # and a flag to indicate if the field passed is observation or forecast. # After a successful call, a MET-readable gridded observation dataset is passed back to GridStat for evaluation. ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (INIT_BEG):** 2021070312 # # **End time (INIT_END):** 2021070312 # # **Increment between beginning and end times (INIT_INCREMENT):** 12H # # **Sequence of forecast leads to process (LEAD_SEQ):** 36 # # Because instance names are used, GridStat will run 3 times for this 1 initalization time. Each of the # instance names correspond to different regridding, neighborhood evaluations, thresholding, output line types, and output # prefix names. For the first GridStat instance, total cloud cover fraction and low cloud cover fraction are verified # at 10 separate thresholds. The observation dataset is provided via Python Embedding. Various output line types are requested # and placed in stat files, with a unique output prefix indicating which output are from which GridStat instance. All of the # evaluation takes place within the masked region, read in via a poly line file. # For the nbr GridStat instance, the same variables are verified, but the thresholds are expanded to include forecast # and observation percentiles, ranging from 20 to 80. This instance also creates 4 neighborhoods of varying width using # a circle definition. The related neighborhood line types are requested as output and a new ouput prefix is used. # Finally, the prob GridStat instance updates the thresholds for the two variable fields to range from 0 to 1 and # changes the forecast field to probabilistic space via the FCST_IS_PROB setting. Neighborhoods using the same definitions # from the nbr instance are used, and the respective line types are requested with a new output prefix for output files # from this instance. ############################################################################## # METplus Configuration # --------------------- # # METplus first loads the default configuration file found in parm/metplus_config, # then it loads any configuration files passed to METplus via the command line, # i.e. parm/use_cases/model_applications/clouds/GridStat_fcstGFS_obsMERRA2_lowAndTotalCloudFrac.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/clouds/GridStat_fcstGFS_obsMERRA2_lowAndTotalCloudFrac.conf ############################################################################## # MET Configuration # ----------------- # # METplus sets environment variables based on user settings in the METplus configuration file. # See :ref:`How METplus controls MET config file settings` for more details. # # **YOU SHOULD NOT SET ANY OF THESE ENVIRONMENT VARIABLES YOURSELF! THEY WILL BE OVERWRITTEN BY METPLUS WHEN IT CALLS THE MET TOOLS!** # # If there is a setting in the MET configuration file that is currently not supported by METplus you'd like to control, please refer to: # :ref:`Overriding Unsupported MET config file settings` # # .. dropdown:: GridStatConfig_wrapped # # .. highlight:: bash # .. literalinclude:: ../../../../parm/met_config/GridStatConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This use case utilizes one Python script to read and process the observation fields to become # METplus usable. From the configuration file it collects an input file, model name, # variable field name being analyzed, the initialization and valid times, # and a flag to indicate if the field passed is observation or forecast. Once the script runs, # it uses the model name to extract the correct grid definition from the griddedDatasets dictionary, # and the variable field name is used to extract the correct file variable field name (in combination # with the name of the model) from the verifVariables dictionary. These are combined into a dictionary # filled with values and keys used by the rest of the code, specifically set for the model and field # being used. A total cloud fraction call results in the field being extracted and set to a 0-100 range # with a similar behavior for low cloud fraction. The grid type associated with each model (determined by the # griddedDatasets dictionary) helps the script create the grid's corresponding latitude and longitude arrays. # Finally, the valid and initialization times that were passed at runtime are used to finalize the attrs dictionary # and the dataset is passed back to METplus for evaluation. # # .. dropdown:: parm/use_cases/model_applications/clouds/common/read_input_data.py # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/clouds/common/read_input_data.py ############################################################################## # User Scripting # -------------- # # User Scripting is not used in this use case. ############################################################################## # Running METplus # --------------- # # Pass the use case configuration file to the run_metplus.py script # along with any user-specific system configuration files if desired:: # # run_metplus.py /path/to/METplus/parm/use_cases/model_applications/clouds/GridStat_fcstGFS_obsMERRA2_lowAndTotalCloudFrac.conf /path/to/user_system.conf # # See :ref:`running-metplus` for more information. ############################################################################## # Expected Output # --------------- # # A successful run will output the following both to the screen and to the logfile:: # # INFO: METplus has successfully finished running. # # Refer to the value set for **OUTPUT_BASE** to find where the output data was generated. # Output for this use case will be found in {OUTPUT_BASE}/model_applications/clouds/GridStat_fcstGFS_obsMERRA2_lowAndTotalCloudFrac # and will contain the following files:: # # * grid_stat_GFS_TO_MERRA2_F36_CloudFracs_360000L_20210705_000000V_pairs.nc # * grid_stat_GFS_to_MERRA2_F36_CloudFracs_360000L_20210705_000000V.stat # * grid_stat_GFS_to_MERRA2_F36_CloudFracs_NBR_360000L_20210705_000000V_pairs.nc # * grid_stat_GFS_to_MERRA2_F36_CloudFracs_NBR_360000L_20210705_000000V.stat # * grid_stat_GFS_to_MERRA2_F36_CloudFracs_PROB_360000L_20210705_000000V_pairs.nc # * grid_stat_GFS_to_MERRA2_F36_CloudFracs_PROB_360000L_20210705_000000V.stat # # The netCDF files from the first GridStat instance and prob instance will contain # the raw fields, difference fields, and gradient fields using the supplied masking area. # For the nbr instance, there will be additional fields for the fractional coverage # fields. Each of the instance's output can be identified by an additional tag in the file # name (NBR for nbr, PROB for prob, and none for the first GridStat instance). For the # accompanying stat files, the first GridStat instance contains FHO, CTC, CTS, CNT, SL1L2, and # GRAD line types. The nbr instance will contain only the neighborhood-related line types # (NBRCTC, NBRCTS, and NBRCNT). Finally, the prob GridStat instance will contain probabilistic # line types only (PCT, PSTD, PJC, and PRC). ############################################################################## # Keywords # -------- # # .. note:: # # * GridStatToolUseCase # * NetCDFFileUseCase # * CloudsAppUseCase # * PythonEmbeddingFileUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # sphinx_gallery_thumbnail_path = '_static/clouds-GridStat_fcstGFS_obsMERRA2_lowAndTotalCloudFrac.png'