""" GridStat: Use Python embedding to evaluate GeoTIFF imagery across multiple fields ================================================================================= model_applications/marine_and_cryosphere/GridStat_fcstGSWR_obsMRMS_GeoTIFF_multiField.conf """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # This use case provides an example for utilizing Python Embedding to read # an unsupported file format into METplus and leverage the verification # capabilities. GeoTIFF format forecasts are read in via Python and verified against # the MRMS dataset, providing useful evaulation statistics for a numerical forecast that # previously would not have been able to utilize METplus. ############################################################################## # Version Added # ------------- # # METplus version 6.1 ############################################################################## # Datasets # -------- # **Forecast:** MIT-LL Global Synthetic Weather Radar (GSWR), ~0.045 degree global resolution # # **Observation:** MultiRadar/MultiSensor (MRMS) ~0.01 degree CONUS resolution # # **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 # ------------------ # # The only tool this use case calls is GridStat, with six separate instances of GridStat used. # Within GridStat a Python script is used for ingesting forecast data. ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (INIT_BEG):** 2024-10-15 # # **End time (INIT_END):** 2024-10-15 # # **Increment between beginning and end times (INIT_INCREMENT):** 15 minutes # # **Sequence of forecast leads to process (LEAD_SEQ):** 60, 75, 90, 105, 120, 240, 300, 360, 420, and 480 minutes # # The use case runs six instances of GridStat; one for each of the three variable fields # to be verified across the two different types of forecast files. # In the first GridStat call, the configuration options that will not # change across the other instances are set, along with the initial five lead times # separated by 15 minute intervals. Due to the observation dataset having irregular # seconds attached to the valid time, the OBS_VAR1_OPTIONS sets the valid time # equal to the valid time being verified. This will ensure that the forecast # being evaluated is always the same valid time as the observation. # The irregular valid time extends to the name of the observation files as well, # so OBS_GRID_STAT_FILE_WINDOW has a +/- 120 seconds setting that will capture # the file's slight irregular offset in seconds. Once the forecast python embedding is complete, # the verification is completed with a regrid to the coarser forecast grid and the requested # line type is created. The two GridStat instances containing "adv" keep the same timing information, # changing only the observation file naming template and the corresponding # forecast file passed to the Python script, as well as slight changes to the # thresholding and output location. The remaining three GridStat instances with "blend" # need their own LEAD_SEQ, with five different lead times incremented by one hour. # These instances also update the observation input file templates, thresholding, and # output location. ############################################################################## # METplus Configuration # --------------------- # # METplus first loads all of the configuration files 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/short_range/GridStat_fcstGSWR_obsMRMS_GeoTIFF_multiField.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/short_range/GridStat_fcstGSWR_obsMRMS_GeoTIFF_multiField.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 # # .. literalinclude:: ../../../../parm/met_config/GridStatConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This script is necessary to read in the forecast file, which is in the unsupported # GeoTIFF format. The only input required is a full path to the file to be evaluated, # as each file only contains one field for analysis. After checking for exactly one input, # two separate routines are used to extract the variable name from the filename, # as well as the timing information from the file name. The data from the file # is extracted using another routine, followed by two final routines that extract the # latitude and longitude information from the file contents and preparing the data # to be passed back to GridStat. That final data routine also adjusts the data if # the variable field in the file is VIL, which requires additional steps to change # the units to the expected kg/m^2. Any negative values are set to bad data (the negative values # are the result of pixels with no data but bad pixel data could be seen as an actual value in MET # if not thresholded) and finally the MET-required dictionary is constructed. # The location of the code is # # .. dropdown:: parm/use_cases/model_applications/short_range/GridStat_fcstGSWR_obsMRMS_GeoTIFF_multiField/TIFF_readin.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/short_range/GridStat_fcstGSWR_obsMRMS_GeoTIFF_multiField/TIFF_readin.py # # For more information on the basic requirements to utilize Python Embedding in METplus, # please refer to the MET User’s Guide section on `Python Embedding `_. ############################################################################## # User Scripting # -------------- # # There is no User Scripting 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/short_range/GridStat_fcstGSWR_obsMRMS_GeoTIFF_multiField.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 one of two directories: # # * advected # * blended # # Under these two directories will be three subdirectories, corresponding to the # variable fields of interest: # # * echo_tops # * vil # * reflectivity # # Finally, each variable will have subdirectories corresponding to each of the leads that were # evaluated. This should look like the following for advected: # # * 202410150200 # * 202410150215 # * 202410150230 # * 202410150245 # * 202410150300 # # And for blended, the following subdirectories are present: # # * 202410150500 # * 202410150600 # * 202410150700 # * 202410150800 # * 202410150900 # # Regardless of which directory is chosen, the lowest directory will contain a netCDF file # with the raw forecast and observation fields, and a stat file with CTS output. ############################################################################## # Keywords # -------- # # .. note:: # # * GridStatToolUseCase # * PythonEmbeddingFileUseCase # * GRIB2FileUseCase # * ShortRangeAppUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # # sphinx_gallery_thumbnail_path = '_static/short_range-GridStat_fcstGSWR_obsMRMS_GeoTIFF_multiField.png'