""" UserScript: Reformat MET .stat ECNT data, calculate aggregation statistics, and generate a spread skill plot ============================================================================================================ model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot.py """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ################################################################################# # Scientific Objective # -------------------- # # This use case illustrates how to use MET .stat output (using the ECNT linetype data) to generate a # spread skill plot using a subset of the METplus Analysis Tools (METdataio, METcalcpy, # and METplotpy). The METdataio METreformat module extracts the ECNT linetype data and # performs reformatting, the METcalcpy agg-stat module performs aggregation, and the # METplotpy line plot is used to generate the spread skill plot. ############################################################################## # Version Added # ------------- # # METplus version 6.0 ################################################################################# # Datasets # -------- # # **Forecast:** RRFS GEFS (Rapid Refresh Forecast System Global Ensemble Forecast System) # Ensemble-Stat output files # # **Observation:** None # # **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. ############################################################################# # External Dependencies # --------------------- # # You will need to use the version of Python that is required for the METplus version # in use. Refer to the Installation section of the User's Guide for basic Python requirements: # https://metplus.readthedocs.io/en/latest/Users_Guide/installation.html # # The METplus Analysis tools: METdataio, METcalcpy, and METplotpy have the additional third-party # Python package requirements. The version numbers are found in the requirements.txt file found at the # top-level directory of each repository. # # * lxml # * pandas # * pyyaml # * numpy # * netcdf4 # * xarray # * scipy # * metpy # * pint # * python-dateutil # * kaleido (python-kaleido) # * plotly # * matplotlib ############################################################################## # METplus Components # ------------------ # # This use case runs the UserScript wrapper tool to run a user provided script, # in this case, reformat_ecnt_linetype.py, agg_stat_ecnt.py and plot_spread_skill.py. # It also requires the METdataio, METcalcpy and METplotpy source code to reformat the MET .stat output, # perform aggregation, and generate the plot. Clone the METdataio repository # (https://github.com/dtcenter/METdataio), # METcalcpy repository (https://github.com/dtcenter/METcalcpy, and the METplotpy # repository (https://github.com/dtcenter/METplotpy) under the same base directory as the # METPLUS_BASE directory so that the METdataio, METcalcpy, and METplotpy directories are under the # same base directory (i.e. if the METPLUS_BASE directory is /home/username/working/METplus, # then clone the METdataio, METcalcpy and METplotpy source code into the /home/username/working directory) # # Clone the METdataio, METcalcpy, and METplotpy source code from their repositories under a base directory. The # repositories are located: # # * https://github.com/dtcenter/METdataio # * https://github.com/dtcenter/METcalcpy # * https://github.com/dtcenter/METplotpy # # # # Define the OUTPUT_BASE, INPUT_BASE, and MET_INSTALL_DIR settings in the user # configuration file. For instructions on how to set up the user configuration file, refer to the `User ConfigurationFile # `_ section. ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (VALID_BEG):** 20220506_000000 # # **End time (VALID_END):** None # # **Increment between beginning and end times (VALID_INCREMENT):** None # # **Sequence of forecast leads to process (LEAD_SEQ):** None # # This use case reads in the MET .stat output that contains the ECNT linetype (from # the MET ensemble-stat tool). The .stat output *MUST* reside under one directory. # If .stat files are spread among multiple directories, these must be consolidated under a # single directory. # The use case loops over three processes: reformatting, aggregating, and plotting. ############################################################################## # METplus Configuration # --------------------- # # METplus first loads all 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/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot.conf # ############################################################################# # MET Configuration # ----------------- # # There are no MET tools used in this use case. The use case uses MET .stat # output as input for the reformatting step. ############################################################################## # Python Embedding # ---------------- # # This use case does not use Python embedding. ############################################################################## # User Scripting # -------------- # # This use case uses Python scripts to invoke the METdataio reformatter, the METcalcpy aggregator, # and the METplotpy line plot. # # The following Python script (from METdataio) is used to reformat the MET .stat ECNT linetype data # into a format that can be used by the aggregating script. # # .. dropdown:: parm/use_cases/model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot/reformat_ecnt_linetype.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot/reformat_ecnt_linetype.py # # This Python script (from METcalcpy) is used to calculate aggregation statistics for the ECNT linetype. # # .. dropdown:: parm/use_cases/model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot/aggregate_ecnt.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot/aggregate_ecnt.py # # Finally,this Python script (from METplotpy) is used to generate a spread-skill plot using the METplotypy line plot code. # # .. dropdown:: parm/use_cases/model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot/plot_spread_skill.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/short_range/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot/plot_spread_skill.py ############################################################################## # 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/UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot.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, one for the # reformat, aggregate, and plot steps of the use case:: # # INFO: METplus has successfully finished running. # # # **Reformat Output** # # The reformatted ensemble-stat ECNT linetype data should exist in the location specified in the user # configuration file (OUTPUT_BASE). Verify that the ensemble_stat_ecnt.data file exists. The file now has all # the statistics under the stat_name and stat_value columns, all ECNT statistic columns labelled with their # corresponding names (e.g. crps, crpss, rmse, etc.) and confidence level values under the # following columns: stat_btcl and stat_btcu # # # **Aggregation Output** # # The METcalcpy agg_stat module is used to calculate aggregated statistics and confidence intervals for each # series (line) point. # # **Plot Output** # # A spread-skill plot of temperature for the RMSE, SPREAD_PLUS_OERR, and a ratio line of SPREAD_PLUS_OERR/RMSE is # created and found in the output location specified in the user configuration file (OUTPUT_BASE). The plot is named # short-range_UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot.png # # ############################################################################## # Keywords # -------- # # .. note:: # # * UserScriptUseCase # * ShortRangeAppUseCase # * METdataioUseCase # * METcalcpyUseCase # * METplotpyUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # # sphinx_gallery_thumbnail_path = '_static/short_range_UserScript_fcstRRFS_fcstOnly_Reformat_Aggregate_Plot.png'