""" Point-Stat, Stat-Analysis, UserScript, DataIngest: CREDIT and GFS point statistics, Data Download, Plots ======================================================================================================== model_applications/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.conf """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # This use case is an example for verification within RAL to illustrate how to # compare two models and also how to download data automatically. The use case was # originally set up to be run in real-time using the now keyword in VALID_BEG and # VALID_END. However, a specified date is provided used here for our automated # testing. The case demonstrates how to run statistics for two models, # NSF NCAR Community Research Earth Digital Intelligence Twin (CREDIT) and GFS, # and how to make plots of continuous statistics (ME, MAE and RMSE) and categorical # statistics (CSI and FBIAS) on some of the variables. In contrast to the # GridStat_fcstCREDIT_GFS_obsGFS_6hrRealtime use case, this case uses point observations # whereas that one used gridded observations. Also, that use case shows how to process # multiple valid times while this one uses only a single valid time. ############################################################################## # Version Added # ------------- # # METplus version 6.2 ############################################################################## # Datasets # -------- # # **Forecast**: CREDIT ~0.28 degree model and GFS 0.25 degree model # # **Observation**: GDAS # # **Climatology:** None # # **Location:** The CREDIT model 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. # # The GFS model data and GDAS observations are downloaded automatically. Make sure you # have an internet connection. ############################################################################## # METplus Components # ------------------ # # This use case calls DataIngest once, PointStat 3 times, StatAnalysis once, and UserScript # 4 times. One of the PointStat calls is for the CREDIT model and 2 are for the GFS model # (one for the surface data and one for the upper air data). The 4 UserScripts require # METcalcpy, METplotpy, and METdataio are required to run this use case. The METcalcpy # scripts accessed include the following: # # * metcalcpy/util/read_env_vars_in_config.py # # The METplopty scrips accessed include the following: # # * metplotpy/plots/line/line.py # # The METdataio scripts accessed include the following: # # * METdbLoad/ush/read_data_files.py # # * METdbLoad/ush/read_load_xml.py # # * METreformat/write_stat_ascii.py ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (VALID_BEG):** 2025-09-24 00Z # # **End time (VALID_END):** 2025-09-24 00Z # # **Increment between beginning and end times (VALID_INCREMENT):** 6 hours # # **Sequence of forecast leads to process (LEAD_SEQ):** 0 - 120 in 6 hour intervals # # DataIngest, PointStat, and StatAnalysis tools are run for each time, whereas StatAnalysis is # run once. This example loops by valid times. It processes 20 lead times for 1 valid # time for a total of 20 runs. All 4 UserScripts are each run once. The first UserScript reformats # the PointStat CNT output while the second reformats the PointStat CTS output so that they can be # used for plotting. The third and fourth UserScript calls creates plots, one for the CNT output # and the other for the CTS output. ############################################################################## # 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, # e.g. parm/use_cases/model_applications/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.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 # # .. dropdown:: StatAnalysisConfig_wrapped # # .. literalinclude:: ../../../../parm/met_config/STATAnalysisConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This use case does not use Python embedding. ############################################################################## # User Scripting # -------------- # # There are three Python scripts used in this use case, called using the "UserScript" keyword # in the METplus wrappers PROCESS_LIST configuration item. These scripts provide an # interface to the functions in the METdataio, METcalcpy, and METplotpy Python modules # of METplus. The functions used in these scripts demonstrate reformatting # aggregated StatAnalysis output to meet the format required by METcalcpy and METplotpy, # and then plotting that reformatted output using functions from METcalcpy and METplotpy. # # The first Python scripts is called reformat_linetype.py. This script takes the aggregated # output linetype from Stat Analysis and reformats it so that the data can be plotted. # The script takes an input .yaml file of which there are 2, reformat_CNT.yaml and reformat_CTS.yaml. # Environment variables in the yaml file are specified in the [user_env_vars] section of the # PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.conf METplus configuration file. # # The second Python script is plot_line_stats.py. This script creates line plots for CREDIT and GFS # of MAE, ME and RMSE with lead time, using the YAML file custom_2line_6h.yaml. Input variables to this # script are also specified in the [user_env_vars] section of the # PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.conf METplus configuration file. # # The third Python script is plot_line_stats_CTS.py. This script creates line plots for CREDIT and GFS # of CSI and Frequency Bias with lead time, using the YAML file custom_2line_cat_6h.yaml. Input variables # to this script are also specified in the [user_env_vars] section of the # PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.conf METplus configuration file. # # For more information about YAML configuration options for the line plots shown here, see the METplotpy # `line plot documentation `_. # # Both Python scripts are located at:: # # parm/use_cases/model_applications/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime # # .. dropdown:: reformat_linetype.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime/reformat_linetype.py # # .. dropdown:: plot_line_stats.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime/plot_line_stats.py # # .. dropdown:: plot_line_stats_CTS.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime/plot_line_stats_CTS.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/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.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/medium_range/PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime. There will # be 6 directories, data_ingest, GDAS, plots, point_stat, reformatted, and StatAnalaysis. The # data_ingest directory contains the observation dat that has been downloaded # and contains 2 subdirectories, GDAS and GFS. The GDAS directory contains 1 subdirectory, 20250924, # with 2 files: # # * 20250924_0000.prepbufr.nr # * 20250924_0600.prepbufr.nr # # The data inside the GFS directory is sorted by model initialization time and contains both surface # and upper air data. These files have the format, where II is the model initialzation hours and HHH # is the lead time in hours: # # * gfs.tIIz.pgrb2.0p25.fHHH # * gfs.tIIz.sfluxgrbfHHH.grib2 # # Inside the GDAS directory contains the GDAS observations that have been converted from prepbufr to # netCDF. The directory contains 2 files: # # * prepbufr.gdas.2025092400.nc # * prepbufr.gdas.2025092406.nc # # The plots directory contains one subdirectory, 6h, that has 11 plots: # # * T500_FULL_MAE.png # * T500_FULL_ME.png # * T500_FULL_RMSE.png # * TMP_FULL_CSI.png # * TMP_FULL_FBIAS.png # * TMP_FULL_MAE.png # * TMP_FULL_ME.png # * TMP_FULL_RMSE.png # * Z500_FULL_MAE.png # * Z500_FULL_ME.png # * Z500_FULL_RMSE.png # # The point_stat directory also has 2 subdirectories, CREDIT/6h and GFS. The files output to CREDIT/6h are # also stored in subdirectories dated with model initialization time in the format of YYYYMMDDHH. These files # have the following format, where the dates labeled are lead time, valid year, month, and day, and valid hour, # minute, and second: # # * point_stat_CREDIT_surface_HHMMSSL_YYYYMMDD_HHMMSSV.stat # # The files output to the GFS directory are also stored in subdirectories dated with model # initialization time in the format of YYYYMMDDHH. These files have the same format as above where # the dates labeled are lead time, valid year, month, and day, and valid hour, minute, and second: # # * point_stat_GFS_surface_HHMMSSL_YYYYMMDD_HHMMSSV.stat # * point_stat_GFS_upper_air_HHMMSSL_YYYYMMDD_HHMMSSV.stat # # The reformatted directory contains the reformatted output from StatAnalysis that is used in plotting. # There is one subdirectory (6h) that contains 2 files: # # * reformat_CNT_6h.data # * reformat_CTS_6h.data # # The StatAnalysis directory contains 1 subdirectory (6h). Inside that directory are # 6 output files: # # * CREDIT_GDAS_2025092400_2025092406_allleads_all_stations_allValidHours_CNT.stat # * CREDIT_GDAS_2025092400_2025092406_allleads_all_stations_allValidHours_CTS.stat # * CREDIT_GDAS_2025092400_2025092406_allleads_all_stations_hourly_VCNT.stat # * GFS_GDAS_2025092400_2025092406_allleads_all_stations_allValidHours_CNT.stat # * GFS_GDAS_2025092400_2025092406_allleads_all_stations_allValidHours_CTS.stat # * GFS_GDAS_2025092400_2025092406_allleads_all_stations_hourly_VCNT.stat ############################################################################## # Keywords # -------- # # .. note:: # # * DataIngestToolUseCase # * PB2NCToolUseCase # * PointStatToolUseCase # * StatAnalysisToolUseCase # * UserScriptUseCase # * MediumRangeAppUseCase # * NCAROrgUseCase # * GRIB2FileUseCase # * NetCDFFileUseCase # * METdbLoadUseCase # * METcalcpyUseCase # * METplotpyUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # sphinx_gallery_thumbnail_path = '_static/medium_range-PointStat_fcstCREDIT_GFS_obsGDAS_6hrRealtime.png'