""" Grid-Stat, MODE, Stat-Analysis, UserScript, Gen-Vx-Mask: GFS Cloud Statistics by Type ===================================================================================== model_applications/clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer.conf """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # This use case evaluates clouds classified into low, mid, and high levels. The # evaluation covers multiple types of statistics including contingency table # statistics, neighborhood statistics, distance maps, and an object based evaluation. # Additionally, plots are created to demonstrate how to reformat and visualize GSS, # CSI, and frequency bias from the command line. ############################################################################## # Version Added # ------------- # # METplus version 6.1 ############################################################################## # Datasets # -------- # # **Forecast**: GFS 0.25 degree model # # **Observation**: GFS Analysis # # **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 # ------------------ # # This use case calls GenVxMask once, GridStat once, MODE three times, StatAnalysis # once, and UserScript twice. MODE has three entries to facilitate calling MODE # for three separate variables without invoking Muli-variate MODE, while GridStat is able # to run for three separate variables with only one process list entry. # Additionally, 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 (INIT_BEG):** 2024030700 # # **End time (INIT_END):** 2024030700 # # **Increment between beginning and end times (INIT_INCREMENT):** 6 hours # # **Sequence of forecast leads to process (LEAD_SEQ):** 0, 6, 12, 18 # # GenVxMask is run once. The Grid-Stat, MODE, and Stat-Analysis tools run for each time. # This example loops by model initialization time. It processes one initialization time and # three lead times for a total of 3 valid times, listed below. # # | **Valid:** 2024-03-07_00Z # | **Forecast lead:** 00 # # | **Valid:** 2024-03-07_06Z # | **Forecast lead:** 06 # # | **Init:** 2024-03-07_12Z # | **Forecast lead:** 12 # # | **Init:** 2024-03-07_18Z # | **Forecast lead:** 18 # # Both UserScripts are each run once. The first UserScript reformats the GridStat CTS # output so that it can be used to create a plot. The second UserScript creates three # plots. ############################################################################## # 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/clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer.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:: MODEConfig_wrapped # # .. highlight:: bash # .. literalinclude:: ../../../../parm/met_config/MODEConfig_wrapped # # .. dropdown:: StatAnalysisConfig_wrapped # # .. literalinclude:: ../../../../parm/met_config/STATAnalysisConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This use case does not use Python embedding. ############################################################################## # User Scripting # -------------- # # There are two 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 script is called reformat_CTS_linetype.py. This script takes the aggregated # output CTS linetype from Stat Analysis and reformats it so that the data can be plotted. # The script takes an input .yaml file, reformat_CTS.yaml. Environment variables in the yaml # file are specified in the [user_env_vars] section of the # GridStat_fcstGFS_obsGFS_cloudFracLayer.conf METplus configuration file. # # The second Python script is plot_line_stats.py. This script creates line plots for low # and high clouds for GSS, CSI, and Frequency bias with lead time, using the YAML files # custom_line_CSI.yaml, custom_line_FBIAS.yaml, and custom_line_GSS.yaml Input variables to both # scripts are set in the [user_env_vars] section of the # GridStat_fcstGFS_obsGFS_cloudFracLayer.conf 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/clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer # # .. dropdown:: reformat_CTS_linetype.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer/reformat_CTS_linetype.py # # .. dropdown:: plot_line_stats.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer/plot_line_stats.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/clouds/GridStat_fcstGFS_obsGFS_cloudFracLayer.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_obsGFS_cloudFracLayer. There will # be 6 directories with output data, masks, grid_stat, mode, stat_analysis, reformatted, and # plots. The mask directory will contain an input mask and the following output mask file:: # # * West_Pacific.nc # # The grid_stat directory will contain 4 .stat file sand 4 .nc files:: # # * grid_stat_GFS_cloud_000000L_20240307_000000V_pairs.nc # * grid_stat_GFS_cloud_000000L_20240307_000000V.stat # * grid_stat_GFS_cloud_060000L_20240307_060000V_pairs.nc # * grid_stat_GFS_cloud_060000L_20240307_060000V.stat # * grid_stat_GFS_cloud_120000L_20240307_120000V_pairs.nc # * grid_stat_GFS_cloud_120000L_20240307_120000V.stat # * grid_stat_GFS_cloud_180000L_20240307_180000V_pairs.nc # * grid_stat_GFS_cloud_180000L_20240307_180000V.stat # # The mode directory will contain the following files:: # # * mode_GFS_high_cloud_000000L_20240307_000000V_000000A_cts.txt # * mode_GFS_high_cloud_000000L_20240307_000000V_000000A_obj.nc # * mode_GFS_high_cloud_000000L_20240307_000000V_000000A_obj.txt # * mode_GFS_high_cloud_000000L_20240307_000000V_000000A.ps # * mode_GFS_high_cloud_060000L_20240307_060000V_000000A_cts.txt # * mode_GFS_high_cloud_060000L_20240307_060000V_000000A_obj.nc # * mode_GFS_high_cloud_060000L_20240307_060000V_000000A_obj.txt # * mode_GFS_high_cloud_060000L_20240307_060000V_000000A.ps # * mode_GFS_high_cloud_120000L_20240307_120000V_000000A_cts.txt # * mode_GFS_high_cloud_120000L_20240307_120000V_000000A_obj.nc # * mode_GFS_high_cloud_120000L_20240307_120000V_000000A_obj.txt # * mode_GFS_high_cloud_120000L_20240307_120000V_000000A.ps # * mode_GFS_high_cloud_180000L_20240307_180000V_000000A_cts.txt # * mode_GFS_high_cloud_180000L_20240307_180000V_000000A_obj.nc # * mode_GFS_high_cloud_180000L_20240307_180000V_000000A_obj.txt # * mode_GFS_high_cloud_180000L_20240307_180000V_000000A.ps # * mode_GFS_low_cloud_000000L_20240307_000000V_000000A_cts.txt # * mode_GFS_low_cloud_000000L_20240307_000000V_000000A_obj.nc # * mode_GFS_low_cloud_000000L_20240307_000000V_000000A_obj.txt # * mode_GFS_low_cloud_000000L_20240307_000000V_000000A.ps # * mode_GFS_low_cloud_060000L_20240307_060000V_000000A_cts.txt # * mode_GFS_low_cloud_060000L_20240307_060000V_000000A_obj.nc # * mode_GFS_low_cloud_060000L_20240307_060000V_000000A_obj.txt # * mode_GFS_low_cloud_060000L_20240307_060000V_000000A.ps # * mode_GFS_low_cloud_120000L_20240307_120000V_000000A_cts.txt # * mode_GFS_low_cloud_120000L_20240307_120000V_000000A_obj.nc # * mode_GFS_low_cloud_120000L_20240307_120000V_000000A_obj.txt # * mode_GFS_low_cloud_120000L_20240307_120000V_000000A.ps # * mode_GFS_low_cloud_180000L_20240307_180000V_000000A_cts.txt # * mode_GFS_low_cloud_180000L_20240307_180000V_000000A_obj.nc # * mode_GFS_low_cloud_180000L_20240307_180000V_000000A_obj.txt # * mode_GFS_low_cloud_180000L_20240307_180000V_000000A.ps # * mode_GFS_mid_cloud_000000L_20240307_000000V_000000A_cts.txt # * mode_GFS_mid_cloud_000000L_20240307_000000V_000000A_obj.nc # * mode_GFS_mid_cloud_000000L_20240307_000000V_000000A_obj.txt # * mode_GFS_mid_cloud_000000L_20240307_000000V_000000A.ps # * mode_GFS_mid_cloud_060000L_20240307_060000V_000000A_cts.txt # * mode_GFS_mid_cloud_060000L_20240307_060000V_000000A_obj.nc # * mode_GFS_mid_cloud_060000L_20240307_060000V_000000A_obj.txt # * mode_GFS_mid_cloud_060000L_20240307_060000V_000000A.ps # * mode_GFS_mid_cloud_120000L_20240307_120000V_000000A_cts.txt # * mode_GFS_mid_cloud_120000L_20240307_120000V_000000A_obj.nc # * mode_GFS_mid_cloud_120000L_20240307_120000V_000000A_obj.txt # * mode_GFS_mid_cloud_120000L_20240307_120000V_000000A.ps # * mode_GFS_mid_cloud_180000L_20240307_180000V_000000A_cts.txt # * mode_GFS_mid_cloud_180000L_20240307_180000V_000000A_obj.nc # * mode_GFS_mid_cloud_180000L_20240307_180000V_000000A_obj.txt # * mode_GFS_mid_cloud_180000L_20240307_180000V_000000A.ps # # The stat_analysis directory will contain the following files:: # # * stat_analysis_GFS_ANAL_West_Pacific_000000L_CTS.stat # * stat_analysis_GFS_ANAL_West_Pacific_060000L_CTS.stat # * stat_analysis_GFS_ANAL_West_Pacific_120000L_CTS.stat # * stat_analysis_GFS_ANAL_West_Pacific_180000L_CTS.stat # # The reformatted directory will contain the following files:: # # * reformat_CTS.data # # The plots directory will contain the following files:: # # * High_low_cloud_CSI.png # * High_low_cloud_FBIAS.png # * High_low_cloud_GSS.png # # For the Grid-Stat netCDF files, 6 variable fields are present (not including the lat/lon # fields). Those variables are:: # # * FCST_DMAP_ge50_LCDC_L0_FULL(lat, lon) # * OBS_DMAP_ge50_LCDC_L0_FULL(lat, lon) # * FCST_DMAP_ge50_MCDC_L0_FULL(lat, lon) # * OBS_DMAP_ge50_MCDC_L0_FULL(lat, lon) # * FCST_DMAP_ge50_HCDC_L0_FULL(lat, lon) # * OBS_DMAP_ge50_HCDC_L0_FULL(lat, lon) # # For the MODE netCDF files, 18 variable fields are present (not including the lat/lon fields). # Those variables are:: # # * fcst_raw(lat, lon) # * fcst_obj_raw(lat, lon) # * fcst_obj_id(lat, lon) # * fcst_clus_id(lat, lon) # * obs_raw(lat, lon) # * obs_obj_raw(lat, lon) # * obs_obj_id(lat, lon) # * obs_clus_id(lat, lon) # * fcst_conv_radius # * obs_conv_radius # * fcst_conv_threshold(fcst_thresh_length) # * obs_conv_threshold(obs_thresh_length) # * fcst_variable(fcst_variable_length) # * obs_variable(obs_variable_length) # * fcst_level(fcst_level_length) # * obs_level(obs_level_length) # * fcst_units(fcst_units_length) # * obs_units(obs_units_length) ############################################################################## # Keywords # --------- # # .. note:: # # * GridStatToolUseCase # * MODEToolUseCase # * StatAnalysisToolUseCase # * UserScriptUseCase # * ShortRangeAppUseCase # * METdataioUseCase # * METcalcpyUseCase # * METplotpyUseCase # * GRIB2FileUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # sphinx_gallery_thumbnail_path = '_static/clouds-GridStat_fcstGFS_obsGFS_cloudFracLayer.png'