""" EnsembleStat: Using Python Embedding for Aerosol Optical Depth ============================================================== model_applications/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod.conf """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################ # Scientific Objective # -------------------- # # To provide useful statistical information on the relationship between # observation data for aersol optical depth (AOD) to an ensemble forecast. # These values can be used to help correct ensemble member deviations from observed values. ############################################################################## # Version Added # ------------- # # METplus version 4.0 ############################################################################## # Datasets # -------- # # **Forecast:** International Cooperative for Aerosol Prediction (ICAP) ensemble netCDF file, 7 members # # **Observation:** Aggregate netCDF file with MODIS observed AOD field # # **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 utilizes the METplus EnsembleStat wrapper to read in files using Python Embedding. ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (INIT_BEG):** 201608150000 # # **End time (INIT_END):** 201608150000 # # **Increment between beginning and end times (INIT_INCREMENT):** 06H # # **Sequence of forecast leads to process (LEAD_SEQ):** 12H # # EnsembleStat is the only tool called in this example. It processes a single run time with seven ensemble members, # with each ensemble member receiving its own verification. # Preprocessing of the ensemble forecast data is completed with Python Embedding, which takes 4 inputs: # the full path to the forecast file, variable name, valid time of verification, and ensemble member number. The script passes # back the variable field requested to EnsembleStat for verification. A similar process is completed # for the observation data, which is preprocessed by a separate Python Embedding script which takes 3 inputs: # the full path to the observation file, group name that contains the variable field, and variable name. # The script passes back the requested variable field and begins the verification process. # Three of the ensemble members do not have data for the AOD field, so EnsembleStat # will only process four of the members for statistics. # After a successful run, EnsembleStat will create the requested output and its corresponding files. ############################################################################## # 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/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod.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:: EnsembleStatConfig_wrapped # # .. highlight:: bash # .. literalinclude:: ../../../../parm/met_config/EnsembleStatConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This use case uses two Python embedding scripts to read input data: one for # the forecast ensemble data, and one for the observation data. The script processing # the ensemble data receives four input arguments: the full path to the forecast file, # variable name, valid time of verification, and ensemble member number. Since seven ensemble # members are being verified, this script will run seven times. The processing is very simple, # with the script grabbing the initialization time from the file name, calculating the lead # by finding the difference between the valid time argument and the initialization time, grabbing # the variable name and index corresponding to the ensemble member input value, and then masking bad data # (anything less than -800) to the expected METplus bad data value of -9999. The latitude and longitude # variables are also extracted, and all of the information is returned to METplus for # verification via array and accompanying attribute dictionary. # # The second script for observational data behaves very similarly to the ensemble data # script. The script receives three inputs at runtime: # the full path to the observation file, group name that contains the variable field, and variable name. # The requested variable field is extracted from the group name provided at runtime, bad data is # deemed to be any value less than -800 and reset to METplus' bad value of -9999, and the data # array is inverted to properly align with METplus' expected orientation. The latitude and longitude # variables are also extracted, and all of the information is returned to METplus for # verification via array and accompanying attribute dictionary. # # 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 `_ # # # .. dropdown:: parm/use_cases/model_applications/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod/forecast_embedded.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod/forecast_embedded.py # # .. dropdown:: parm/use_cases/model_applications/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod/analysis_embedded.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod/analysis_embedded.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/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod.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/air_quality_and_comp/EnsembleStat_fcstICAP_obsMODIS_aod # and will contain the following files:: # # * ensemble_stat_aod_20160815_120000V_ecnt.txt # * ensemble_stat_aod_20160815_120000V_ens.nc # * ensemble_stat_aod_20160815_120000V_orank.nc # * ensemble_stat_aod_20160815_120000V_phist.txt # * ensemble_stat_aod_20160815_120000V_relp.txt # * ensemble_stat_aod_20160815_120000V_rhist.txt # * ensemble_stat_aod_20160815_120000V_ssvar.txt # * ensemble_stat_aod_20160815_120000V.stat ############################################################################## # Keywords # -------- # # .. note:: # # * EnsembleStatToolUseCase # * PythonEmbeddingFileUseCase # * AirQualityAndCompAppUseCase # * PythonEmbeddingFileUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # # sphinx_gallery_thumbnail_path = '_static/air_quality_and_comp-EnsembleStat_fcstICAP_obsMODIS_aod.png'