""" SeriesAnalysis: Verifying one or more seasonal models using Python embedding ============================================================================ model_applications/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCN_SingleOrMultimodel.conf """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # By leveraging METplus' Python Embedding, this use case calculates climatologies, # anomalies, and ensemble means "on the fly" in memory. Using this method, it # demonstrates how climatology does not need to be ingested directly by METplus, # but instead can be calculated from the forecast and observation files when the series # scale is large enough to allow seasonal considerations. It also demonstrates that # leveraging Python Embedding allows for numerous model combinations in an ensemble, # removing pre-packed ensemble limitations. ############################################################################## # Version Added # ------------- # # METplus version 13.0 ############################################################################## # Datasets # -------- # **Forecast:** Climate Forecast System ver 2 (CFSv2) and Seasonal Forecast System (SFS) Baseline, 1 degree resolution, 2m temperature # # **Observation:** Global Historical Climatology Network ver. 2, Climatology Anomaly Monitoring System (GHCNCAMS) 1 degree resolution, 2m temperature # # **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 SeriesAnalysis. A Python # script is used for ingesting forecast and observation data, once for each year # of data in the CFSv2 and SFS ensembles. ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (INIT_BEG):** 1994-11-01 # # **End time (INIT_END):** 2020-11-01 # # **Increment between beginning and end times (INIT_INCREMENT):** 1 year # # **Sequence of forecast leads to process (LEAD_SEQ):** None # # With an increment of 1 year, all November 1st’s from 1994 to 2020 are processed # for a total of 27 years. This use case utilizes Python Embedding to process the # forecast and observation inputs, reading user-set variables like TIME_PERIOD and # CLIM to fine-tune what temporal size the means are calcuated over and what climatology # period is available, respectively. Outputs of ME, MAE, and RMSE, among others, provide # climate-useful statistical output. Instead of utilizing LEAD_SEQ, this use case # uses the CUSTOM_LOOP_LIST for 1, 2, and 3 month leads. This is done so that the Python script can # access an actual number rather than a date for processing. ############################################################################## # 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/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel.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:: SeriesAnalysisConfig_wrapped # # .. literalinclude:: ../../../../parm/met_config/SeriesAnalysisConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This use case utilizes two Python scripts. The first, function_library.py, # serves the purpose of data handling. It open and reads in NMME and observational data, # while also calculating climatologies, anomalies, and tercile probabilities using CPC methodologies # (including non-normal assumption for precipitation terciles, or other variables as needed). # For more simple changes, users can add additional models under MODEL_SPECS # (in the format 'model_name': nMembers); and additional model groupings can # be added under MODEL_GROUPS, in the format 'short_name’: ['list', 'of', 'models']. # Both of these are near the top of the function library. Additional observed datasets # can be added under the file_map and var_map, under create_obs_anomalies. # The second script, wrapper_combined.py, serves as the interface between python logic and METplus. # Based on options in the METplus config file, it formats model and observational # data (e.g., standardizing to lat x lon grids) and holds it in memory for METplus # to ingest. It also feature options for flags (FLIP_OBS, FLIP_MODELS) to handle # latitude orientation mismatches, ensuring data is geometrically correct before MET sees it. # # .. dropdown:: parm/use_cases/model_applications/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel/function_library.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel/function_library.py # # .. dropdown:: parm/use_cases/model_applications/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel/wrapper_combined.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel/wrapper_combined.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 # -------------- # # This use case does not use additional scripts. ############################################################################## # 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/s2s_mme/SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel.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 will be in a directory named SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel # relative to **OUTPUT BASE** and will contain the following files: # # * Bias_SeriesAnalysis_tmp2m_raw_SFS_Baseline_IC11_Lead01seasonal_1994_2020.nc # * Bias_SeriesAnalysis_tmp2m_raw_SFS_Baseline_IC11_Lead02seasonal_1994_2020.nc # * Bias_SeriesAnalysis_tmp2m_raw_SFS_Baseline_IC11_Lead03seasonal_1994_2020.nc # # Each of the three files is the result of the 1, 2, and 3 month lead times. # In each netCDF file, six variable fields are present (not including the lat/lon fields). # These correspond to the statistics that were requested in the configuration file, # and are as follows: # # * series_cnt_ME(lat, lon) # * series_cnt_MAE(lat, lon) # * series_cnt_RMSE(lat, lon) # * series_cnt_TOTAL(lat, lon) # * series_cnt_FBAR(lat, lon) # * series_cnt_OBAR(lat, lon) ############################################################################## # Keywords # -------- # # .. note:: # # * SeriesAnalysisUseCase # * PythonEmbeddingFileUseCase # * CustomStringLoopingUseCase # * S2SMMEAppUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # sphinx_gallery_thumbnail_path = '_static/s2s_mme-SeriesAnalysis_fcstCFSandSFS_obsGHCNCAMS_SingleOrMultimodel.png'