""" UserScript and StatAnalysis: Calculate and Evaluate Weather Regimes for GFS and ERA =================================================================================== model_applications/s2s_mid_lat/UserScript_fcstGFS_obsERA_WeatherRegime.py """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # Weather regimes are defined as atmospheric patterns with high likelihoods of recurrence. # They can modulate many atmospheric phenomena including tornado and severe weather # occurrence. This use case computes the top 6 most frequent weather regimes using K-means # clustering for the forecast and observations, and computes multi-category contingency table # statistics on these regimes. It also computes the frequency of occurrence of each # weather regime over a 7 day period for the forecast and observation and continuous # statistics are computed comparing these two frequencies. # # The code for computing weather regimes comes from Douglas Miller. # # * Miller, D. E., Wang, Z., Trapp, R. J., &Harnos, D. S., 2020: Hybrid prediction of weekly tornado activity out to Week 3: Utilizing weather regimes. Geophysical Research Letters, 47, https://doi.org/10.1029/2020GL087253. ############################################################################## # Version Added # ------------- # # METplus version 4.0.0 ############################################################################## # Datasets # -------- # # **Forecast:** GFS Forecast 500 mb height # # **Observation:** ERA Reanlaysis 500 mb height # # **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 calles UserScript once and Stat-Analysis twice. There are two optional # pre-processing steps, Regrid-Data-Plane and PCP-Combine. Additionally, METcalcpy # and METplotpy are required to run this use case. The METcalcpy scripts accessed include # the following: # # * metcalcpy/contributed/blocking_weather_regime/WeatherRegime.py # # * metcalcpy/contributed/blocking_weather_regime/Blocking_WeatherRegime_util.py # # * metcalcpy/util/write_mpr.py # # The METplopty scrips accessed include the following: # # * metplotpy/contributed/weather_regime/plot_weather_regime.py ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (VALID_BEG):** 12-01-2000 # # **End time (VALID_END):** 02-28-2017 # # **Increment between beginning and end times (VALID_INCREMENT):** 1 day # # **Sequence of forecast leads to process (LEAD_SEQ):** 24 hours # # This use case does not loop, but the UserScript and both calls to Stat-Analysis are # each run once. The UserScript runs the weather regime driver script. The weather regime # driver script performs the weather regime calculation for the forecast and observation. This # calculation is divided up into steps, which the user can select by setting STEPS_FCST and # STEPS_OBS in the [user_env_vars] section of the configuration. More information on the steps and # how the calculation proceeds is given in the User Scripting section below. # # The two optional pre-processing steps loop by valid time when they are turned on, with different timing # settings needed for the different steps. These steps are turned off due to data size and processing # time. The first optional step calls Regrid-Data-Plane to regrid the data to a 1 degree latitude/longitude # grid. The second calls PCP-Combine to compute daily means. These omitted steps can be turned back on by # using the PROCESS_LIST that is commented out: # # PROCESS_LIST = RegridDataPlane(regrid_obs), PcpCombine(daily_mean_obs), UserScript(script_wr), StatAnalysis(sanal_wrclass), StatAnalysis(sanal_wrfreq) # # Settings for the optional pre-processing steps can be found in the respective sections of the configuration, # regrid_obs and daily_mean_obs. Data is not provided in the tarball to run these steps, but the # configurations are provided for reference on how to set up these calculations. ############################################################################## # 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/s2s_mid_lat/UserScript_fcstGFS_obsERA_WeatherRegime.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s_mid_lat/UserScript_fcstGFS_obsERA_WeatherRegime.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:: StatAnalysisConfig_wrapped # # .. literalinclude:: ../../../../parm/met_config/STATAnalysisConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This use case does not use python embedding. ############################################################################## # User Scripting # -------------- # # This use case runs WeatherRegime_driver.py. This driver script runs the selected steps # of the weather regime calculation that are specified in FCST_STEPS and OBS_STEPS in the # [user_env_vars] section of the UserScript .conf file. All steps are run for this use # case, as specified in the following format: # # | OBS_STEPS = ELBOW+PLOTELBOW+EOF+PLOTEOF+KMEANS+PLOTKMEANS+TIMEFREQ+PLOTFREQ # | FCST_STEPS = ELBOW+PLOTELBOW+EOF+PLOTEOF+KMEANS+PLOTKMEANS+TIMEFREQ+PLOTFREQ # # The possible steps are computing the elbow or optimal number of clusters (ELBOW), plotting the elbow # (PLOTELBOW), computing EOFs (EOF), plotting EOFs (PLOTEOF), computing the weather regimes using # K means clustering (KMEANS), plotting the weather regimes (PLOTKMEANS), computing a user specified # time frequency of weather regimes (TIMEFREQ) and plotting the time frequency (PLOTFREQ). The # TIMEFREQ and PLOTFREQ steps require that the KMEANS step be run first, while the ELBOW, EOF, and # KMEANS steps can be run individally. Input variables to the WeatherRegime driver are set and # described in the [user_env_vars] section of the configuration file. # # Elbow computes the optimal number of clusters using the sum of squared distances for # clusters 1 - 14 and draws a straight line from the sum of squared distance for the # clusters. This helps determine the optimal cluster number by examining the largest # difference between the curve and the straight line. The EOFs step computes empirical orthogonal # functions. These EOFs are used to reconstruct the height field, with this reconstructed data used # in the K means calculation. If EOFs are not compted, the original height field is used in the K means # calculation. The K means step uses clustering to compute the frequency of occurrence and anomalies # for each cluster to give the most common weather regimes. Then, the time frequency computes the # frequency of each weather regime over a user specified time frame. # # .. dropdown:: parm/use_cases/model_applications/s2s_mid_lat/common/WeatherRegime_driver.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s_mid_lat/common/WeatherRegime_driver.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/s2s_mid_lat/UserScript_fcstGFS_obsERA_WeatherRegime.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/s2s_mid_lat/WeatherRegime # and will contain output for the steps requested. The output includes 8 plots in the plots directory:: # # * fcst_elbow.png # * obs_elbow.png # * fcst_eof.png # * obs_eof.png # * fcst_kmeans.png # * obs_kmeans.png # * fcst_freq.png # * obs_freq.png # # The output also includes a daily classification of weather regimes as text files for both # the forecast and observation:: # # * fcst_weather_regime_class.txt # * obs_weather_regime_class.txt # # There are numerous matched pair files output in two subdirectories of the mpr directory. These contain # output classified weather regimes and also the frequency for each weather regime. For the classified # weather regimes, one file is written for each day to the WeatherRegime subdirectory (1513 files total) # in the format below for 12-02-2000:: # # * weather_regime_stat_GFS_240000L_20001202_000000V.stat # # for the frequency matched pair files, one file is written for each day and each weather regime (8466 files # total) to the freq subdirectory in the format below for 12-02-2000, weather regime 1:: # # * weather_regime01_freq_stat_GFS_240000L_20001202_000000V.stat # # Stat-Analysis Output:: # # * GFS_ERA_WRClass_240000L_MCTS.stat # * GFS_ERA_WR_freq_240000L_CNT.stat # # If the pre-processing steps are turned on, the output will include the regridded data and daily averaged files. ############################################################################## # Keywords # -------- # # .. note:: # # * RegridDataPlaneToolUseCase # * PCPCombineToolUseCase # * StatAnalysisToolUseCase # * S2SAppUseCase # * S2SMidLatAppUseCase # * UserScriptUseCase # * NetCDFFileUseCase # * GRIB2FileUseCase # * METcalcpyUseCase # * METplotpyUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # # sphinx_gallery_thumbnail_path = '_static/s2s_mid_lat-UserScript_fcstGFS_obsERA_WeatherRegime.png'