""" UserScript and StatAnalysis: Calculate and evaluate Blocking for the GFS and ERA ================================================================================ model_applications/s2s_mid_lat/UserScript_fcstGFS_obsERA_Blocking.py """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # Atmospheric blocking is associated with extreme weather events. This use case computes # atmospheric blocking events using the methodology in Miller & Wang (2019, 2022), which # identifies blocks from 500 mb height. Various studies (Masato et al. 2013; Kitano and # Yamada 2016) have suggested that 500 mb height produces a similar blocking climatology as # results from potential temperature on a 2-PVU surface. # # The methodology in Miller & Wang (2019, 2022) first computes the Central Blocking Latitude # (CBL) or storm track. Allowing for an offset north and south of the storm track, reversals # in geopotential height are then identified as Instantaneously Blocked longitudes (IBLs). These # IBLs are grouped when consective longitudes are blocked (GIBLs) and then blocks are identified # by applying thresholds to ensure the large-scale, quasi-stationary characteristics of blocking # anticyclones are met. The IBLs, GIBLs, and blocks are computed separately for the model and # observations, and contingency table statistics are computed to compare model performance at # identifying IBLs and blocks. The original code for computing blocking came from Douglas Miller. # # * Miller, D. E., and Z. Wang, 2019a: Skillful seasonal prediction of Eurasian winter blocking and extreme temperature frequency. Geophys. Res. Lett., 46, 11 530–11 538, https://doi.org/10.1029/2019GL085035. # * Miller, D. E., and Z. Wang, 2022: Northern Hemisphere Winter Blocking: Differing Onset Mechanisms across regions. J. Atmos. Sci., 79, 1291-1309, https://doi.org/10.1175/JAS-D-21-0104.1. # * Masato, G., B. J. Hoskins, and T. J. Woollings, 2013: Winter and summer Northern Hemisphere blocking in CMIP5 models. J. Climate, 26, 7044–7059, https://doi.org/10.1175/JCLI-D-12-00466.1. # * Kitano, Y., and T. J. Yamada, 2016: Relationship between atmospheric blocking and cold day extremes in current and RCP8.5 future climate conditions over Japan and the surrounding area. Atmos. Sci. Lett., 17, 616–622, https://doi.org/10.1002/asl.711. ############################################################################## # Version Added # ------------- # # METplus version 4.0.0 ############################################################################## # Datasets # -------- # # **Forecast:** GFS Forecast 500 mb height for DJF 2000 - 2017 # # **Observation:** ERA Reanlaysis 500 mb height for DJF 2000 - 2017 for the blocking evaluation and 1979 - 2017 for the CBL calculation # # **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 UserScript twice and StatAnalysis twice. The first StatAnalysis # run computes contingency table statistics on the IBLs, while the second computes contintency # table statistics on the computed blocks. There are 6 optional pre-processing steps, 2 calls to # RegridDataPlane and 4 calls to 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/Blocking.py # # * metcalcpy/contributed/blocking_weather_regime/Blocking_WeatherRegime_util.py # # * metcalcpy/util/write_mpr.py # # The METplopty scrips accessed include the following: # # * metplotpy/contributed/blocking_s2s/CBL_plot.py # # * metplotpy/contributed/blocking_s2s/plot_blocking.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 2 UserScripts calls are run once for # all valid times of the forecast and observations. The first UserScript creates a file list # with the observed ERA data to use for the storm track climatology. This is done separately # because it's a different (longer) time frame from the data used in the other steps of the blocking # calculation. The second UserScript runs the blocking driver which calls the code to perform the # blocking calculation. The blocking calculation is divided up into steps, which the user can select # by setting STEPS_OBS and STEPS_FCST 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 calls to StatAnalysis also don't loop but are run once for all valid times. The first # StatAnalysis run computes contingency table statistics on the IBLs, while the second computes # contintency table statistics on the computed blocks. # # The 6 optional pre-processing steps loop by loop by valid time with different timing settings # needed used for the different steps. These include 2 runs of RegridDataPlane to regrid both # the model and observations to a 1 degree grid. Then, there are 2 calls to PcpCombine. These # compute daily average 500 mb height for the model and observations. The next two calls to # PcpCombine compute a 5 day running mean and daily anomalies on the observations, which are used # to compute the storm track climatology. These omitted steps can be turned back on by using the # PROCESS_LIST that is commented out: # # PROCESS_LIST = RegridDataPlane(regrid_fcst), RegridDataPlane(regrid_obs), PcpCombine(daily_mean_fcst), PcpCombine(daily_mean_obs), PcpCombine(running_mean_obs), PcpCombine(anomaly_obs), UserScript(create_cbl_filelist), UserScript(script_blocking), StatAnalysis(sanal_ibls), StatAnalysis(sanal_blocks) # # Settings for the optional pre-processing steps can be found in the respective sections of # the configuration, regrid_fcst, regrid_obs, daily_mean_fcst, daily_mean_obs, running_mean_obs, # and anomaly_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_Blocking.conf # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s_mid_lat/UserScript_fcstGFS_obsERA_Blocking.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 the blocking driver. The blocking driver runs the user selected steps # of the blocking calculation for both the forecast and observation. These steps are specified # in FCST_STEPS and OBS_STEPS in the [user_env_vars] section fo the configuration file in the # following format: # # | FCST_STEPS = CBL+IBL+PLOTIBL+GILB+CALCBLOCKS+PLOTBLOCKS # | OBS_STEPS = CBL+PLOTCBL+IBL+PLOTIBL+GILB+CALCBLOCKS+PLOTBLOCKS # # The possible steps are computing the CBLs or central blocking latitude (CBL), plotting CBLs # (PLOTCBL), computing instantaneously blocked longitudes (IBL), plotting IBL frequency (PLOTIBL), # computing grouped instantaneously blocked longitudes (GIBL), computing blocks (CALCBLOCKS), and # plotting the blocking frequency (PLOTBLOCKS). This use case runs all steps although not all of # them are required to be run. The CBL, IBL, GIBL, and CALCBLOCKS steps must be run in order as the # IBL step requires previously computed CBLs, and GIBLs requires previously computed IBLs. Plotting # also requires the associated step to be run (e.g. PLOTCBL requires CBL to be run first). The # methodology used in these calculations is described in Miller & Wang (2019, 2022) listed in the # Scientific Objective section. # # There are many input variables that can be changed for the driver script and blocking calculation. # These can be changed and are described in the [user_env_vars] section of the configuration file. # # .. dropdown:: parm/use_cases/model_applications/s2s_mid_lat/common/Blocking_driver.py # # .. highlight:: python # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s_mid_lat/common/Blocking_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_Blocking.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. # # Warnings of missing files will also be output to the log file. In this case, the warnings are a result of # the 5 day running mean calculation. They should alert the user about missing data fir 12/01, 12/02, 02/27, # and 02/28 of each year the calculation runs. # # 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/Blocking. There should be 4 different # graphics output to the plot directory in the location above, but each will have png and pdf version to make # for 8 output plots:: # # * ERA_CBL_avg.png # * ERA_CBL_avg.pdf # * GFS_ERA_IBL_Freq_DJF.png # * GFS_ERA_IBL_Freq_DJF.pdf # * obs_Block_Freq.png # * obs_Block_Freq.pdf # * fcst_Block_Freq.png # * fcst_Block_Freq.pdf # # There are numerous matched pair files output in two subdirectories of the mpr directory. These contain # output computed IBLs and blocks. For the IBLs, one file is written for each day to the IBL subdirectory # in the format below for 12-02-2000:: # # * IBL_stat_GFS_240000L_20001202_000000V.stat # # For the blocks .stat files, one file is also written for each day to the Blocks subdirectory in the format # below for 12-02-2000:: # # * blocking_stat_GFS_240000L_20001202_000000V.stat # # There are also 2 files output from the StatAnalysis runs containing contingency table statistics:: # # * GFS_ERA_IBLS_240000L_CTS_CNT.stat # * GFS_ERA_Blocks_240000L_CTS.stat # # If the pre-processing steps are turned on, regridded data, daily averaged files, running mean files, # and anomaly files will also be output to Regrid, Daily,Rmean5d, and Anomaly directories in the ERA # directory and Regrid and Daily directories in the GFS directory. ############################################################################## # 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_Blocking.png'