.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "generated/model_applications/precipitation/GridStat_fcstHRRR-TLE_obsStgIV_GRIB.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_generated_model_applications_precipitation_GridStat_fcstHRRR-TLE_obsStgIV_GRIB.py: Grid-Stat: 6hr PQPF Probability Verification ============================================ model_applications/precipitation/GridStat_fcstHRRR-TLE_obsStgIV_GRIB.conf .. GENERATED FROM PYTHON SOURCE LINES 9-13 .. contents:: :depth: 1 :local: :backlinks: none .. GENERATED FROM PYTHON SOURCE LINES 15-31 Scientific Objective -------------------- This use case demonstrates the evaluation of a probabilistic field. The HRRR-Time Lag Ensemble (TLE) used in this example was used to demonstrate prototype ensemble post-processing techniques. A time-lagged ensemble can provide higher temporal resolution and be used to compute several different accumulation amounts based on what data is available for each run time. 6 hour and 1 hour observation data is available at 6Z, so the 6 hour accumulation data is used. However, at 7Z only a 1 hour accumulation field is available, so it uses the 1 hour field, then steps back in time trying to build a 6 hour accumulation with earlier data. METplus is configured to only allow 1 hour or 6 hour accumulations in the input files, so a set of six 1 hour accumulation fields are combined to create a 6 hour accumulation field. The result is compared to the 6 hour forecast data. .. GENERATED FROM PYTHON SOURCE LINES 33-37 Version Added ------------- METplus version 3.0 .. GENERATED FROM PYTHON SOURCE LINES 39-57 Datasets -------- **Forecast:** NOAA High Resolution Rapid Refresh Time-Lagged Ensemble (HRRR-TLE) probabilistic forecasts in GRIB2 **Observation:** Stage IV GRIB 1 and 6 hour precipitation accumulation **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. .. GENERATED FROM PYTHON SOURCE LINES 59-66 METplus Components ------------------ This use case first runs PCPCombine on the observation data to build a 6 hour precipitation accumulation from 1 hour files or a single 6 hour file. Then the observation data is regridded to the model grid using the RegridDataPlane. Finally, the observation files are compared to the forecast data using GridStat. .. GENERATED FROM PYTHON SOURCE LINES 68-94 METplus Workflow ---------------- **Beginning time (INIT_BEG):** 2016090412 **End time (INIT_END):** 2016090412 **Increment between beginning and end times (INIT_INCREMENT):** 60 **Sequence of forecast leads to process (LEAD_SEQ):** 6, 7 The following tools are used for each run time: PCPCombine (observation) > RegridDataPlane (observation) > GridStat This example loops by initialization time. For each initialization time it will process forecast leads 6 and 7. There is only one initialization time in this example, so the following will be run: Run times: | **Init:** 2016-09-04_12Z | **Forecast lead:** 6 | | **Init:** 2016-09-04_12Z | **Forecast lead:** 7 .. GENERATED FROM PYTHON SOURCE LINES 96-105 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/precipitation/GridStat_fcstHRRR-TLE_obsStgIV_GRIB.conf .. highlight:: bash .. literalinclude:: ../../../../parm/use_cases/model_applications/precipitation/GridStat_fcstHRRR-TLE_obsStgIV_GRIB.conf .. GENERATED FROM PYTHON SOURCE LINES 107-121 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 .. GENERATED FROM PYTHON SOURCE LINES 123-127 Python Embedding ---------------- This use case does not use Python embedding. .. GENERATED FROM PYTHON SOURCE LINES 129-133 User Scripting -------------- User Scripting is not used in this use case. .. GENERATED FROM PYTHON SOURCE LINES 135-144 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/precipitation/GridStat_fcstHRRR-TLE_obsStgIV_GRIB.conf /path/to/user_system.conf See :ref:`running-metplus` for more information. .. GENERATED FROM PYTHON SOURCE LINES 146-168 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 model_applications/precipitation/GridStat_fcstHRRR-TLE_obsStgIV_GRIB/grid_stat/201609041200 (relative to **OUTPUT_BASE**) and will contain the following files: * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_180000V_pct.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_180000V_pjc.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_180000V_prc.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_180000V_pstd.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_180000V.stat * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_190000V_pct.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_190000V_pjc.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_190000V_prc.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_060000L_20160904_190000V_pstd.txt * grid_stat_PROB_PHPT_APCP_vs_STAGE4_GRIB_APCP_A06_070000L_20160904_190000V.stat .. GENERATED FROM PYTHON SOURCE LINES 170-192 Keywords -------- .. note:: * GridStatToolUseCase * PrecipitationAppUseCase * PCPCombineToolUseCase * RegridDataPlaneToolUseCase * GRIBFileUseCase * GRIB2FileUseCase * NetCDFFileUseCase * NOAAWPCOrgUseCase * NOAAHMTOrgUseCase * NOAAHWTOrgUseCase * ConvectionAllowingModelsAppUseCase * ProbabilityVerificationUseCase Navigate to the :ref:`quick-search` page to discover other similar use cases. .. GENERATED FROM PYTHON SOURCE LINES 192-194 .. code-block:: Python # .. _sphx_glr_download_generated_model_applications_precipitation_GridStat_fcstHRRR-TLE_obsStgIV_GRIB.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: GridStat_fcstHRRR-TLE_obsStgIV_GRIB.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: GridStat_fcstHRRR-TLE_obsStgIV_GRIB.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: GridStat_fcstHRRR-TLE_obsStgIV_GRIB.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_