""" Grid-Stat and Series-Analysis: BMKG APIK Seasonal Forecast ========================================================== model_applications/s2s/GridStat_SeriesAnalysis_fcstNMME_obsCPC_seasonal_forecast.conf """ ############################################################################## # .. contents:: # :depth: 1 # :local: # :backlinks: none ############################################################################## # Scientific Objective # -------------------- # # The process of seasonal forecasting with a time horizon of one to many months # (typically 6 to 9 months) poses new challenges to tools primarily developed for # weather forecasting that cover a few days. These challenges include two aspects # in particular: (1) a dramatically expanded time variable, and (2) a verification # that is by design backward oriented using extensive hindcasts over past decades # rather than the rapid verification possible in short-range weather forecasting. # Therefore, the scientific objective of the seasonal forecast usecase involves # the expansion of options to describe time as well as the strategic selection # of hindcasts. # # Time: # Commonly METplus expresses time intervals in the minutes, hours, and days. # Month and year intervals were not supported since there is not a constant length for these units. # Therefore, modifications to METplus were made to support these intervals by determining the offset # relative to a given time. # # Input data: # The input data from seasonal forecasts is generally based on daily, weekly, decadal # (10-day), monthly or seasonal time integrated intervals. The time variable therefore is # often no longer a simple snapshot of the system but rather representing an average, a # sum (precipitation), or a particular statistic (maximum wind, minimum temperature, wind # variability) over the integration time period. This requires some adjustment from the # traditional approach in forecast verification where forecast time ("valid-time") is # simply a snapshot out of a continuous run. # # Hindcasts: # The objective of seasonal forecasts is no longer the exact location and intensity # of one particular weather event, such as a storm, a frontal passage, or high wind # conditions. Rather, seasonal forecasting focuses more on the statistical properties # over a period of time, be it a 10-day interval, a month, or even a three month season. # The verification of a new, forward looking seasonal forecast requires assessments of # the forecast systems ability to appropriately forecast that longrange behavior of the # weather (here, only atmospheric verification is considered, but the same concept would # apply ocean or any other longrange forecast system). Because weather properties # commonly change significantly over the course of the season, samples to verify the # prognostic system can not be taken from the immediate days, weeks of months before the # forecast. Hindcasting in the seasonal context requires a complete set of forecasts # based on the same season but during past years. A current July-1 2019 forecast, therefore # requires many July-1 forecasts for as many years in the past as possible, given that # the forecast system is the same as the one used for the current forecast cycle into the # future. Operational centers offer hindcasts, also sometimes called "re-forecasts", with # the current, most up-to-date forecast system. MET and METplus therefore need to be able # to extract the appropriate collection of past forecasts. This includes the identification # of the same Julian-day-of-Year init-dates from forecasts cycles from past years, and then # identify the different lead-times of interest generally ranging from one to 6 or more # months. # # Verification: # The verification steps can then utilize the existing collection of verification tools. # In comparison to weather forecasts, the only difference is that the data, as stated # above, are not snapshots but time-integrated values (averages, sums, statistics) that # are representing a whole period of time. The verification then focuses on comparisons # of these derivatives of the forecast simulations. In practice, a further step might be # added prior to, or as a key step during verification: the formation of anomalies of the # forecasts compared to long-term expected averages. A rainfall forecasts can therefore # be verified in both absolute as well as anomaly context where some analyses might focus # on extreme rainfall threshold exeedance of, for example, 500mm per month. At the same # time, the same forecast might be verified for the 3 months rainfall average in comparison # with the long-term expected mean. The verification might then assess how well the system # can foresee the occurrence of below average rainfall over the season, and possibly some # selected thresholds there (e.g., ability to forecast mean seasonal rainfall below # the 10-th percentile of seasonal rainfall). Finally, flexibility in formulating forecast # verification strategies is important as forecast skill might vary by location, the timing # within the seasonal cycle, or the state of the evolving coupled system (the rapid onset # of a strong El Nino will lead to significantly different forecast skill compared to a # neutral state in the Pacific). Memory from past months, for example when considering # accumulated soil moisture, might also influence the forecast skills. Seasonal forecast # verification therefore requires understanding of the climate system; MET and METplus # then need to offer the flexibility to tailor verification strategies and to potentially # craft conditional approaches. # # Overall, seasonal forecasts don't require a new verification approach. It does however # put demands on the flexibility of dealing with a significantly exapanded range of the # time variable as well as logistic infrastructure to select appropriate hindcast samples # from long hindcast or re-forecast archives. Scientifically, the challenges are mostly # restricted in the appropriate formulation of verificaation questions that address # specific forecast objectives. Compared to weather forecastsing, seasonal forecasts need # to draw their skill from slowly changing components in the coupled Earth system while # acknowledging the high-frequency noise of weather superposed on these 'climatologically' # evolving background conditions. In many regions of the world, the noise might dominate # that background climate and forecast skill is low. It is therefore the task of seasonal # forecast verification to identify where there is actually skill for particular properties # of the forecasts over a wide range of lead-times. The skill might be dependent on # location, on the timing within the seasonal cycle, or even on the evolving state of the # coupled system. ############################################################################## # Version Added # ------------- # # METplus version 3.0 ############################################################################## # Datasets # -------- # # All datasets are traditionally in netCDF format. Grids are either regular gaussian # Latitude/Longitude grids or they are Lambert-conformal WRF grids. # # The forecast datasets contain weekly, monthly or seasonally integrated data. Here, the # time format of the use-case is monthly. Since the verification is done on the hindcasts # rather than the forecast (would require another 6 months of waiting), the key # identification here is the month of initialization and then the lead-time of the forecast # of interest. # # The hindcast data, the 'observational' data that is to be compared to the forecast, # is a collection of datasets formatted in equivalent format to the forecast. The # hindcast ensemble is identified through the year in the filename (as well as in the # time variable inside the netCDF file). # # **Forecast:** North American Multi-Model Ensemble (NMME) # # **Observation:** CPC Precipitation Reference Data # # **Climatology:** CPC Precipitation Reference Data # # **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 loops over initialization years and processes forecast lead months with GridStat # It also processes the output of GridStat using two calls to SeriesAnalysis. # ############################################################################## # External Dependencies # --------------------- # # You will need to use a version of Python 3.6+ that has the following packages installed: # # * netCDF4 ############################################################################## # METplus Workflow # ---------------- # # **Beginning time (INIT_BEG):** 198207 # # **End time (INIT_END):** 201007 # # **Increment between beginning and end times (INIT_INCREMENT):** 1Y # # **Sequence of forecast leads to process (LEAD_SEQ):** 1m, 2m, 3m, 4m, 5m, 6m # # The following tools are used for each run time: GridStat # # This example loops by initialization time. Each initialization time is July of each year from 1982 to 2010. For each init time it will run once, processing forecast leads 1 month through 5 months. The following times are processed: # # Run times: # # | **Init:** 1982-07 # | **Forecast leads:** 1 month, 2 months, 3 months, 4 months, 5 months # | # | **Init:** 1983-07 # | **Forecast leads:** 1 month, 2 months, 3 months, 4 months, 5 months # | # | **Init:** 1984-07 # | **Forecast leads:** 1 month, 2 months, 3 months, 4 months, 5 months # | # | **Init:** 1985-07 # | **Forecast leads:** 1 month, 2 months, 3 months, 4 months, 5 months # | # | ... # | # | **Init:** 2009-07 # | **Forecast leads:** 1 month, 2 months, 3 months, 4 months, 5 months # | # | **Init:** 2010-07 # | **Forecast leads:** 1 month, 2 months, 3 months, 4 months, 5 months ############################################################################## # METplus Configuration # --------------------- # # .. highlight:: bash # .. literalinclude:: ../../../../parm/use_cases/model_applications/s2s/GridStat_SeriesAnalysis_fcstNMME_obsCPC_seasonal_forecast.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:: GridStatConfig_wrapped # # .. literalinclude:: ../../../../parm/met_config/GridStatConfig_wrapped # # .. dropdown:: SeriesAnalysisConfig_wrapped # # .. literalinclude:: ../../../../parm/met_config/SeriesAnalysisConfig_wrapped ############################################################################## # Python Embedding # ---------------- # # This use case does not use Python embedding. ############################################################################## # 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/s2s/GridStat_SeriesAnalysis_fcstNMME_obsCPC_seasonal_forecast.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 model_applications/s2s/GridStat_SeriesAnalysis_fcstNMME_obsCPC_seasonal_forecast/GridStat (relative to **OUTPUT_BASE**) # # For each month and year there will be two files written:: # # * grid_stat_NMME-hindcast_precip_vs_CPC_IC{%Y%b}01_2301360000L_20081001_000000V.stat # * grid_stat_NMME-hindcast_precip_vs_CPC_IC{%Y%b}01_2301360000L_20081001_000000V_pairs.nc # # Output from SeriesAnalysis will be found in model_applications/s2s/GridStat_SeriesAnalysis_fcstNMME_obsCPC_seasonal_forecast/SeriesAnalysis (relative to **OUTPUT_BASE**) # # For each month there will be two files written:: # # * series_analysis_NMME_CPC_stats_ICJul_{%m}_climo.nc # * series_analysis_NMME_CPC_stats_ICJul_{%m}_full_stats.nc # ############################################################################## # Keywords # -------- # # .. note:: # # * GridStatToolUseCase # * SeriesAnalysisUseCase # * NetCDFFileUseCase # * LoopByMonthFeatureUseCase # * NCAROrgUseCase # * RuntimeFreqUseCase # * ClimatologyUseCase # # Navigate to the :ref:`quick-search` page to discover other similar use cases. # # # # sphinx_gallery_thumbnail_path = '_static/s2s-GridStat_SeriesAnalysis_fcstNMME_obsCPC_seasonal_forecast.png' #