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Statistical Postprocessing for Weather Forecasts: Review, Challenges, and Avenues in a Big Data World

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  • 1 Royal Meteorological Institute of Belgium, and European Meteorological Network (EUMETNET), Brussels, Belgium
  • | 2 Norwegian Meteorological Institute, Oslo, Norway
  • | 3 Royal Meteorological Institute of Belgium, and European Meteorological Network (EUMETNET), Brussels, Belgium
  • | 4 Met Office, Exeter, United Kingdom
  • | 5 Met Office, Exeter, United Kingdom
  • | 6 Federal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland
  • | 7 Institute for Stochastics, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • | 8 MetOffice@Reading, Met Office, United Kingdom
  • | 9 Deutscher Wetterdienst, Offenbach, Germany
  • | 10 Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria
  • | 11 European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
  • | 12 Federal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland
  • | 13 Zentralanstalt für Meteorologie und Geodynamik, Vienna, Austria
  • | 14 Royal Meteorological Institute of Belgium, Brussels, Belgium
  • | 15 Finnish Meteorological Institute, Helsinki, Finland
  • | 16 Météo-France, CNRM-UMR 3589, Toulouse, France
  • | 17 Federal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland
  • | 18 Croatian Meteorological and Hydrological Service, Zagreb, Croatia
  • | 19 Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
  • | 20 Météo-France, CNRM-UMR 3589, Toulouse, France
  • | 21 Royal Meteorological Institute of Belgium, Brussels, Belgium
  • | 22 Royal Meteorological Institute of Belgium, Brussels, Belgium
  • | 23 Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
  • | 24 Finnish Meteorological Institute, Helsinki, Finland
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Abstract

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space–time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Stéphane Vannitsem, stephane.vannitsem@meteo.be

Abstract

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space–time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Stéphane Vannitsem, stephane.vannitsem@meteo.be
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