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

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  • 1 Croatian Meteorological and Hydrological Service, Borongajska cesta 83d/1, 10000 Zagreb, Croatia
  • 2 Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach, Germany
  • 3 European Center for Medium-range Weather Forecasts, Shinfield Park, Reading, United Kingdom
  • 4 Federal Office of Meteorology and Climatology, MeteoSwiss, Operation Center 1, 8058 Zürich-Flughafen, Switzerland
  • 5 Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00560 Helsinki, Finland
  • 6 Karlsruhe Institute of Technology, Institute for Stochastics, Englerstr. 2, D-76131 Karlsruhe, Germany
  • 7 Météo-France, CNRM - UMR 3589, Toulouse, France
  • 8 Met Office, Exeter, United Kingdom
  • 9 MetOffice@Reading, Met Office, United Kingdom
  • 10 Norwegian Meteorological Institute, Box 43 Blindern, Oslo, Norway
  • 11 Royal Meteorological Institute of Belgium, Avenue Circulaire, 3, 1180 Brussels, Belgium
  • 12 Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE De Bilt, The Netherlands
  • 13 Zentralanstalt für Meteorologie und Geodynamik, Hohe Warte 38, 1190 Wien, Austria
  • 14 European Meteorological Network (EUMETNET), Avenue Circulaire, 3, 1180 Brussels, Belgium
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Capsule

State-of-the-Art statistical postprocessing techniques for ensemble forecasts are reviewed, together with the challenges posed by a demand for timely, high-resolution and reliable probabilistic information. Possible research avenues are also discussed.

Corresponding author: Stéphane Vannitsem, Stephane.Vannitsem@meteo.be

Capsule

State-of-the-Art statistical postprocessing techniques for ensemble forecasts are reviewed, together with the challenges posed by a demand for timely, high-resolution and reliable probabilistic information. Possible research avenues are also discussed.

Corresponding author: Stéphane Vannitsem, Stephane.Vannitsem@meteo.be
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