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Reliable Probabilities Through Statistical Post-processing of Ensemble Forecasts

  • Bert Van SchaeybroeckEmail author
  • Stéphane Vannitsem
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

We develop post-processing approaches based on linear regression that make ensemble forecasts more reliable. First of all we enforce climatological reliability (CR) in the sense that the total variability of the forecast is equal the variability of the observations. Second, we impose ensemble reliability (ER) such that the spread around the ensemble mean of the observation coincides with the one of the ensemble members. Since, generally, different ensembles have different sizes, standard post-processing methods tend to overcorrect ensembles with large spreads. By taking variable values of the error variances, our forecast becomes more reliable at short lead times as reflected by a flatter rank histogram. We illustrate our findings using the Lorenz 1963 model.

Keywords

Lead Time Bias Correction Ensemble Forecast Ensemble Spread Short Lead Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Royal Meteorological InstituteBrusselsBelgium

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