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Tricks for improving non-homogeneous regression for probabilistic precipitation forecasts: Perfect predictions, heavy tails, and link functions

Listed author(s):
  • Manuel Gebetsberger


  • Jakob W. Messner


  • Georg J. Mayr


  • Achim Zeileis


Raw ensemble forecasts display large errors in predicting precipitation amounts and its forecast uncertainty, especially in mountainous regions where local effects are often not captured. Therefore, statistical post-processing is typically applied to obtain automatically corrected weather forecasts where precipitation represents one of the most challenging quantities. This study applies the non-homogenous regression framework as a start-of-the-art ensemble post-processing technique to predict a full forecast distribution and improves its forecast performance with three statistical tricks. First of all, a novel split-type approach effectively accounts for perfect ensemble predictions that can occur. Additionally, the statistical model assumes a censored logistic distribution to deal with the heavy tails of precipitation amounts. Finally, the optimization of regression coefficients for the scale parameter is investigated with suitable link-functions. These three refinements are tested for stations in the European Alps for lead-times from +24h to +48h and accumulation periods of 24 and 6 hours. Results highlight an improvement due to a combination of the three statistical tricks against the default post-processing method which does not account for perfect ensemble predictions. Probabilistic forecasts for precipitation amounts as well as the probability of precipitation events could be improved, especially for 6 hour sums.

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Paper provided by Faculty of Economics and Statistics, University of Innsbruck in its series Working Papers with number 2016-28.

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Length: 25 pages
Date of creation: Oct 2016
Handle: RePEc:inn:wpaper:2016-28
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  1. Thordis L. Thorarinsdottir & Tilmann Gneiting, 2010. "Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 371-388.
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