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arXiv · 1711.10937

Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting

Maxime Taillardat·Anne-Laure Fougères·Philippe Naveau·Olivier Mestre
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Abstract

Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-tailed distributions. Our goal is to improve ensemble quality for all types of precipitation events, heavy-tailed included, subject to a good overall performance. Our hybrid proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the M{é}t{é}o-France ensemble prediction system called PEARP. They provide calibrated pre-dictive distributions and compete favourably with state-of-the-art methods like Analogs method or Ensemble Model Output Statistics. In particular, hybrid forest-based procedures appear to bring an added value to the forecast of heavy rainfall.

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Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau, Olivier Mestre. 2017-11-29. Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting. https://doi.org/10.1175/waf-d-18-0149.1

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