Forest-based and semi-parametric methods for the postprocessing of rainfall ensemble forecasting

Maxime Taillardat, Anne-Laure Fougères, Philippe Naveau, Olivier Mestre
2019 Weather and forecasting  
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
more » ... pitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called PEARP. They provide calibrated predictive 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.
doi:10.1175/waf-d-18-0149.1 fatcat:5cta7h65rzamtp33m3cphkzfra