Title: Forest-based methods and ensemble model output statistics for rainfall ensemble forecasting,
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.