Forest-based and semi-parametric methods for the postprocessing of rainfall ensemble forecasting
Résumé
To satisfy a wide range of end-users, rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We introduce local statistical post-processing methods based on Quantile Regression Forests and Gradient Forests with a semi-parametric extension for heavy-tailed distributions. These hybrid methods make use of the forest-based outputs to fit a parametric distribution that is suitable to model jointly low, medium, and heavy rainfall intensities. Our goal is to improve ensemble quality and value for all rainfall intensities. The proposed methods are applied to daily 51-h forecasts of 6-h accumulated precipitation from 2012 to 2015 over France using the Météo-France ensemble prediction system called PEARP. They are verified with a cross validation strategy and compete favorably with state-of-the-art methods like Analog Ensemble or Ensemble Model Output Statistics. Our methods do not assume any parametric links between the variable to calibrate and possible covariates. They do not require any variable selection step, and can make use of more than 60 predictors available such as summary statistics on the raw ensemble, deterministic forecasts of other parameters of interest, or probabilities of convective rainfall. In addition to improvements in overall performance, hybrid forest-based procedures produced the largest skill improvements for forecasting heavy rainfall events.
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