Abstract
Abstract 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 postprocessing methods based on quantile regression forests and gradient forests with a semiparametric 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 Prévision d’Ensemble ARPEGE (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 variables 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.
Highlights
1.1 Post-processing of ensemble forecastsAccurately forecasting weather is paramount for a wide range of end-users, e.g. air traffic controllers, emergency managers and energy providers
Scores used concern respectively (i) global performance measured by the continuous ranked probability score (CRPS); (ii) reliability performance, measured by the mean, the normalized variance and the entropy of the PIT histograms, denoted by Ω in the sequel; (iii) gain in CRPS compared to the raw ensemble, measured by the Skill of the CRPS using the raw ensemble as baseline
The tail-extended methods get a lower CRPS, that can be explained by their skill for extreme events
Summary
1.1 Post-processing of ensemble forecastsAccurately forecasting weather is paramount for a wide range of end-users, e.g. air traffic controllers, emergency managers and energy providers (see, e.g. Pinson et al, 2007; Zamo et al, 2014). Despite its recent developments in national meteorological services, ensemble forecasts still suffer of bias and underdispersion Hamill and Colucci, 1997) At least two types of statistical methods have emerged in the last decades: analogs method and ensemble model output statistics (EMOS) Xm) the corresponding m ensemble member forecasts, the EMOS predictive distribution is a distribution whose parameters depend on the values of Van Schaeybroeck and Vannitsem (2015) investigated member-by-member post-processing techniques and Taillardat et al (2016) found that quantile regression forests (QRF) techniques performed well for temperatures and wind speed data
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