Mixtures of (skewed) Gaussian distributions for statistical post-processing

Abstract

The implementation of statistical post-processing of ensemble forecasts is increasingly developed among national weather services. The so-called Ensemble Model Output Statistics (EMOS) method, which consists in generating a given distribution whose parameters depend on the raw ensemble, leads to significant improvments in forecast performance for a low computational cost, and so is particularly appealing for reduced performance computing architectures. However, the choice of a parametric distribution has to be sufficiently consistent so as not to lose information on predictability such as multimodalities or asymmetries.