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

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.

Different distributions are applied to the post-processing of the ECMWF ensemble forecast of surface temperature. More precisely, mixture of Gaussian and skew-Normal distributions are tryed from 3 up to 360h lead time forecasts. For this work, analytical formulas of the continuous ranked probability score have been derived. We will discuss the first results obtained judging both overall performance and tolerance to mispecification.