Statistical postprocessing of ensemble forecasts for severe weather at Deutscher Wetterdienst - R. Hess (Nonlinear Processes in Geophysics, 2020)
This paper gives an overview of Deutscher Wetterdienst's (DWD's) postprocessing system called Ensemble-MOS together with its motivation and the design consequences for probabilistic forecasts of extreme events based on ensemble data. Forecasts of the ensemble systems COSMO-D2-EPS and ECMWF-ENS are statistically optimised and calibrated by Ensemble-MOS with a focus on severe weather in order to support the warning decision management at DWD. Ensemble mean and spread are used as predictors for linear and logistic multiple regressions to correct for conditional biases. The predictands are derived from synoptic observations and include temperature, precipitation amounts, wind gusts and many more and are statistically estimated in a comprehensive model output statistics (MOS) approach. Long time series and collections of stations are used as training data that capture a sufficient number of observed events, as required for robust statistical modelling. Logistic regressions are applied to probabilities that predefined meteorological events occur. Details of the implementation including the selection of predictors with testing for significance are presented. For probabilities of severe wind gusts global logistic parameterisations are developed that depend on local estimations of wind speed. In this way, robust probability forecasts for extreme events are obtained while local characteristics are preserved. The problems of Ensemble-MOS, such as model changes and consistency requirements, which occur with the operative MOS systems of the DWD are addressed.