Mesoscale Weather Extremes - Theory, Spatial Modeling and Prediction

High-impact extreme weather events, such as heavy rainfall and/or strong winds are very often associated with intensive atmospheric fronts with strong gradients or deep moist convection. These events are governed by mesoscale atmospheric dynamics, operating on horizontal scales ranging from a few to several hundred kilometers. Forecasts of high-impact mesoscale weather are generated using ensemble prediction systems (EPS) of limited area numerical weather prediction (NWP) models. For example, the German Meteorological Service (Deutscher Wetterdienst; DWD) operates an ensemble based on the COSMO-DE model, which is a state of the art convection resolving version of the Consortium for Small-Scale Modeling (COSMO) limited area model. Realizing the full potential of an ensemble of NWP forecasts requires statistical postprocessing, in that model biases and dispersion errors need to be addressed. Furthermore, assessing the risk of the occurrence of high impact weather events requires postprocessing approaches that are specifically tailored to extremes, and that are physically coherent across weather variables, space and time. Finally, understanding the physical processes that contribute to the building-up of severe weather, and incorporating this knowledge in the statistical postprocessing helps improving predictions of high-impact weather. The combined expertise of the WEX-MOP research group is dynamical and statistical meteorology, spatial statistics, statistical ensemble postprocessing, and methodology and theory for extremes.