Data Driven Prediction of Severe Convection at DWD. An Overview of Recent Developments

Abstract

Thunderstorms endager life and infrastructure. Accurate and precise prediction of thunderstorms are therefore helpful to enable protections measures and to reduce the risks. This manuscript presents the latest developments to improve thunderstorm forecasting in the first few hours. This includes the description and discussion of a new Julia-based method (JuliaTSnos) for the temporal extrapolation of thunderstorms and the blending of this method with the numerical weather prediction model (NWP) ICON. The combination of ICON and JuliaTSnow attempts to overcome the limitations associated with the pure extrapolation of observations wit Atmospheric Motion Vectors (AMVs) and thus increase the prediction horizon. For the blending the operational ICON-D2 is used, but also the experimental ICON-RUC, which is performed with a higher data assimilation update cycle. The blended products are evaluated against lightning data. The Critical Success Index (CSI) for the blended RUC product is higher for all forecast time steps. This is mainly due to higher resolution of the AMVs (prediction hours 0-2) and the rapid update cycle of ICON-RUC (prediction hours 2-6). The results demonstrate the potential of the rapid update cycle to improve the short term forecasts of thunderstorms. Also the transition between AMV driven nowcasting to NWP is much smoother in the blended RUC product, which points to the advantages of fast data assimilation for seamless predictions. The CSI is well above the critical value of 0.5 for the 0-2 hour forecasts. However, also with RUC the CSI drops below 0.5 as soon as the last forecast is more than 3 hours away from the last data assimilation, indicating the lack of the model physics to accurately predict thunderstorms. This lack is simply a result of chaos theory. Within this scope the role of NWP in comparison with Artificial Intelligence (AI) is discussed and it is concluded that AI could replace physical short term forecasts in the near future.