Deriving Event-Based Warnings from Ensemble Forecasts

National weather services play a crucial role in mitigating the impact of severe weather events by issuing timely warnings for conditions such as frost (prolonged sub-threshold temperatures) or heavy rain (intense rain over a defined period). Automating the production of weather warnings has the potential to improve the consistency and uniformity of warnings while improving operational efficiency. Here, we present advancements in a prototype designed to automate critical processes within the new warning system of the German Meteorological Service (Deutscher Wetterdienst, DWD), which is developed in the program RainBoW ("Risk-based, Application-oriented and INdividualizaBle Provision of Optimized Warning Information"). This work introduces an algorithm designed to derive consistent weather events from forecast members drawn from various model versions and types.

By standardizing event detection within ensemble forecasts, the prototype aims to improve the accuracy and coherence of automated warnings. The system currently integrates real-time meteorological data from the numerical weather model ICON in four setup variations (rapid update cycle and normal cycle of local area version, EU version and global version). It applies rule-based decision-making to assess severe weather conditions within individual ensemble members and aggregates the various outputs into a unified result. The objective is to generate seamless warning information with lead times of up to seven days. By processing ensemble members from different model variants collectively while allowing adjustable weighting of inputs, we ensure a balanced and adaptable mechanism that accounts for variations in model performance. Preliminary results suggest that improvements can be expected especially in complex terrain, where the characteristics of deep valleys and high peaks are much better captured when deriving frost warnings. Therefore, this enhancement has the potential to increase user benefit by delivering more precise and actionable warnings.