From Ensembles to Alerts: Deriving Event-Based Information from Forecasts

Weather warnings issued by national meteorological services are commonly derived through manual interpretation of numerical weather prediction output. While this approach allows for expert judgement, it limits update frequency and lead time and introduces variability between forecasters. Within the RainBoW program ("Risk-based, Application-oriented and INdividualizaBle Provision of Optimized Warning Information"), the German Meteorological Service (Deutscher Wetterdienst, DWD) is developing a prototype system that derives warning-relevant weather events from ensemble forecast data. This contribution presents the implementation and refinement of this prototype, with focus on frost and rain. It demonstrates how automated event detection can be used to supplement analysis that have traditionally been performed manually.

The prototype system integrates ensemble data of multiple configurations of the ICON numerical weather prediction model as input, to maximise the provided forecast lead time and accuracy. The different setups are ICON-D2 Rapid Update Cycle (RUC), ICON-D2, ICON-EU, and ICON with forecast lead times ranging from 14 hours up to seven days. These forecast datasets are supplemented by additional information, such as radar-based precipitation data. Event detection is performed per warning element, but similar across various input data sources. A rule-based approach is used to detect events in each ensemble member individually. Detected events are subsequently aggregated across ensemble members and model configurations in order to derive consistent event signals that are suitable for warning generation. This approach enables the combination of information from multiple models while also utilizing the ensemble character of the input data.

A key achievement of this work is the development of a processing chain that automatically generates updated event information whenever new model data become available. This enables a substantially higher update frequency than workflows based on human forecasters, while maintaining longer lead times. Currently, it operates as a research prototype and is not yet part of the operational warning workflow at DWD. Selected case studies demonstrate the detection of frost and rain events from ensemble forecasts. The results are compared with warning information derived from observational datasets to investigate the behaviour and interpretability of the prototype.

Future work will focus on comprehensive statistical verification, further refinement of the methodology, and the extension of the system to additional warning elements.