First Results and Evaluation of Automated Wind Gust Warnings

As part of the ongoing renewal of the warning system in the program RainBoW ("Risk-based, Application-oriented and INdividualizaBle Provision of Optimized Warning Information"), the German Meteorological Service (Deutscher Wetterdienst, DWD) aims to produce warning information for up to seven days to inform the public early-on about severe weather. Maintaining such a forecast horizon at a high update frequency requires automated processing chains. In this contribution, we present the current state of a running prototype for automated wind gust warnings.

The prototype’s processing chain uses modern messaging techniques to combine ensemble wind gust forecasts from DWD's in-house model ICON in multiple setups (local area version, EU version, global version). The model data is subsequently translated into gridded threshold exceedance probabilities for different warning levels. If exceedance probabilities are high enough, the according warning level will be assigned. New incoming forecast data from an ICON model version is used to update the exceedance probabilities, which in turn may lead to changes in the assigned warning level for a fixed location and forecast date. We therefore subsequently apply a smoothing across updates to limit the amount of such changes. For completeness, the prototype also provides consistent information about the wind direction, about possible worst-case scenarios, represented by the ensemble maximum of wind gusts, and about uncertainties linked to the ICON model predictions as well as threshold exceedance probabilities. Wherever the spatial resolution of ICON input data is sufficiently high, exceedance thresholds are calculated across multiple topographic layers (i.e., elevation intervals). This approach helps better represent the vertical structure of warnings and reduces false alarms in areas near mountain peaks and valleys.

Monitoring and verification tools were developed to compare (smoothed) gust warning fields with SYNOP observations for case studies of extreme weather situations as well as longer periods. Immediate forecast monitoring indicated that the spatial extent of resulting automated warning data is generally in good agreement with observations. Longer-term verification statistics showed that smoothing increased the probability of detection compared to the original automated warning results. These insights have helped us to improve the configuration of the wind gust prototype.