Closing the gap between nowcasting and warning: A prototype showcase

At the German Meteorological Service (Deutscher Wetterdienst, DWD), thunderstorm warnings are currently issued on-detection. In a manual workflow, detected individual thunderstorms are used as a base to delineate warning regions. Depending on the synoptic situation, this can result in a significant workload that may lead to inconsistencies in the spatio-temporal granularity of delineated thunderstorm events. Automating the production of thunderstorm warnings can ameliorate this issue. Therefore, within the RainBoW program ("Risk-based, Application-oriented and INdividualizaBle Provision of Optimized Warning Information"), the automation of thunderstorm warnings is currently implemented using the in-house nowcasting model NowCastMIX¹. It provides thunderstorm warning regions with lead times up to one hour, including information about the associated hazards (such as gusts and precipitation) and their derived warning level. Given the model’s update rate of five minutes, the system must process warning information at a high frequency. This high update rate can lead to undesirable fluctuations in the warning levels prior to an event, potentially confusing end users.

One approach to stabilize the warning level over multiple model updates is temporal smoothing. In this work, we present a prototype for temporal smoothing of automated thunderstorm warnings based on NowCastMix. The smoothing is applied individually to the warning level of each associated hazard on an event basis. The overall thunderstorm warning level is then derived as the maximum warning level among all associated hazards. To further enhance stability, warning durations are binned to prevent rapid changes in the start and end times. The smoothing is applied to gridded data which is subsequently converted to polygons to facilitate mapping onto administrative regions.

To evaluate the impact of the smoothing procedure, we employ an object-based verification approach. Various performance scores are used to determine optimal smoothing parameters for our purposes. These results provide a starting point for further evaluations using administrative regions as a base. Preliminary results suggest that this methodology reduces warning fluctuations while maintaining the spatial and temporal accuracy for effective warnings from a nowcasting system.

¹Paul James et al. DOI:10.1175/WAF-D-18-0038.1