Temporal Smoothing of Automated Wind Gust and Thunderstorm Warnings

The automatization of weather warnings is an ongoing endeavor and plays an important role in the program RainBoW ("Risk-based, Application-oriented and INdividualizaBle Provision of Optimized Warning Information"), which aims to renew the warning system of the German Meteorological Service (Deutscher Wetterdienst, DWD). Automated warnings are often generated at relatively high frequency, depending on the availability of updated forecast data. For a fixed forecast date, these updates of the warning information may lead to frequent changes of, for example, the warning level, that could confuse end-users. One method to circumvent frequent jumps in the warning level is called smoothing. This work presents a prototype system for temporal smoothing of automated weather warnings, focusing on wind gusts and thunderstorm events to gain temporal stability and to minimize fluctuating warnings.

For wind gusts, this study uses automatically generated warnings based on ensemble wind gust forecasts from the in-house ICON model, which is updated hourly. Smoothing of thunderstorm warnings is based on the output of the NowCastMIX nowcasting application, which predicts the development of thunderstorm cells and their characteristics in 5-minute increments for up to one hour. The techniques to enhance the temporal warning stability are applied to a grid-based framework. For thunderstorm and wind gust warnings, temporal smoothing is achieved by evaluating the maximum warning level across a series of the most recent forecast data for the same forecast date. Wind-related parameters that complement the warning, including gust speeds and wind direction, undergo a weighted smoothing procedure that accounts for the higher precision of more recent forecasts. This methodology aims to reduce unnecessary fluctuations in warning outputs without compromising the responsiveness of the system to genuine changes in weather conditions.

This prototype for smoothing wind gust warnings contributes to the ongoing effort to automatize weather warnings at the DWD and is an important step towards more user-friendly warning-products.