Developing a new warning system at the German Meteorological Service

One of the most important tasks of the German Meteorological Service (DWD) is to issue weather warnings. In the current operational warning system, forecasters interpret a multitude of meteorological data with respect to a set of threshold-based warning criteria. Warning regions are then manually identified and sent to an automatic post-processing chain, which generates the final warning products to be distributed in relevant communication channels.

Even though DWD’s weather warnings are mostly perceived well by its end-users, there are some drawbacks to the current system. They include a short lead time of warnings, a complex catalog of warning criteria, and missing flexibility towards specialized user requirements. To tackle these shortcomings, DWD launched a program called RainBoW (“Risikobasierte, anwendungsorientierte, indiviualisierbare Bereitstellung optimierter Warninformationen” or “Risk-based, application-oriented and individualizable delivery of optimized weather warnings” in English) to optimize its warning system.

RainBoW focusses on three fields of action. First, the forecast horizon of warnings will be extended up to 7 days into the future to inform users early on, while also communicating the uncertainties resulting from larger lead times. Second, the comprehensibility of warnings will be enhanced by reducing the complexity of the warning criteria catalog and by taking weather impacts into account. Third, warnings will be made individualizable. This means, that users with specific requirements, e.g. in terms of warning thresholds and/or considered areas, will get the possibility to configure individual warnings matching their particular use case and their individual meteorological thresholds. These individualized warnings will be generated automatically based on user-created warning profiles. 

The three fields of action serve RainBoW’s overarching goal to tailor warnings more strongly towards the needs of end-users, such that they are enabled to take appropriate action in case of significant and extreme weather.

This contribution will describe the conceptual ideas behind RainBoW along with some first results.