An extreme weather identifier for severe weather warnings: general concept and first results

At MeteoSwiss, we are currently developing a modular framework to automatically identify extreme weather events that will allow us to generate a broad range of user-tailored warning products. Here, we introduce this Extreme Weather Identifier (EWI) framework in the context of public warnings, namely warning polygon proposals for forecasters to support them in issuing public warnings of high quality in a timely manner. The EWI translates NWP ensemble forecasts into warning products – in our case, warning polygon proposals – by employing a pre-defined sequence of processing steps with configurable parameters. By applying grid-point-specific warning thresholds to each NWP ensemble member, it obtains local-scale warning information and by aggregating the underlying information in space and time, it accounts for spatio-temporal representativeness issues and facilitates communication. In a first aggregation step, the spatio-temporal representativeness issues are addressed by employing neighborhood approaches to detect extreme weather in each ensemble member individually. Subsequently, all members are evaluated jointly in order to assess the probability that the extreme weather actually takes place. Afterwards, areas exceeding a minimal probability threshold are grouped together into individual regionally-valid warning polygons. At this point, the communication-motivated aggregation to the visual scale of interest starts and all further changes simply serve the goal to produce warning products that can be easily communicated to their target audience without any additional physical justifications. To allow the forecasters to obtain an in-depth understanding of the EWI’s proposals and thoroughly assess their quality, not only the proposals themselves will be distributed but also outcomes of key intermediate steps of the EWI’s processing sequence. Products covering the EWI’s processing steps until the communication-motivated aggregation starts are intended to be made available to forecasters in real-time towards the end of this year and we will illustrate them in this contribution with examples from past warning events.