Toward seamless weather forecasts.

Abstract

On a daily basis, MeteoSwiss provides a wide range of automatic weather forecasts to the general public, the aviation and to private customers. These data are provided by an ensemble of heterogeneous individual system wich forces the end-user to choose between different sources and sometimes to combine them despite a limited knowledge of their quality and shortcomings. In addition, the forecasts of the different systems are provided in different formats and through different channels, making combined use even more difficult. Furthermore, from a scientific point of view, combining nowcasting and post-processing approaches in a single step using statistical and/or machine learning methods has been shown to give the best forecast performance for twelve hour precipitation forecasts Deep learning for twelve-hour precipitation forecasts. Nat Commun 13, 5145 (2022)). In such an approach, there is no need for a separate system for the nowcasting range, and therefore no need to combine different forecasts a posteriori, which requires further assumptions and introduces further source of errors and inconsistencies. MeteoSwiss has therefore initiated a project to build a system that will integrate data from different weather observation and weather forecasts data (ECMWF, MeteoSwiss regional ICON implementation) in order to provide a consolidated and easily accessible weather forecast dataset. We aim as well for probabilistic gridded forecasts that are seamless in space and time which can be used by a wide range of application, like hydrological models, automatic generation of warning proposals for forecasters, probabilistic animation of precipitation for the MeteoSwiss App. This project is very much oritented toward end-users and a large effort will be made to assess and meet their needs.