DERISC: Deep learning based Extreme Rainfall and flood warnIngs through Seamless foreCasting​

The extreme precipitation event of July 2021 and the ensuing floods caused 41 deaths and over 2 billion euro in damages in Belgium alone. An impact-based early warning system can improve preparedness and reduce the societal and economic impacts of such extreme precipitation events, as it allows local authorities, emergency services and industry to make better-informed, timely decisions.

Early warning systems require forecasts for weeks ahead, as provided by global numerical weather prediction (NWP) models. However, such models generally fail to capture precipitation extremes, in part due to their limited spatial resolution. Km-scale NWP models, integrated in seamless observation-driven short-term prediction systems such as RMIB's Project IMA, better represent these extremes, making them more suitable for high-resolution (urban) flood models. However, their time horizon is limited to 1-2 days, making them unsuitable for early warnings and proper management of extreme events.

This project aims to meet the need for a consistent and calibrated ensemble forecasting system by seamlessly combining models at different time horizons, and to integrate them in impact models.