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

By increasing the forecast reliability and sharpness at longer forecast lead times, we ensure that mitigating actions can be made in a more timely and effective manner at all decision levels, thereby reducing the impact of these extreme events. The improved precipitation forecasts can also promote the sustainable exploitation of resources by enabling more efficient water management such as smart water buffering. While the focus of the project is on extreme precipitation events, the methodology developed will be transferable to other weather variables.

Embedded in the research groups of the two FED-tWIN researchers for "DEEP" and "EXPRIMA", the project bridges the two objectives, culminating in an end-to-end system: from multimodal observations, through AI and physical multi-modelling, to accurate impact-based warnings. It will support and strengthen scientific collaborations between RMI, KU Leuven and VUB.