This project leverages the deep learning (DL) approach, highly effective for data fusion of multimodal observations, by extending it to the fusion or blending of three types of forecasts in a residual learning paradigm:

• Nowcasts based on multimodal observations,

• Short-range (0-3 days) NWP

• Medium-range (0-2 weeks) global NWP forecasts

We will integrate nowcasts for the first time driven by a multimodal DL-based QPE in our seamless prediction system.

Downscaling, calibration and blending will be considered in a single framework, and different DL architectures such as Unets and GANs will be compared.

The coupling to hydrological models will be implemented relatively early in the project, to ensure that impact models can optimally use the ensemble forecasts at their full resolution and time range, and to enable impact-based validation.