DERISC: Deep learning based Extreme Rainfall and flood warnIngs through Seamless foreCasting​
Objectives
- The realization of a real-time deep learning (DL) based multimodal quantitative precipitation estimate (QPE)
- The creation of a seamless precipitation forecast product that is:
- frequently updated, to ingest the most recent observations;
- probabilistic and ensemble-based,
- sharp and calibrated
- based on state-of-the-art ensemble prediction systems: pySTEPS nowcasting, ACCORD’s limited area NWP, and ECMWF’s medium-range ensemble forecasts.
- Integration of these forecasts in hydrological models to enable impact-based early warnings for extreme precipitation events.