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


  1. The realization of a real-time deep learning (DL) based multimodal quantitative precipitation estimate (QPE)
  2. 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.
  3. Integration of these forecasts in hydrological models to enable impact-based early warnings for extreme precipitation events.

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