High-quality near-real time Quantitative Precipitation Estimation (QPE) and its prediction for the next hours (Quantitative Precipitation Nowcasting, QPN) is of high importance for many applications in meteorology, hydrology, agriculture, construction, water and sewer system management. Especially for the timely prediction of floods in small/meso-scale catchments and of intense precipitation over cities, the value of high-resolution and high-quality QPE/QPN cannot be overrated. Area-covering and high-resolution polarimetric weather radar observations provide the undisputed core information for QPE/QPN providing precipitation intensity, hydrometeor types, and wind. Despite extensive investments in such weather radars, QPE is still based primarily on rain gauge measurements, and no operational flood forecasting system actually dares to employ radar observations for QPE.
RealPEP will advance QPE/QPN to verifiably outperform rain gauge observations when employed for flood predictions in small to medium-sized catchments. To this goal radar polarimetry will be combined with attenuation estimates from commercial microwave link networks for QPE improvement. In addition, information on convection initiation and evolution from satellites and lightning counts from surface networks will be exploited for QPN improvement.
With increasing forecast horizons the predictive power of observation-based nowcasting quickly deteriorates and is outperformed by Numerical Weather Prediction (NWP) based on data assimilation, which fails, however, for the first hours due to the lead time required for model integration and spin-up.
RealPEP will merge observation-based QPN with NWP towards seamless prediction in order to provide optimal forecasts of surface precipitation from the time of observation to days ahead. Despite recent advances, hydrologic components for operational flood prediction are still conceptual, need calibration, and are often unable to objectively digest observational information on the state of the catchments. RealPEP will apply physics-based hydrological models, combined with advanced QPE/QPN/NWP and the assimilation of catchment state observations, to propose an alternative for traditional flood forecasting in small to meso-scale catchments.