Quantitative evaluation of regional precipitation forecasts using multi-dimensional remote sensing observations (QUEST)

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Quantitative precipitation forecasts will be evaluated by considering the spatial-temporal structure of water in all its three phases using new remote sensing observations. By studying the whole process chain from the water vapour distribution through cloud processes to the amount of precipitation reaching the ground, weaknesses in the treatment of cloud processes in weather forecasting models will be identified. Improvements in predictions should be achieved by improving the assumptions about cloud and precipitation microphysics (e.g. conversion rates, drop size distributions, particle phase and shape) as well as the sub-grid variability. Existing observational datasets will be used in both observation-to-model and model-to-observation approaches. The most important are: detailed observations of the vertical hydrometeor distribution available at observatories equipped with advanced ground-based remote sensors, three dimensional distributions of polarimetric radar parameters and simultaneous observations of the 3D wind field, and high spatial resolution water vapour fields, cloud parameters, and precipitation-relevant microwave radiances from satellite. The use of forward operators allows the full exploitation of the information content of the remote Sensors and is an important step towards future data assimilation methods. The focus of the proposed research is an short-term predictions by the Lokal-Modell of the German Weather Service, however, the created tools will be transferable to other models.