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Efficient Local Precipitation Prediction through Machine Learning (DeepRain)

DeepRain will combine modern methods of machine learning with high-performance data provisioning and processing systems to generate spatially and temporally high-resolution maps with improved and validated precipitation predictions including their uncertainties based on high-resolution regional weather models.

Radar-Online-Aneichung (RADOLAN) hourly numbers


The DeepRain project is a collaboration among the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich, the German Weather Service (DWD), the Universities of Osnabrück and Bonn and the Jacobs University in Bremen. During the three years of funding from BMBF, the partners investigate how modern methods of machine learning can be applied to improve precipitation forecasts in Germany. Precise predictions of rain and snow with a reliable indication of the expected amount of precipitation are still an extreme challenge for weather modelling, especially at the local scales where they are most relevant. more: DeepRain …


DeepRain is funded by the Bundesministerium fuer Bildung und Forschung (BMBF) under grant agreement 01 IS18047A-E.