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001     916111
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020 _ _ |a 978-3-95806-675-5
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037 _ _ |a FZJ-2022-05942
100 1 _ |0 P:(DE-Juel1)6952
|a Schultz, Martin
|b 0
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245 _ _ |a Final report of the DeepRain project - Abschlußbericht des DeepRain Projektes
260 _ _ |a Jülich
|b Forschungszentrum Jülich GmbH Zentralbibliothek Verlag
|c 2022
300 _ _ |a 77 p.
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490 0 _ |a Schriften des Forschungszentrums Jülich IAS Series
|v 51
520 _ _ |a The DeepRain project was launched to develop new approaches to combine modern machine learning methods with high performance IT systems for data processing and dissemination in order to produce high-resolution spatial maps of precipitation over Germany. The foundation of this project was the multi-year archive of ensemble model forecasts from the numerical weather model COSMO of the German Weather Service (DWD). Six trans-disciplinary research institutions worked together in DeepRain to develop an end-to-end processing chain which could potentially be used in the future operational weather forecasting context. The project proposal had identified several challenges which had to be overcome in this regard. Next to the technical challenges in establishing a novel data fusion of rather diverse data sets (numerical model data, radar data, ground-based station observations), building scalable machine learning solutions and optimising the performance of data processing and machine learning, there were various scientific challenges related to the small local-scale structures ofprecipitation events, difficulties with finding robust evaluation methods for precipitation forecasts and non-gaussian precipitation statistics combined with highly imbalanced data sets
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536 _ _ |0 G:(BMBF)01IS18047A
|a Verbundprojekt DeepRain: Effiziente Lokale Niederschlagsvorhersage durch Maschinelles Lernen (01IS18047A)
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|a Earth System Data Exploration (ESDE)
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|a Mozaffari, Amirpasha
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700 1 _ |0 P:(DE-Juel1)180790
|a Langguth, Michael
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856 4 _ |u https://juser.fz-juelich.de/record/916111/files/IAS_Series_51.pdf
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