000916111 001__ 916111
000916111 005__ 20230201120434.0
000916111 0247_ $$2Handle$$a2128/33144
000916111 0247_ $$2URN$$aurn:nbn:de:0001-2023013106
000916111 020__ $$a978-3-95806-675-5
000916111 037__ $$aFZJ-2022-05942
000916111 1001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b0$$eEditor
000916111 245__ $$aFinal report of the DeepRain project  - Abschlußbericht des DeepRain Projektes
000916111 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek Verlag$$c2022
000916111 300__ $$a77 p.
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000916111 4900_ $$aSchriften des Forschungszentrums Jülich IAS Series$$v51
000916111 520__ $$aThe 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
000916111 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000916111 536__ $$0G:(BMBF)01IS18047A$$aVerbundprojekt DeepRain: Effiziente Lokale Niederschlagsvorhersage durch Maschinelles Lernen (01IS18047A)$$c01IS18047A$$x1
000916111 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x2
000916111 7001_ $$0P:(DE-Juel1)166264$$aMozaffari, Amirpasha$$b1$$eEditor$$ufzj
000916111 7001_ $$0P:(DE-Juel1)180790$$aLangguth, Michael$$b2$$eEditor$$ufzj
000916111 8564_ $$uhttps://juser.fz-juelich.de/record/916111/files/IAS_Series_51.pdf$$yOpenAccess
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000916111 9141_ $$y2022
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