This title appears in the Scientific Report :
2022
Please use the identifier:
http://hdl.handle.net/2128/33144 in citations.
Final report of the DeepRain project - Abschlußbericht des DeepRain Projektes
Final report of the DeepRain project - Abschlußbericht des DeepRain Projektes
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-y...
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Personal Name(s): | Schultz, Martin (Editor) |
---|---|
Mozaffari, Amirpasha (Editor) / Langguth, Michael (Editor) | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Imprint: |
Jülich
Forschungszentrum Jülich GmbH Zentralbibliothek Verlag
2022
|
Physical Description: |
77 p. |
ISBN: |
978-3-95806-675-5 |
Document Type: |
Book |
Research Program: |
Earth System Data Exploration Verbundprojekt DeepRain: Effiziente Lokale Niederschlagsvorhersage durch Maschinelles Lernen Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups |
Series Title: |
Schriften des Forschungszentrums Jülich IAS Series
51 |
Link: |
OpenAccess |
Publikationsportal JuSER |
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 |