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@BOOK{Schultz:916111,
      key          = {916111},
      editor       = {Schultz, Martin and Mozaffari, Amirpasha and Langguth,
                      Michael},
      title        = {{F}inal report of the {D}eep{R}ain project -
                      {A}bschlußbericht des {D}eep{R}ain {P}rojektes},
      volume       = {51},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek Verlag},
      reportid     = {FZJ-2022-05942},
      isbn         = {978-3-95806-675-5},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {77 p.},
      year         = {2022},
      abstract     = {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},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Verbundprojekt
                      DeepRain: Effiziente Lokale Niederschlagsvorhersage durch
                      Maschinelles Lernen (01IS18047A) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(BMBF)01IS18047A /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)3},
      urn          = {urn:nbn:de:0001-2023013106},
      url          = {https://juser.fz-juelich.de/record/916111},
}