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@INPROCEEDINGS{Langguth:1034626,
      author       = {Langguth, Michael and Lehner, Sebastian and Harder, Paula
                      and Patnala, Ankit and Schicker, Irene and Dabernig, Markus
                      and Schultz, Martin},
      title        = {{D}ownscale{B}ench: {A} benchmark dataset for
                      statisticaldownscaling of meteorological fields},
      reportid     = {FZJ-2024-07387},
      year         = {2024},
      abstract     = {Statistical downscaling with deep neural networks has
                      recently gained a lot of momentum in the meteorological
                      community. While several studies show promising results,
                      direct comparison between different approaches is often
                      impeded due to a large variety in the related downscaling
                      task (target quantity), the deployed dataset and the
                      evaluation framework. Furthermore, the performance of deep
                      neural networks for statistical downscaling is barely
                      compared to classical downscaling methods that have been
                      established in the meteorological domain over the last
                      decades. To enable fair comparison between different
                      downscaling techniques, we suggest DownscaleBench, a
                      benchmark dataset based on the coarse-grained ERA5 and the
                      high-resolved COSMO REA6-dataset. DownscaleBench provides a
                      standardized set of predictors for three (deterministic)
                      downscaling tasks, that are downscaling of 2m temperature,
                      100m wind and solar irradiance. The ready-to-use datasets
                      are complemented by a set of baseline models (standard deep
                      neural networks and a classical method) whose performance is
                      accessed in a task-specific, but standardized evaluation
                      framework.},
      month         = {Aug},
      date          = {2024-08-29},
      organization  = {Workshop on Large-Scale Deep Learning
                       for the Earth System, Bonn (Germany),
                       29 Aug 2024 - 30 Aug 2024},
      subtyp        = {Other},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / MAELSTROM - MAchinE
                      Learning for Scalable meTeoROlogy and cliMate (955513) /
                      Verbundprojekt: MAELSTROM - Skalierbarkeit von Anwendungen
                      des Maschinellen Lernens in den Bereichen Wetter und
                      Klimawissenschaften für das zukünftige Supercomputing
                      (16HPC029) / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)955513 / G:(BMBF)16HPC029
                      / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.34734/FZJ-2024-07387},
      url          = {https://juser.fz-juelich.de/record/1034626},
}