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@INPROCEEDINGS{Langguth:1034617,
      author       = {Langguth, Michael and Harder, Paula and Schicker, Irene and
                      Patnala, Ankit and Lehner, Sebastian and Dabernig, Markus
                      and Mayer, Konrad},
      title        = {{A} {B}enchmark {D}ataset for {M}eteorological
                      {D}ownscaling},
      reportid     = {FZJ-2024-07378},
      pages        = {N/A},
      year         = {2024},
      abstract     = {High spatial resolution in atmospheric representations is
                      crucial across Earth science domains, but global reanalysis
                      datasets like ERA5 often lack the detail to capture local
                      phenomena due to their coarse resolution. Recent efforts
                      have leveraged deep neural networks from computer vision to
                      enhance the spatial resolution of meteorological data,
                      showing promise for statistical downscaling. However,
                      methodological diversity and insufficient comparisons with
                      traditional downscaling techniques challenge these
                      advancements. Our study introduces a benchmark dataset for
                      statistical downscaling, utilizing ERA5 and the
                      finer-resolution COSMO-REA6, to facilitate direct
                      comparisons of downscaling methods for 2m temperature,
                      global (solar) irradiance and 100m wind fields. Accompanying
                      U-Net, GAN, and transformer models with a suite of
                      evaluation metrics aim to standardize assessments and
                      promote transparency and confidence in applying deep
                      learning to meteorological downscaling.},
      month         = {May},
      date          = {2024-05-07},
      organization  = {International Conference on Learning
                       Representations, Vienna (Austria), 7
                       May 2024 - 11 May 2024},
      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)8},
      doi          = {10.34734/FZJ-2024-07378},
      url          = {https://juser.fz-juelich.de/record/1034617},
}