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@INPROCEEDINGS{Gong:908363,
      author       = {Gong, Bing and Langguth, Michael and Ji, Yan and Mozaffari,
                      Amirpasha and Mache, Karim and Schultz, Martin},
      title        = {{S}tatistical {D}ownscaling of {S}urface {T}emperature and
                      {P}recipitation with {D}eep {N}eural {N}etworks},
      reportid     = {FZJ-2022-02565},
      year         = {2022},
      abstract     = {In light of the success of superresolution (SR)
                      applications in computer vision, recent studies have started
                      to develop statistical downscaling methods for
                      meteorological data based on deep neural networks (DNNs).
                      DNNs are attractive, because they are computationally cheap,
                      once they are trained.In this study, deep neural networks
                      are developed to downscale hourly 2 meter temperature and
                      precipitation over the complex terrain of Central Europe.
                      Our approach is based on advanced generative adversarial
                      networks (GANs) and transformer networks. The merit of this
                      choice is that GANs encourage the generator to preserve the
                      strong spatial variability from the data, while the
                      transformer can capture the temporal dependencies. The
                      experiments are designed to generate high-resolution
                      temperature (0.1°) from low resolution (0.8°), and
                      time-evolving high-resolution precipitation (1 km) from low
                      resolution (4 km/8 km). The DNNs are fed with several
                      relevant static and dynamic predictors and comprehensively
                      evaluated by grid point-level errors, and error metrics for
                      spatial variability and the generated probability
                      distribution. Our results motivate the further development
                      of DNNs that can be potentially leveraged to downscale other
                      challenging Earth system data such as cloud cover or wind in
                      operational workflows.},
      month         = {Jun},
      date          = {2022-06-27},
      organization  = {Platform for Advanced Scientific
                       Computing Conference 2022, Basel
                       (Switzerland), 27 Jun 2022 - 30 Jun
                       2022},
      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) /
                      Earth System Data Exploration (ESDE) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)955513 /
                      G:(DE-Juel-1)ESDE / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/908363},
}