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@INPROCEEDINGS{Langguth:908083,
      author       = {Langguth, Michael and Gong, Bing and Ji, Yan and Mozaffari,
                      Amirpasha and Schultz, Martin},
      title        = {{S}tochastic downscaling of meteorological fields with deep
                      neural networks},
      reportid     = {FZJ-2022-02362},
      year         = {2022},
      abstract     = {Weather forecasts at high spatio-temporal resolution are of
                      great relevance for industry and society. However,
                      contemporary global NWP models deploy grids with a spacing
                      of about 10 km which is too coarse to capture relevant
                      variability in the presence of complex topography. To
                      overcome the limitations of coarse-grained model output,
                      statistical downscaling with deep neural networks is
                      attaining increasing attention.<br>In this study, a powerful
                      generative adversarial network (GAN) for downscaling the 2m
                      temperature is presented. The generator of the GAN model is
                      built upon a U-net architecture and furthermore equipped
                      with a recurrent layer to obtain a temporarily coherent
                      downscaling product. As an exemplary case study, coarsened
                      2m temperature fields from the ERA5 reanalysis dataset are
                      downscaled to the same horizontal resolution (0.1°) as the
                      Integrated Forecasting System (IFS) model which runs
                      operationally at the European Centre for Medium-Range
                      Weather Forecasts (ECMWF). We choose Central Europe
                      including the Alps as a proper target region for our
                      downscaling experiment.Our GAN model is evaluated in terms
                      of several evaluation metrics which measure the error on
                      grid point-level as well as the goodness of the downscaled
                      product in terms of the spatial variability and the produced
                      probability distribution function. Furthermore, we
                      demonstrate how different input quantities help the model to
                      create an improved downscaling product. These quantities
                      comprise dynamic variables such as wind and temperature on
                      different pressure levels, but also static fields such as
                      the surface elevation and the land-sea mask. Incorporating
                      the selected input variables ensures that our neural network
                      for downscaling is capable of capturing challenging
                      situations with the presence of temperature inversions over
                      complex terrain.<br>The results motivate further development
                      of the deep neural network including a further increase in
                      the spatial resolution of the target product as well as
                      applications to other meteorological variables such as wind
                      or precipitation.},
      month         = {May},
      date          = {2022-05-23},
      organization  = {Living Planet Symposium 2022, Bonn
                       (Germany), 23 May 2022 - 27 May 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)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)955513 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/908083},
}