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@INPROCEEDINGS{Gong:890172,
      author       = {Gong, Bing and Stadtler, Scarlet and Langguth, Michael and
                      Mozaffari, Amirpasha and Vogelsang, Jan and Schultz, Martin},
      title        = {{D}eep learning for short-term temperature forecasts with
                      video prediction methods},
      reportid     = {FZJ-2021-00761},
      year         = {2020},
      abstract     = {This study explores the adaptation of state-of-the-art deep
                      learning architectures for video frame prediction in the
                      context of weather and climate applications. A
                      proof-of-concept case study was performed to predict surface
                      temperature fields over Europe for up to 20 hours based on
                      ERA5 reanalyses weather data. Initial results have been
                      achieved with a PredNet and a GAN-based architecture by
                      using various combinations of temperature, surface pressure,
                      and 500 hPa geopotential as inputs. The results show that
                      the GAN-based architecture outperforms the PredNet. To
                      facilitate the massive data processing and testing of
                      various deep learning architectures, we have developed a
                      containerized parallel workflow for the full life-cycle of
                      the application, which consists of data extraction, data
                      pre-processing, training, post-processing and visualisation
                      of results. The training for PredNet was parallelized on
                      JUWELS supercomputer at JSC, and the training scalability
                      performance was also evaluated.},
      month         = {May},
      date          = {2020-05-04},
      organization  = {European Geosciences Union 2020,
                       Virtual (Austria), 4 May 2020 - 8 May
                       2020},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / IntelliAQ - Artificial Intelligence for Air
                      Quality (787576) / Earth System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/890172},
}