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@ARTICLE{Schultz:890552,
      author       = {Schultz, Martin and Betancourt, Clara and Gong, Bing and
                      Kleinert, Felix and Langguth, Michael and Leufen, Lukas
                      Hubert and Mozaffari, Amirpasha and Stadtler, Scarlet},
      title        = {{C}an deep learning beat numerical weather prediction?},
      journal      = {Philosophical transactions of the Royal Society of London /
                      A},
      volume       = {379},
      number       = {2194},
      issn         = {0080-4614},
      address      = {London},
      publisher    = {Royal Society},
      reportid     = {FZJ-2021-01034},
      pages        = {20200097},
      year         = {2021},
      abstract     = {The recent hype about artificial intelligence has sparked
                      renewed interest in applying the successful deep learning
                      (DL) methods for image recognition, speech recognition,
                      robotics, strategic games and other application areas to the
                      field of meteorology. There is some evidence that better
                      weather forecasts can be produced by introducing big data
                      mining and neural networks into the weather prediction
                      workflow. Here, we discuss the question of whether it is
                      possible to completely replace the current numerical weather
                      models and data assimilation systems with DL approaches.
                      This discussion entails a review of state-of-the-art machine
                      learning concepts and their applicability to weather data
                      with its pertinent statistical properties. We think that it
                      is not inconceivable that numerical weather models may one
                      day become obsolete, but a number of fundamental
                      breakthroughs are needed before this goal comes into reach.},
      cin          = {JSC},
      ddc          = {510},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / IntelliAQ -
                      Artificial Intelligence for Air Quality (787576) / Earth
                      System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33583266},
      UT           = {WOS:000649132600009},
      doi          = {10.1098/rsta.2020.0097},
      url          = {https://juser.fz-juelich.de/record/890552},
}