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@INPROCEEDINGS{Patnala:1041713,
      author       = {Patnala, Ankit and Semcheddine, Asma and Langguth, Michael
                      and Schultz, Martin and Lessig, Christian and Luise, Ilaria},
      title        = {{A}pplying {A}tmo{R}ep for {D}iverse {W}eather
                      {A}pplications},
      volume       = {52},
      reportid     = {FZJ-2025-02398},
      pages        = {301- 311},
      year         = {2025},
      note         = {Proceedings: https://doi.org/10.34734/FZJ-2025-01965 ISBN:
                      978-3-95806-793-6},
      comment      = {NIC Symposium 2025 Proceedings},
      booktitle     = {NIC Symposium 2025 Proceedings},
      abstract     = {Machine learning has recently seen a rapid wide-spread
                      adoption across various fields of science including
                      atmospheric and weather research. The emergence of
                      foundation models has marked a transformation in the science
                      of machine learning. These foundation models are
                      general-purpose models trained on huge amounts of data using
                      self-supervised methods, eliminating the need for labeled
                      data. Once trained, the parameters of these models can be
                      utilized as a starting point for a range of domain-specific
                      tasks. This approach is advantageous in terms of both cost
                      and performance, as it minimizes the reliance on annotated
                      data compared to models trained from scratch. Motivated by
                      this, our study explores the foundational capabilities of
                      AtmoRep, a stochastic atmospheric foundation model, for two
                      distinct weather-related applications, data compression and
                      statistical downscaling. The training of the 3.5 billion
                      parameter AtmoRep model consumed about a few weeks of
                      compute time on 32 JUWELS Booster nodes.},
      month         = {Mar},
      date          = {2025-03-06},
      organization  = {The 12th John von Neumann Institute
                       for Computing (NIC) Symposium, Jülich
                       (Germany), 6 Mar 2025 - 7 Mar 2025},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.34734/FZJ-2025-02398},
      url          = {https://juser.fz-juelich.de/record/1041713},
}