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@ARTICLE{Govett:1034098,
      author       = {Govett, Mark and Bah, Bubacar and Bauer, Peter and Berod,
                      Dominique and Bouchet, Veronique and Corti, Susanna and
                      Davis, Chris and Duan, Yihong and Graham, Tim and Honda,
                      Yuki and Hines, Adrian and Jean, Michel and Ishida, Junishi
                      and Lawrence, Bryan and Li, Jian and Luterbacher, Juerg and
                      Muroi, Chiasi and Rowe, Kris and Schultz, Martin and
                      Visbeck, Martin and Williams, Keith},
      title        = {{E}xascale {C}omputing and {D}ata {H}andling: {C}hallenges
                      and {O}pportunities for {W}eather and {C}limate
                      {P}rediction},
      journal      = {Bulletin of the American Meteorological Society},
      volume       = {105},
      number       = {12},
      issn         = {0003-0007},
      address      = {Boston, Mass.},
      publisher    = {ASM},
      reportid     = {FZJ-2024-06919},
      pages        = {E2385–E2404},
      year         = {2024},
      abstract     = {The emergence of exascale computing and artificial
                      intelligence offer tremendous potential to significantly
                      advance Earth system prediction capabilities. However,
                      enormous challenges must be overcome to adapt models and
                      prediction systems to use these new technologies
                      effectively. A 2022 WMO report on exascale computing
                      recommends “urgency in dedicating efforts and attention to
                      disruptions associated with evolving computing technologies
                      that will be increasingly difficult to overcome, threatening
                      continued advancements in weather and climate prediction
                      capabilities.” Further, the explosive growth in data from
                      observations, model and ensemble output, and postprocessing
                      threatens to overwhelm the ability to deliver timely,
                      accurate, and precise information needed for
                      decision-making. Artificial intelligence (AI) offers
                      untapped opportunities to alter how models are developed,
                      observations are processed, and predictions are analyzed and
                      extracted for decision-making. Given the extraordinarily
                      high cost of computing, growing complexity of prediction
                      systems, and increasingly unmanageable amount of data being
                      produced and consumed, these challenges are rapidly becoming
                      too large for any single institution or country to handle.
                      This paper describes key technical and budgetary challenges,
                      identifies gaps and ways to address them, and makes a number
                      of recommendations.},
      cin          = {JSC},
      ddc          = {550},
      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)16},
      UT           = {WOS:001382177000003},
      doi          = {10.1175/BAMS-D-23-0220.1},
      url          = {https://juser.fz-juelich.de/record/1034098},
}