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DOE OSTI · 2589529

Exascale Computing and Data Handling: Challenges and Opportunities for Weather and Climate Prediction

Govett, Mark [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)]·Bah, Bubacar [African Institute for Mathematical Sciences (AIMS), Cape Town (South Africa)]·Bauer, Peter [European Centre for Medium Range Weather Forecast, Reading (United Kingdom)]·Berod, Dominique [World Meteorological Organization (WMO), Geneva (Switzerland)]·Bouchet, Veronique [Meteorological Service of Canada (MSC), Dorval, QC (Canada) ]·Corti, Susanna [National Research Council (CNR), Bologna (Italy). Institute of Atmospheric Sciences and Climate (CNR-ISAC)]·Davis, Chris [National Center for Atmospheric Research (NCAR), Boulder, CO (United States)]·Duan, Yihong [Chinese Academy of Meteorological Sciences (CAMS), Beijing (China)]·Graham, Tim [United Kingdom Meteorological Office, Exeter, Devon (United Kingdom)]·Honda, Yuki [World Meteorological Organization (WMO), Geneva (Switzerland)]·Hines, Adrian [Science and Technology Facilities Council (STFC), Didcot (United Kingdom). Centre for Environmental Data Analysis (CEDA)]·Jean, Michel [World Meteorological Organization (WMO), Geneva (Switzerland)]·Ishida, Junishi [Japan Meteorological Agency, Tokyo (Japan)]·Lawrence, Bryan [Univ. of Reading (United Kingdom)]·Li, Jian [Chinese Academy of Meteorological Sciences (CAMS), Beijing (China)]·Luterbacher, Juerg [World Meteorological Organization (WMO), Geneva (Switzerland)]·Muroi, Chiasi [Japan Meteorological Agency, Tokyo (Japan)]·Rowe, Kris [Argonne National Laboratory (ANL), Argonne, IL (United States)]·Schultz, Martin [Forschungszentrum Juelich (Germany)]·Visbeck, Martin [GEOMAR Helmholtz Centre for Ocean Research, Kiel (Germany)]·Williams, Keith [United Kingdom Meteorological Office, Exeter, Devon (United Kingdom)]
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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.

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Govett, Mark [National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)], Bah, Bubacar [African Institute for Mathematical Sciences (AIMS), Cape Town (South Africa)], Bauer, Peter [European Centre for Medium Range Weather Forecast, Reading (United Kingdom)], Berod, Dominique [World Meteorological Organization (WMO), Geneva (Switzerland)], Bouchet, Veronique [Meteorological Service of Canada (MSC), Dorval, QC (Canada) ], Corti, Susanna [National Research Council (CNR), Bologna (Italy). Institute of Atmospheric Sciences and Climate (CNR-ISAC)], Davis, Chris [National Center for Atmospheric Research (NCAR), Boulder, CO (United States)], Duan, Yihong [Chinese Academy of Meteorological Sciences (CAMS), Beijing (China)], Graham, Tim [United Kingdom Meteorological Office, Exeter, Devon (United Kingdom)], Honda, Yuki [World Meteorological Organization (WMO), Geneva (Switzerland)], Hines, Adrian [Science and Technology Facilities Council (STFC), Didcot (United Kingdom). Centre for Environmental Data Analysis (CEDA)], Jean, Michel [World Meteorological Organization (WMO), Geneva (Switzerland)], Ishida, Junishi [Japan Meteorological Agency, Tokyo (Japan)], Lawrence, Bryan [Univ. of Reading (United Kingdom)], Li, Jian [Chinese Academy of Meteorological Sciences (CAMS), Beijing (China)], Luterbacher, Juerg [World Meteorological Organization (WMO), Geneva (Switzerland)], Muroi, Chiasi [Japan Meteorological Agency, Tokyo (Japan)], Rowe, Kris [Argonne National Laboratory (ANL), Argonne, IL (United States)], Schultz, Martin [Forschungszentrum Juelich (Germany)], Visbeck, Martin [GEOMAR Helmholtz Centre for Ocean Research, Kiel (Germany)], Williams, Keith [United Kingdom Meteorological Office, Exeter, Devon (United Kingdom)]. 2024-12-01. Exascale Computing and Data Handling: Challenges and Opportunities for Weather and Climate Prediction. https://doi.org/10.1175/bams-d-23-0220.1

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