TY - JOUR
T1 - Exascale Computing and Data Handling
T2 - Challenges and Opportunities for Weather and Climate Prediction
AU - Govett, Mark
AU - Bah, Bubacar
AU - Bauer, Peter
AU - Berod, Dominique
AU - Bouchet, Veronique
AU - Corti, Susanna
AU - Davis, Chris
AU - Duan, Yihong
AU - Graham, Tim
AU - Honda, Yuki
AU - Hines, Adrian
AU - Jean, Michel
AU - Ishida, Junishi
AU - Lawrence, Bryan
AU - Li, Jian
AU - Luterbacher, Juerg
AU - Muroi, Chiasi
AU - Rowe, Kris
AU - Schultz, Martin
AU - Visbeck, Martin
AU - Williams, Keith
N1 - Publisher Copyright:
© 2024 American Meteorological Society.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Atmosphere
KW - Climate
KW - General circulation models
KW - Numerical weather prediction/forecasting
KW - Ocean
KW - Optimization
UR - https://www.scopus.com/pages/publications/85197188665
U2 - 10.1175/BAMS-D-23-0220.1
DO - 10.1175/BAMS-D-23-0220.1
M3 - Article
AN - SCOPUS:85197188665
SN - 0003-0007
VL - 105
SP - E2385-E2404
JO - Bulletin of the American Meteorological Society
JF - Bulletin of the American Meteorological Society
IS - 12
ER -