Exascale computing and data handling: challenges and opportunities for weather and climate prediction

Govett, M., Bah, B., Bauer, P., Berod, D., Bouchet, V., Corti, S., Davis, C., Duan, Y., Graham, T., Honda, Y., Hines, A., Jean, M., Ishida, J., Lawrence, B. ORCID: https://orcid.org/0000-0001-9262-7860, Li, J., Luterbacher, J., Muroi, C., Rowe, K., Schultz, M., Visbeck, M. and Williams, K. (2024) Exascale computing and data handling: challenges and opportunities for weather and climate prediction. Bulletin of the American Meteorological Society, 105 (12). E2385-E2404. ISSN 0003-0007 doi: 10.1175/BAMS-D-23-0220.1

Abstract/Summary

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 recent 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 post processing threatens to overwhelm the ability to deliver timely, accurate, and precise information needed for decision making. 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/116611
Identification Number/DOI	10.1175/BAMS-D-23-0220.1
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	American Meteorological Society
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