The rapid emergence of deep learning is attracting growing private interest in the traditionally public enterprise of numerical weather and climate prediction. A public–private partnership would be a pioneering step to bridge between physics- and data-based methods, and necessary to effectively address future societal challenges.
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References
Bauer, P. et al. The digital revolution of Earth-system science. Nat. Comput. Sci. 1, 104–113 (2021).
Brown, T. et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).
Buontempo, C. et al. The Copernicus Climate Change Service: Climate science in action. Bull. Amer. Meteor. Soc. 103, E2669–E2687 (2022).
Dueben, P. D. et al. Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status, and outlook. Artif. Intell. Earth Syst. 1, e210002 (2022).
Bi, K. et al. Accurate medium-range global weather forecasting with 3D neural networks. Nature https://doi.org/10.1038/s41586-023-06185-3 (2023).
Lam, R. et al. GraphCast: Learning skillful medium-range global weather forecasting. Preprint at arXiv https://doi.org/10.48550/arXiv.2212.12794 (2022).
Pathak, J. et al. Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. Preprint at arXiv https://doi.org/10.48550/arXiv.2202.11214 (2022).
Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J. K. & Grover, A. ClimaX: A foundation model for weather and climate. Preprint at arXiv https://doi.org/10.48550/arXiv.2301.10343 (2023).
Chase, R. J., Harrison, D. R., Burke, A., Lackmann, G. M. & McGovern, A. A machine learning tutorial for operational meteorology. Part I: Traditional machine learning. Wea. Forecasting 37, 1509–1529 (2022).
Schick, T. et al. Toolformer: Language models can teach themselves to use tools. Preprint at arXiv https://doi.org/10.48550/arXiv.2302.04761 (2023).
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P.B. conceived the original concept of the paper, and all authors contributed to the final version of the manuscript.
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Bauer, P., Dueben, P., Chantry, M. et al. Deep learning and a changing economy in weather and climate prediction. Nat Rev Earth Environ 4, 507–509 (2023). https://doi.org/10.1038/s43017-023-00468-z
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DOI: https://doi.org/10.1038/s43017-023-00468-z
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