Document Type
Article
Publication Date
10-16-2024
Abstract
The traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the perturbations. Eliminating small scales from the perturbations also yields similar forecast improvements. Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates.
Source Publication
Geophysical Research Letters (ISSN 0094-8276 |e-ISSN 1944-8007)
Recommended Citation
Vonich, P. T., & Hakim, G. J. (2024). Predictability Limit of the 2021 Pacific Northwest Heatwave From Deep‐Learning Sensitivity Analysis. Geophysical Research Letters, 51(19), e2024GL110651. https://doi.org/10.1029/2024GL110651
Comments
© 2024 The Authors.
This article is published by Wiley on behalf of the American Geophysical Union (AGU), licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Funding notes: Author Gregory Hakim acknowledges support from NSF award 2202526 and Heising-Simons Foundation award 2023–4715.