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Status: Unchecked ×Keyword: sensitivity to initial conditions ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Predictability Limit of the 2021 Pacific Northwest Heatwave from Deep-Learning Sensitivity Analysis

   Vonich and Hakim · arXiv (Cornell University) · 2024

   The authors used deep-learning gradient methods to tune initial conditions and reduce forecast errors for the 2021 Pacific Northwest heatwave, finding improvement out to about 23 days.

   Unchecked3 claims
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   1. UncheckedOptimising starting conditions in the GraphCast model cut 10-day forecast errors over the Pacific Northwest by over 90% for the June 2021 heatwave.“We apply this approach to forecasts of the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10-day forecast errors over the Pacific Northwest.”
   2. Unchecked“Similar improvements are found for Pangu-Weather model forecasts initialized with the GraphCast-derived optimal, suggesting that model error is not an important part of the initial perturbations.”
   3. UncheckedLengthening the optimisation window, the authors find the heatwave forecast can be improved out to about 23 days, at the upper end of recent predictability estimates.“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.”