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Yingkai Sha on Muck Rack

Yingkai Sha

As seen in: Wiley Online Library, Journal of Applied Meteorology and Climatology, Monthly Weather Review
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Articles

Improving AI Weather Prediction Models Using Global Mass and Energy Conservation Schemes

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By Yingkai Sha, John Schreck, William Chapman, David Gagne
| Wiley Online Library Verified
1 Introduction Artificial Intelligence (AI) weather prediction (hereafter AIWP) models have attracted attention from the atmospheric science community following the successful releases of Pangu-Weather (Bi et al., 2023), GraphCast (Lam et al., 2023), and many others (e.g., Bodnar et al., 2024; Bonev et al., 2023; Chen et al., 2023; Lang et al., 2024; Nguyen, Brandstetter, et al., 2023; Nguyen, Shah, et al., 2023; Schreck et al., 2025; Willard et al., 2024).
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Improving ensemble extreme precipitation forecasts using generative artificial intelligence

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By Yingkai Sha, Ryan A Sobash, David Gagne
| ametsoc.org
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Generative ensemble deep learning severe weather prediction from a deterministic convection-allowing model

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By Yingkai Sha, Ryan A Sobash, David Gagne
| ametsoc.org
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