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arXiv AI By Wenbo Hu, Xinlei Xiong, Shuxun Zhou, Kaifeng Bi, Lingxi Xie, Jun Zhu, Richang Hong, Qi Tian

Resolving sources of uncertainty in AI weather forecasting

Read the original on arXiv AI →

The paper introduces Pangu‑Bayes, a probabilistic forecasting hierarchy that separates atmospheric‑state uncertainty from learned‑model uncertainty as distinct stochastic variables, allowing cross‑flow perturbations of the evolving state with Bayesian parameter samples. In tests on 90 held‑out 2023 tropical cyclones, Pangu‑Bayes reduces track, pressure, and wind errors by 54.2%, 17.2%, and 24.9% respectively, and improves rapid‑intensification detection. The study finds that atmospheric‑state variability more consistently improves track prediction, while learned‑model variability more often enhances intensity prediction, demonstrating how model‑defined uncertainty resolution can be linked to target‑dependent value and dynamical interpretation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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