Resolving sources of uncertainty in AI weather forecasting

Abstract:Weather forecast uncertainty arises from imperfect analyses and forecast models, but ensemble spread alone does not reveal how distinct sources relate to downstream targets. We introduce Pangu-Bayes, a probabilistic forecasting hierarchy that treats atmospheric-state and learned-model uncertainty as distinct stochastic variables, crossing flow-dependent perturbations of the evolving state with Bayesian parameter samples. This construction yields model-defined source-resolved variance components and matched pathway evaluation. Across 90 held-out 2023 tropical cyclones, Pangu-Bayes reduces track, pressure and wind errors by 54.2%, 17.2% and 24.9%, respectively, while improving rapid-intensification detection. Among 88 cyclones supporting pathway comparison, atmospheric-state variability is more consistently associated with improved track prediction, whereas learned-model variability is more often associated with improved intensity prediction. During Mawar and Khanun, state perturbations sample alternative steering-flow evolutions associated with recurvature, whereas parameter sampling broadens intensity evolution. Pangu-Bayes thus connects model-defined uncertainty resolution to target-dependent value and dynamical interpretation while retaining competitive global probabilistic skill.

Substantially revised and extended version with an updated title and author list. Wenbo Hu and Xinlei Xiong contributed equally to this version

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2511.14218 [cs.LG]

(or arXiv:2511.14218v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2511.14218 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.