融合贝叶斯深度学习与集合气象预报，提升不确定性量化精度与效率
Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts
- 构建混合框架，分离认知与随机不确定性
- 在ERA5数据上准确率提升，计算效率优于现有扩散模型
- 适合气候建模与高精度气象预测研究者
气象预报受大气混沌特性制约，需采用概率方法量化不确定性。传统集合预报（EPS）依赖计算密集型模拟，而贝叶斯深度学习（BDL）虽具潜力却常与之脱节。本文提出统一的混合贝叶斯深度学习框架，显式分解预测不确定性为认知与随机两类，分别通过变分推断和物理引导的随机扰动方案建模流依赖的大气动力学。进一步建立统一理论框架，给出正式定理，证明在该框架下总预测不确定性可被精确分解。在1979-2019年、0.25°空间分辨率的40年ERA5再分析数据集上验证，本方法不仅提升预报准确性并实现更优校准的不确定性估计，还显著优于当前最先进的概率扩散模型，在计算效率上更具优势。代码将在论文接收后开源。
原文摘要 · Abstract (English)
Weather forecasting is fundamentally challenged by the chaotic nature of the atmosphere, necessitating probabilistic approaches to quantify uncertainty. While traditional ensemble prediction (EPS) addresses this through computationally intensive simulations, recent advances in Bayesian Deep Learning (BDL) offer a promising but often disconnected alternative. We bridge these paradigms through a unified hybrid Bayesian Deep Learning framework for ensemble weather forecasting that explicitly decomposes predictive uncertainty into epistemic and aleatoric components, learned via variational inference and a physics-informed stochastic perturbation scheme modeling flow-dependent atmospheric dynamics, respectively. We further establish a unified theoretical framework that rigorously connects BDL and EPS, providing formal theorems that decompose total predictive uncertainty into epistemic and aleatoric components under the hybrid BDL framework. We validate our framework on the large-scale 40-year ERA5 reanalysis dataset (1979-2019) with 0.25° spatial resolution. Experimental results show that our method not only improves forecast accuracy and yields better-calibrated uncertainty quantification but also achieves superior computational efficiency compared to state-of-the-art probabilistic diffusion models. We commit to making our code open-source upon acceptance of this paper.
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