Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts

TL;DR

This paper introduces a hybrid Bayesian Deep Learning framework that unifies ensemble weather forecasting and Bayesian methods, improving accuracy, uncertainty calibration, and computational efficiency using a physics-informed approach.

Contribution

It presents a novel hybrid BDL framework that decomposes uncertainty, connects BDL with ensemble prediction theory, and demonstrates superior performance on large-scale weather data.

Findings

Improved forecast accuracy and uncertainty calibration.

Better computational efficiency than diffusion models.

Validated on 40-year ERA5 dataset.

Abstract

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{\deg} 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.