Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling

2026-09-02

AI Summary

研究通过分布式模型-代理耦合，在Met Office统一模型中使用在线强化学习方法，对模型进行动态一致性和数值稳定性校正，以提高天气预报准确性。

Abstract

Machine-learnt corrections can complement numerical weather prediction only if they adapt to the evolving model state while preserving dynamical consistency and numerical stability. To test this within a global forecasting model, we couple the Met Office (UKMO) Unified Model (UM) with distributed RL agents through rank-local tensors. A DDPG actor shares weights across the 70 vertical model levels of each atmospheric column and applies bounded potential-temperature corrections to the model tendencies. Across ten nudged training forecasts, nudging calculations towards the UKMO operational analysis provides an immediate counterfactual target. The frozen policy is then evaluated in a non-nudged forecast for inference. The coupled workflow successfully completes training and remains numerically stable in the evaluated case. Relative to a matched native UM forecast at +6 h, the learnt policy reduces Z$_{500}$ MAE in four of six latitude bands, including reductions of 45.8% and 40.8% in the northern and southern tropics. MSLP error too decreases in three bands, with a maximum reduction of 27.3% at 0-30°N. This single-case experiment demonstrates significant promise and feasibility of distributed online learning followed by non-nudged inference, laying the groundwork for RL-based bias correction and parametrisations within operational systems.

Problem

• Reinforcement Learning
• Numerical Weather Prediction
• Dynamical Consistency
• Numerical Stability

Innovation

• Distributed Reinforcement Learning
• Model-Agent Coupling
• DDPG
• Numerical Stability
• Potential-Temperature Corrections