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

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The study couples the Met Office Unified Model with distributed reinforcement learning agents that apply bounded temperature corrections across 70 vertical levels. Training on ten nudged forecasts and evaluating on a non‑nudged run, the learned policy remains numerically stable and improves forecast accuracy, reducing Z₅₀₀ MAE by up to 45.8% in tropical bands and MSLP error by up to 27.3% in certain latitudes. This experiment demonstrates the feasibility of online RL for bias correction in operational weather models.

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