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arXiv Machine Learning By Pritthijit Nath, Sebastian Schemm, Peter Haynes, Emily Shuckburgh, Mark Webb

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, using a DDPG actor that applies bounded potential‑temperature corrections across 70 vertical levels. Training is performed on ten nudged forecasts, after which the frozen policy is evaluated in a non‑nudged forecast, demonstrating numerical stability. The learned policy reduces Z₅₀₀ MAE in four of six latitude bands—up to 45.8% in the northern tropics—and decreases MSLP error by up to 27.3% in certain bands, indicating promising bias‑correction potential.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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