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

תקציר מקורי באנגליתarXiv:2609.02566v2 Announce Type: replace Abstract: Machine-learnt corrections can complement numerical weather prediction provided that they operate stably within an evolving numerical model. In this study, we couple the Met Office (UKMO) Unified Model (UM) with distributed reinforcement-learning agents through rank-local tensors. A column-aware deep deterministic policy gradient (DDPG) actor uses local vertical structure together with full-column context to apply bounded corrections to potential temperature and horizontal wind. During training, we perform ten nudged 6-hr 12-min forecasts, with nudging towards the UKMO operational analysis providing an immediate counterfactual target from which the policy learns. The resulting actor is then frozen and applied to a non-nudged forecast with