3 papers
cs.LG2026
Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling
Pritthijit Nath, Sebastian Schemm, Peter Haynes +2
Machine-learnt corrections can complement numerical weather prediction only if they adapt to the evolving model state while preserving dynamical consistency and numerical stability…
cs.LG2026
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
Pritthijit Nath, Sebastian Schemm, Henry Moss +3
Weather and climate models rely on parametrisations to represent unresolved sub-grid processes. Traditional schemes rely on fixed coefficients that are weakly constrained and tuned…
cs.LG2025
FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models
Pritthijit Nath, Sebastian Schemm, Henry Moss +3
Sub-grid parameterisations in climate models are traditionally static and tuned offline, limiting adaptability to evolving states. This work introduces FedRAIN-Lite, a federated re…