AI RESEARCH
Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning
arXiv CS.LG
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ArXi:2601.04268v2 Announce Type: replace 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 offline, contributing to persistent biases that limit their ability to adapt to underlying physics.