Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning

Researchers have developed a framework using reinforcement learning (RL) to dynamically adjust parameters in weather and climate models based on the evolving model state, replacing traditional fixed coefficients that often introduce biases. This approach enhances adaptability by reducing persistent errors across various testbeds, with RL algorithms like TQC, DDPG, and TD3 showing superior performance compared to static tuning methods.

Read the full article at arXiv cs.LG (ML)

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