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Found 3 papers, 1 papers with code
A Practical Probabilistic Benchmark for AI Weather Models
We also reveal how multiple time-step loss functions, which many data-driven weather models have employed, are counter-productive: they improve deterministic metrics at the cost of increased dissipation, deteriorating probabilistic skill.
ACE: A fast, skillful learned global atmospheric model for climate prediction
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency.
Machine-learned climate model corrections from a global storm-resolving model
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes.
