Neural scaling laws can predict weather model performance and guide efficient resource allocation—models trained with periodic cooldowns outperform standard approaches and enable longer, more accurate forecasts.
This paper studies how neural networks for weather forecasting improve as you scale up the model size, training data, and compute.