Scaling Laws of Global Weather Models
A new research paper explores the scaling laws of data-driven global weather models, analyzing how performance relates to model size, dataset size, and compute budget. The study found that weather models favor wider architectures over deeper ones and that increasing training data yields greater performance gains than increasing model size under fixed compute budgets. Specifically, the Aurora model showed strong data-scaling behavior, with a 10x increase in training data leading to a 3.2x reduction in validation loss. AI
IMPACT Provides insights into optimizing AI model development for weather forecasting, suggesting wider architectures and larger datasets are key.