Scaling Laws of Global Weather Models

Abstract:Data-driven models are revolutionizing weather forecasting. To optimize training efficiency and model performance, this paper analyzes empirical scaling laws within this domain. We investigate the relationship between model performance (validation loss) and three key factors: model size ($N$), dataset size ($D$), and compute budget ($C$). Across a range of models, we find that Aurora exhibits the strongest data-scaling behavior: increasing the training dataset by 10x reduces validation loss by up to 3.2x. GraphCast demonstrates the highest parameter efficiency, yet suffers from limited hardware utilization. Our compute-optimal analysis indicates that, under fixed compute budgets, allocating resources to more total training data yields greater performance gains than increasing model size. Furthermore, we analyze model shape and uncover scaling behaviors that differ fundamentally from those observed in language models: weather forecasting models consistently favor increased width over depth. These findings suggest that future weather models should prioritize wider architectures and larger effective training datasets to maximize predictive performance.

Accepted at ICML 2026. 21 pages, 7 figures

Machine Learning (cs.LG)

arXiv:2602.22962 [cs.LG]

(or arXiv:2602.22962v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.22962 Focus to learn more arXiv-issued DOI via DataCite

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