arXiv:2602.22962v2 Announce Type: replace 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 pa
The rapid advancement of data-driven models in weather forecasting is prompting a deeper analysis into their foundational 'scaling laws' to optimize performance and efficiency.
Understanding scaling laws in weather models provides critical insights for resource allocation, computational infrastructure planning, and the future reliability of climate prediction and disaster preparedness.
This research provides empirical data on the efficiency and performance gains achievable by investing in larger datasets, model sizes, or compute budgets for specific weather forecasting models.
- · AI compute infrastructure providers
- · Weather forecasting agencies
- · Insurance sector
- · Agricultural technology
- · Traditional meteorological modeling approaches
- · Regions lacking compute or data access
More accurate and timely weather predictions become possible, leading to better disaster preparedness and economic planning.
Increased demand for specialized AI hardware and massive datasets, further stressing existing compute supply chains.
Nations with advanced AI weather capabilities gain strategic advantages in climate resilience and resource management, potentially influencing geopolitical power dynamics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG