Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts
arXiv:2605.30184v1 Announce Type: new Abstract: While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons. This work addresses the lack of a formal taxonomy by categorizing these failures into three distinct regimes: blow-up, drift, and loss of seasonality, through year-long rollouts of nine state-of-the-art AI weather models. Our analysis reveals that stability hinges on the treatment of small spatio-temporal scales: unstable models amplify high-frequency energy, while stable m
This research provides a formal framework for understanding the limitations of current AI weather models, which is crucial as the field matures and seeks to extend its predictive capabilities.
The ability to accurately predict long-term weather and climate is foundational for national security, economic planning, and disaster preparedness, impacting sectors from agriculture to energy.
This work introduces a taxonomy of AI weather model failures, shifting the discussion from 'instabilities' to specific, addressable issues like blow-up, drift, and loss of seasonality.
- · AI model developers specializing in stability
- · Climate scientists
- · Agriculture sector
- · Renewable energy companies
- · AI weather models without robust stability mechanisms
- · Forecasting methods reliant solely on short-term AI models
Improved understanding and mitigation of long-term instability in AI weather prediction models.
More reliable long-range weather and climate forecasts, enabling better strategic planning across various industries.
Potential for AI to replace or significantly augment traditional numerical weather prediction, impacting resource allocation and infrastructure resilience.
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