Advancing Subseasonal Forecasting with Machine Learning
Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented accuracy out to two weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence (AI) models. However, model skill drops precipitously at subseasonal timescales (2 - 6 weeks ahead), due to compounding errors, systemic model biases, and the chaotic nature of the atmosphere. To counter this degradation, we introduce probabilistic bias correction (PBC), a machine learning framework that substantially reduces systematic error by learning to correct historical probabilistic...
| Authors | Hannah Guan, Soukayna Mouatadid, Paulo Orenstein et al. |
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| Primary category | cs.LG |
| Published | 17 April 2026 |
| Last updated at source | 4 September 2026 |
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