AI weather models show promise for extreme event prediction with uncertainty quantification

A new study published on arXiv investigates the effectiveness of AI-based weather models in predicting extreme events by quantifying their uncertainty. Researchers found that while models like FuXi, GraphCast, and SFNO show competitive skill, their ability to represent uncertainty and capture extreme events is limited. The study explored various perturbation strategies to generate ensembles, concluding that simpler methods like Gaussian noise can extend deterministic models for probabilistic forecasting, though native probabilistic models and numerical weather prediction ensembles still outperform them. AI

IMPACT Enhances understanding of AI weather model limitations for extreme events, guiding development of more reliable early warning systems.

RANK_REASON Academic paper on AI weather model performance and uncertainty quantification.

Read on arXiv cs.LG →

• AIFS ENS
• arXiv
• ERA5
• FuXi
• GraphCast
• IFS ENS

• paper
• safety

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