AI天气模型能提前10-15天预测高温，但峰值预报仍不准确。
Weather Emulators at the Frontier of Heat Extremes Predictability
- 用深度学习天气模拟器替代传统物理模型进行长期温度预测
- 部分AI模型在温度预报上超越了物理模型，但存在细节模糊问题
- 适合关注气候预警与极端天气建模的研究者参考
大气可预测性在十天后迅速下降，长期预报主要反映大尺度趋势而非具体状态。但在全球变暖背景下，提升极端高温的早期预警能力日益关键。本文评估六种前沿深度学习天气模拟器（Pangu-Weather、FuXi、ArchesWeather、AIFS、GraphCast、Aurora）及主流动力系统与统计基线，在10-15天预报周期内对全球地表温度和极端高温的预测表现。结果显示，多个模拟器在确定性温度预测上达到甚至超过基于物理的预报水平，但伴随显著的频谱保真度下降，即广泛存在的“模糊化”现象。尽管所有模型均具备一定极端高温预测能力，多数模拟器仍低估峰值强度，且IFS模型的回忆率高于所有模拟器。这些结果凸显了人工智能在延长温度预报时效方面的潜力，也揭示了在气候变化背景下提供可靠、可行动预警所面临的挑战。
原文摘要 · Abstract (English)
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge. Here we evaluate six state-of-the-art deep learning weather emulators - Pangu-Weather, FuXi, ArchesWeather, AIFS, GraphCast and Aurora - alongside leading dynamical systems and statistical baselines in forecasting global near-surface temperature and extreme heat at lead times of 10-15 days. We find that several emulators rival or even surpass physics-based forecasts in deterministic temperature skill, but do so at the cost of reduced spectral fidelity, in a process widely known as blurring. While all models show some degree of predictive skill for extreme heat, most emulators under-represent peak intensities, and IFS recall is greater than that of any of the emulators. These results highlight both the emerging potential of AI to enhance extended range temperature prediction, and the remaining challenges in delivering reliable, actionable early warnings in a changing climate.
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