处于极端高温可预测性前沿的天气模拟器
Weather Emulators at the Frontier of Heat Extremes Predictability
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中文总结 AI 辅助
该研究评估六种深度学习天气模拟器及基线，发现部分模拟器在10-15天确定性温度预报上可媲美或超越物理预报，同时指出其在极端高温峰值预报及频谱保真度上的不足，凸显AI在延伸期温度预报的潜力与挑战。
中文摘要 AI 辅助
大气可预测性在未来10天后迅速下降，因此更长时效的预报主要传达大尺度趋势而非具体状态。然而在变暖的世界中，改进极端高温的早期预警正成为日益关键的挑战。本文评估了六种最先进的深度学习天气模拟器——Pangu-Weather、FuXi、ArchesWeather、AIFS、GraphCast和Aurora，以及领先的动力系统和统计基线，用于预报10-15天时效的全球近地面温度和极端高温。我们发现，若干模拟器在确定性温度技巧上可与基于物理的预报相媲美甚至超越，但这以降低频谱保真度为代价，该过程被广泛称为“模糊化”。尽管所有模型对极端高温都表现出一定程度的预测技巧，但大多数模拟器低估了峰值强度，且IFS的召回率高于任何一种模拟器。这些结果凸显了AI在延伸期温度预报方面的新兴潜力，以及在不断变化的气候中提供可靠、可操作的早期预警仍面临的挑战。
英文摘要
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.
发表机构
- Ghent University（根特大学）
- European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
机构由 AI 辅助整理，请以论文原文为准。
