闪电般快速的对流展望：利用全球AI气象模型预测强对流环境
Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models
AI总结：
本文评估了三个全球AI气象模型对强对流环境参数的10天预报能力，发现GraphCast和Pangu-Weather在稳定度和风切变上达到或超过IFS，为快速低成本强天气预测提供新途径。
摘要：
强对流风暴是最危险的气象现象之一，准确的预报可以减轻其影响。最近发布的一套基于AI的气象模型能在数秒内生成中期预报，其在单层变量上的技巧与最先进的业务预报相当。然而，预测强雷暴环境需要准确结合动力与热力变量以及大气的垂直结构。推动对AI模型进行面向过程的评估，为面向灾害的应用奠定基础。我们评估了三个表现最佳的AI模型在最长10天预报时效内对对流参数的预报技巧，并与再分析资料及ECMWF的业务数值天气预报模型IFS进行对比。在案例研究和季节分析中，GraphCast和Pangu-Weather表现最佳：这些模型在稳定度和风切变方面达到甚至超过IFS的性能。这为快速且低成本地预测强天气环境开辟了机会。
英文摘要：
Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
