标题： 基于观测的南亚季风期间全球AI天气预测模型评估 显示英文标题

标题： An Observations-focused Assessment of Global AI Weather Prediction Models During the South Asian Monsoon

摘要： 七种最先进的AI天气模型（FourCastNet、FourCastNet-SFNO、Pangu-Weather、GraphCast、Aurora、AIFS和GenCast）在南亚季风期间与观测数据进行对比评估。这些模型在温度、风、全球动能谱、区域降水、云量、气旋路径预测以及极端天气事件周围的超本地预测方面进行了测试。这些模型在大尺度动力学预测上具有合理的准确性，但在对季风期天气预测至关重要的关键指标上表现不足。与地面气象站数据相比，这些模型的误差明显更高，相较于再分析数据或传统预报。AI天气预测模型在季风期间的中尺度动能和极端降水方面存在显著差异，并且在印度次大陆上预测的季风期气旋路径明显不同，这引发了对其在操作应用中准备就绪程度的疑问。我们的分析发现，ECMWF的确定性AIFS模型表现出最可靠的表现和可用性，GraphCast和GenCast紧随其后。 显示英文摘要

摘要： Seven state-of-the-art AI weather models (FourCastNet, FourCastNet-SFNO, Pangu-Weather, GraphCast, Aurora, AIFS, and GenCast) are evaluated against observational data during the South Asian Monsoon. The models are tested on temperature, winds, global kinetic energy spectrum, regional precipitation, cloud cover, cyclone trajectory prediction, and hyperlocal predictions around extreme weather events. The models forecast large-scale dynamics with reasonable accuracy, but fall short on key metrics critical to Monsoon-time weather prediction. The models exhibit substantially higher errors when compared against ground-based weather station data than against reanalysis or conventional forecasts. The AI weather prediction models show key differences in mesoscale kinetic energy and extreme precipitation during the Monsoon, and predict markedly different Monsoon-time cyclone trajectories over the Indian subcontinent, raising questions about their readiness for operational applications. Our analysis finds that ECMWF's deterministic AIFS model offers the most reliable performance and usability, with GraphCast and GenCast being close seconds.