标题： MAUSAM：南亚季风期间基于观测的全球人工智能天气预测模型评估 显示英文标题

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

摘要： 准确的天气预报对于社会规划和灾害准备至关重要。 然而，这些预报的制作和评估仍然具有挑战性，特别是在观测覆盖稀疏的地区。 目前对人工智能（AI）天气预测的评估主要依赖于再分析数据，这可能会掩盖重要的缺陷。 在这里，我们介绍了MAUSAM（南亚季风期间测量AI不确定性），这是对七个领先的基于AI的预报系统——FourCastNet、FourCastNet-SFNO、Pangu-Weather、GraphCast、Aurora、AIFS和GenCast——在南亚季风期间的评估，使用地面气象站、雨量计网络和静止卫星图像。 AI模型在季风期间的广泛变量上表现出令人印象深刻的预报技能，从大尺度地表温度和风力到降水、云量以及次季节到季节的涡旋统计，突显了数据驱动天气预测的优势。 然而，这些模型在更细尺度上仍表现出系统性误差，如极端降水的低估、气旋路径的差异以及中尺度动能谱，突出了未来改进的方向。 与观测结果的比较显示，预报误差比相对于再分析数据和传统预报的误差高出15-45%，表明以再分析数据为中心的基准可能高估了预报技能。 在评估的模型中，AIFS对大气变量的表示最为一致，GraphCast和GenCast也表现出强大的技能。 该分析提供了一个在区域预测中评估AI天气模型的框架，并突出了AI天气预测在数据稀疏地区的潜力和当前局限性，强调了观测评估对未来业务采用的重要性。 显示英文摘要

摘要： Accurate weather forecasts are critical for societal planning and disaster preparedness. Yet these forecasts remain challenging to produce and evaluate, especially in regions with sparse observational coverage. Current evaluation of artificial intelligence (AI) weather prediction relies primarily on reanalyses, which can obscure important deficiencies. Here we present MAUSAM (Measuring AI Uncertainty during South Asian Monsoon), an evaluation of seven leading AI-based forecasting systems - FourCastNet, FourCastNet-SFNO, Pangu-Weather, GraphCast, Aurora, AIFS, and GenCast - during the South Asian Monsoon, using ground-based weather stations, rain gauge networks, and geostationary satellite imagery. The AI models demonstrate impressive forecast skill during monsoon across a broad range of variables, ranging from large-scale surface temperature and winds to precipitation, cloud cover, and subseasonal to seasonal eddy statistics, highlighting the strength of data-driven weather prediction. However, the models still exhibit systematic errors at finer scales like the underprediction of extreme precipitation, divergent cyclone tracks, and the mesoscale kinetic energy spectra, highlighting avenues for future improvement. A comparison against observations reveals forecast errors up to 15-45% larger than those relative to reanalysis and traditional forecasts, indicating that reanalysis-centric benchmarks can overstate forecast skill. Of the models assessed, AIFS achieves the most consistent representation of atmospheric variables, with GraphCast and GenCast also showing strong skill. The analysis presents a framework for evaluating AI weather models on regional prediction and highlights both the promise and current limitations of AI weather prediction in data-sparse regions, underscoring the importance of observational evaluation for future operational adoption.