Speaker
Abstract
Artificial Intelligence Weather Prediction (AIWP) models excel in global mean-error metrics, yet their efficacy in detecting low-probability, high-impact extreme events--critical for emergency response--remains under-examined. This study evaluates three leading models (GraphCast, FuXi, and Artificial Intelligence Forecasting System (AIFS)) against satellite observations and a numerical baseline across four diverse historical crises. Using a crisis-centric evaluation framework comprising Peak Amplitude Ratio (PAR), Spatial Correlation (SC), Root Mean Square Error (RMSE), volumetric Bias, and the Symmetric Extremal Dependence Index (SEDI), preliminary results reveal a systemic intensity deficit in AIWP models. While GFS maintains a PAR above 0.65 across most scenarios, AI models underestimate peak rainfall by over 90% and exhibit significant spatial displacement. These findings suggest that inherent statistical smoothing transforms catastrophic signals into benign forecasts. Consequently, over-reliance on current AIWP models for crisis detection may yield a false sense of security, potentially exacerbating rather than mitigating emergency vulnerabilities.
摘要
人工智能天气预报（AIWP）模型在全球平均误差指标上表现出色，但其在检测低概率、高影响极端事件（对应急响应至关重要）方面的有效性仍缺乏检验。本研究将三个领先模型（GraphCast、FuXi和人工智能预测系统（AIFS））与卫星观测和数值基准在四个不同的历史危机中进行评估。使用以危机为中心的评估框架，包括峰值振幅比（PAR）、空间相关性（SC）、均方根误差（RMSE）、体积偏差和对称极端依赖指数（SEDI），初步结果表明AIWP模型存在系统性的强度缺陷。虽然GFS在大多数情况下保持PAR高于0.65，但AI模型低估峰值降雨量超过90%，并表现出显著的空间位移。这些发现表明，固有的统计平滑将灾难性信号转化为良性预测。因此，过度依赖当前的AIWP模型进行危机检测可能会产生虚假的安全感，反而可能加剧而不是减轻应急脆弱性。
| 关键词 | 危机检测，极端降雨，气象大模型，回溯评估 |
|---|---|
| Keywords | Crisis Detection, Extreme Precipitation, AI Weather Prediction, Hindcast Evaluation. |
