发表机构
University of Oxford; University of Manchester（牛津大学; 曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对UTCI多项式近似在极端天气下精度不足的问题，提出神经网络Neural-UTCI，将RMSE从2.78°C降至0.36°C，误分类率从5.3%降至1.7%，提升热应激评估可靠性。
AI 中文摘要
极端温度是全球气候相关死亡的主要原因。气候健康研究和业务天气预报需要准确估算人体热应激。通用热气候指数（UTCI）是最复杂且广泛使用的体感温度指标之一。然而，其普遍采用的多项式近似在极端天气条件下泛化能力不佳。在此，我们提出Neural-UTCI，这是一种神经网络，能够以更高的精度计算全球条件下的UTCI，同时降低业务使用的计算成本。Neural-UTCI将多项式近似的均方根误差（RMSE）从2.78°C降低至0.36°C，提升了87%，并将热应激误分类率从5.3%降至1.7%，在重采样实验中表现一致。这些差异影响热暴露指标。例如，在2003年意大利罗马的欧洲热浪夏季，使用Neural-UTCI相比ERA5-HEAT等业务产品，非常强热应激天数从15天增加至35天。同时，Neural-UTCI能可靠分类极端寒冷应激条件，支持连续全球应用。通过提高UTCI精度，Neural-UTCI可加强气候健康风险评估和公共天气预警系统，尤其是在全球变暖增加极端事件发生率的背景下。
英文摘要
Extreme temperatures are the leading cause of climate-related mortality world-wide. Climate-health research and operational weather forecasting require accurate estimates of human thermal stress. The Universal Thermal Climate Index (UTCI) is among the most sophisticated and widely used feels-like temperature metrics. However, its ubiquitous polynomial approximation does not generalize well to extreme weather conditions. Here, we introduce Neural-UTCI, a neural network that calculates UTCI with substantially higher accuracy across global conditions at a lower computational cost for operational use. Neural-UTCI reduces the polynomial approximation RMSE from 2.78°C to 0.36 °C, an 87% improvement, and lowers thermal stress misclassification rates from 5.3% to 1.7%, with consistent performance across resampling experiments. These differences affect thermal exposure metrics. For example, during the 2003 European heatwave summer in Rome, Italy, the number of very strong heat stress days increases from 15 to 35 days when using Neural-UTCI compared to operational products like ERA5-HEAT. Simultaneously, Neural-UTCI reliably classifies extreme cold stress conditions, allowing continuous global application. By improving UTCI accuracy, Neural-UTCI can strengthen climate-health risk assessments and public weather warning systems, especially as global warming increases the incidence of extreme events.
