1940–2022年逐日最大简化湿球黑球温度及其遥感相关研究用气候网络数据集
收藏资源简介：
本数据集提供1940年至2022年期间全球逐日最大简化湿球黑球温度（simplified Wet-Bulb Globe Temperature, sWBGT）数据，空间分辨率为2°×2°。sWBGT是一种综合了气温、露点温度和地面气压的热应激指标，用于评估人体热负荷。本数据集基于ERA5逐小时单层再分析数据计算得出。除主变量sWBGT外，数据集中还包含用于遥相关研究的气候网络数据及其网络结构度量指标，涵盖自1940年至2020年每隔五年的代表年份。该部分数据有助于揭示全球热应激事件在时空上的相关性与传播机制，支持对热应激遥相关现象的深入研究。sWBGT数据以NetCDF格式发布，适用于主流气候科学数据分析工具；气候网络数据及其指标以pickle格式存储，便于Python环境下的高效读写与处理。同时，数据集附带全部所用Python脚本，用户可基于现有方法对数据进行重建或开展个性化研究。该数据集适用于气候科学、公共健康及环境政策等研究领域，旨在为热应激历史演变趋势分析与未来风险评估提供数据支撑与方法基础。
This dataset provides global daily maximum simplified Wet-Bulb Globe Temperature (sWBGT) data from 1940 to 2022, with a spatial resolution of 2° × 2°. sWBGT is a thermal stress index that integrates air temperature, dew point temperature and surface air pressure, and is used to assess human heat load. This dataset is calculated based on hourly ERA5 single-level reanalysis data. In addition to the main variable sWBGT, the dataset also includes climate network data and its network structure metrics for teleconnection research, covering representative years every five years from 1940 to 2020. This portion of data helps reveal the spatiotemporal correlation and propagation mechanisms of global heat stress events, and supports in-depth research on heat stress teleconnection phenomena. The sWBGT data is released in NetCDF format, which is compatible with mainstream climate science data analysis tools; the climate network data and its metrics are stored in pickle format, facilitating efficient reading, writing and processing in Python environments. Additionally, the dataset accompanies all employed Python scripts, allowing users to either reconstruct the dataset or conduct personalized studies using existing methodologies. This dataset is applicable to research fields such as climate science, public health and environmental policy, and aims to provide data support and methodological foundations for the analysis of historical evolution trends of heat stress and future risk assessment.
