PDF(Pubmed)
Abstract：
Robust spatio-temporal delineation of extreme climate events and accurate identification of areas that are impacted by an event is a prerequisite for identifying population-level and health-related risks. In prior research, attributes such as temperature and humidity have often been linearly assigned to the population of the study unit from the closest weather station. This could result in inaccurate event delineation and biased assessment of extreme heat exposure. We have developed a spatio-temporal model to dynamically delineate boundaries for Extreme Heat Events (EHE) across space and over time, using a relative measure of Apparent Temperature (AT). Our surface interpolation approach offers a higher spatio-temporal resolution compared to the standard nearest-station (NS) assignment method. We show that the proposed approach can provide at least 80.8 percent improvement in identification of areas and populations impacted by EHEs. This improvement in average adjusts the misclassification of about one million Californians per day of an extreme event, who would be either unidentified or misidentified under EHEs between 2017 and 2021.
摘要：
对极端气候事件进行稳健的时空划分和准确识别受事件影响的区域是研究气候变化流行病学的前提。在先前的研究中，气候属性，如温度和湿度，通常被线性分配给研究单位的人口从最近的气象站。这可能导致不准确的事件描述和对极端热暴露的偏见评估。我们开发了一个时空模型，以动态地描绘跨空间和随时间的极端热事件（EHE）的边界。使用视在温度（AT）的相对测量。与标准的最近站（NS）分配方法相比，我们的表面插值方法提供了更高的时空分辨率。我们表明，所提出的方法可以在识别受EHEs影响的区域和人口方面提供至少80.8％的改进。平均值的这种提高调整了极端事件每天约100万加利福尼亚人的错误分类，在2017年至2021年期间，根据EHEs，他们要么身份不明，要么被误认。
