Spatio-temporal interpolation and delineation of extreme heat events in California between 2017 and 2021.
Spatio-temporal interpolation and delineation of extreme heat events in California between 2017 and 2021.
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DOI：
10.1016/j.envres.2023.116984
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发表时间：
2023-08
影响因子：
8.3
通讯作者：
P. Fard;M. Chung;Hossein Estiri;C. Patel
中科院分区：
文献类型：
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作者：
P. Fard;M. Chung;Hossein Estiri;C. Patel
对极端气候事件进行强有力的时空划分，并准确确定受事件影响的地区，是确定人口层面和与健康有关的风险的先决条件。在以前的研究中，温度和湿度等属性通常被线性分配给最近气象站的研究单元的人口。这可能导致不准确的事件描述和对极端热暴露的偏倚评估。我们已经开发了一个时空模型，动态划定边界的极端高温事件（EHE）在空间和时间上，使用相对测量表观温度（AT）。我们的表面插值方法提供了更高的时空分辨率相比，标准的最近站（NS）分配方法。我们表明，所提出的方法可以提供至少80.8%的改进，在识别受EHE影响的地区和人群。这一平均值的改善调整了每天约100万加利福尼亚人对极端事件的错误分类，这些极端事件在2017年至2021年期间将无法识别或错误识别。
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.