Spatio-temporal interpolation and delineation of extreme heat events in California between 2017 and 2021

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.