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The combined effects of climate change and urban expansion are elevating urban temperatures, amplifying health risks, and increasing summertime energy demand. Effective heat reduction and adaptation strategies rely on a thorough understanding of urban heat island (UHI) dynamics, which vary within cities and over time due to local urban form and meteorological conditions. While existing studies have examined the spatiotemporal structure of UHIs, they have not examined the space-time variability of air temperature at high spatial and temporal resolutions. This study addresses this gap by utilizing a ground-based sensor network of 53 iButton thermochrons, synoptic weather stations, and satellites to capture these spatiotemporal patterns. Using these data, we developed generalized additive models to predict daily, daytime and nighttime air temperature anomalies throughout Baltimore City. We found that 1) meteorological variables are more influential in characterizing the nighttime UHI than the daytime UHI; 2) spatiotemporal variability of nighttime UHIs is greatest in areas with sparse vegetation canopy cover; and 3) interactions between urban form and meteorology have a systematic but modest impact on the spatial structure of UHIs. The methodology and findings presented in this study offer tools for developing outdoor heat mitigation and adaptation strategies to reduce the UHI effect.
Corpuz et al. (Mon,) studied this question.