Spatiotemporal Variability of Heat Storage in Major U.S. Cities—A Satellite-Based Analysis

Abstract

Heat storage, ΔQs, is quantified for 10 major U.S. cities using a method called the thermal variability scheme (TVS), which incorporates urban thermal mass parameters and the variability of land surface temperatures. The remotely sensed land surface temperature (LST) is retrieved from the GOES-16 satellite and is used in conjunction with high spatial resolution land cover and imperviousness classes. New York City is first used as a testing ground to compare the satellite-derived heat storage model to two other methods: a surface energy balance (SEB) residual derived from numerical weather model fluxes, and a residual calculated from ground-based eddy covariance flux tower measurements. The satellite determination of ΔQs was found to fall between the residual method predicted by both the numerical weather model and the surface flux stations. The GOES-16 LST was then downscaled to 1-km using the WRF surface temperature output, which resulted in a higher spatial representation of storage heat in cities. The subsequent model was used to predict the total heat stored across 10 major urban areas across the contiguous United States for August 2019. The analysis presents a positive correlation between population density and heat storage, where higher density cities such as New York and Chicago have a higher capacity to store heat when compared to lower density cities such as Houston or Dallas. Application of the TVS ultimately has the potential to improve closure of the urban surface energy balance.

Satellite-Derived Land Surface Temperature Dynamics in the Context of Global Change—A Review

“…A second factor is the urban landscape, where high buildings and trees can have a shading effect on the thermal environment [74,102,110,112,122]. Further factors are the reflective properties of the materials used in the urban environment [88,100,112], climate [65,85,118,121,135,140], the size of the city [96,142] and human activities, such as heating in winter [126,135]. Additionally, the SUHI intensity and footprint underlies diurnal and seasonal variations [100], with higher intensity during summer [143,169] and daytime [114,135].…”

“…Nearly half of the studies (77, 47%) were conducted with a medium resolution, which is the original resolution of MODIS LST. Only two of these studies did not use MODIS LST, namely Ouyang et al [190], who used AATSR LST to analyze local LST trends over the Heihe River Basin in China, and Hrisko et al [65], who used GOES LST to analyze LST trends over major US cities. After medium resolution, the second-most reviewed studies (57, 35%) used high-resolution LST, which comprises the original resolution of Landsat, ASTER and ECOSTRESS.…”

Urban Land Surface Temperature Downscaling in Chicago: Addressing Ethnic Inequality and Gentrification

“…On the other hand, geostationary satellites, especially the GOES-R series, stand out for their ability to continuously monitor a fixed point on Earth, making them ideal for the sub-hourly LST data capture over North America, despite their lower spatial resolution (2 km) compared to polar-orbiting satellites. This capability has led to their increasing use in urban LST monitoring [27][28][29][30].…”