Urban Heat Islands as Viewed by Microwave Radiometers and Thermal Time Indices

Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower temporal coverage due to revisit frequency, limited swath width, and cloud cover. Algorithms have recently been developed to retrieve near-surface air temperatures using microwave radiometer data, which enables characterization of UHIs in metropolitan areas, major conurbations, and global megacities at regional to continental scales using temporally denser time series than those that have been available from TIR sensors. Here we examine how UHIs appear across the entire Western Hemisphere using surface air temperatures derived from the Advanced Microwave Scanning Radiometers (AMSRs), AMSR-E onboard the National Aeronautics and Space Administration’s (NASA’s) Aqua and AMSR2 onboard the Japan Aerospace eXploration Agency’s Global Change Observation Mission-Water1 (JAXA’s GCOM-W1) satellites. We compare these data with station observations from the Global Historical Climate Network (GHCN) for 27 major cities across North America (in 83 urban-rural groupings) to demonstrate the capability of microwave data in a UHI study. Two measures of thermal time, accumulated diurnal and nocturnal degree-days, are calculated from the remotely sensed surface air temperature time series to characterize the urban-rural thermal differences over multiple growing seasons. Daytime urban thermal accumulations from the microwave data were sometimes lower than in adjacent rural areas. In contrast, station observations showed consistently higher day and night thermal accumulations in cities. UHIs are more pronounced at night, with 55% (AMSRs) and 93% (GHCN) of urban-rural groupings showing higher accumulated nocturnal degree-days in cities. While urban-rural thermal gradients may vary according to different datasets or locations, day-night differences in thermal time metrics were consistently lower (>90% of urban-rural groupings) in urban areas than in rural areas for both datasets. We propose that the normalized difference accumulated thermal time index (NDATTI) is a more robust metric for comparative UHI studies than simple temperature differences because it can be calculated from either station or remotely sensed data and it attenuates latitudinal effects.