PNNL

Abstract

Understanding of urban weather and climate is severely limited by data poverty resulting from a dearth of true urban weather stations. As a result, land surface temperature (Ts), obtained from remote sensing platforms, has been widely used as a stand-in for near-surface air temperature (Ta) despite their fundamental differences, especially in urban areas. This has led to erroneous characterization of urban heat stress and urban climate impacts. Here we develop a novel urban transfer learning framework (U-TL) to address this critical gap and to provide urban high-resolution air temperature (U-HAT) data at large scales across the contiguous United States (CONUS). U-TL demonstrates high accuracy and strong robustness in predicting urban Ta, even with limited training data. The resulting U-HAT is the first high-resolution urban Ta dataset capable of accurately reproducing observed and well-established urban climatology. U-HAT demonstrates substantial Ts–Ta discrepancies and therefore cautions the use of Ts to characterize urban heat. We demonstrate that satellite-measured Ts substantially overestimates both urban heat stress magnitude and intra-city disparities, which have very consequential implications for urban heat exposure, vulnerability, and adaptation policy making.