Urban surface temperature observations from ground-based thermography: intra- and inter-facet variability

Morrison, W., Kotthaus, S. and Grimmond, S. ORCID: https://orcid.org/0000-0002-3166-9415 (2021) Urban surface temperature observations from ground-based thermography: intra- and inter-facet variability. Urban Climate, 35. 100748. ISSN 2212-0955 doi: 10.1016/j.uclim.2020.100748

Abstract/Summary

Ground based thermal cameras are used to observe urban surface temperatures (Ts) with an unprecedented combination of: temporal and spatial resolution (5 min and ~0.5 m → 2.5 m), spatial extent (3.9 ha), instrument number (6 static cameras) and surface heterogeneity (mixed high rise and vegetation). Unsupervised classification of images by geometry and material properties (surface orientation, albedo, solar irradiance, and shadow history) is facilitated by a detailed three-dimensional surface model (430 m x 430 m extent) and sensor view modelling. From detailed source area analysis, 9.5 % of the area is observed by the cameras. Across all camera pixels, the 5th - 95th percentile Ts differences reach 37.5 K around midday. Roofs have the greatest diurnal Ts range (290.6 K → 329.0 K). Ts differences across sunlit sloped roofs reach 23.3 K. Walls of different cardinal orientations consistently differ by > 10 K between 10:00 and 15:00. Shadow tracking within images is used to model cooling rates, where recently shaded (< 30 min) ground can be 18.6 K warmer than equivalent unshaded Ts. West walls remain warm past sunset and are 1.2 K warmer than north walls at 23:00 (~4 hours after sunset). Recently shaded walls cool exponentially to ambient Ts at a similar rate as the ground, but four times slower than roofs. The observed Ts characteristics are anticipated to have a wide range of applications (e.g. evaluation of urban surface energy balance models, ground-truthing of satellite thermal remote sensing).

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/94803
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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