Lookup NU author(s): Tomas Du Chemin Holderness,
Professor Stuart Barr,
Professor Richard Dawson
Urban areas have a high sensitivity to extreme temperature events such as heatwaves due to increased absorption and re-radiation of thermal energy from man-made materials as well as anthropogenic heat outputs. Variations in urban form, land use and surface cover result in spatial variability in temperatures across urban areas, meaning that exposure to extreme events is variable at the sub-city scale. Such variability must be quantified in order to better understand urban temperature interactions and identify areas with the greatest potential exposure to extreme heatwave events. Earth observed data offer a spatially complete and homogenous data source to supplement observations from sparse weather station networks in order to quantify the spatial temperature variability across cities. This paper presents an evaluation of the thermal data acquired by the Advanced Very High Resolution Radiometer (AVHRR) instrument to quantify the spatial temperature dynamics of London. 81 cloud-free AVHRR scenes from summers between 1996 and 2006 are analysed in association with air temperature measurements from four London weather stations in order to characterise the year-on-year temperature dynamics of London. The data were employed to investigate the viability of using AVHRR scenes to distinguish a heatwave year from background years using the commonly employed Urban Heat Island Intensity (UHII) metric. Results show that AVHRR thermal data are highly sensitive to local-meteorological and diurnal effects, requiring temporal averaging to the monthly and seasonal scales to provide robust data for a comparison between different years. Resulting UHII scenes highlight the spatial variability of intensity across London. However, comparison of UHII scenes between summers indicate that the UHII metric is a relatively poor means by which to distinguish between a heatwave summer in London and the 75th, median and 25th percentile summer temperatures of the time series investigated.
Author(s): Holderness T, Barr SL, Dawson R, Hall J
Publication type: Article
Journal: International Journal of Remote Sensing
Print publication date: 02/10/2012
ISSN (print): 0143-1161
ISSN (electronic): 1366-5901
Publisher: Taylor & Francis Ltd.
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