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Advances in Meteorology
Volume 2016 (2016), Article ID 8736263, 14 pages
Research Article

Assessing Urban Landscape Variables’ Contributions to Microclimates

1Department of Geography, Virginia Polytechnic Institute and State University, 220 Stanger Street, Blacksburg, VA 24061, USA
2Department of Statistics, Virginia Polytechnic Institute and State University, 410 Hutcheson Hall, 250 Drillfield Drive, Blacksburg, VA 24061, USA

Received 2 July 2015; Accepted 16 September 2015

Academic Editor: Stefania Bonafoni

Copyright © 2016 Tammy E. Parece et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The well-known urban heat island (UHI) effect recognizes prevailing patterns of warmer urban temperatures relative to surrounding rural landscapes. Although UHIs are often visualized as single features, internal variations within urban landscapes create distinctive microclimates. Evaluating intraurban microclimate variability presents an opportunity to assess spatial dimensions of urban environments and identify locations that heat or cool faster than other locales. Our study employs mobile weather units and fixed weather stations to collect air temperatures across Roanoke, Virginia, USA, on selected dates over a two-year interval. Using this temperature data, together with six landscape variables, we interpolated (using Kriging and Random Forest) air temperatures across the city for each collection period. Our results estimated temperatures with small mean square errors (ranging from 0.03 to 0.14); landscape metrics explained between 60 and 91% of temperature variations (higher when the previous day’s average temperatures were included as a variable). For all days, similar spatial patterns appeared for cooler and warmer areas in mornings, with distinctive patterns as landscapes warmed during the day and over successive days. Our results revealed that the most potent landscape variables vary according to season and time of day. Our analysis contributes new dimensions and new levels of spatial and temporal detail to urban microclimate research.