Theoretical and Applied Climatology

, Volume 108, Issue 1–2, pp 301–324 | Cite as

High-frequency fluctuations of surface temperatures in an urban environment

  • Andreas ChristenEmail author
  • Fred Meier
  • Dieter Scherer
Original Paper


This study presents an attempt to resolve fluctuations in surface temperatures at scales of a few seconds to several minutes using time-sequential thermography (TST) from a ground-based platform. A scheme is presented to decompose a TST dataset into fluctuating, high-frequency, and long-term mean parts. To demonstrate the scheme’s application, a set of four TST runs (day/night, leaves-on/leaves-off) recorded from a 125-m-high platform above a complex urban environment in Berlin, Germany is used. Fluctuations in surface temperatures of different urban facets are measured and related to surface properties (material and form) and possible error sources. A number of relationships were found: (1) Surfaces with surface temperatures that were significantly different from air temperature experienced the highest fluctuations. (2) With increasing surface temperature above (below) air temperature, surface temperature fluctuations experienced a stronger negative (positive) skewness. (3) Surface materials with lower thermal admittance (lawns, leaves) showed higher fluctuations than surfaces with high thermal admittance (walls, roads). (4) Surface temperatures of emerged leaves fluctuate more compared to trees in a leaves-off situation. (5) In many cases, observed fluctuations were coherent across several neighboring pixels. The evidence from (1) to (5) suggests that atmospheric turbulence is a significant contributor to fluctuations. The study underlines the potential of using high-frequency thermal remote sensing in energy balance and turbulence studies at complex land–atmosphere interfaces.


Temperature Fluctuation Surface Energy Balance Urban Surface Turbulent Exchange Digital Surface Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The infrastructure and the experimental part of this study were funded by “Energy eXchange and Climates of Urban Structures and Environments (EXCUSE)” supported by TU Berlin (Scherer). The data analysis and computing infrastructure were supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, discovery grant 342029-07, Christen).


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Department of GeographyUniversity of British ColumbiaVancouverCanada
  2. 2.Department of EcologyTechnische Universität BerlinBerlinGermany

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