Theoretical and Applied Climatology

, Volume 105, Issue 3–4, pp 311–323 | Cite as

The influence of vegetation and building morphology on shadow patterns and mean radiant temperatures in urban areas: model development and evaluation

  • Fredrik LindbergEmail author
  • C. S. B. Grimmond
Original Paper


The solar and longwave environmental irradiance geometry (SOLWEIG) model simulates spatial variations of 3-D radiation fluxes and mean radiant temperature (T mrt) as well as shadow patterns in complex urban settings. In this paper, a new vegetation scheme is included in SOLWEIG and evaluated. The new shadow casting algorithm for complex vegetation structures makes it possible to obtain continuous images of shadow patterns and sky view factors taking both buildings and vegetation into account. For the calculation of 3-D radiation fluxes and T mrt, SOLWEIG only requires a limited number of inputs, such as global shortwave radiation, air temperature, relative humidity, geographical information (latitude, longitude and elevation) and urban geometry represented by high-resolution ground and building digital elevation models (DEM). Trees and bushes are represented by separate DEMs. The model is evaluated using 5 days of integral radiation measurements at two sites within a square surrounded by low-rise buildings and vegetation in Göteborg, Sweden (57°N). There is good agreement between modelled and observed values of T mrt, with an overall correspondence of R 2 = 0.91 (p < 0.01, RMSE = 3.1 K). A small overestimation of T mrt is found at locations shadowed by vegetation. Given this good performance a number of suggestions for future development are identified for applications which include for human comfort, building design, planning and evaluation of instrument exposure.


Root Mean Square Error Digital Elevation Model Shortwave Radiation Longwave Radiation Physiological Equivalent Temperature 
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.



This work is financially supported by FORMAS—the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning and by European Community’s Seventh Framework Programme FP/2007–2011 BRIDGE (211345) project. The authors would like to thank the Meteorological Institute, University of Freiburg for providing human-biometeorological data from the KLIMES-project. The interface can be downloaded from the Göteborg Urban Climate Group-website (


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Environmental Monitoring and Modelling Group, Department of GeographyKing’s College LondonLondonUK
  2. 2.Department of Earth SciencesUniversity of GothenburgGothenburgSweden

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