Theoretical and Applied Climatology

, Volume 105, Issue 3–4, pp 521–527 | Cite as

Comparison of models calculating the sky view factor used for urban climate investigations

  • Martin Hämmerle
  • Tamás Gál
  • János Unger
  • Andreas Matzarakis
Original Paper


The sky view factor (SVF) describes the surface geometry and is a commonly used and important measure in urban climate investigations whose aim is the exploration of effects of a complex urban surface on climatological processes in built-up areas. A selection of methods and models for calculating the SVF was compared. For this purpose, fish eye images were taken at several locations in the city of Szeged, southern Hungary. The fish eye images equidistantly follow linear transects to cover a range of SVF values and to analyze the reaction of the methods to a continuously changing environment. The fish eye pictures were evaluated by three methods: the method according to Steyn (Atmos-Ocean 18(3):245–258, 1980) implemented in a GIS-Script, the “Edit free sky view factor” tool of the RayMan model and BMSkyView. The SVF values at the coordinates of the fish eye pictures were calculated with three numerical models (SkyHelios, ArcView SVF extension, and SOLWEIG) with a 3D building data base as input. After comparing the results of the first run, a deviation occurs. The deviation disappears after implementing an option to include a weighting factor in some of the models.


Digital Elevation Model Zenith Angle Street Canyon Local Climate Zone RayMan 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 research was supported by the Hungarian Scientific Research Fund (OTKA K-67626). The first author was supported by the ERASMUS program.


  1. Gál T, Lindberg F, Unger J (2009) Computing continuous sky view factors using 3D urban raster and vector databases: comparison and application to urban climate. Theor Appl Climatol 95:111–123ADSCrossRefGoogle Scholar
  2. Grimmond CSB, Potter SK, Zutter HN, Souch C (2001) Rapid methods to estimate sky view factors applied to urban areas. Int J Climatol 21:903–913CrossRefGoogle Scholar
  3. Holmer B, Postgård U, Eriksson M (2001) Sky view factors in forest canopies calculated with IDRISI. Theor Appl Climatol 68:33–40ADSCrossRefGoogle Scholar
  4. Lin T-P, Matzarakis A, Hwang RL (2010) Shading effect on long-term outdoor thermal comfort. Build Environ 45:213–211CrossRefGoogle Scholar
  5. Lindberg F, Thorsson S, Holmer B (2008) SOLWEIG 1.0—modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. Int J Biometeorol 52:697–713PubMedCrossRefGoogle Scholar
  6. Makra L, Sánta T, Matyasovsky I, Damialis A, Karatzas K, Bergmann KC, Vokou D (2010) Airborne pollen in three European cities: detection of atmospheric circulation pathways by applying three-dimensional clustering of backward trajectories. J Geophys Res 115:16. doi: 10.1029/2010JD014743 CrossRefGoogle Scholar
  7. Matuschek O, Matzarakis A (2010) Estimation of sky view factor in complex environment as a tool for applied climatological studies. Berichte des Meteorologischen Instituts der Albert-Ludwigs-Universität Freiburg 20:534–539Google Scholar
  8. Matzarakis A (2001) Die thermische Komponente des Stadtklimas. Berichte des Meteorologischen Institutes der Universität Freiburg. Nr. 6Google Scholar
  9. Matzarakis A, Matuschek O (2010) Sky view factor as a parameter in applied climatology—rapid estimation by the SkyHelios model. Meteorol Z 20:39–45Google Scholar
  10. Matzarakis A, Rutz F, Mayer H (2007) Modeling radiation fluxes in simple and complex environments—application of the RayMan model. Int J Biometeorol 51:323–334PubMedCrossRefGoogle Scholar
  11. Matzarakis A, Rutz F, Mayer H (2010) Modeling radiation fluxes in simple and complex environments—basics of the RayMan model. Int J Biometeorol 54:131–139PubMedCrossRefGoogle Scholar
  12. Oke TR (1981) Canyon geometry and the nocturnal urban heat island: comparison of scale model and field observations. J Climatol 1:237–254CrossRefGoogle Scholar
  13. Oke TR (1987) Boundary layer climates. Methuen, LondonGoogle Scholar
  14. Ratti C, Richens P (1999) Urban texture analysis with image processing techniques. In: Augenbroe G, Eastman Ch (eds) Proceedings of the 8th International Conference on Computer Aided Architectural Design Futures held in Atlanta, GeorgiaGoogle Scholar
  15. Richert C (2010) GIS-gestützte Analyse des klimatischen Potentials der Windenergie in der Region Freiburg im Breisgau auf der Grundlage von Messungen und Klimasimulationen. Freiburg. Magister Scientiarum Thesis, unpublished. Supervisors: Prof. Dr. Rüdiger Glaser (Department of Physical Geography), Prof. Dr. Andreas Matzarakis (Meteorological Institute), Albert-Ludwigs-University of FreiburgGoogle Scholar
  16. Rzepa M, Gromek B (2006) Variability of sky view factor in the main street canyon in the center of Łódź. Preprints of the Sixth International Conference on Urban Climate, Göteborg, Sweden, pp. 854–857Google Scholar
  17. Stewart ID, Oke T (2010) Thermal differentiation of “local climate zones” using temperature observations from urban and rural field sites. In: Sailor DJ, Chen F, Lundquist JK (eds) Proceedings of the Ninth Symposium on the Urban Environment held by the American Meteorological Society in Keystone, ColoradoGoogle Scholar
  18. Steyn DG (1980) The calculation of view factors from fisheye-lens photographs. Atmos-Ocean 18(3):245–258MathSciNetADSGoogle Scholar
  19. Tukey JW (1985) The problem of multiple comparisons (1953) (unpublished manuscript). In: The Collected Works of John W. Tukey. Vol. II: Time Series. Wadsworth Advanced Books & Software, Monterey, pp. 1965–1984Google Scholar
  20. Unger J (2004) Intra-urban relationship between surface geometry and urban heat island: review and new approach. Clim Res 27:253–264CrossRefGoogle Scholar
  21. Unger J (2006) Modelling the annual mean maximum urban heat island with the application of 2 and 3D surface parameters. Clim Res 30:215–226CrossRefGoogle Scholar
  22. Unger J (2009) Connection between urban heat island and sky view factor approximated by a software tool on a 3D urban database. Int J Environ Pollut 36:59–80CrossRefGoogle Scholar
  23. VDI (1998) VDI 3787, Part I: Environmental Meteorology, Methods for the human biometeorological evaluation of climate and air quality for the urban and regional planning at regional level. Part I: Climate. Beuth, BerlinGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Martin Hämmerle
    • 1
  • Tamás Gál
    • 2
  • János Unger
    • 2
  • Andreas Matzarakis
    • 1
  1. 1.Meteorological InstituteAlbert-Ludwigs-University of FreiburgFreiburgGermany
  2. 2.Department of Climatology and Landscape EcologyUniversity of SzegedSzegedHungary

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