Modelling the urban climate using a local governmental geo‐database

In this study, local government digital spatial data are used to describe urban geometry and analyse spatial variations of the urban climate within the central areas of Goteborg, Sweden. A high-resolution raster digital elevation model (1 m pixel resolution) consisting of building structures and ground heights is derived from a local government geo-database, as well as land use patterns and artificial heat sources. Parameters such as the sky view factor (SVF) and daily averages of solar radiation are calculated. Results obtained from the model are compared with intra-urban air temperature variations which are derived from mobile measurements, as well as surface temperature variations derived from thermal infrared images. Results show that high-resolution digital elevation models in raster format are very useful sources of data for the investigation of intra-urban temperature variations. Results also show that the areal mean of SVF correlates with intra-urban air temperature variations to a higher degree than SVF that is taken from a point source location. The correlation between the modelled SVF and surface temperature is high during both spring and winter. Adding information about daily averages of global radiation for the spring measurement causes the correlation between SVF and surface temperature variations to increase. Copyright © 2007 Royal Meteorological Society

[1]  Marie K. Svensson,et al.  Sky view factor analysis – implications for urban air temperature differences , 2004 .

[2]  J. Monteith,et al.  Boundary Layer Climates. , 1979 .

[3]  F. Lindberg,et al.  Different methods for estimating the mean radiant temperature in an outdoor urban setting , 2007 .

[4]  János Unger,et al.  Temperature cross-section features in an urban area , 2001 .

[5]  I. Saito,et al.  Study of the effect of green areas on the thermal environment in an urban area , 1990 .

[6]  Ingegärd Eliasson,et al.  Urban nocturnal temperatures, street geometry and land use , 1996 .

[7]  Juan Pedro Montávez,et al.  A study of the urban heat island of Granada , 2000 .

[8]  I. D. Watson,et al.  The determination of view-factors in urban canyons , 1984 .

[9]  János Unger,et al.  Intra-urban relationship between surface geometry and urban heat island: review and new approach , 2004 .

[10]  Chew Hung Chang,et al.  The relationship between height to width ratios and the heat island intensity at 22:00 h for Singapore , 1999 .

[11]  J. Mattsson,et al.  Canyon geometry, street temperatures and urban heat island in malmö, sweden , 1985 .

[12]  Douw G. Steyn,et al.  The calculation of view factors from fisheye‐lens photographs: Research note , 1980 .

[13]  J. Chen,et al.  Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand , 1991 .

[14]  A. Arnfield.  Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island , 2003 .

[15]  J. Unger.  Modelling of the annual mean maximum urban heat island using 2D and 3D surface parameters , 2006 .

[16]  C. S. B. Grimmond,et al.  Rapid methods to estimate sky‐view factors applied to urban areas , 2001 .

[17]  Lee Chapman,et al.  Rapid determination of canyon geometry parameters for use in surface radiation budgets , 2001 .

[18]  C. Ratti,et al.  Analysis of 3-D Urban Databases with Respect to Pollution Dispersion for a Number of European and American Cities , 2001 .

[19]  Ingegärd Eliasson,et al.  The influence of green areas on nocturnal temperatures in a high latitude city (Göteborg, Sweden) , 1998 .

[20]  Ingegärd Eliasson,et al.  Spatial air temperature variations and urban land use — a statistical approach , 2003 .

[21]  C. Ratti,et al.  COMPARISON OF METHODOLOGIES FOR COMPUTING SKY VIEW FACTOR IN URBAN ENVIRONMENTS , 2001 .

[22]  C. Ratti,et al.  Urban texture analysis with image processing techniques , 1999 .

[23]  Koen Steemers,et al.  Radiation absorption and urban texture , 1998 .

[24]  J. Unger,et al.  A multiple linear statistical model for estimating the mean maximum urban heat island , 2003 .

[25]  M. Eriksson,et al.  Sky view factors in forest canopies calculated with IDRISI , 2001 .

[26]  Jacques Teller,et al.  Townscope II—A computer system to support solar access decision-making , 2001 .

[27]  Daniel Souto Rodrigues,et al.  The 3dskyview extension: an urban geometry access tool in a geographical information system , 2003 .

[28]  Ingegärd Eliasson,et al.  Infrared thermography and urban temperature patterns , 1992 .