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THOMPSON WIND TUNNEL DATA USED FOR VALIDATION OF OML AND AERMOD/PRIME

This page gives technically minded readers the possibility to explore the behavior of dispersion when a stack is placed close to a rectangular building.

Some background information is appropriate:

In 1990 a comprehensive data set on dispersion behind rectangular buildings was compiled in the US EPA wind tunnel by R.S. Thompson. The data set systematically describes dispersion for a variety of building shapes, stack heights and stack locations. These data were originally used to estimate so-called Building Amplification Factor, but the potential of the data set extends much beyond this application.

In 2006, a group at the National Environmental Research Institute in Denmark conducted a study where we used this data set to analyse the performance of a number of dispersion models with more or less sophisticated approaches for handling building effects. The models are the Danish OML model, the US AERMOD/PRIME model, and the German MISKAM model.

The study is briefly described in a conference paper for the 6th International Conference on Urban Air Quality (Cyprus, March 27-29, 2007). It is also discussed in a technical report on revision of the Danish OML model that contains numerous graphs based on the data set.

Thompson’s data set is very comprehensive, and it deserves to be used much more than it has been in the past. The present page gives relevant links, as listed below.

  • The above-mentioned conference paper  gives a brief introduction to the data and to model performance: Olesen, H.R., Berkowicz, R.B, Ketzel, M., Løfstrøm, P., 2007: Validation of OML, AERMOD/PRIME and MISKAM using the Thompson wind tunnel data set for simple stack-building configurations.  6th International Conference on Urban Air Quality, Cyprus, March 27-29, 2007.
  • A PowerPoint presentation  with some interesting sequences showing how the concentrations change, depending on stack height, building geometry and stack location. The ground-level concentrations along the plume centerline are shown, according to both wind tunnel and models.
  • 4 Excel workbooks (one for each building shape). Each workbook contains one central worksheet with a chart as shown in the figure. The chart is dynamic, so the user can vary the stack height or the stack location (using arrow keys), and inspect the resulting changes in concentrations according to both measurements and models. The Power Point presentation referred to above is derived from these Excel sheets.
  • A Wiki on atmospheric dispersion modelling  exists. The Wiki can be used to communicate experiences on data sets. Therefore check the page specifically dealing with the Thompson wind tunnel data set
  • There is a comprehensive technical report  on the OML dispersion model (Olesen et al., 2007b). Page 92-94 of this report gives a further explanation of Thompson's wind tunnel data and the graphs.
  • If you wish to use the wind tunnel data, you should request the original data report from Steve Perry of the US EPA (Perry.Steven AT epamail.epa.gov). The report is available as a scanned PDF report. Also the data are available from the EPA. However, they are available as well in a rearranged format in the Excel workbooks developed at NERI.

Literature references:

Olesen, H.R., Berkowicz, R.B, Ketzel, M., Løfstrøm, P., 2007a: Validation of OML, AERMOD/PRIME and MISKAM using the Thompson wind tunnel data set for simple stack-building configurations.  6th International Conference on Urban Air Quality, Cyprus, March 27-29, 2007.  4-page extended abstract at 

Olesen, H.R., Berkowicz, R.B, Løfstrøm, P., 2007b: OML: Review of model formulation. National Environmental Research Institute, Denmark. NERI Technical Report No. 609, pp. 130. 

Thompson, R. S. (1993): Building Amplification Factors for Sources Near Buildings - A Wind-Tunnel Study. Atmospheric Environment Part A-General Topics 27, 2313-2325.

Thompson, R.S. (1991): Data report. Project: Building Amplification Factors. Available as a scanned PDF file from the US EPA (contact Steve Perry).