Editorial Type:
Article
Article Type:
Research Article

Evaluation of a Fast-Running Urban Dispersion Modeling System Using Joint Urban 2003 Field Data

Eric A. Hendricks Advanced Engineering and Sciences, ITT Corporation, Colorado Springs, Colorado

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Steve R. Diehl Advanced Engineering and Sciences, ITT Corporation, Colorado Springs, Colorado

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Donald A. Burrows Advanced Engineering and Sciences, ITT Corporation, Colorado Springs, Colorado

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Robert Keith Advanced Engineering and Sciences, ITT Corporation, Colorado Springs, Colorado

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Print Publication:
01 Dec 2007
Collections:
Joint Urban 2003 Experiment (JU2003)
DOI:
https://doi.org/10.1175/2006JAMC1289.1
Page(s):
2165–2179
Restricted access

Abstract

An urban dispersion modeling system was evaluated using the Joint Urban 2003 field data. The system consists of a fast-running urban airflow model (RUSTIC, for Realistic Urban Spread and Transport of Intrusive Contaminants) that is coupled with a Lagrangian particle transport and diffusion model (MESO) that uses random-walk tracer diffusion techniques. Surface measurements from fast-response and integrated bag samplers were used to evaluate model performance in predicting near-field (less than 1 km from the source) dispersion in the Oklahoma City, Oklahoma, central business district. Comparisons were made for six different intense operating periods (IOPs) composed of three different release locations and stable nighttime and unstable daytime meteorological conditions. Overall, the models were shown to have an underprediction bias of 47%. A possible influence to this underprediction is that the higher density of sulfur hexafluoride in comparison with air was not taken into account in the simulations. The models were capable of predicting 42% of the sampler data within a factor of 2 and 83% of the data within a factor of 10. When the effects of large-scale atmospheric turbulence were included, the models were shown to be capable of predicting 51% of the data within a factor of 2. The results were further broken down into performance for varying meteorological conditions. For daytime releases, the models performed reasonably well; for nighttime releases the models performed more poorly. Two possible causes of the poorer nighttime comparisons are (a) an inability to model the suppression of vertical turbulence because of the assumption of isotropy in RUSTIC’s k–ω turbulence model and (b) difficulty in modeling the light and variable inflow winds. The best comparisons were found for the three continuous daytime releases of IOP-4. It was hypothesized that these good comparisons were a result of steadier inflow conditions combined with the fact that the release site was more exposed and closer to the sodar used for the inflow meteorological conditions.

* Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Corresponding author address: Eric A. Hendricks, Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: eric.@atmos.colostate.edu

This article included in the Urban 2003 Experiment (JU2003) special collection.

Abstract

An urban dispersion modeling system was evaluated using the Joint Urban 2003 field data. The system consists of a fast-running urban airflow model (RUSTIC, for Realistic Urban Spread and Transport of Intrusive Contaminants) that is coupled with a Lagrangian particle transport and diffusion model (MESO) that uses random-walk tracer diffusion techniques. Surface measurements from fast-response and integrated bag samplers were used to evaluate model performance in predicting near-field (less than 1 km from the source) dispersion in the Oklahoma City, Oklahoma, central business district. Comparisons were made for six different intense operating periods (IOPs) composed of three different release locations and stable nighttime and unstable daytime meteorological conditions. Overall, the models were shown to have an underprediction bias of 47%. A possible influence to this underprediction is that the higher density of sulfur hexafluoride in comparison with air was not taken into account in the simulations. The models were capable of predicting 42% of the sampler data within a factor of 2 and 83% of the data within a factor of 10. When the effects of large-scale atmospheric turbulence were included, the models were shown to be capable of predicting 51% of the data within a factor of 2. The results were further broken down into performance for varying meteorological conditions. For daytime releases, the models performed reasonably well; for nighttime releases the models performed more poorly. Two possible causes of the poorer nighttime comparisons are (a) an inability to model the suppression of vertical turbulence because of the assumption of isotropy in RUSTIC’s k–ω turbulence model and (b) difficulty in modeling the light and variable inflow winds. The best comparisons were found for the three continuous daytime releases of IOP-4. It was hypothesized that these good comparisons were a result of steadier inflow conditions combined with the fact that the release site was more exposed and closer to the sodar used for the inflow meteorological conditions.

* Current affiliation: Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

Corresponding author address: Eric A. Hendricks, Dept. of Atmospheric Science, Colorado State University, Fort Collins, CO 80523. Email: eric.@atmos.colostate.edu

This article included in the Urban 2003 Experiment (JU2003) special collection.

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Journal of Applied Meteorology and Climatology

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