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Yansen Wang, Cheryl L. Klipp, Dennis M. Garvey, David A. Ligon, Chatt C. Williamson, Sam S. Chang, Rob K. Newsom, and Ronald Calhoun

; Rotach 1995 ; Roth and Oke 1995 ; Westcott 1989 ). Uno et al. (1988) have reported some turbulence observation results within the nocturnal UBL. Roth (2000) has given an extensive review of observational studies of atmospheric turbulence over cities. The results are mostly from high-density urban areas with relatively short buildings. In recent years, there have been some developments on the urban atmospheric boundary layer parameterization in mesoscale numerical weather prediction models

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Donald A. Burrows, Eric A. Hendricks, Steve R. Diehl, and Robert Keith

sources are modeled explicitly but require information on the atmospheric conditions, especially turbulence due to mesoscale circulations and boundary layer processes. For mesoscale models, estimates of the heat and momentum fluxes due to urban effects are needed, as is a characterization of the city in terms of surface roughness or drag coefficient. Urban-scale models could be used to provide better information to mesoscale models regarding the local influence of large building areas of a city. Of

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M. A. Nelson, E. R. Pardyjak, M. J. Brown, and J. C. Klewicki

1. Introduction Urban landscapes affect the turbulent and mean flow characteristics throughout the atmospheric surface layer (ASL). Urban roughness influences the ASL turbulence on a vast range of length and time scales. Flow scales of interest span from the mesoscale to the smallest scales where energy is dissipated into heat. The ASL over urban roughness is subdivided into regions based on the extent to which the urban roughness affects the mean and turbulent flow characteristics ( Grimmond

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M. A. Nelson, E. R. Pardyjak, J. C. Klewicki, S. U. Pol, and M. J. Brown

applications as neighborhood or mesoscale models of flow through urban areas, it averages out the localized flow features that may have significant effects on, for example, near-source dispersion of a contaminant. In contrast to most UFM urban canopy studies, this work limits the averaging period to, at most, 3 h in order to focus on the complicated flow structures present within the urban canopy over these time scales. This work also takes advantage of what is to date one of the highest instrument

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Stevens T. Chan and Martin J. Leach

1. Introduction Urban areas are the mostly likely locations for atmospheric releases of hazardous material, whether resulting from industrial accidents or from terrorist acts. To protect the population effectively, there is a great need for observational and modeling tools to track and to forecast the transport and dispersion of the hazardous material from such releases. The need for robust modeling tools, among others, was stressed in a recent report by the Committee on the Atmospheric

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Eric A. Hendricks, Steve R. Diehl, Donald A. Burrows, and Robert Keith

the simulations presented in this paper are β = 0.072, β * = 0.09, σ = 0.5, σ * = 0.5, and α = 0.52 ( Wilcox 1998 ). MESO is a mesoscale Lagrangian particle-based transport and diffusion code. For plumes, it uses random-walk techniques ( Diehl et al. 1982 ) for vertical dispersion and Langevin techniques ( van Dop et al. 1985 ) for horizontal dispersion. The random-walk techniques are used because they are numerically fast in comparison with other stochastic techniques. For clouds (or

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