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Boundary-Layer Meteorology

, Volume 16, Issue 1, pp 3–22 | Cite as

Markov chain simulations of vertical dispersion in the neutral surface layer for surface and elevated releases

  • John D. Reid
Article

Abstract

Vertical dispersion in the neutral surface layer is investigated using a Markov Chain simulation procedure. The conceptual basis of the procedure is discussed and computation procedures outlined. Wind and turbulence parameterizations appropriate to the neutral surface layer are considered with emphasis on the Lagrangian time scale. Computations for a surface release are compared with field data. Good agreement is found for the variation of surface concentration and cloud height to distances 500 m downwind of the source. The functional form of the vertical concentration profile is examined and an exponential with exponent ∼ 1.6 is found to give the best fit with simulations.

For elevated releases, it is demonstrated that an initial dip of the mass mean height from the simulation can be normalized for various release heights using a non-dimensionalized downwind coordinate incorporating advective wind speed and wind shear. The vertical distribution standard deviation (σ z ), as employed in Gaussian models, shows a fair degree of independence with source height but close examination reveals an optimum source height for maximum σz at a given downwind distance,x. This source height increases with downwind distance. Also the simulations indicate that vertical wind shear is more important than vertical variation of Lagrangian time scale close to the source, with a reverse effect farther downwind.

Keywords

Wind Shear Vertical Wind Vertical Wind Shear Surface Release Turbulence Parameterization 
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.

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Copyright information

© D. Reidel Publishing Co. 1979

Authors and Affiliations

  • John D. Reid
    • 1
  1. 1.Atmospheric Environment ServiceDownsviewCanada

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