Sensitivity of Urban Airshed Model (UAM-IV) Calculated Air Pollutant Concentrations to the Vertical Diffusion Parameterization during Convective Meteorological Situations

Peter Nowacki Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Perry J. Samson Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Sanford Sillman Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Abstract

It is shown that Urban Airshed Model (UAM-IV) calculated air pollutant concentrations during photochemical smog episodes in Atlanta, Georgia, depend strongly on the numerical parameterization of the daytime vertical diffusivity. Results found suggest that vertical mixing is overestimated by the UAM-IV during unstable daytime conditions, as calculated vertical diffusivity values exceed measured and comparable literature values. Although deviations between measured and UAM-IV calculated air pollutant concentrations may only in part be due to the UAM-IV diffusivity parameterization, results indicate the large error potential in vertical diffusivity parameterization. Easily implemented enhancements to UAM-IV algorithms are proposed, thus improving UAM-IV modeling performance during unstable stratification.

Abstract

It is shown that Urban Airshed Model (UAM-IV) calculated air pollutant concentrations during photochemical smog episodes in Atlanta, Georgia, depend strongly on the numerical parameterization of the daytime vertical diffusivity. Results found suggest that vertical mixing is overestimated by the UAM-IV during unstable daytime conditions, as calculated vertical diffusivity values exceed measured and comparable literature values. Although deviations between measured and UAM-IV calculated air pollutant concentrations may only in part be due to the UAM-IV diffusivity parameterization, results indicate the large error potential in vertical diffusivity parameterization. Easily implemented enhancements to UAM-IV algorithms are proposed, thus improving UAM-IV modeling performance during unstable stratification.

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