Evaluation of TAPM, a prognostic meteorological and air pollution model, using urban and rural point-source data

Abstract

Abstract The Air Pollution Model (TAPM) (version 2.0), a 3D prognostic model that solves the fundamental fluid dynamics and scalar transport equations to predict both meteorology and air pollution concentrations, is evaluated using the 1985 Indianapolis and 1980−1981 Kincaid field data sets on point-source plume dispersion. The data sets represent urban and rural conditions, respectively, in relatively flat terrain. Multi-level nesting is applied, the Lagrangian particle approach is used to describe near-source dispersion, and the model is operated both with and without local wind data assimilation. Comparison with (published) results obtained from some commonly used plume/puff models, which do not calculate meteorology, indicates that the performance of TAPM is comparable to the best of these models. TAPM gives better concentration predictions when the observed winds are assimilated, but the results without data assimilation are almost as good. The latter implies that the model predicts the local meteorology well. The Indianapolis results point to some bias in TAPM to underpredict in the nighttime stable/neutral conditions and to slightly overpredict in the daytime convective/neutral conditions. The latter is also highlighted by the comparison with the convective Kincaid data. The nighttime underprediction is perhaps due to the fact that the local urban effects are not properly accounted for by the generic single-layer canopy scheme used. The comparison also highlights the roles that light winds, wind direction shear and unsteadiness of the flow play in influencing the locations of concentration maxima.