45 The impacts of sidewall canopy cover on the formation of temperature inversions in an idealized valley

Wednesday, 11 June 2014
Palm Court (Queens Hotel)
Michael T. Kiefer, Michigan State University, East Lansing, MI; and S. Zhong

While numerous studies have examined drainage flows and the formation of temperature inversions inside basins and valleys, relatively little is known of the impacts of sidewall canopies on the formation and evolution of temperature inversions inside basins and valleys. A lack of observations makes numerical modeling a useful tool, but until recently the dual requirements of fine resolution and the inclusion of a canopy sub-model made investigating this problem difficult.

In the current study, a set of fine-scale idealized numerical simulations were performed using the Advanced Regional Prediction System (ARPS) atmospheric model to investigate the impact of sidewall canopy distribution on the structure and evolution of temperature inversions inside a valley. Recently, ARPS has been modified to allow simulation of flow through a multi-layer canopy (ARPS-CANOPY). The effects of vegetation elements (e.g., branches, leaves) on drag, turbulence production/dissipation, radiation transfer, and the surface energy budget are accounted for through modifications to the original ARPS model equations and physical parameterization schemes.

We examine the impact of sidewall canopy cover on valley inversions in four scenarios that differ in canopy distribution: (i) vegetation along the lower valley sidewall, (ii) vegetation along the upper valley sidewall, (iii) vegetation along the entire sidewall, and (iv) no vegetation. For each scenario, profiles of plant area density (Ap) are specified, where Ap is defined as the one-sided area of all plant material per unit volume of canopy. Inversion characteristics are compared between cases during various stages of inversion development. Findings gleaned from this study have relevance to numerical prediction of inversion strength and persistence as well as air quality prediction.

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