Evaluation of Urban Scale Contaminant Transport and Dispersion Modeling using Loosely Coupled CFD and Mesoscale Models

A study has recently been conducted to evaluate the potential improvement in urban area contaminant transport and dispersion modeling accuracy using a combined mesoscale and microscale (CFD) modeling approach. The coupling of urban area and mesoscale models offers the potential of improving both models accuracy by providing more accurate prevailing conditions data to supply to the urban scale model, as well as more detailed urban parameterization model data to feedback to the mesoscale model. A Computational Fluid Dynamics (CFD) based urban area transport and dispersion model, CFD-Urban, has been loosely coupled with the Weather Research and Forecasting model, WRF, to investigate transport and dispersion modeling accuracy as well as to evaluate improved urban area parameterizations. Recent results regarding the use of combined micro-scale and meso-scale models are presented, under the context of modeling the transport and dispersion through the Salt Lake City area during Intensive Operating Period (IOP) 10 of the Urban 2000 field test. High-resolution (500 meter grid spacing) meso-scale calculations performed using the community Weather Research and Forecasting (WRF) meso-scale model are presented, with a detailed urban land use classification and with various urban parameterizations, for a time period covering the selected tracer gas release durations of the IOP 10 of Urban 2000. The velocity, pressure, and turbulence fields are extracted from the meso-scale model output, and used to supply boundary conditions to CFD-Urban. Statistical measures of the transport and dispersion model accuracy are shown for CFD-Urban calculations for isolated (Raging Waters station input) and from WRF-forecasted-meteorological-conditions input. A description of both models, the coupling procedures and details from the statistical comparisons are shown, and recommendations for further research and development regarding a more tightly coupled approach are presented.

Acknowledgements The authors gratefully acknowledge the financial support for this work, performed under a Small Business Innovation Research Phase I grant from the Defense Threat Reduction Agency, Technical Monitor Cmdr. Stephanie Hamilton.