In recent years there have been dramatic advances in high performance desktop computing as computer hardware has decreased in cost as computing speeds, memory and disk storage have increased. This increased computing capability has given many dispersion modelers the capability to run mesoscale models that produce high-resolution three dimensional meteorological data. The dispersion model AERMOD, which is on track to be the model of choice for short-range regulatory scenarios, has the capability of utilizing more advanced meteorological information such as that produced by a mesoscale model. We have developed a technique to extract data from the mesoscale model RAMS, ingest it into AERMOD meteorological preprocessor AERMET, and then run AERMOD to produce concentration output. Some of the RAMS data extracted for AERMET includes mixing height, sensible heat flux, and long and short wave radiation along with the standard variables of wind speed and direction. For this paper we made a series of AERMOD runs using RAMS data as input and compared the results with AERMOD runs made using surface and upper air data from National Weather Service observations. When it was possible, we compared model results with field tracer observations. The results show some of the advantages of using mesoscale model data as input into AERMOD but they also show some of the limitations as well.