With the greater Salt Lake area (GSLA) growing rapidly every year, it is important to understand how metropolitan growth can affect local meteorology. The physical layout and anthropogenic heating in urban environments can cause an increase in temperature up to 7º C relative to a comparable rural area, which often leads to higher levels of PM2.5 and ozone. The Weather Research and Forecasting Model (WRF) can predict meteorological behavior of the GSLA. The use of modeling techniques like an Urban Canopy Model (UCM), and Local Climate Zones (LCZ) can improve the representation of urban characteristics. These tools give specific classifications to different parts of the GSLA and allow us to compute data regarding temperature, wind speed, humidity, and an array of other variables. A more accurate representation of the urban environment allows for better simulation results. To assess the impacts of future growth scenarios, a baseline model of current urban properties and meteorological conditions must be established. Part of this process is to assess model sensitivities to discretization parameters. In this case computational domain resolution. Resolution is determined by how many grid cells of uniform size can reside within the domain. More grid cells with smaller areas within the domain give a higher resolution and more precise results but take longer to calculate. Fewer grid cells per domain result in lower resolution which saves time and allows for more simulations, but at the cost of precision. This presentation discusses how grid resolution was changed and the resulting differences in predicted meteorological properties. Results showed that predicted temperatures varied based on grid size for a downtown location.