With increasing reliance on numerical weather prediction models by the operational forecasting community, more accurate and detailed data assimilation systems are being studied. The FASDAS (flux-adjusting surface data assimilation system) uses the surface layer temperature and humidity to estimate the soil moisture and temperature evolutions in numerical model predictions. Both soil moisture and soil temperatures are important variables in the development of deep convection. In this study, FASDAS is coupled within the MM5 model to obtain realistic soil moisture and temperature fields over the IHOP (2002) region to study convective initiation. Surface temperature, dew point temperature and wind observations were directly assimilated by using the analyzed surface observations collected during this study. Two 72-hr numerical simulations were performed. A Control Simulation was run that assimilated all available IHOP data into the standard MM5 Four-Dimensional Data Assimilation. An Experimental Simulation was completed that assimilated all available IHOP data into the FASDAS version of the MM5. Results from the case study suggest that the Experimental run simulated precipitation amounts and distribution better than the Control Simulation over the region. Soil temperature and moisture fields in the Experimental Simulation matched observations more closely than the Control Simulation. Results suggest that the FASDAS can be successfully assimilated into a mesoscale model provided high quality surface observations are available.