Assimilation of various forms of satellite data into numerical weather prediction models has led to a significant increase in forecast skill during the past 25 years. Only recently have these efforts begun to be transitioned to mesoscale and storm-scale forecasts and initial research has shown promising results. One particular challenge in storm-scale data assimilation is properly identifying the location and intensity of convective features and the characteristics of the surrounding enviromnent prior to initiating forecasts. To examine whether or not hi-resolution satellite data can provide value added information, this research assimilates GOES cloud properties such as cloud top pressure, temperature, and cloud fraction into a forecast simulation of a severe weather outbreak that occurred in Oklahoma on 10 May 2010. The hypothesis posed by this research is that satellite derived cloud properties can provide information on the atmospheric state above and surrounding thunderstorms that will enable an improved model analysis of their characteristics, leading to improved short term forecasts. Traditional atmospheric observations are assimilated into the WRF-ARW model using a 36 member EnKF assimilation technique over a continental U.S. domain at 15 km resolution. GOES cloud data are then assimilated using the same technique on a 3 km nested grid domain centered around this event. Mesoscale data assimilation begins at 1200 UTC and continues until 2100 UTC 10 May with hourly GOES cloud data assimilated within the nested grid from 1800 to 2100 UTC. The effects of the satellite data assimilation are assessed by comparing two identical model experiments, one with and one without the satellite data, using 0 – 3 hour forecasts of thermodynamic conditions and simulated reflectivity. Observations from the VORTEX-2 expermient as well as radar and satellite observations are used to verify model output within the storm-scale domain.