Study of temporal and spatial distribution of highly variable dust storms through numerical models can help us to forecast these events at regional and global scales and also improve timely decision making in this field. The purpose of this study was to assess and forecast dust storms in Khozestan province using WRF atmospheric mesoscale model and HYSPLIT simulation model of motion and dispersion particles and satellite dust imagehy;. For this purpose, MODIS satellite images and horizontal visibility data were obtained from 2 to 7 July 2009 . After coupling WRF and HYSPLIT models two-part of HYSPLIT model was used including emission and trajectory simulatiohy;. Then, for evaluating HYSPLIT model, the outputs of WRF atmospheric model, thermal-infrared dust index (TDI), MODIS Deep Blue AOD and OMI AI Products were used. According to the HYSPLIT model, the first dust plume arrived Khuzestan province 27 and 12 hours after entraining from the first and second points on the third of July 2009, respectively. The area of dust plume was gradually increased from 2 to 5 July moving towards the north-west, south- east, south-west and central parts of Iran. The longitudinal and latitudinal dust profiles of HYSPLIT model indicated that dust particles can reach the high levels of atmosphere (up to 8,500 m above ground level) and with help of Jetstreams can traort to the furthest eastern point of Iran and the Persian Gulf. The map of atmospheric parameters indicated an increase of wind speed in the Shamal Wind from 2 to 5 July as a factor for particle entrainment and traorting dust from the Tigris- Euphrates basin. The results of TDI algorithm and also Deep Blue AOD and OMI AI Products confirmed the appropriateness of the HYSPLIT model in forecasting dust storms in the region. Overall, the results of this study showed that the integration of numerical models (WRF and HYSPLIT) and satellite dust images can be used as an effective system in assessment and early warning of dust crisis. Key words : Dust Storm, WRF Model, HYSPLIT Model, Satellite Images