In the near future the NASA Global Precipitation Measurement (GPM) Mission will provide new sources of precipitation observations with unprecedented accuracy and coverage of the globe. For hydrological applications, the satellite observations need to be downscaled to the required finer spatial and temporal resolution precipitation fields. A WRF ensemble data assimilation system is developed to explore the potential of using ensemble data assimilation techniques and cloud-resolving models to dynamically downscale satellite observations. A high-resolution regional WRF model with multiple nesting grids is used to provide the first guess and ensemble forecasts. An ensemble assimilation algorithm based on Maximum Likelihood Ensemble Filter (MLEF) is used to perform analysis. Precipitation-affected radiances are assimilated along with the observations from the NCEP regional data stream. Prognostic hydrometeors and dynamical variables are simultaneously updated by the analysis every 3 hours. The experiments using the current available satellite precipitation data will be presented to discuss some of the challenging issues: direct radiance assimilation using microwave high-frequency channels in precipitation region overland, model-predicted hydrometeor control variables and associated background error covariance, and data impact to precipitation analysis.