Cloud properties are required to test and improve cloud parameterizations in weather prediction and climate models and to study role of clouds in the atmospheric system. The cloud properties are also desired in initial condition in the weather prediction. Cloud property retrievals from site observations (e.g., the Atmospheric Radiation Measurements) and global satellite remote sensing measurements (e.g., International Satellite Cloud Climatology Project) and simulations of cloud resolving models (CRMs) have been used to estimate the statistical cloud properties. Neither of the approaches have been fully successful. The retrievals at the single site are not representative of large domains, while the cloud analysis based on the global satellite measurement retrievals do not have sufficient resolution to represent high variability at spatial scales which control the cloud properties. Furthermore, other information required for parameterization work and understanding of cloud evolution, such as advection of cloud profiles and the relationships between clouds and 3D dynamics are not available from the retrievals. The CRM simulations, on the other hand, represent the cloud microphysical and the associated dynamical properties but are poorly constrained if at all with cloud observations. The desired 4D analysis of cloud properties could be obtained with objective 4D data assimilation methodology which allows that the information from the cloud resolving model and cloud sensitive remote sensing measurements is integrated. We developed a 4D variational (4DVAR) data assimilation numerical algorithm for the Regional Atmospheric Modeling System (RAMS) with cloud resolving capability. The entire 4DVAR algorithm with the model is designated Regional Atmospheric Modeling and Data Assimilation system (RAMDAS). This project supported by the Army Research Lab is intended to improve high resolution atmospheric state estimation with clouds in 4D. The current applications of the new 4DVAR system emphasize assimilation of cloudy satellite radiances from GOES imager in IR. wavelengths. The imager observations are selected for the high temporal and spatial resolution and strong sensitivity to clouds and water vapor.