A regional 4DVAR data assimilation system was developed using a mesoscale model with explicit cloud microphysics and a suite of optical property and radiative transfer (RT) models to study the potential impact of satellite radiance measurements on 3D cloudy state analysis. Cloudy states consist of the 3D clouds and their environment. The set of optical property and RT models suitable for use with an explicit cloud microphysics background is referred to as an observational operator, for short denoted VISIROO, which was then included in a newly developed regional 4DVAR data assimilation system (RAMDAS, Regional Atmospheric Modeling and Data Assimilation System). The background includes time sequence of 3D states of 7 hydrometeor types and the associated surface and atmospheric conditions.

The complex problem of assessing quantitative information content of visible and IR measurements on explicit cloudy states was divided into three phases. First, we performed a sequence of sensitivity analyses of the separate components (cloud optical property models and RT models) using the associated adjoint of VISIROO. These tests were done for a set of idealized perturbations across a wide range of hydrometeor sizes and number concentrations to test the accuracy and feasibility of the adjoint analysis. Then the adjoint analysis was applied to three cases of cloudy weather evolution simulated by the mesoscale weather prediction model: a) continental stratiform low level liquid cloud deck, b) deep convective summer storm and c) winter storm. These analyses show strong potential for the visible and IR radiance measurements to improve quantitative estimates of 3D cloudy states and their initialization in numerical weather prediction.

The data assimilation experiments using GOES 9 imager channels 1-5 were then assimilated into the mesoscale model for the case of stratiform low-level cloud. The results show positive impact of observations on the cloud microphysical quantities in the cloudy region and associated physically consistent change in the cloud environment.