The Development of 1-D Ice Cloud Microphysics Data Assimilation System (IMDAS) for Cloud Parameter Retrievals by Integrating Satellite Data

Reliable prediction of precipitation by Numerical Weather Prediction (NWP) models depends on the appropriate representation of cloud microphysical processes and accurate initial conditions of observations of atmospheric variables. Therefore, 1D Variational (1D-Var) Ice Cloud Microphysics Data Assimilation System (IMDAS) is developed for retrieving reasonable cloud distributions to improve the predictability of NWP models. The general framework of IMDAS includes the Lin ice cloud microphysics scheme as a model operator, a 4-stream fast microwave radiative transfer model (RTM) in the atmosphere as an observation operator, and a global minimization method known as Shuffled Complex Evolution (SCE). The IMDAS assimilates the satellite microwave radiometer data set of Advanced Microwave Scanning Radiometer (AMSR-E) and retrieves integrated water vapor (IWV) and integrated cloud liquid water content (ICLWC). This new method successfully introduces the heterogeneity into the initial state of the atmosphere, and the modeled microwave brightness temperatures agree well with observations of Wakasa Bay Experiment 2003 in Japan. It has improved the performance of cloud microphysics scheme significantly by the intrusion of heterogeneity into the external Global Reanalysis (GANAL) data, which may improve atmospheric initial conditions.