Development of a satellite land and cloud data assimilation system coupled with WRF

To obtain the satellite observations of cloud over the land and to assimilate them into the model are effective for rain prediction. However it cannot be easily achieved, because emissivity of clouds is weaker than that of land surface. In order to observe cloud over the land, we have to adequately represent the heterogeneity of land state, especially soil moisture distribution, which has large effect on emissivity of the land, and estimate the surface emissivity, then remove it as background information for cloud observation. For this purpose, we developed a satellite-based land and cloud data assimilation system coupled with the Weather Research and Forecasting Model (CALDAS-WRF), based on the Coupled Land and Atmosphere Data Assimilation System (CALDAS) (Rasmy et al. 2012). The CALDAS-WRF includes Simple Biosphere model version 2 (SiB2) as a land surface driver, radiative transfer models for surface soil layer and atmosphere as observation operators, and Ensemble Kalman Filter (EnKF) and 1DVAR as assimilation algorithms for land and cloud, respectively. The CALDAS-WRF first assimilates the soil moisture heterogeneity, using passive microwave brightness temperature (Tb) at lower frequency, which has a high sensitivity to soil moisture, and then assimilates cloud and water vapor, using Tb at higher frequency and optimized emissivity of land as a background information. To evaluate this system, the CALDAS-WRF was applied to a mesoscale region in the Tibetan Plateau. The experimental results show that the CALDAS-WRF effectively assimilated information of clouds contained in higher frequency microwave data and improved the representation of cloud distribution compared with satellite observation.