抄録
We developed a satellite-based land and cloud data assimilation system coupled with the Weather Research and Forecasting Model (CALDAS-WRF) and applied to the Kanto area. 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. Then the CALDAS-WRF assimilates cloud over the land, using Tb at higher frequency, which is sensitive to cloud, and optimized emissivity of land as a background information. The experimental results in the Kanto area show that the CALDAS-WRF effectively assimilated information of clouds, improved the representation of cloud distribution, and also affected atmospheric field around the cloud area about one hour after assimilation.
