Abstract
A satellite-based Land Data Assimilation System (LDAS-WRF) was developed by coupling the Weather Research and Forecasting Model (WRF) and Simple Biosphere model version 2 (SiB2), to physically introduce the soil moisture observations and improve the representation of land surface and lower boundary conditions in Numerical Weather Prediction (NWP). The LDAS-WRF assimilates the soil moisture, using passive microwave brightness temperature at the lower frequency, which has a high sensitivity to soil moisture. This system consists of a radiative transfer model which treats surface and volume scattering of surface soil layer as an observation operator and Ensemble Kalman Filter (EnKF) as a sequential assimilation algorithm. To evaluate the capability of the system, the LDAS-WRF was applied to a mesoscale region in the Tibetan Plateau. The results show that the soil moisture and land surface energy fluxes obtained by the LDAS-WRF are successfully improved compared with no assimilation case.
