Development of a WRF and SiB2 based satellite land data assimilation system

A satellite-based Land Data Assimilation System (LDAS-WRF) was developed by coupling the Weather Research and Forecasting Model (WRF), as an atmospheric driver, and Simple Biosphere model version 2 (SiB2), as a land surface driver and a model operator, 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 heterogeneity, 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, where the land-atmosphere interactions affect the atmospheric dynamics considerably. The experimental results show that the soil moisture and land surface energy fluxes obtained by the LDAS-WRF are successfully improved compared with no assimilation case. It was demonstrated that the LDAS-WRF has ability to apply satellite land observations to estimation of land conditions with high accuracy and provide more correct lower boundary condition to atmosphere in NWP.