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Soil moisture is the central focus of land surface and atmospheric modeling because it controls surface water and energy fluxes and consequently affects land-atmosphere interactions. Although global or regional satellite-derived surface soil moisture data sets are readily available, knowledge about assimilating them into numerical weather prediction (NWP) models is limited. The methods of assimilating soil moisture products in NWP models have several limitations, and they cannot be applied in near-real-time applications. As a result, this paper focuses on the development of a system [a Land Data Assimilation System coupled with a mesoscale Atmospheric model (LDAS-A)] that couples satellite land data assimilation with a mesoscale model to physically introduce land surface heterogeneities into the mesoscale model. The LDAS-A consists of a sequential LDAS that directly assimilates the lower frequency passive microwave brightness temperatures, and therefore, its use is feasible for near-real-time NWP applications. The LDAS-A was validated for the Tibetan Plateau using surface, radiosonde, and satellite observations. The simulation results show that the LDAS-A effectively improved the land surface variables (i.e., surface soil moisture and skin temperature) compared with the no-assimilation case and that it has the potential to correct uncertainties resulting from model initialization, model-specific parameters, and model forcing on a wider scale. The improved land surface conditions in the LDAS-A improve the land-atmosphere feedback mechanism, and the assimilated results provide better prediction of atmospheric profiles (i.e., potential temperature and specific humidity) than the no-assimilation case when compared with radiosonde soundings. Improvements in solar radiation, in addition to soil moisture, are necessary to introduce realistic land-atmosphere interactions into a mesoscale model.