Abstract:
Satellite measurements (retrievals) of surface soil moisture from the planned Soil Moisture Active Passive (SMAP) mission are subject to errors and cannot by themselves provide the space-time coverage that is often needed (for example, in forecast initialization applications). A land data assimilation system can merge the SMAP soil moisture retrievals with information from land surface models and antecedent meteorological data, information that is spatio-temporally complete but likewise uncertain. This merger yields the suite of SMAP Level 4 data assimilation products (including root zone soil moisture and evapotranspiration).

For the design of the SMAP mission it is critical to understand just how uncertain the surface soil moisture retrievals can be while still achieving the science objectives of the mission. Here, we present a synthetic data assimilation experiment that determines the contribution of surface soil moisture retrievals to the skill of land data assimilation products as a function of retrieval and land model skill. As expected, the skill of the assimilation products increases with the skill of the model and that of the retrievals. The skill of the soil moisture assimilation products always exceeds that of the model acting alone; even retrievals of low quality contribute information to the assimilation products, particularly if model skill is modest.