Tuesday, 24 January 2017
4E (Washington State Convention Center )
Satellite-based surface soil moisture retrievals are commonly assimilated into hydrological models in order to obtain improved profile soil moisture estimates. However, differences in temporal auto-correlation structure between these retrievals and comparable model-based predictions can potentially undermine the efficiency of such assimilation. In this study, we conduct a series of synthetic experiments to examine the magnitude of this problem and the potential for detecting the presence of retrieval/model auto-correlation differences using a simple diagnostic procedure. Our synthetic experiments are based on modifying the observation operator within a data assimilation system to artificially induce differences in temporal auto-correlation between assimilated surface soil moisture retrievals and comparable surface soil moisture estimates made by off-line hydrological models. Results demonstrate that neglecting a mismatch in retrieval/model auto-correlation can reduce the benefit of surface soil moisture data assimilation. The impact is especially large for soil profiles with limited vertical coupling. However, the presence of this source of retrieval/model auto-correlation misfit is detectable using a simple diagnostic index derived from a time series of soil moisture retrievals and open loop model predictions. The diagnostic is capable of using relatively short data sets to identify the worst-case scenarios leading to the most significant degradation of assimilation results.
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