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Impact of Temporal Autocorrelation Mismatch on the Assimilation of Satellite-Derived Surface Soil Moisture Retrievals

Qiu, Jianxiu, Crow, Wade T., Mo, Xingguo, Liu, Suxia
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014 v.7 no.8 pp. 3534-3542
autocorrelation, data collection, models, prediction, remote sensing, satellites, soil profiles, soil water, temporal variation, time series analysis
Satellite-based surface soil moisture retrievals are commonly assimilated into ecohydrological models in order to obtain improved profile soil moisture estimates. However, differences in temporal autocorrelation structure between these retrievals and comparable model-based predictions can potentially undermine the efficiency of such assimilation. Here, we conduct a series of synthetic experiments to examine the magnitude of this problem and the potential for detecting the presence of retrieval/model autocorrelation 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 autocorrelation between assimilated surface soil moisture retrievals and comparable surface soil moisture estimates made by an off-line ecohydrological assimilation model. Results demonstrate that neglecting a mismatch in retrieval/assimilation model autocorrelation 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 autocorrelation misfit is detectable using a simple diagnostic index derived from a time series of soil moisture retrievals and open loop model predictions. Using relatively short data sets (∼2 years in length), the diagnostic is capable of identifying worst-case scenarios leading to the most significant degradation of assimilation results.