Satellite-based Surface Soil Moisture Research Articles

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

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.

Improving long-term, retrospective precipitation datasets using satellite-based surface soil moisture retrievals and the Soil Moisture Analysis Rainfall Tool

Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the submonthly scale accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain gauge observations. In order to adapt to the irregular retrieval frequency of heritage soil moisture products, a new variable correction window method is developed that allows for better efficiency in leveraging temporally sparse satellite soil moisture retrievals. Results confirm the advantage of using this variable window method relative to an existing fixed-window version of SMART over a range of one- to 30-day accumulation periods. Using this modified version of SMART and heritage satellite surface soil moisture products, a 1.0-deg, 20-year (1979 to 1998) global rainfall dataset over land is corrected and validated. Relative to the original precipitation product, the corrected dataset demonstrates improved correlation with a global gauge-based daily rainfall product, lower root-mean-square-error (−13%) on a 10-day scale and provides a higher probability of detection (þ5%) and lower false alarm rates (−3.4%) for five-day rainfall accumulation estimates. This corrected rainfall dataset is expected to provide improved rain- fall forcing data for the land surface modeling community. © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). (DOI: 10.1117/1.JRS.6.063604)

Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations

Abstract Root-zone soil moisture controls the land–atmosphere exchange of water and energy, and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root-zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments, synthetic surface soil moisture observations are assimilated into four different models [Catchment, Mosaic, Noah, and Community Land Model (CLM)] using the ensemble Kalman filter. The authors demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root-zone information is higher when the surface–root zone coupling is stronger. The experiments also suggest that (faced with unknown true subsurface physics) overestimating surface–root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Last, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.