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Title: Leveraging microwave polarization information for calibration of a land data assimilation system

item HOLMES, THOMAS - Science Systems, Inc
item Crow, Wade
item DE JEU, R.A.M. - Vrije University

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/20/2014
Publication Date: 12/23/2014
Publication URL:
Citation: Holmes, T., Crow, W.T., De Jeu, R. 2014. Leveraging microwave polarization information for calibration of a land data assimilation system. Geophysical Research Letters. 41(24):8878-8886. DOI: 10.1002/2014GL061991.

Interpretive Summary: Over the past decade, a large number of studies have demonstrated that high-quality soil moisture information can be extracted from satellite-based microwave observations of the land surface. Such soil moisture information can be integrated into land surface models to improve our ability to numerically predict the weather and anticipate future variations in water resource availability. However, the efficient integration of satellite information into land surface models requires the accurate specification of the sensitivity of the microwave radiance measured by satellite to variations in surface soil moisture. Failure to accurately specify this sensitivity will lead to cases where the integration of satellite observations into a land surface model actually degrades the performance of the model. This study provides a technique for more accurately determining the sensitivity of satellite-based microwave radiance to variations in soil moisture. As a result, it contributes to on-going efforts which seek to improve the accuracy of land surface model predictions via the ingestion of microwave satellite observations. Better land surface model predictions will, in turn, lead to better water management information for agricultural producers and USDA operational activities.

Technical Abstract: This letter contributes a new approach to calibrating a tau-omega radiative transfer model coupled to a land surface model with low frequency (< 10 GHz) microwave brightness temperature (TB) observations. The problem of calibrating this system is generally poorlyposed because various parameter combinations may yield indistinguishable (least square error) results. This is theoretically important for a land data assimilation system since alternative parameter combinations have different impacts on the sensitivity of TB to soil moisture and misattribution of systematic error may therefore disrupt the performance of a data assimilation system. In several synthetic experiments we demonstrate that the use of multi-polarization TB information to constrain vegetation opacity can improve the stability of calibrated soil moisture/TB sensitivities relative to the more typical approach of utilizing ancillary information to parameterize opacities. Even in the most challenging scenarios, sensitivity variations were reduced by more than 50% by a multipolarization approach.