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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: An EnKF Dual assimilation of thermal-infrared and microwave satellite observations of soil moisture into the Noah land surface model

Authors
item Hain, C -
item Crow, Wade
item Anderson, Martha
item Mecikalski, J -

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: December 15, 2011
Publication Date: January 21, 2012
Citation: Hain, C., Crow, W.T., Anderson, M.C., Mecikalski, J. 2012. An EnKF Dual assimilation of thermal-infrared and microwave satellite observations of soil moisture into the Noah land surface model [abstract]. Meeting Abstract. 2012 CDROM.

Technical Abstract: Studies which have attempted to assimilate remotely-sensed soil moisture (SM) into land-surface models have mainly focused on the application of retrievals from active and passive microwave (MW) sensors. However, SM retrievals from thermal (TIR) sensors have been shown to add unique information especially in areas where MW sensors are not able to provide accurate SM retrievals (i.e. moderate to dense vegetation). In this study, the TIR-based product is provided by a soil moisture methodology associated with surface flux estimates from the Atmosphere Land Exchange Inverse (ALEXI) model, while the MW-based product is provided by Vrijie Universiteit Amsterdam (VUA)-NASA surface SM product based on Land Surface Parameter (LPRM) model. Here a series of data assimilation experiments using an ensemble Kalman filter (EnKF) are proposed to quantify the impact of assimilating TIR and MW SM retrievals in isolation and within a dual assimilation framework: (a) no assimilation, (b) only ALEXI SM, (c) only LPRM SM and (d) ALEXI and LPRM SM. The relative skill of each assimilation configuration is assessed through a data-denial experimental design, where the LSM is forced with an inferior precipitation dataset. The ability of each assimilation configuration to correct for precipitation errors is quantified through the comparison of SM predictions with a LSM simulation which is forcing with a high-quality precipitation dataset. Finally, the entire assimilation framework is repeated using a new technique based on the estimation of triple collocation error as an estimate of retrieval error in the EnKF. These results are compared to the first series of assimilation experiments which employ a constant, ad-hoc specification of model and retrieval error in the EnKF to assess the impact of a more sophisticated representation of error. USDA is an equal opportunity provider and employer.

Last Modified: 8/1/2014
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