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Title:   Author  item
                                            Crow, Wade
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 2/15/2014
                                    Publication  Date: 2/28/2014
                                    Citation: Crow, W.T. 2014. A parsimonious data assimilation system for optimally integrating multi-sensor satellite observations over semi-arid areas [abstract]. Proceedings of the 2014 Texas A&M University Arid Zone Hydrology Workshop, February 27-28, 2014, College Station, Texas. 2014 CDROM.Interpretive Summary:
                                Technical Abstract: Land data assimilation systems are commonly tasked with merging remotely sensed surface soil moisture retrievals with information derived from a soil water balance model driven by observed rainfall. The performance of such systems can be degraded by the incorrect specification of parameters describing modeling and observation errors. Here, the Auto-tuned Land Data Assimilation System (ATLAS) is introduced to simultaneously solve for all parameters required for the application of a simple land data assimilation system to integrate satellite-based rainfall and soil moisture retrievals for drought monitoring applications. The approach is based on combining a triple collocation (TC) strategy with the statistical analysis of Kalman filtering innovations and designed to leverage the simultaneous availability of satellite-based soil moisture and rainfall products acquired from both active and passive microwave remote sensing. As such, ATLAS provides an optimal approach for integrating error-prone satellite observations of both rainfall and soil moisture into an agricultural drought analysis. This talk will describe the theoretical basis of ATLAS and present preliminary ATLAS results over the south-west and south-central United States.


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