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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #303054
Title: A parsimonious land data assimilation system for the SMAP/GPM satellite era

Author
item Crow, Wade
item YILMAZ, M.T. - Collaborator

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/12/2014
Publication Date: 7/5/2014
Citation: Crow, W.T., Yilmaz, M.T. 2014. A Parsimonious Land Data Assimilation System for the SMAP/GPM Satellite Era [abstract]. 7th International Scientific Conference on the Global Water and Envergy Cycle. Abstract S8.T14.2.

Interpretive Summary:

Technical Abstract: Land data assimilation systems typically require complex parameterizations in order to: define required observation operators, quantify observing/forecasting errors and calibrate a land surface assimilation model. These parameters are commonly defined in an arbitrary manner and, if poorly specified, can degrade the accuracy of state estimates provided by the system. This presentation will describe the development of a parsimonious land data assimilation which attempts to auto-calibrate all parameters necessary to simultaneously assimilate active/passive satellite-based surface soil moisture retrievals into a simple land surface model driven by uncertain precipitation forcing. The approach, coined the Auto-Tuned Land Assimilation System (ATLAS), is based on combining aspects of a triple collocation analysis with a statistical analysis of filtering innovations and is coupled with a new understanding of the proper theoretical basis for rescaling soil moisture observations prior to their assimilation into a land model. As a result, ATLAS is able to internally-derive – and adaptively pass to a Colored Kalman Filter implementation – key error statistical information that is typically either neglected or arbitrarily specified by existing land data assimilation systems. Key simplifying assumptions required by ATLAS will be justified, and preliminary ATLAS results (using existing active/passive soil moisture retrieval data sets) over the contiguous United States will be presented. Overall, results demonstrate that ATLAS provides a valuable tool for intelligently integrating uncertain remotely-sensed surface soil moisture and rainfall accumulation information acquired during the SMAP/GPM satellite era.