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United States Department of Agriculture

Agricultural Research Service

Research Project: QUANTIFYING LANDSCAPE FACTORS INFLUENCING SOIL PRODUCTIVITY AND THE ENVIRONMENT Title: The optimality of potential rescaling approaches in land data assimilation

Authors
item Yilmaz, Mustafa
item Crow, Wade

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: October 15, 2012
Publication Date: January 6, 2013
Citation: Yilmaz, M.T., Crow, W.T. 2013. The optimality of potential rescaling approaches in land data assimilation[abstract]. 2012 American Meteorological Society Joint Meeting. 2013 CDROM.

Technical Abstract: It is well-known that systematic differences exist between modeled and observed realizations of hydrological variables like soil moisture. Prior to data assimilation, these differences must be removed in order to obtain an optimal analysis. A number of rescaling approaches have been proposed for removing systematic differences between models and observations. These methods include rescaling techniques based on: matching sampled temporal statistics (i.e. variance), minimizing the least-squares distance between observations and models, and the application of triple collocation. Here we evaluate the optimality and relative performances of these rescaling methods both analytically and numerically and find that a triple collocation-based rescaling method results in an optimal solution whereas variance matching- and least squares-regression approaches result in only approximations to this optimal solution.

Last Modified: 4/25/2014
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