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Original Research Papers

On discretization error and its control in variational data assimilation

Authors:

David Furbish,

Department of Earth and Environmental Sciences, Vanderbilt University, US
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M. Y. Hussaini ,

School of Computational Science, Florida State University, Tallahassee, FL 32306-4120, US
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F.-X. Le Dimet,

Laboratoire Jean-Kuntzmann, Université de Grenoble and INRIA, Grenoble, FR
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Pierre Ngnepieba,

Department of Mathematics, Florida A&M, Tallahassee, FL 32307, US
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Yonghui Wu

School of Computational Science, Florida State University, Tallahassee, FL 32306-4120, US
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Abstract

In four-dimensional variational data assimilation (4D-Var), the model equations are treated as strong constraints on an optimization problem. In reality, the model does not represent the system behaviour exactly and errors arise due to physical approximations, discretization, variability of physical parameters, and inaccuracy of initial and boundary conditions. Errors are also inherent in observation due to inaccuracies in the direct measurement and mapping of the state (model) space onto the observational space or vice versa. The purpose of this work is to define these errors, in particular the discretization and projection errors, and to formulate a canonical problem to study their impact on the quality of the data assimilation process and resulting predictions.

How to Cite: Furbish, D., Hussaini, M.Y., Le Dimet, F.-X., Ngnepieba, P. and Wu, Y., 2008. On discretization error and its control in variational data assimilation. Tellus A: Dynamic Meteorology and Oceanography, 60(5), pp.979–991. DOI: http://doi.org/10.1111/j.1600-0870.2008.00358.x
  Published on 01 Jan 2008
 Accepted on 10 Jul 2008            Submitted on 7 Feb 2008

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