Publication: Accounting for Model Error in Data Assimilation Using Adjoint Models
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Accounting for Model Error in Data Assimilation Using Adjoint Models

- incollection -
 

Author(s)
Anne K. Griffith , Nancy K. Nichols

Published in
Computational Differentiation: Techniques, Applications, and Tools

Editor(s)
Martin Berz, Christian Bischof, George Corliss, Andreas Griewank

Year
1996

Publisher
SIAM

Abstract
We consider a modification to the variational assimilation method, in which a correction term is added to the model equations. This correction term is determined in the assimilation procedure and produces a solution that is closer to the observed data and compensates to some extent for model error. The 1-D shallow water equations are used to illustrate the variational assimilation method, with and without the correction term, in the presence of model error.

Cross-References
Berz1996CDT

BibTeX
@INCOLLECTION{
         Griffith1996AfM,
       author = "Anne K. Griffith and Nancy K. Nichols",
       editor = "Martin Berz and Christian Bischof and George Corliss and Andreas Griewank",
       title = "Accounting for Model Error in Data Assimilation Using Adjoint Models",
       booktitle = "Computational Differentiation: Techniques, Applications, and Tools",
       pages = "195--204",
       publisher = "SIAM",
       address = "Philadelphia, PA",
       key = "Griffith1996AfM",
       crossref = "Berz1996CDT",
       abstract = "We consider a modification to the variational assimilation method, in which a
         correction term is added to the model equations. This correction term is determined in the
         assimilation procedure and produces a solution that is closer to the observed data and compensates
         to some extent for model error. The 1-D shallow water equations are used to illustrate the
         variational assimilation method, with and without the correction term, in the presence of model
         error.",
       keywords = "Data assimilation, adjoint model, model error, control variable.",
       year = "1996"
}


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