A reduced adjoint approach to variational data assimilation

Handle URI:
http://hdl.handle.net/10754/562626
Title:
A reduced adjoint approach to variational data assimilation
Authors:
Altaf, Muhammad; El Gharamti, Mohamad ( 0000-0002-7229-8366 ) ; Heemink, Arnold W.; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
The adjoint method has been used very often for variational data assimilation. The computational cost to run the adjoint model often exceeds several original model runs and the method needs significant programming efforts to implement the adjoint model code. The work proposed here is variational data assimilation based on proper orthogonal decomposition (POD) which avoids the implementation of the adjoint of the tangent linear approximation of the original nonlinear model. An ensemble of the forward model simulations is used to determine the approximation of the covariance matrix and only the dominant eigenvectors of this matrix are used to define a model subspace. The adjoint of the tangent linear model is replaced by the reduced adjoint based on this reduced space. Thus the adjoint model is run in reduced space with negligible computational cost. Once the gradient is obtained in reduced space it is projected back in full space and the minimization process is carried in full space. In the paper the reduced adjoint approach to variational data assimilation is introduced. The characteristics and performance of the method are illustrated with a number of data assimilation experiments in a ground water subsurface contaminant model. © 2012 Elsevier B.V.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC); Earth Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier
Journal:
Computer Methods in Applied Mechanics and Engineering
Issue Date:
Feb-2013
DOI:
10.1016/j.cma.2012.10.003
Type:
Article
ISSN:
00457825
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorAltaf, Muhammaden
dc.contributor.authorEl Gharamti, Mohamaden
dc.contributor.authorHeemink, Arnold W.en
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-03T10:58:54Zen
dc.date.available2015-08-03T10:58:54Zen
dc.date.issued2013-02en
dc.identifier.issn00457825en
dc.identifier.doi10.1016/j.cma.2012.10.003en
dc.identifier.urihttp://hdl.handle.net/10754/562626en
dc.description.abstractThe adjoint method has been used very often for variational data assimilation. The computational cost to run the adjoint model often exceeds several original model runs and the method needs significant programming efforts to implement the adjoint model code. The work proposed here is variational data assimilation based on proper orthogonal decomposition (POD) which avoids the implementation of the adjoint of the tangent linear approximation of the original nonlinear model. An ensemble of the forward model simulations is used to determine the approximation of the covariance matrix and only the dominant eigenvectors of this matrix are used to define a model subspace. The adjoint of the tangent linear model is replaced by the reduced adjoint based on this reduced space. Thus the adjoint model is run in reduced space with negligible computational cost. Once the gradient is obtained in reduced space it is projected back in full space and the minimization process is carried in full space. In the paper the reduced adjoint approach to variational data assimilation is introduced. The characteristics and performance of the method are illustrated with a number of data assimilation experiments in a ground water subsurface contaminant model. © 2012 Elsevier B.V.en
dc.publisherElsevieren
dc.subject4DVARen
dc.subjectModel order reductionen
dc.subjectProper orthogonal decompositionen
dc.titleA reduced adjoint approach to variational data assimilationen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalComputer Methods in Applied Mechanics and Engineeringen
dc.contributor.institutionDelft University of Technology, Delft, Netherlandsen
kaust.authorAltaf, Muhammaden
kaust.authorEl Gharamti, Mohamaden
kaust.authorHoteit, Ibrahimen
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