On a nonlinear Kalman filter with simplified divided difference approximation

Handle URI:
http://hdl.handle.net/10754/562110
Title:
On a nonlinear Kalman filter with simplified divided difference approximation
Authors:
Luo, Xiaodong; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Moroz, Irene M.
Abstract:
We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling's interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling's interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling's interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier BV
Journal:
Physica D: Nonlinear Phenomena
Issue Date:
Mar-2012
DOI:
10.1016/j.physd.2011.12.003
Type:
Article
ISSN:
01672789
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLuo, Xiaodongen
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorMoroz, Irene M.en
dc.date.accessioned2015-08-03T09:45:01Zen
dc.date.available2015-08-03T09:45:01Zen
dc.date.issued2012-03en
dc.identifier.issn01672789en
dc.identifier.doi10.1016/j.physd.2011.12.003en
dc.identifier.urihttp://hdl.handle.net/10754/562110en
dc.description.abstractWe present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling's interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling's interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling's interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.en
dc.publisherElsevier BVen
dc.subjectData assimilationen
dc.subjectDivided difference approximationen
dc.subjectEnsemble Kalman filteren
dc.titleOn a nonlinear Kalman filter with simplified divided difference approximationen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalPhysica D: Nonlinear Phenomenaen
dc.contributor.institutionMath Inst, Oxford OX1 3LB, Englanden
kaust.authorLuo, Xiaodongen
kaust.authorHoteit, Ibrahimen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.