An Adjoint-Based Adaptive Ensemble Kalman Filter

Handle URI:
http://hdl.handle.net/10754/552769
Title:
An Adjoint-Based Adaptive Ensemble Kalman Filter
Authors:
Song, Hajoon; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Cornuelle, Bruce D.; Luo, Xiaodong; Subramanian, Aneesh C.
Abstract:
A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
An Adjoint-Based Adaptive Ensemble Kalman Filter 2013, 141 (10):3343 Monthly Weather Review
Publisher:
American Meteorological Society
Journal:
Monthly Weather Review
Issue Date:
Oct-2013
DOI:
10.1175/MWR-D-12-00244.1
Type:
Article
ISSN:
0027-0644; 1520-0493
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00244.1
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSong, Hajoonen
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorCornuelle, Bruce D.en
dc.contributor.authorLuo, Xiaodongen
dc.contributor.authorSubramanian, Aneesh C.en
dc.date.accessioned2015-05-14T06:45:28Zen
dc.date.available2015-05-14T06:45:28Zen
dc.date.issued2013-10en
dc.identifier.citationAn Adjoint-Based Adaptive Ensemble Kalman Filter 2013, 141 (10):3343 Monthly Weather Reviewen
dc.identifier.issn0027-0644en
dc.identifier.issn1520-0493en
dc.identifier.doi10.1175/MWR-D-12-00244.1en
dc.identifier.urihttp://hdl.handle.net/10754/552769en
dc.description.abstractA new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.en
dc.publisherAmerican Meteorological Societyen
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-12-00244.1en
dc.rights© Copyright 2013 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyrights@ametsoc.org.en
dc.subjectFiltering techniquesen
dc.subjectKalman filtersen
dc.subjectOptimizationen
dc.subjectVariational analysisen
dc.subjectData assimilationen
dc.subjectEnsemblesen
dc.titleAn Adjoint-Based Adaptive Ensemble Kalman Filteren
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalMonthly Weather Reviewen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionOcean Sciences Department, University of California, Santa Cruz, Santa Cruz, Californiaen
dc.contributor.institutionScripps Institution of Oceanography, University of California, San Diego, San Diego, Californiaen
kaust.authorHoteit, Ibrahimen
kaust.authorLuo, Xiaodongen
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