Ensemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operators

Handle URI:
http://hdl.handle.net/10754/347331
Title:
Ensemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operators
Authors:
Luo, Xiaodong; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
The ensemble Kalman filter (EnKF) is an efficient algorithm for many data assimilation problems. In certain circumstances, however, divergence of the EnKF might be spotted. In previous studies, the authors proposed an observation-space-based strategy, called residual nudging, to improve the stability of the EnKF when dealing with linear observation operators. The main idea behind residual nudging is to monitor and, if necessary, adjust the distances (misfits) between the real observations and the simulated ones of the state estimates, in the hope that by doing so one may be able to obtain better estimation accuracy. In the present study, residual nudging is extended and modified in order to handle nonlinear observation operators. Such extension and modification result in an iterative filtering framework that, under suitable conditions, is able to achieve the objective of residual nudging for data assimilation problems with nonlinear observation operators. The 40-dimensional Lorenz-96 model is used to illustrate the performance of the iterative filter. Numerical results show that, while a normal EnKF may diverge with nonlinear observation operators, the proposed iterative filter remains stable and leads to reasonable estimation accuracy under various experimental settings.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
Ensemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operators 2014, 142 (10):3696 Monthly Weather Review
Publisher:
American Meteorological Society
Journal:
Monthly Weather Review
Issue Date:
Oct-2014
DOI:
10.1175/MWR-D-13-00328.1
ARXIV:
arXiv:1406.4307
Type:
Article
ISSN:
0027-0644; 1520-0493
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00328.1; http://arxiv.org/abs/1406.4307
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLuo, Xiaodongen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-03-31T06:14:19Zen
dc.date.available2015-03-31T06:14:19Zen
dc.date.issued2014-10en
dc.identifier.citationEnsemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operators 2014, 142 (10):3696 Monthly Weather Reviewen
dc.identifier.issn0027-0644en
dc.identifier.issn1520-0493en
dc.identifier.doi10.1175/MWR-D-13-00328.1en
dc.identifier.urihttp://hdl.handle.net/10754/347331en
dc.description.abstractThe ensemble Kalman filter (EnKF) is an efficient algorithm for many data assimilation problems. In certain circumstances, however, divergence of the EnKF might be spotted. In previous studies, the authors proposed an observation-space-based strategy, called residual nudging, to improve the stability of the EnKF when dealing with linear observation operators. The main idea behind residual nudging is to monitor and, if necessary, adjust the distances (misfits) between the real observations and the simulated ones of the state estimates, in the hope that by doing so one may be able to obtain better estimation accuracy. In the present study, residual nudging is extended and modified in order to handle nonlinear observation operators. Such extension and modification result in an iterative filtering framework that, under suitable conditions, is able to achieve the objective of residual nudging for data assimilation problems with nonlinear observation operators. The 40-dimensional Lorenz-96 model is used to illustrate the performance of the iterative filter. Numerical results show that, while a normal EnKF may diverge with nonlinear observation operators, the proposed iterative filter remains stable and leads to reasonable estimation accuracy under various experimental settings.en
dc.publisherAmerican Meteorological Societyen
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-13-00328.1en
dc.relation.urlhttp://arxiv.org/abs/1406.4307en
dc.rights© 2015 American Meteorological Society Privacy Policy and Disclaimer Headquarters: 45 Beacon Street Boston, MA 02108-3693 DC Office: 1120 G Street, NW, Suite 800 Washington DC, 20005-3826 amsinfo@ametsoc.org Phone: 617-227-2425 Fax: 617-742-8718 Allen Press, Inc. assists in the online publication of AMS journalsen
dc.titleEnsemble Kalman Filtering with Residual Nudging: An Extension to State Estimation Problems with Nonlinear Observation Operatorsen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalMonthly Weather Reviewen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionInternational Research Institute of Stavanger, Bergen, Norwayen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
dc.identifier.arxividarXiv:1406.4307en
kaust.authorHoteit, Ibrahimen
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