Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF

Handle URI:
http://hdl.handle.net/10754/565937
Title:
Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF
Authors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Pham, D.-T.; El Gharamti, Mohamad ( 0000-0002-7229-8366 ) ; Luo, X.
Abstract:
The stochastic ensemble Kalman filter (EnKF) updates its ensemble members with observations perturbed with noise sampled from the distribution of the observational errors. This was shown to introduce noise into the system and may become pronounced when the ensemble size is smaller than the rank of the observational error covariance, which is often the case in real oceanic and atmospheric data assimilation applications. This work introduces an efficient serial scheme to mitigate the impact of observations’ perturbations sampling in the analysis step of the EnKF, which should provide more accurate ensemble estimates of the analysis error covariance matrices. The new scheme is simple to implement within the serial EnKF algorithm, requiring only the approximation of the EnKF sample forecast error covariance matrix by a matrix with one rank less. The new EnKF scheme is implemented and tested with the Lorenz-96 model. Results from numerical experiments are conducted to compare its performance with the EnKF and two standard deterministic EnKFs. This study shows that the new scheme enhances the behavior of the EnKF and may lead to better performance than the deterministic EnKFs even when implemented with relatively small ensembles.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
Mitigating Observation Perturbation Sampling Errors in the Stochastic EnKF 2015, 143 (7):2918 Monthly Weather Review
Publisher:
American Meteorological Society
Journal:
Monthly Weather Review
Issue Date:
17-Mar-2015
DOI:
10.1175/MWR-D-14-00088.1
Type:
Article
ISSN:
0027-0644; 1520-0493
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-14-00088.1
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorPham, D.-T.en
dc.contributor.authorEl Gharamti, Mohamaden
dc.contributor.authorLuo, X.en
dc.date.accessioned2015-08-12T08:51:08Zen
dc.date.available2015-08-12T08:51:08Zen
dc.date.issued2015-03-17en
dc.identifier.citationMitigating Observation Perturbation Sampling Errors in the Stochastic EnKF 2015, 143 (7):2918 Monthly Weather Reviewen
dc.identifier.issn0027-0644en
dc.identifier.issn1520-0493en
dc.identifier.doi10.1175/MWR-D-14-00088.1en
dc.identifier.urihttp://hdl.handle.net/10754/565937en
dc.description.abstractThe stochastic ensemble Kalman filter (EnKF) updates its ensemble members with observations perturbed with noise sampled from the distribution of the observational errors. This was shown to introduce noise into the system and may become pronounced when the ensemble size is smaller than the rank of the observational error covariance, which is often the case in real oceanic and atmospheric data assimilation applications. This work introduces an efficient serial scheme to mitigate the impact of observations’ perturbations sampling in the analysis step of the EnKF, which should provide more accurate ensemble estimates of the analysis error covariance matrices. The new scheme is simple to implement within the serial EnKF algorithm, requiring only the approximation of the EnKF sample forecast error covariance matrix by a matrix with one rank less. The new EnKF scheme is implemented and tested with the Lorenz-96 model. Results from numerical experiments are conducted to compare its performance with the EnKF and two standard deterministic EnKFs. This study shows that the new scheme enhances the behavior of the EnKF and may lead to better performance than the deterministic EnKFs even when implemented with relatively small ensembles.en
dc.language.isoenen
dc.publisherAmerican Meteorological Societyen
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/MWR-D-14-00088.1en
dc.rightsArchived with thanks to Monthly Weather Reviewen
dc.subjectData assimilationen
dc.titleMitigating Observation Perturbation Sampling Errors in the Stochastic EnKFen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalMonthly Weather Reviewen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionCentre National de la Recherche Scientifique, Grenoble, Franceen
dc.contributor.institutionInternational Research Institute of Stavanger, Bergen, Norwayen
dc.contributor.institutionNansen Environmental and Remote Sensing Center, Bergen, Norwayen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHoteit, Ibrahimen
kaust.authorGharamti, M. E.en
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