Assessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Sea

Handle URI:
http://hdl.handle.net/10754/562939
Title:
Assessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Sea
Authors:
Triantafyllou, George N.; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Luo, Xiaodong; Tsiaras, Kostas P.; Petihakis, George
Abstract:
An application of an ensemble-based robust filter for data assimilation into an ecosystem model of the Cretan Sea is presented and discussed. The ecosystem model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The filtering scheme is based on the Singular Evolutive Interpolated Kalman (SEIK) filter which is implemented with a time-local H∞ filtering strategy to enhance robustness and performances during periods of strong ecosystem variability. Assimilation experiments in the Cretan Sea indicate that robustness can be achieved in the SEIK filter by introducing an adaptive inflation scheme of the modes of the filter error covariance matrix. Twin-experiments are performed to evaluate the performance of the assimilation system and to study the benefits of using robust filtering in an ensemble filtering framework. Pseudo-observations of surface chlorophyll, extracted from a model reference run, were assimilated every two days. Simulation results suggest that the adaptive inflation scheme significantly improves the behavior of the SEIK filter during periods of strong ecosystem variability. © 2012 Elsevier B.V.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier BV
Journal:
Journal of Marine Systems
Issue Date:
Sep-2013
DOI:
10.1016/j.jmarsys.2012.12.006
Type:
Article
ISSN:
09247963
Sponsors:
This work was carried out within the framework of the FP7 project MEECE (Marine Ecosystem Evolution in a Changing Environment). Luo acknowledges partial financial support from the Research Council of Norway and industrial partners through the project "Transient well flow modelling and modern estimation techniques for accurate production allocation". We would like to thank the anonymous reviewers for their constructive comments and suggestions.
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorTriantafyllou, George N.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorLuo, Xiaodongen
dc.contributor.authorTsiaras, Kostas P.en
dc.contributor.authorPetihakis, Georgeen
dc.date.accessioned2015-08-03T11:16:19Zen
dc.date.available2015-08-03T11:16:19Zen
dc.date.issued2013-09en
dc.identifier.issn09247963en
dc.identifier.doi10.1016/j.jmarsys.2012.12.006en
dc.identifier.urihttp://hdl.handle.net/10754/562939en
dc.description.abstractAn application of an ensemble-based robust filter for data assimilation into an ecosystem model of the Cretan Sea is presented and discussed. The ecosystem model comprises two on-line coupled sub-models: the Princeton Ocean Model (POM) and the European Regional Seas Ecosystem Model (ERSEM). The filtering scheme is based on the Singular Evolutive Interpolated Kalman (SEIK) filter which is implemented with a time-local H∞ filtering strategy to enhance robustness and performances during periods of strong ecosystem variability. Assimilation experiments in the Cretan Sea indicate that robustness can be achieved in the SEIK filter by introducing an adaptive inflation scheme of the modes of the filter error covariance matrix. Twin-experiments are performed to evaluate the performance of the assimilation system and to study the benefits of using robust filtering in an ensemble filtering framework. Pseudo-observations of surface chlorophyll, extracted from a model reference run, were assimilated every two days. Simulation results suggest that the adaptive inflation scheme significantly improves the behavior of the SEIK filter during periods of strong ecosystem variability. © 2012 Elsevier B.V.en
dc.description.sponsorshipThis work was carried out within the framework of the FP7 project MEECE (Marine Ecosystem Evolution in a Changing Environment). Luo acknowledges partial financial support from the Research Council of Norway and industrial partners through the project "Transient well flow modelling and modern estimation techniques for accurate production allocation". We would like to thank the anonymous reviewers for their constructive comments and suggestions.en
dc.publisherElsevier BVen
dc.subjectCoupled biophysical modelsen
dc.subjectData assimilationen
dc.subjectEnsemble Kalman filteringen
dc.subjectH infinity filteren
dc.subjectKalman filteren
dc.titleAssessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Seaen
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.journalJournal of Marine Systemsen
dc.contributor.institutionHellenic Centre for Marine Research, P.O. Box 712 Anavyssos, Attica GR-190 13, Greeceen
dc.contributor.institutionInternational Research Institute of Stavanger, 5008 Bergen, Norwayen
kaust.authorHoteit, Ibrahimen
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