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dc.contributor.authorTriantafyllou, George N.
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorLuo, Xiaodong
dc.contributor.authorTsiaras, Kostas P.
dc.contributor.authorPetihakis, George
dc.date.accessioned2015-08-03T11:16:19Z
dc.date.available2015-08-03T11:16:19Z
dc.date.issued2013-09
dc.identifier.citationTriantafyllou, G., Hoteit, I., Luo, X., Tsiaras, K., & Petihakis, G. (2013). Assessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Sea. Journal of Marine Systems, 125, 90–100. doi:10.1016/j.jmarsys.2012.12.006
dc.identifier.issn09247963
dc.identifier.doi10.1016/j.jmarsys.2012.12.006
dc.identifier.urihttp://hdl.handle.net/10754/562939
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.
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.
dc.publisherElsevier BV
dc.subjectCoupled biophysical models
dc.subjectData assimilation
dc.subjectEnsemble Kalman filtering
dc.subjectH infinity filter
dc.subjectKalman filter
dc.titleAssessing a robust ensemble-based Kalman filter for efficient ecosystem data assimilation of the Cretan Sea
dc.typeArticle
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalJournal of Marine Systems
dc.contributor.institutionHellenic Centre for Marine Research, P.O. Box 712 Anavyssos, Attica GR-190 13, Greece
dc.contributor.institutionInternational Research Institute of Stavanger, 5008 Bergen, Norway
kaust.personHoteit, Ibrahim


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