A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean

Handle URI:
http://hdl.handle.net/10754/623311
Title:
A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean
Authors:
Tsiaras, Kostas P.; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Kalaroni, Sofia; Petihakis, George; Triantafyllou, George
Abstract:
A hybrid ensemble data assimilation scheme (HYBRID), combining a flow-dependent with a static background covariance, was developed and implemented for assimilating satellite (SeaWiFS) Chl-a data into a marine ecosystem model of the Mediterranean. The performance of HYBRID was assessed against a model free-run, the ensemble-based singular evolutive interpolated Kalman (SEIK) and its variant with static covariance (SFEK), with regard to the assimilated variable (Chl-a) and non-assimilated variables (dissolved inorganic nutrients). HYBRID was found more efficient than both SEIK and SFEK, reducing the Chl-a error by more than 40% in most areas, as compared to the free-run. Data assimilation had a positive overall impact on nutrients, except for a deterioration of nitrates simulation by SEIK in the most productive area (Adriatic). This was related to SEIK pronounced update in this area and the phytoplankton limitation on phosphate that lead to a built up of excess nitrates. SEIK was found more efficient in productive and variable areas, where its ensemble exhibited important spread. SFEK had an effect mostly on Chl-a, performing better than SEIK in less dynamic areas, adequately described by the dominant modes of its static covariance. HYBRID performed well in all areas, due to its “blended” covariance. Its flow-dependent component appears to track changes in the system dynamics, while its static covariance helps maintaining sufficient spread in the forecast. HYBRID sensitivity experiments showed that an increased contribution from the flow-dependent covariance results in a deterioration of nitrates, similar to SEIK, while the improvement of HYBRID with increasing flow-dependent ensemble size quickly levels off.
KAUST Department:
King Abdullah University of Sciences and Technology, Thuwal, Saudi Arabia
Citation:
Tsiaras KP, Hoteit I, Kalaroni S, Petihakis G, Triantafyllou G (2017) A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean. Ocean Dynamics. Available: http://dx.doi.org/10.1007/s10236-017-1050-7.
Publisher:
Springer Nature
Journal:
Ocean Dynamics
Issue Date:
20-Apr-2017
DOI:
10.1007/s10236-017-1050-7
Type:
Article
ISSN:
1616-7341; 1616-7228
Sponsors:
This work was supported by EU OPEC project, funded from the European Union’s Seventh Framework Program (FP7/2007-2013) under grant agreement n° 283291. We thank Dionysios Raitsos for kindly providing the SeaWiFS Chl-a data.
Additional Links:
http://link.springer.com/article/10.1007/s10236-017-1050-7
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Full metadata record

DC FieldValue Language
dc.contributor.authorTsiaras, Kostas P.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorKalaroni, Sofiaen
dc.contributor.authorPetihakis, Georgeen
dc.contributor.authorTriantafyllou, Georgeen
dc.date.accessioned2017-05-02T13:22:28Z-
dc.date.available2017-05-02T13:22:28Z-
dc.date.issued2017-04-20en
dc.identifier.citationTsiaras KP, Hoteit I, Kalaroni S, Petihakis G, Triantafyllou G (2017) A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean. Ocean Dynamics. Available: http://dx.doi.org/10.1007/s10236-017-1050-7.en
dc.identifier.issn1616-7341en
dc.identifier.issn1616-7228en
dc.identifier.doi10.1007/s10236-017-1050-7en
dc.identifier.urihttp://hdl.handle.net/10754/623311-
dc.description.abstractA hybrid ensemble data assimilation scheme (HYBRID), combining a flow-dependent with a static background covariance, was developed and implemented for assimilating satellite (SeaWiFS) Chl-a data into a marine ecosystem model of the Mediterranean. The performance of HYBRID was assessed against a model free-run, the ensemble-based singular evolutive interpolated Kalman (SEIK) and its variant with static covariance (SFEK), with regard to the assimilated variable (Chl-a) and non-assimilated variables (dissolved inorganic nutrients). HYBRID was found more efficient than both SEIK and SFEK, reducing the Chl-a error by more than 40% in most areas, as compared to the free-run. Data assimilation had a positive overall impact on nutrients, except for a deterioration of nitrates simulation by SEIK in the most productive area (Adriatic). This was related to SEIK pronounced update in this area and the phytoplankton limitation on phosphate that lead to a built up of excess nitrates. SEIK was found more efficient in productive and variable areas, where its ensemble exhibited important spread. SFEK had an effect mostly on Chl-a, performing better than SEIK in less dynamic areas, adequately described by the dominant modes of its static covariance. HYBRID performed well in all areas, due to its “blended” covariance. Its flow-dependent component appears to track changes in the system dynamics, while its static covariance helps maintaining sufficient spread in the forecast. HYBRID sensitivity experiments showed that an increased contribution from the flow-dependent covariance results in a deterioration of nitrates, similar to SEIK, while the improvement of HYBRID with increasing flow-dependent ensemble size quickly levels off.en
dc.description.sponsorshipThis work was supported by EU OPEC project, funded from the European Union’s Seventh Framework Program (FP7/2007-2013) under grant agreement n° 283291. We thank Dionysios Raitsos for kindly providing the SeaWiFS Chl-a data.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/article/10.1007/s10236-017-1050-7en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10236-017-1050-7en
dc.subjectData assimilationen
dc.subjectBiogeochemical modelen
dc.subjectKalman filteren
dc.subjectOcean coloren
dc.subjectMediterraneanen
dc.titleA hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterraneanen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Sciences and Technology, Thuwal, Saudi Arabiaen
dc.identifier.journalOcean Dynamicsen
dc.eprint.versionPost-printen
dc.contributor.institutionHellenic Centre for Marine Research, Attica, Greeceen
dc.contributor.institutionHellenic Centre for Marine Research, Iraklio, Greeceen
kaust.authorHoteit, Ibrahimen
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