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dc.contributor.authorDreano, Denis
dc.contributor.authorTsiaras, Kostas
dc.contributor.authorTriantafyllou, George
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2017-06-15T05:24:19Z
dc.date.available2017-06-15T05:24:19Z
dc.date.issued2017-05-24
dc.identifier.citationDreano D, Tsiaras K, Triantafyllou G, Hoteit I (2017) Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea. Ocean Dynamics 67: 935–947. Available: http://dx.doi.org/10.1007/s10236-017-1065-0.
dc.identifier.issn1616-7341
dc.identifier.issn1616-7228
dc.identifier.doi10.1007/s10236-017-1065-0
dc.identifier.urihttp://hdl.handle.net/10754/625039
dc.description.abstractForecasting the state of large marine ecosystems is important for many economic and public health applications. However, advanced three-dimensional (3D) ecosystem models, such as the European Regional Seas Ecosystem Model (ERSEM), are computationally expensive, especially when implemented within an ensemble data assimilation system requiring several parallel integrations. As an alternative to 3D ecological forecasting systems, we propose to implement a set of regional one-dimensional (1D) water-column ecological models that run at a fraction of the computational cost. The 1D model domains are determined using a Gaussian mixture model (GMM)-based clustering method and satellite chlorophyll-a (Chl-a) data. Regionally averaged Chl-a data is assimilated into the 1D models using the singular evolutive interpolated Kalman (SEIK) filter. To laterally exchange information between subregions and improve the forecasting skills, we introduce a new correction step to the assimilation scheme, in which we assimilate a statistical forecast of future Chl-a observations based on information from neighbouring regions. We apply this approach to the Red Sea and show that the assimilative 1D ecological models can forecast surface Chl-a concentration with high accuracy. The statistical assimilation step further improves the forecasting skill by as much as 50%. This general approach of clustering large marine areas and running several interacting 1D ecological models is very flexible. It allows many combinations of clustering, filtering and regression technics to be used and can be applied to build efficient forecasting systems in other large marine ecosystems.
dc.description.sponsorshipThis research was funded by King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia. This research made use of the resources of the supercomputing laboratory at KAUST. We thank the ESA Ocean Colour CCI Team for providing OC-CCI chlorophyll data.
dc.publisherSpringer Nature
dc.relation.urlhttp://link.springer.com/article/10.1007/s10236-017-1065-0
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10236-017-1065-0
dc.subjectData assimilation
dc.subjectEnsemble Kalman filter
dc.subjectSEIK
dc.subjectERSEM
dc.subjectMarine ecosystem modelling
dc.subjectRed Sea
dc.subjectClustering
dc.subjectChlorophyll remote sensing
dc.titleEfficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalOcean Dynamics
dc.eprint.versionPost-print
dc.contributor.institutionInstitute of Oceanography, Hellenic Centre for Marine Research, Attica, Greece
kaust.personDreano, Denis
kaust.personHoteit, Ibrahim
refterms.dateFOA2018-05-24T00:00:00Z
dc.date.published-online2017-05-24
dc.date.published-print2017-07


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