Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea

Handle URI:
http://hdl.handle.net/10754/625039
Title:
Efficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Sea
Authors:
Dreano, Denis ( 0000-0001-7956-5538 ) ; Tsiaras, Kostas; Triantafyllou, George; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
Forecasting 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.
KAUST Department:
Applied Mathematics and Computational Science Program; Earth Science and Engineering Program
Citation:
Dreano 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.
Publisher:
Springer Nature
Journal:
Ocean Dynamics
Issue Date:
24-May-2017
DOI:
10.1007/s10236-017-1065-0
Type:
Article
ISSN:
1616-7341; 1616-7228
Sponsors:
This 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.
Additional Links:
http://link.springer.com/article/10.1007/s10236-017-1065-0
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorDreano, Denisen
dc.contributor.authorTsiaras, Kostasen
dc.contributor.authorTriantafyllou, Georgeen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2017-06-15T05:24:19Z-
dc.date.available2017-06-15T05:24:19Z-
dc.date.issued2017-05-24en
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.en
dc.identifier.issn1616-7341en
dc.identifier.issn1616-7228en
dc.identifier.doi10.1007/s10236-017-1065-0en
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.en
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.en
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/article/10.1007/s10236-017-1065-0en
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s10236-017-1065-0en
dc.subjectData assimilationen
dc.subjectEnsemble Kalman filteren
dc.subjectSEIKen
dc.subjectERSEMen
dc.subjectMarine ecosystem modellingen
dc.subjectRed Seaen
dc.subjectClusteringen
dc.subjectChlorophyll remote sensingen
dc.titleEfficient ensemble forecasting of marine ecology with clustered 1D models and statistical lateral exchange: application to the Red Seaen
dc.typeArticleen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalOcean Dynamicsen
dc.eprint.versionPost-printen
dc.contributor.institutionInstitute of Oceanography, Hellenic Centre for Marine Research, Attica, Greeceen
kaust.authorDreano, Denisen
kaust.authorHoteit, Ibrahimen
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