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

Denis Dreano, Kostas Tsiaras, George Triantafyllou, Ibrahim Hoteit

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)935-947
Number of pages13
JournalOcean Dynamics
Issue number7
StatePublished - May 24 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.


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