Ocean Dynamics

, Volume 67, Issue 7, pp 915–933 | Cite as

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

  • Habib Toye
  • Peng Zhan
  • Ganesh Gopalakrishnan
  • Aditya R. Kartadikaria
  • Huang Huang
  • Omar Knio
  • Ibrahim HoteitEmail author
Part of the following topical collections:
  1. Topical Collection on the 18th Joint Numerical Sea Modelling Group Conference, Oslo, Norway, 10-12 May 2016


We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.


Red Sea Data assimilation Seasonal variability Ensemble Kalman filter Ensemble optimal interpolation 



This research work was supported by King Abdullah University of Science and Technology (KAUST), Saudi Arabia, and the Saudi ARAMCO Marine Environmental Research Center at KAUST (SAMERCK). The research made use of the resources of the Super computing Laboratory and computer clusters at KAUST.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Habib Toye
    • 1
  • Peng Zhan
    • 2
  • Ganesh Gopalakrishnan
    • 3
  • Aditya R. Kartadikaria
    • 2
    • 4
  • Huang Huang
    • 1
  • Omar Knio
    • 1
  • Ibrahim Hoteit
    • 1
    • 2
    Email author
  1. 1.Division of Computer, Electrical and Mathematical Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia
  2. 2.Division of Physical Sciences and EngineeringKing Abdullah University of Science and Technology (KAUST)ThuwalSaudi Arabia
  3. 3.Scripps Institution of OceanographyUniversity of California San DiegoLa JollaUSA
  4. 4.Study Program of OceanographyBandung Institute of TechnologyBandungIndonesia

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