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

Ocean Dynamics volume 67, pages915–933(2017)Cite this article

Abstract

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.

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    In CPU time and not necessarily in real time as the ensemble members can be integrated in parallel.

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Acknowledgments

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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Affiliations

  1. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Habib Toye, Huang Huang, Omar Knio & Ibrahim Hoteit

  2. Division of Physical Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Peng Zhan, Aditya R. Kartadikaria & Ibrahim Hoteit

  3. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Ganesh Gopalakrishnan

  4. Study Program of Oceanography, Bandung Institute of Technology, Bandung, Indonesia

    Aditya R. Kartadikaria

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  1. Habib Toye
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  2. Peng Zhan
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  3. Ganesh Gopalakrishnan
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  4. Aditya R. Kartadikaria
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  5. Huang Huang
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  6. Omar Knio
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  7. Ibrahim Hoteit
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Correspondence to Ibrahim Hoteit.

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This article is part of the Topical Collection on the 18th Joint Numerical Sea Modelling Group Conference, Oslo, Norway, 10–12 May 2016

Responsible Editor: Ulf Gräwe

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Toye, H., Zhan, P., Gopalakrishnan, G. et al. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing. Ocean Dynamics 67, 915–933 (2017). https://doi.org/10.1007/s10236-017-1064-1

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Keywords

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