Acta Oceanologica Sinica

, Volume 33, Issue 7, pp 72–82 | Cite as

Assimilating the along-track sea level anomaly into the regional ocean modeling system using the ensemble optimal interpolation

  • Guokun Lyu
  • Hui Wang
  • Jiang Zhu
  • Dakui Wang
  • Jiping Xie
  • Guimei Liu


The ensemble optimal interpolation (EnOI) is applied to the regional ocean modeling system (ROMS) with the ability to assimilate the along-track sea level anomaly (TSLA). This system is tested with an eddy-resolving system of the South China Sea (SCS). Background errors are derived from a running seasonal ensemble to account for the seasonal variability within the SCS. A fifth-order localization function with a 250 km localization radius is chosen to reduce the negative effects of sampling errors. The data assimilation system is tested from January 2004 to December 2006. The results show that the root mean square deviation (RMSD) of the sea level anomaly decreased from 10.57 to 6.70 cm, which represents a 36.6% reduction of error. The data assimilation reduces error for temperature within the upper 800 m and for salinity within the upper 200 m, although error degrades slightly at deeper depths. Surface currents are in better agreement with trajectories of surface drifters after data assimilation. The variance of sea level improves significantly in terms of both the amplitude and position of the strong and weak variance regions after assimilating TSLA. Results with AGE error (AGE) perform better than no AGE error (NoAGE) when considering the improvements of the temperature and the salinity. Furthermore, reasons for the extremely strong variability in the northern SCS in high resolution models are investigated. The results demonstrate that the strong variability of sea level in the high resolution model is caused by an extremely strong Kuroshio intrusion. Therefore, it is demonstrated that it is necessary to assimilate the TSLA in order to better simulate the SCS with high resolution models.

Key words

ensemble optimal interpolation regional ocean modeling system along-track sea level anomaly South China Sea variability 


  1. Bueh C, Cubasch U, Hagemann S. 2003. Impacts of global warming on changes in the East Asian monsoon and the related river discharge in a global time-slice experiment. Climate Research, 24(1): 47–57CrossRefGoogle Scholar
  2. Counillon F, Bertino L. 2009. Ensemble optimal interpolation: multivariate properties in the Gulf of Mexico. Tellus A, 61(2): 296–308CrossRefGoogle Scholar
  3. Dibarboure G, Lauret O, Mertz F, et al. 2008. SSALTO/DUACS User Handbook:(M) SLA and (M) ADT Near-real Time and Delayed Time Products. Rep CLS DOS NT, Vol. 6 Ramonville St Agne: Centre National D’études Spatiales, 39Google Scholar
  4. Evensen G. 1994. Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statics. Journal of Geophysical Research: Oceans, 99(C5): 10143–10162CrossRefGoogle Scholar
  5. Evensen G. 2003. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4): 343–367CrossRefGoogle Scholar
  6. Fu Weiwei, Zhu Jiang, Yan Changxiang. 2009. A comparison between 3DVAR and EnOI techniques for satellite altimetry data assimilation. Ocean Modelling, 26(3): 206–216CrossRefGoogle Scholar
  7. Gaspari G, Cohn S E. 1999. Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society, 125(554): 723–757CrossRefGoogle Scholar
  8. Hamill T M, Whitaker J S, Snyder C. 2001. Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter. Monthly Weather Review, 129(11): 2776–2790CrossRefGoogle Scholar
  9. Huang Xiang-Yu, Morgensen K, Yang Xiaohua. 2002. First-guess at the appropriate time: the HIRLAM implementation and experiments. In: Proceedings of the HIRLAM Workshop on Variational Data Assimilation and Remote Sensing, Helsinki, Finland, 28–43Google Scholar
  10. Hurlburt H E, Brassington G B, Drillet Y, et al 2009. High-resolution global and basin-scale ocean analyses and forecasts. Oceanography, 22(3):110–127CrossRefGoogle Scholar
  11. Nan Feng, Xue Huijie, Chai Fei, et al. 2011. Identification of different types of Kuroshio intrusion into the South China Sea. Ocean Dynamics, 61(9): 1291–1304CrossRefGoogle Scholar
  12. Oke P R, Allen J S, Miller R N, et al. 2002. Assimilation of surface velocity data into a primitive equation coastal ocean model. Journal of Geophysical Research: Oceans, 107(C9): 3122CrossRefGoogle Scholar
  13. Oke P R, Brassington G B, Griffin D A, et al. 2008. The Bluelink ocean data assimilation system (BODAS). Ocean Modelling, 21(1): 46–70CrossRefGoogle Scholar
  14. Oke P R, Sakov P, Corney S. 2007. Impacts of localisation in the EnKF and EnOI: experiments with a small model. Ocean Dynamics, 57(1): 32–45CrossRefGoogle Scholar
  15. Oke P R, Schiller A, Griffin D A, et al. 2005. Ensemble data assimilation for an eddy-resolving ocean model of the Australian region. Quarterly Journal of the Royal Meteorological Society, 131(613): 3301–3311CrossRefGoogle Scholar
  16. Ott E, Hunt B R, Szunyogh I, et al. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus A, 56(5): 415–428CrossRefGoogle Scholar
  17. Shchepetkin A F, McWilliams J C. 2005. The regional oceanic modeling system (ROMS): a split-explicit, free-surface, topography-following- coordinate oceanic model. Ocean Modelling, 9(4): 347–404CrossRefGoogle Scholar
  18. Tang Liqun, Sheng Jinyu, Ji Xiaomei, et al. 2009. Investigation of threedimensional circulation and hydrography over the Pearl River Estuary of China using a nested-grid coastal circulation model. Ocean Dynamics, 59(6): 899–919CrossRefGoogle Scholar
  19. Wang Guihua, Su Jilan, Chu P C. 2003. Mesoscale eddies in the South China Sea observed with altimeter data. Geophysical Research Letters, 30(21): 2121CrossRefGoogle Scholar
  20. Wong LaiAh A, Chen Jay-Chung, Xue Huijie, et al. 2003. A model study of the circulation in the Pearl River Estuary (PRE) and its adjacent coastal waters. 1: Simulations and comparison with observations. Journal of Geophysical Research: Oceans, 108(C5): 3156CrossRefGoogle Scholar
  21. Xie Jiping, Counillon F, Zhu Jiang, et al. 2011. An eddy resolving tidaldriven model of the South China Sea assimilating along-track SLA data using the EnOI. Ocean Science, 7(5): 609–627CrossRefGoogle Scholar
  22. Xie Jiping, Zhu Jiang. 2010. Ensemble optimal interpolation schemes for assimilating Argo profiles into a hybrid coordinate ocean model. Ocean Modelling, 33(3): 283–298CrossRefGoogle Scholar

Copyright information

© The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Guokun Lyu
    • 1
    • 3
  • Hui Wang
    • 2
    • 3
  • Jiang Zhu
    • 4
  • Dakui Wang
    • 3
  • Jiping Xie
    • 5
  • Guimei Liu
    • 3
  1. 1.Physical Oceanography LaboratoryOcean University of ChinaQingdaoChina
  2. 2.College of Environmental Science and EngineeringOcean University of ChinaQingdaoChina
  3. 3.Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting CenterState Oceanic AdministrationBeijingChina
  4. 4.State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  5. 5.International Center for Climate and Environmental Sciences, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

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