An ensemble adjustment Kalman filter study for Argo data

  • Xunqiang Yin (尹训强)
  • Fangli Qiao (乔方利)
  • Yongzeng Yang (杨永增)
  • Changshui Xia (夏长水)


An ensemble adjustment Kalman filter system is developed to assimilate Argo profiles into the Northwest Pacific MASNUM wave-circulation coupled model, which is based on the Princeton Ocean Model (POM). This model was recoded in FORTRAN-90 style, and some new data types were defined to improve the efficiency of system design and execution. This system is arranged for parallel computing by using UNIX shell scripts: it is easier with single models running separately with the required information exchanged through input/output files. Tests are carried out to check the performance of the system: one for checking the ensemble spread and another for the performance of assimilation of the Argo data in 2005. The first experiment shows that the assimilation system performs well. The comparison with the Satellite derived sea surface temperature (SST) shows that modeled SST errors are reduced after assimilation; at the same time, the spatial correlation between the simulated SST anomalies and the satellite data is improved because of Argo assimilation. Furthermore, the temporal evolution/trend of SST becomes much better than those results without data assimilation. The comparison against GTSPP profiles shows that the improvement is not only in the upper layers of ocean, but also in the deeper layers. All these results suggest that this system is potentially capable of reconstructing oceanic data sets that are of high quality and are temporally and spatially continuous.


ensemble adjustment Kalman filter Argo profile data assimilation 


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  1. Anderson J L. 2001. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev. 129: 2 884–2 903.Google Scholar
  2. Anderson J L. 2003. A local least squares framework for ensemble filtering. Mon. Wea. Rev., 131: 634–642.CrossRefGoogle Scholar
  3. Bishop C H, Etherton B J and Majumdar S. 2001. Adaptive sampling with the ensemble transform Kalman filter, part I. Mon. Wea. Rev., 129: 420–436.CrossRefGoogle Scholar
  4. Blumberg A F, L. Mellor G. 1987. A description of a three dimensional coastal ocean circulation model, in Three Dimensional Coastal Ocean Models, Coastal Estuarine Science, AGU, Washington, D. C. vol. 4, p. 1–16.Google Scholar
  5. Burgers G, van Leeuwen P J, Evensen G. 1998. Analysis scheme in the ensemble Kalman Filter. Mon. Wea. Rev. 126: 1719–1724.CrossRefGoogle Scholar
  6. Cummings J A. 2005. Operational Multivariate Ocean Data Assimilation. Q. J. R. Meteorol. Soc., 131: 3583–3604.CrossRefGoogle Scholar
  7. da Silva A M, Young C C, Levitus S. 1994a. Atlas of Surface Marine Data 1994, Volume 3, Anomalies of Heat and Momentum Fluxes. NOAA Atlas NESDIS 8. U.S. Department of Commerce, NOAA, NESDIS, p. 4llGoogle Scholar
  8. da Silva A M, Young C C, Levitus S. 1994b. Atlas of Surface Marine Data 1994, Volume 4, Anomalies of Fresh Water Fluxes. NOAA Atlas NESDIS 9. U.S. Department of Commerce, NOAA, NESDIS, p. 308.Google Scholar
  9. Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geotropic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99: 10 143–10 162CrossRefGoogle Scholar
  10. Evensen G. 2003. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dynamics 53: 343–367CrossRefGoogle Scholar
  11. Evensen G., 2004. Sampling strategies and square root analysis schemes for the EnKF. Ocean Dynamics, 54: 539–560.CrossRefGoogle Scholar
  12. Jeffrey S W, Andrew F L. 1998. The Relationship between Ensemble Spread and Ensemble Mean Skill. Mon. Wea. Rev., 126: 3 292–3 302.Google Scholar
  13. Kalman R. 1960. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering, 82(D): 35–45.Google Scholar
  14. Kalman R. Bucy R. 1961. New results in linear filtering and prediction theory. Transactions of the ASME—Journal of Basic Engineering, 82(D): 95–109.Google Scholar
  15. Liu Y, Jiang Z, She J et al., 2009. Assimilating temperature and salinity profile observations using an anistropic recursive filter in a coastal ocean model. Ocean Modelling, 30: 75–87.CrossRefGoogle Scholar
  16. Lü X G, Qiao F L, Wang G S et al. 2008. Upwelling off the west coast of Hainan Island in summer: Its detection and mechanisms. Geophys. Res. Lett., 35: L02604, doi:10.1029/2007GL032 440.CrossRefGoogle Scholar
  17. Martin M J, Hines A, Bell M J. 2007. Data assimilation in the FOAM operational short-range ocean forecasting system: a description of the scheme and its impact. Q. J. R. Meteorol. Soc., 133: 981–995.CrossRefGoogle Scholar
  18. Gao S H, Wu Z M, Xie H Q. 2000. The developments and applications of Kalman filters in meteorological data assimilation. Advance in Earth Sciences, 15(5): 571–575. (in Chinese with English abstract)Google Scholar
  19. Oke P R, Schiller A, Griffin D A et al. 2005. Ensemble data assimilation for an eddy-resolving ocean model of the Australian Region. Q. J. R. Meteorol. Soc., 131: 3 301–3 311.CrossRefGoogle Scholar
  20. Oke P R, Brassington G B, Griffin D A et al., 2008. The Bluelink ocean data assimilation system (BODAS). Ocean Modelling, 21: 46–70.CrossRefGoogle Scholar
  21. Qiao F L, Yuan Y L, Yang Y Z et al. 2004. Wave induced mixing in the upper ocean: Distribution and application to a global ocean circulation model. Geophys. Res. Lett., 31, L11303, doi:10.1029/2004GL019824.CrossRefGoogle Scholar
  22. Qiao F L, Zhang S Q. 2002. Unification and applications of modern oceanic/atmospheric data assimilation algorithms. Advances in Oceanography, 20(4): 79–93. (in Chinese with English abstract)Google Scholar
  23. Qiao F L, Zhang S Q, Yuan Y L. 2004. Unification and applications of modern oceanic/atmospheric data assimilation algorithms. Journal of Hydrodynamics, B(5): 1–15.Google Scholar
  24. Tippett M K, Anderson J L, Bishop C H et al. 2003. Ensemble square-root filters. Mon. Wea. Rev., 131: 485–1 490CrossRefGoogle Scholar
  25. Whitaker J S, Hamill T M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130: 1 913–1 924.CrossRefGoogle Scholar
  26. Xia C S, Qiao F L, Yang Y Z et al. 2006. Three-dimensional structure of the summertime circulation in the Yellow Sea from a wave-tide-circulation coupled model. J. Geophys. Res., 111, C11S03, doi: 10.1029/2005JC003218CrossRefGoogle Scholar
  27. Yin X Q, Oey L Y. 2007. Bred-ensemble ocean forecast during Katrina: Loop current and ring. Ocean Modeling, 17(4): 300–326.CrossRefGoogle Scholar
  28. Zhang S Q, Anderson J L. 2003. Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus, 55A(2): 126–147.Google Scholar
  29. Zhang S Q, Anderson J L, Rosati A et al. 2004. Multiple Time Level Adjustment for Data Assimilation. Tellus, 56A(1): 2–16.Google Scholar
  30. Zhang S Q, Harrison M J, Wittenberg A T et al. 2005. Initialization of an ENSO forecast system using a parallelized ensemble filter. Mon. Wea. Rev., 133: 3 176–3 201.Google Scholar
  31. Zhang S Q, Harrison M J, Rosati A et al. 2007. System Design and Evaluation of Coupled Ensemble Data Assimilation for Global Oceanic Climate Studies. Mon. Wea. Rev., 135: 3 541–3 564.Google Scholar

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Xunqiang Yin (尹训强)
    • 1
    • 2
  • Fangli Qiao (乔方利)
    • 1
    • 2
  • Yongzeng Yang (杨永增)
    • 1
    • 2
  • Changshui Xia (夏长水)
    • 1
    • 2
  1. 1.The First Institute of OceanographySOAQingdaoChina
  2. 2.Key Laboratory of Marine Science and Numerical Modeling (MASNUM)SOAQingdaoChina

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