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An ensemble adjustment Kalman filter study for Argo data

Abstract

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.

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References

  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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  6. Cummings J A. 2005. Operational Multivariate Ocean Data Assimilation. Q. J. R. Meteorol. Soc., 131: 3583–3604.

    Article  Google 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. 4ll

  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.

  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: 10 143–10 162

    Article  Google Scholar 

  10. Evensen G. 2003. The Ensemble Kalman Filter: Theoretical Formulation and Practical Implementation, Ocean Dynamics 53: 343–367

    Article  Google Scholar 

  11. Evensen G., 2004. Sampling strategies and square root analysis schemes for the EnKF. Ocean Dynamics, 54: 539–560.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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: 485–1 490

    Article  Google Scholar 

  25. Whitaker J S, Hamill T M. 2002. Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130: 1 913–1 924.

    Article  Google 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: 10.1029/2005JC003218

    Article  Google 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.

    Article  Google 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 

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Correspondence to Xunqiang Yin.

Additional information

Supported by the Project of National Basic Research Program of China (No. 2007CB816002), Special Fund for Fundamental Scientific Research (No. 2008G08).

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Yin, X., Qiao, F., Yang, Y. et al. An ensemble adjustment Kalman filter study for Argo data. Chin. J. Ocean. Limnol. 28, 626–635 (2010). https://doi.org/10.1007/s00343-010-9017-2

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Keyword

  • ensemble adjustment Kalman filter
  • Argo profile
  • data assimilation