Ocean Dynamics

, Volume 62, Issue 7, pp 1059–1071 | Cite as

Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean

  • Xunqiang Yin
  • Fangli Qiao
  • Yongzeng Yang
  • Changshui Xia
  • Xianyao Chen
Part of the following topical collections:
  1. Topical Collection on the 3rd International Workshop on Modelling the Ocean 2011


The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10 % on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85 % and 80 %, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models.


Argo profiles Ensemble adjustment Kalman filter Ensemble free runs Ensemble spread Mesoscale variability 


  1. Anderson JL (2001) An ensemble adjustment Kalman filter for data assimilation. Mon Weather Rev 129:2884–2903CrossRefGoogle Scholar
  2. Anderson JL (2003) A local least squares framework for ensemble filtering. Mon Weather Rev 131:634–642CrossRefGoogle Scholar
  3. Anderson JL, Hoar T, Raeder K et al (2009) The data assimilation research testbed: a community data assimilation facility. Bull Am Meteorol Soc 90:1283–1296CrossRefGoogle Scholar
  4. Bishop CH, Etherton BJ, Majumdar S (2001) Adaptive sampling with the ensemble transform Kalman filter, part I: theoretical aspects. Mon Weather Rev 129:420–436CrossRefGoogle Scholar
  5. Burgers G, van Leeuwen PJ, Evensen G (1998) Analysis scheme in the ensemble Kalman filter. Mon Weather Rev 126:1719–1724CrossRefGoogle Scholar
  6. Chelton DB, Schlax MG, Samelson RM, de Szoeke RA (2007) Global observations of large oceanic eddies. Geophys Res Lett 34:L15606. doi:10.1029/2007GL030812 CrossRefGoogle Scholar
  7. Chelton DB, Schlax MG, Samelson RM (2011) Global observations of nonlinear mesoscale eddies. Prog Oceanogr 91:167–216CrossRefGoogle Scholar
  8. Chin TM, Milliff RF, Large WG (1998) Basin-scale high-wavenumber sea surface wind fields from multiresolution analysis of scatterometer data. J Atmos Ocean Technol 15:741–763CrossRefGoogle Scholar
  9. Courtier P, Derber J, Errico R et al (1993) Important literature on the use of adjoint, variational methods and the Kalman filter in meteorology. Tellus 45A:342–357Google Scholar
  10. Cummings JA (2005) Operational multivariate ocean data assimilation. Q J R Meteorol Soc 131:3583–3604CrossRefGoogle Scholar
  11. da Silva AM, Young CC, 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
  12. da Silva AM, Young CC, 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. 308Google Scholar
  13. Ducet N, Le Traon PY, Reverdin G (2000) Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and −2. J Geophys Res 105(C8):19477–19498CrossRefGoogle Scholar
  14. ETOP5 (1986) 5′ × 5′ Topography and elevation. Marine Geology and Geophysics Division, National Geophysical Data Center. (Available from National Geophysical Data Center, NOAA, Code E/GC3, Boulder, CO 80303)Google Scholar
  15. 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
  16. Evensen G (2003) The ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367CrossRefGoogle Scholar
  17. Fu LL, Traon PL (2006) Satellite altimetry and ocean dynamics. C R Geosci 338:1063–1076CrossRefGoogle Scholar
  18. Kalman R (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82(D):35–45CrossRefGoogle Scholar
  19. Kalman R, Bucy R (1961) New results in linear filtering and prediction theory. Trans ASME J Basic Eng 82(D):95–109CrossRefGoogle Scholar
  20. Kalnay E, Kanamitsu M, Kistler R et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–470CrossRefGoogle Scholar
  21. Karspeck A, Anderson JL (2007) Experimental implementation of an ensemble adjustment filter for an intermediate ENSO model. J Climate 20:4638–4658CrossRefGoogle Scholar
  22. Liu Y, Zhu J, She J et al (2009) Assimilating temperature and salinity profile observations using an anistropic recursive filter in a coastal ocean model. Ocean Model 30(2–3):75–87CrossRefGoogle Scholar
  23. Martin MJ, Hines A, Bell MJ (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 133:981–995CrossRefGoogle Scholar
  24. Milliff RF, Morzel J, Chelton DB et al (2004) Wind stress curl and wind stress divergence biases from rain effects on QSCAT surface wind retrievals. J Atmos Ocean Technol 21:1216–1231CrossRefGoogle Scholar
  25. Oke PR, Schiller A, Griffin DA et al (2005) Ensemble data assimilation for an eddy-resolving ocean model of the Australian Region. Q J R Meteorol Soc 131:3301–3311CrossRefGoogle Scholar
  26. Oke PR, Brassington GB, Griffin DA et al (2008) The Bluelink ocean data assimilation system (BODAS). Ocean Model 21:46–70CrossRefGoogle Scholar
  27. Pham DT (2001) Stochastic methods for sequential data assimilation in strongly non-linear system. Mon Weather Rev 129:1194–1207CrossRefGoogle Scholar
  28. Qiao F, Yuan Y, Yang Y et al (2004) Wave-induced mixing in the upper ocean: distribution and application in a global ocean circulation model. Geophys Res Lett 31:L11303. doi:10.1029/2004GL019824 CrossRefGoogle Scholar
  29. Tippett MK, Anderson JL, Bishop CH et al (2003) Ensemble square-root filters. Mon Weather Rev 131:1485–1490CrossRefGoogle Scholar
  30. Whitaker JS, Hamill TM (2002) Ensemble data assimilation without perturbed observations. Mon Weather Rev 130:1913–1924CrossRefGoogle Scholar
  31. Xia C, Qiao F, Yang Y 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 CrossRefGoogle Scholar
  32. Yin X, Qiao F, Yang Y (2010) Ensemble adjustment Kalman filter study for Argo data. Chin J Oceanol Limnol 28(3):626–635CrossRefGoogle Scholar
  33. Yin X, Qiao F, Shu Q (2011) Using ensemble adjustment Kalman filter to assimilate Argo profiles in a global OGCM. Ocean Dyn 61:1017–1031. doi:10.1007/s10236-011-0419-2 CrossRefGoogle Scholar
  34. Zhang S, Anderson JL (2003) Impact of spatially and temporally varying estimates of error covariance on assimilation in a simple atmospheric model. Tellus 55A(2):126–147Google Scholar
  35. Zhang S, Harrison MJ, Wittenberg AT et al (2005) Initialization of an ENSO forecast system using a parallelized ensemble filter. Mon Weather Rev 133:3176–3201CrossRefGoogle Scholar
  36. Zhang S, Harrison MJ, Rosati A et al (2007) System design and evaluation of coupled ensemble data assimilation for global oceanic climate studies. Mon Weather Rev 135:3541–3564CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Xunqiang Yin
    • 1
    • 2
  • Fangli Qiao
    • 1
    • 2
  • Yongzeng Yang
    • 1
    • 2
  • Changshui Xia
    • 1
    • 2
  • Xianyao Chen
    • 1
    • 2
  1. 1.First Institute of OceanographyState Oceanic Administration (SOA)QingdaoChina
  2. 2.Key Laboratory of Marine Science and Numerical Modeling (MASNUM)SOAQingdaoChina

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