Science in China Series D: Earth Sciences

, Volume 49, Issue 11, pp 1212–1222 | Cite as

A three-dimensional variational ocean data assimilation system: Scheme and preliminary results

  • Zhu Jiang 
  • Zhou Guangqing 
  • Yan Changxiang 
  • Fu Weiwei 
  • You Xiaobao 


A new 3DVAR-based Ocean Variational Analysis System (OVALS) is developed. OVALS is capable of assimilating in situ sea water temperature and salinity observations and satellite altimetry data. As a component of OVALS, a new variational scheme is proposed to assimilate the sea surface height data. This scheme considers both the vertical correlation of background errors and the nonlinear temperature-salinity relationship which is derived from the generalization of the linear balance constraints to the nonlinear in the 3DVAR. By this scheme, the model temperature and salinity fields are directly adjusted from the altimetry data. Additionally, OVALS can assimilate the temperature and salinity profiles from the ARGO floats which have been implemented in recent years and some temperature and salinity data such as from expendable bathythermograph, moored ocean buoys, etc. A 21-year assimilation experiment is carried out by using OVALS and the Tropical Pacific circulation model. The results show that the assimilation system may effectively improve the estimations of temperature and salinity by assimilating all kinds of observations. Moreover, the root mean square errors of temperature and salinity in the upper depth less than 420 m reach 0.63°C and 0.34 psu.


data assimilation 3DVAR sea surface height ARGO floats 


  1. 1.
    Argo Data management Group. Report of the Firth Argo Data Management Meeting, Hangzhou, China, March 4–6, 2003Google Scholar
  2. 2.
    GHRSST Porject Office. The GHRSST-PP Development and Implementation Plan, June 12, 2003.Google Scholar
  3. 3.
    Xu J P. The Discovery of ARGO Global Ocean Observations (in Chinese). Beijing: China Ocean Press, 2002Google Scholar
  4. 4.
    Zhu J, Kamachi M, Wang D X. Estimation of air-sea heat flux from ocean measurements: an ill-posed problem. J Geophy Res, 2002a, 107, doi: 10.1029/2001JC000995Google Scholar
  5. 5.
    Zhu J, Wang H, Zhou G. SST data assimilation experiments using an adaptive variational method. Chi Sci Bull, 2002, 47(23): 2010–2013CrossRefGoogle Scholar
  6. 6.
    Derber J D, Rosati A. A global oceanic data assimilation systerm. J Phys Oceanogr, 1989, 19: 1333–1347CrossRefGoogle Scholar
  7. 7.
    Clancy R M, Phoebus P A, Pollak K D. An operational global-scale ocean thermal analysis system. J Atmos Oceanic Technol, 1990, 7: 233–254CrossRefGoogle Scholar
  8. 8.
    Meyers G, Phillips H, Smith N, et al. Space and timescales for optimal interpolation of temperature-tropical Pacific. Ocean Prog Oceanogr, 1991, 28: 189–218CrossRefGoogle Scholar
  9. 9.
    Ji M, Leetmaa A, Derber J. An ocean analysis system for seasonal to interannual climate studies, Mon Wea Rev, 1995, 123: 460–481CrossRefGoogle Scholar
  10. 10.
    Kimoto M, Yoshiawa I, Ishii M. An ocean data assimilation system for climate monitoring. J Meteoroe Soc Jpn, 1997, 5: 471–487Google Scholar
  11. 11.
    Behringer D, Ji M, Leetmaa A. An improved coupled model for ENSO prediction and implications for ocean initialization, Part I: The ocean data assimilation system. Mon Wea Rev, 1998, 126: 1013–1021CrossRefGoogle Scholar
  12. 12.
    Zhou G Q, Li X. An oceanic data assimilation system based on a global OGCM, Corpus for Pre-processing System for Data Input of Climate Models, Documents for National Key Project-Studies on Short-Term Climate Prediction System in China (1996–2000), Sub-project 2-6(96-908-02-06), 2000, 1: 34–43Google Scholar
  13. 13.
    Carton J A, Chepurin G, Cao X. A simple ocean data assimilation analysis of the global upper ocean 1950–95, Part I: Methodology. J Phys Oceanogr, 2000,30: 294–309CrossRefGoogle Scholar
  14. 14.
    Carton J A, Giese B S, Cao X, et al. Impact of altimeter, thermistor, and expendable bathythermograph data on retrospective analyses of the tropical Pacific Ocean. J Geophys Res, 1996, 101: 14147–14159CrossRefGoogle Scholar
  15. 15.
    Fischer M. Flugel M, Ji M, et al. The impact of data assimilation on ENSO simulation and predictions. Mon Wea Rev, 1997, 125: 819–829CrossRefGoogle Scholar
  16. 16.
    Ji M, Reynolds R W, Behringer D W. Use of TOPEX/Poseidon Sea Level Data for Ocean Analyses and ENSO Prediction: Some Early Results. J Clim, 2000, 13: 216–231CrossRefGoogle Scholar
  17. 17.
    Maes C. Estimating the influence of salinity on sea level anomaly in the ocean. Geophys Res Lett, 1998, 25: 3551–3554CrossRefGoogle Scholar
  18. 18.
    Henin C, Du Penhoat Y, Ioualalen M. Observations of sea surface salinity in the western Pacific fresh pool: large scale changes in 1992–1995. J Geophys Res, 1998, 103: 7523–7536CrossRefGoogle Scholar
  19. 19.
    Webster P J, Lukas R. The Tropical Ocean/Global Atmosphere Coupled Ocan-Atmpsphere Response Experiment (COARE). Bull Am Meteorol Soc, 1992, 73: 1377–1416CrossRefGoogle Scholar
  20. 20.
    Maes C, Delecluse P, Madec G. Impact of westerly wind bursts on the warm pool of the TOGA-COARE domain in an OGCM. Clim Dyn, 1998, 14: 55–70CrossRefGoogle Scholar
  21. 21.
    Woodgate R A. Can we assimilate temperature data alone into a full equation of state model? Ocean Model, 1997, 114: 4–5Google Scholar
  22. 22.
    Cooper N S. The effect of salinity in tropical ocean models. J Phys Oceanogr, 1988, 18: 697–707CrossRefGoogle Scholar
  23. 23.
    Troccoli A, Balmaseda M A, Segschneider J, et al. Salinity adjustments in the presence of temperature data assimilation. Mon Wea Rev, 2002, 130: 89–102CrossRefGoogle Scholar
  24. 24.
    Vossepoel F, Behringer D W. Impact of sea level assimilation on salinity variability in the western equatorial Pacific. J Phys Ocean, 2000, 30: 1706–1721CrossRefGoogle Scholar
  25. 25.
    Maes C, Behringer D. Using Satellite-derived Sea Level and Temperature Profiles for Determining the Salinity Variability: A New Approach. J Geophys Res, 2000, 105(C4): 8537–8547CrossRefGoogle Scholar
  26. 26.
    Le Traon P Y, Rienecker M, Bahurel P, et al. Operational oceanography and prediction-a GODAE perspective. In: Solicited Papers in the Ocean Observing System for Climate, Ocean OBS, 1999. 99, session 5BGoogle Scholar
  27. 27.
    Cooper M, Haines K. Altimetric assimilation with water property conservation. J Geophys Res, 1996, 101: 1059–1077CrossRefGoogle Scholar
  28. 28.
    Alves J O S, Anderson D L T, Haines K. Sea level assimilation experiments in the Tropical Pacific. J Phys Oceanogr, 2001, 31: 305–323CrossRefGoogle Scholar
  29. 29.
    Maes C. A note on the vertical scales of temperature and salinity and their signature in dynamic height in the western Pacific Ocean: Implications for data assimilation. J Geophys Res, 1999, 104: 15575–15585CrossRefGoogle Scholar
  30. 30.
    Maes C, Behringer D, Reynolds R W, et al. Retrospective analysis of the salinity variability in the western Tropical Pacific Ocean using an indirect minimization approach. J Atmos Oceanic Technol, 2000, 17: 512–524CrossRefGoogle Scholar
  31. 31.
    Fujii Y, Kamachi M. A reconstruction of observed profiles in the Sea East of Japan using vertical coupled temperature-salinity EOF modes. J Oceanogr, 2003, 59: 173–186CrossRefGoogle Scholar
  32. 32.
    Han G, Zhu J, Zhou G. Salinity estimation using the T-S relation in the context of variational data assimilation. J Geophys Res, 2004, 109, C03018, doi:10.1029/2003JC001781CrossRefGoogle Scholar
  33. 33.
    Derber J C, Bouttier F. A reformulation of the background error covariance in the ECMWF global data assimilation system. Tellus, 1999, 51A: 195–221Google Scholar
  34. 34.
    Zhu J, Yan C. Nonlinear balance constraints in 3DVAR data assimilation. Sci China Ser D-Earth Sci, 2005, 49(3): 331–336CrossRefGoogle Scholar
  35. 35.
    Yan C, Zhu J, Li R, et al. Roles of vertical correlations of background error and T-S relations in estimation of temperature and salinity profiles from sea surface dynamic height. J Geophys Res 2004, 109, C08010, doi:10.1029/2003JC002224CrossRefGoogle Scholar
  36. 36.
    Hellerman S, Rosenstein M. Normal monthly wind stress over the world ocean with error estimates. J Phys Oceanogr, 1983, 13: 1093–1104CrossRefGoogle Scholar
  37. 37.
    Bourassa M A, Smith SR, O’Brien J J. A new FSU winds and flux climatology, 11 th Conference on Interactions of the Sea and Atmosphere, San Diego, CA. Amer Meteor Soc, 2001, 912Google Scholar
  38. 38.
    Reynolds R W, Rayner N A, Smith T M, et al. An improved in situ and satellitite SST analysis for climate. J Climate, 2002, 15: 1609–1625CrossRefGoogle Scholar
  39. 39.
    Zhang R H, Endoh M. A free surface general circulation model for the tropical Pacific Ocean. J Geophys Res, 1992, 97(C7): 11237–11255Google Scholar
  40. 40.
    Pacanowski R, Philander S G H. Parameterization of vertical mixing in numerical models of the tropical ocean. J Phys Oceanogr, 1981, 11: 1443–1451CrossRefGoogle Scholar
  41. 41.
    Levitus S, Climatological Atlas of The World Ocean. U.S. Govt Print Office, Washington, D.C., 1982. NOAA Prof Pap 13, 173Google Scholar
  42. 42.
    Haney R L. Surface thermal boundary conditions for ocean circulation models. J Phys Oceanogr, 1971, 1: 241–248CrossRefGoogle Scholar
  43. 43.
    Zhou G, Fu W, Zhu J. The impact of location dependent correlation length scales of background covariance on a ocean data assimilation system. Geophys Res Lett, 2004, 31, L21306, doi: 10.1029/2004GL020579CrossRefGoogle Scholar
  44. 44.
    You X, Zhou G, Zhu J, et al. Sea temperature data assimilation System for the China Sea and adjacent areas. Chin Sci Bulletin, 2003, 48(Supp. II): 70–76Google Scholar

Copyright information

© Science in China Press 2006

Authors and Affiliations

  • Zhu Jiang 
    • 1
    • 2
  • Zhou Guangqing 
    • 1
  • Yan Changxiang 
    • 1
  • Fu Weiwei 
    • 1
  • You Xiaobao 
    • 1
    • 3
  1. 1.International Center for Climate and Environment Sciences, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.Jiangsu Key Laboratory of Meteorological DisasterNanjing University of Information Science and TechnologyNanjingChina
  3. 3.Beijing Institute of Applied MeteorologyBeijingChina

Personalised recommendations