Ocean Dynamics

, Volume 67, Issue 12, pp 1523–1533 | Cite as

An objective algorithm for reconstructing the three-dimensional ocean temperature field based on Argo profiles and SST data

  • Chaojie Zhou
  • Xiaohua Ding
  • Jie Zhang
  • Jungang Yang
  • Qiang Ma


While global oceanic surface information with large-scale, real-time, high-resolution data is collected by satellite remote sensing instrumentation, three-dimensional (3D) observations are usually obtained from in situ measurements, but with minimal coverage and spatial resolution. To meet the needs of 3D ocean investigations, we have developed a new algorithm to reconstruct the 3D ocean temperature field based on the Array for Real-time Geostrophic Oceanography (Argo) profiles and sea surface temperature (SST) data. The Argo temperature profiles are first optimally fitted to generate a series of temperature functions of depth, with the vertical temperature structure represented continuously. By calculating the derivatives of the fitted functions, the calculation of the vertical temperature gradient of the Argo profiles at an arbitrary depth is accomplished. A gridded 3D temperature gradient field is then found by applying inverse distance weighting interpolation in the horizontal direction. Combined with the processed SST, the 3D temperature field reconstruction is realized below the surface using the gridded temperature gradient. Finally, to confirm the effectiveness of the algorithm, an experiment in the Pacific Ocean south of Japan is conducted, for which a 3D temperature field is generated. Compared with other similar gridded products, the reconstructed 3D temperature field derived by the proposed algorithm achieves satisfactory accuracy, with correlation coefficients of 0.99 obtained, including a higher spatial resolution (0.25° × 0.25°), resulting in the capture of smaller-scale characteristics. Finally, both the accuracy and the superiority of the algorithm are validated.


Three-dimensional temperature reconstruction Argo temperature profile Sea surface temperature Fitting method Vertical temperature gradient 



We would like to give thanks to the China Argo Real-Time Data Center for providing the Argo profile data product ( The study is supported by the National Key Research and Development Program of China under contract nos. 2016YFA0600102 & 2016YFC1401800; the National Natural Science Foundation of China under contract no.41576176; the Key Project of Science and Technology of Weihai under contract no. 2014DXG J14 and the Disciplinary Construction Guide Foundation of Harbin Institute of Technology at Weihai under contract no. WH20140206.


  1. Carnes MR, Teague WJ, Mitchell JL (1994) Inference of subsurface thermohaline structure from fields measurable by satellite. J Atmos Ocean Technol 11(2):551–566CrossRefGoogle Scholar
  2. Chu PC, Fralick CR, Haeger SD et al (1997a) A parametric model for the Yellow Sea thermal variability. J Geophys Res Oceans 102(C5):10499–10507CrossRefGoogle Scholar
  3. Chu PC, Tseng HC, Chang CP et al (1997b) South China Sea warm pool detected in spring from the Navy’s master oceanographic observational data set (MOODS). J Geophys Res Oceans 102(C7):15761–15771CrossRefGoogle Scholar
  4. Chu PC, Fan C, Liu WT (2000) Determination of vertical thermal structure from sea surface temperature. J Atmos Ocean Technol 17(7):971–979CrossRefGoogle Scholar
  5. Fox DN, Teague WJ, Barron CN et al (2002) The modular ocean data assimilation system (MODAS). J Atmos Oceanic Technol 15(1):22–28Google Scholar
  6. González-Pola C, Fernández-Díaz JM, Lavín A (2007) Vertical structure of the upper ocean from profiles fitted to physically consistent functional forms. Deep-Sea Res I Oceanogr Res Pap 54(11):1985–2004CrossRefGoogle Scholar
  7. Good SA, Martin MJ, Rayner NA (2013) EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J Geophys Res Oceans 118(12):6704–6716CrossRefGoogle Scholar
  8. Guinehut S, Le Traon PY, Larnicol G et al (2004) Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields—a first approach based on simulated observations. J Mar Syst 46(1):85–98CrossRefGoogle Scholar
  9. Hosoda S, Ohira T, Nakamura T (2008) A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations. Jamstec Rep Res Dev 8:47–59CrossRefGoogle Scholar
  10. Hurlburt HE (1986) Dynamic transfer of simulated altimeter data into subsurface information by a numerical ocean model. J Geophys Res Oceans 91(C2):2372–2400CrossRefGoogle Scholar
  11. Lawson C L, Hanson R J (1974) Solving least squares problems. Prentice-HallGoogle Scholar
  12. Levitus S (1982) Climatological atlas of the World Ocean. NOAA Prof Paper No 13. 64(49):173 ppGoogle Scholar
  13. Li H, Xu F, Zhou W et al (2017) Development of a global gridded Argo data set with Barnes successive corrections. J Geophys Res Oceans 122(2):866–889CrossRefGoogle Scholar
  14. Monterey G, Levitus S (1997) Seasonal variability of mixed layer depth for the world ocean. U S Gov Printing Office, Washington D C, p 96Google Scholar
  15. Nardelli BB, Santoleri R (2004) Reconstructing synthetic profiles from surface data. J Atmos Ocean Technol 21(4):693–703CrossRefGoogle Scholar
  16. Reynolds RW, Smith TM, Liu C et al (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20(22):5473–5496CrossRefGoogle Scholar
  17. Riser SC, Freeland HJ, Roemmich D et al (2016) Fifteen years of ocean observations with the global Argo array. Nat Clim Chang 6(2):145–153CrossRefGoogle Scholar
  18. Roemmich D, Gilson J (2009) The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Prog Oceanogr 82(2):81–100CrossRefGoogle Scholar
  19. Troupin C, Machín F, Ouberdous M et al (2010) High-resolution climatology of the northeast Atlantic using Data-Interpolating Variational Analysis (Diva). J Geophys Res Oceans 115(C8):20CrossRefGoogle Scholar
  20. Yan C, Zhu J, Li R et al (2004) Roles of vertical correlations of background error and TS relations in estimation of temperature and salinity profiles from sea surface dynamic height. J Geophys Res Atmos 109(8):383–402Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of MathematicsHarbin Institute of Technology at WeihaiWeihaiChina
  2. 2.The First Institute of Oceanography, State Oceanic AdministrationQingdaoChina

Personalised recommendations