An objective algorithm for reconstructing the three-dimensional ocean temperature field based on Argo profiles and SST data
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.
KeywordsThree-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 (http://www.argo.org.cn/). 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.
- 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
- Lawson C L, Hanson R J (1974) Solving least squares problems. Prentice-HallGoogle Scholar
- Levitus S (1982) Climatological atlas of the World Ocean. NOAA Prof Paper No 13. 64(49):173 ppGoogle Scholar
- 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
- 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