Articles | Volume 18, issue 7
https://doi.org/10.5194/essd-18-4617-2026
https://doi.org/10.5194/essd-18-4617-2026
Data description article
 | 
06 Jul 2026
Data description article |  | 06 Jul 2026

Attention enhanced 3D-U-Net+ +  ocean temperature and salinity reconstruction in the northwestern Pacific based on transfer learning

Hao Wang, Linlin Zhang, Shuguo Yang, Xiaomei Yan, and Zhen Li

Cited articles

Ali, M. M., Swain, D., and Weller, R. A.: Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach, Geophys. Res. Lett., 31, https://doi.org/10.1029/2004gl021192, 2004. 
AVISO+: Global Ocean Gridded L 4 Sea Surface Heights And Derived Variables Reprocessed 1993 Ongoing, Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00148, 2026. 
Bellucci, A., Masina, S., DiPietro, P., and Navarra, A.: Using Temperature–Salinity Relations in a Global Ocean Implementation of a Multivariate Data Assimilation Scheme, Mon. Weather Rev., 135, 3785–3807, https://doi.org/10.1175/2007MWR1821.1, 2007. 
Chen, Y., Bao, S., Cao, Y., Zhang, W., and Wang, H.: The Yin-He Global Ocean Data Assimilation and Forecast System, Ocean-Land-Atmosphere Research, 4, 0121, https://doi.org/10.34133/olar.0121, 2025. 
Chen, Z., Wang, P., Bao, S., and Zhang, W.: Rapid reconstruction of temperature and salinity fields based on machine learning and the assimilation application, Frontiers in Marine Science, 9, https://doi.org/10.3389/fmars.2022.985048, 2022. 
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Short summary
This study develops a new method to reconstruct daily three-dimensional ocean temperature and salinity fields in the northwestern Pacific using only real-time sea surface temperature and height data. By combining deep learning and attention mechanisms, the approach captures complex vertical structures and temporal changes. The results provide more accurate and consistent subsurface information, helping improve ocean monitoring and climate research.
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