Articles | Volume 22, issue 4
https://doi.org/10.5194/os-22-2161-2026
https://doi.org/10.5194/os-22-2161-2026
Research article
 | 
16 Jul 2026
Research article |  | 16 Jul 2026

TS-Cast: deep learning for subsurface ocean reconstruction from satellite observations in the northwestern Pacific

Jeong-Yeob Chae, Kathleen A. Donohue, and Jae-Hun Park

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Cited articles

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Chen, Y., Liu, L., Chen, X., Wei, Z., Sun, X., Yuan, C., and Gao, Z.: Data driven three-dimensional temperature and salinity anomaly reconstruction of the northwest Pacific Ocean, Front. Mar. Sci., 10, 1121334, https://doi.org/10.3389/fmars.2023.1121334, 2023. a
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 424–432, Springer, https://doi.org/10.48550/arXiv.1606.06650, 2016. a
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Short summary
We introduce TS (Temperature-Salinity)-Cast, a novel deep neural network that reconstructs subsurface thermohaline structures from satellite observations. Validated against independent time-series data, TS-Cast achieves root mean squared errors of < 1 °C and < 0.1 psu in the upper 500 m of the Kuroshio Extension, comparable or surpassing data-assimilated numerical models. Critically, we demonstrate that the physical limitations of the input satellite data fundamentally constrain the model's predictive skill.
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