TS-Cast: Deep Learning for Subsurface Ocean Reconstruction from Satellite Observations in the Northwestern Pacific

Since the 1990s, satellite observations have been providing reliable estimates of the ocean surface states, including absolute dynamic topography (ADT), sea surface temperature (SST), and sea surface salinity (SSS) at sufficient space and time scales to characterize ocean dynamics. Together with the extensive hydrographic dataset from Argo and ship-based hydrographic profiles, these measurements provide a comprehensive view of oceanic conditions. While ADT represents integrated information for subsurface water properties, it is challenging to relate SST, SSS, and ADT with subsurface water profiles due to their complex spatial and temporal variations. To address this issue, we introduce a novel deep neural network, the thermohaline profile estimating network termed TS-Cast. Sourcing from monthly climatological profiles, TS-Cast is designed to adjust these profiles to align with satellite-measured SST, SSS, and ADT data, by training with approximately 150,000 Argo and ship-based thermohaline profiles in the northwestern Pacific. TS-Cast’s excellent capability is demonstrated by comparisons with independent time series data from moorings that measured temperature and salinity or vertical acoustic travel time. The network produces water-property profiles with accuracy that is comparable to, or even surpasses that of data-assimilated numerical models. This work provides a powerful tool for ocean monitoring and introduces a validation framework. Critically, this framework reveals not only the model's high fidelity but also demonstrates that its predictive skill is fundamentally constrained by the physical information content and limitations of the input satellite data, thereby establishing a clear link between AI performance and observational physics.