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Attention enhanced 3D-U-Net+ + ocean temperature and salinity reconstruction in the northwestern Pacific based on transfer learning
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DOI：10.5194/essd-18-4617-2026.png)
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Abstract
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Abstract. Real-time and accurate three-dimensional ocean temperature–salinity (T–S) field are of great significance for a deeper understanding of ocean dynamics and prediction skill improvement of numerical models. However; current ocean observations; especially those below the sea surface; still suffer from significant limitations in temporal and spatial resolution. Several neural network methods using multi-source satellite data for underwater temperature and salinity reconstruction have been proposed; achieving real-time temperature and salinity reconstruction; but their biases relative to in-situ observations are still significant. This study focuses on the northwestern Pacific region (0–40° N; 120–160° E) and proposes an attention-enhanced three dimensional U-Net ++ model; which reconstructs daily T–S fields (26 layers; 1/4 ° resolution; 5–2000 m depth) using real-time available sea surface temperature (SST) and sea surface height (SSH) data. The model introduces cross-scale feature aggregation and selective information gating; allowing it to emphasize temporally coherent surface features most relevant to subsurface variability; while suppressing noise propagation and over-smoothing. By integrating 26 consecutive days of SST and SSH as inputs; the model effectively alleviates the underdetermined problem of mapping limited surface observations to full-depth structures. In addition; a two-stage transfer learning strategy is employed: the model is first pretrained using monthly SST/SSH data and the gridded Argo data to learn observation-dominated low-frequency spatiotemporal patterns; and then fine-tuned using daily SST/SSH data and the high-resolution reanalysis to capture the meso-scale dynamic processes. Evaluation results show that the reconstructed T–S fields agree better with in-situ T–S profiles from World Ocean Database than previous studies; both during the validation period and in long-term statistical analyses; suggesting that the proposed approach is reliable and accurate for subsurface ocean field reconstruction. The reconstructed T–S field is available at https://doi.org/10.57760/sciencedb.31950 (Wang et al.; 2025).
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