Attention enhanced 3D-U-Net+ + ocean temperature and salinity reconstruction in the northwestern Pacific based on transfer learning
TL;DR
An attention-enhanced three dimensional U-Net++ model is proposed, which reconstructs daily T–S fields using real-time available sea surface temperature (SST) and sea surface height (SSH) data, and shows that the reconstructed T–S fields agree better with in-situ T–S profiles from World Ocean Database than previous studies, suggesting that the proposed approach is reliable and accurate for subsurface ocean field reconstruction.
Abstract
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).