Data-Driven Multidecadal Reconstruction and Nowcasting of Coastal and Offshore 3-D Sea Temperature Fields from Satellite Observations: A Case Study in the East/Japan Sea

Understanding ocean temperature structure and its spatiotemporal variability is essential for studying ocean circulation, climate, and marine ecosystems. While previous approaches using observations and numerical models have advanced our understanding, they face limitations such as sparse data coverage and computational bias. To address these issues, we developed an ensemble of data-driven neural network models trained with in situ vertical profiles and daily remote sensing inputs. Unlike previous studies that were limited to open-ocean regions, our model explicitly included coastal areas with complex bathymetry. The model was applied to the East/Japan Sea and reconstructed 31 years (1993–2023) of daily three-dimensional ocean temperature fields at 13 standard depths. The predictions were validated against observations, showing RMSE < 1.33 °C and bias < 0.10 °C. Comparisons with previous studies confirmed the model’s ability to capture short- to mid-term temperature variations. This data-driven approach demonstrates a robust alternative to traditional methods and offers an applicable and reliable tool for understanding long-term ocean variability in marginal seas.