Vertical structure of mesoscale eddies is key to the eddy‐induced heat/material transport that further affects the climate and marine ecosystem. This study explores the vertical structure of mesoscale eddies in the Kuroshio‐Oyashio Extension region (KOE) and its underlying dynamics. By applying the hierarchical ascending classification to the observational and reanalysis data sets, we classify mesoscale eddies with three distinct kinds of vertical structures. Each kind of eddies exhibits clear spatial aggregation along a distinct zonal band. Eddies have core depths of 100–300 m in the northern part of the KOE and core depths of 300–500 m and 0–100 m in the southern. The eddy splitting or merging does not introduce new kind of eddy vertical structure but causes large intra‐kind variability. The different kinds of eddy vertical structures can be partially accounted for by the baroclinic instabilities at the eddy generation sites and local adjustment process.

Physics–Informed Neural Networks (PINNs) have demonstrated efficacy in solving both forward and inverse problems for nonlinear partial differential equations (PDEs). However, they frequently struggle to accurately capture multiscale physical features, particularly in regions exhibiting sharp local variations such as shock waves and discontinuities, and often suffer from optimization difficulties in complex loss landscapes. To address these issues, we propose LocRes–PINN, a physics–informed neural network framework that integrates local awareness mechanisms with residual learning. This framework integrates a radial basis function (RBF) encoder to enhance the perception of local variations and embeds it within a residual backbone to facilitate stable gradient propagation. Furthermore, we incorporate a residual–based adaptive refinement strategy and an adaptive weighted loss scheme to dynamically focus training on high–error regions and balance multi–objective constraints. Numerical experiments on the Extended Korteweg–de Vries, Navier–Stokes, and Burgers equations demonstrate that LocRes–PINN reduces relative prediction errors by approximately 12% to 67% compared to standard benchmarks. The results also verify the model’s robustness in parameter identification and noise resilience.

Vertical structure of mesoscale eddies is key to the eddy-induced heat/material transport that further affects the climate and marine ecosystem. This study explores the vertical structure of mesoscale eddies in the Kuroshio-Oyashio Extension region (KOE) and its underlying dynamics. By applying the hierarchical ascending classification to the observational and reanalysis datasets, we classify mesoscale eddies with three distinct kinds of vertical structures. Each kind of eddies exhibits clear spatial aggregation along a distinct zonal band. Eddies have core depths of 100-300 m in the northern part of the KOE and core depths of 300-500 m and 0-100 m in the southern. The eddy splitting or merging does not introduce new kind of eddy vertical structure but causes large intra-kind variability. The different kinds of eddy vertical structures can be partially accounted for by the inference from the baroclinic instabilities at the eddy generation sites.

Estimating the ocean’s subsurface thermohaline information from satellite measurements is essential for understanding ocean dynamics and El Niño phenomenon. This paper proposes an improved double-output Residual Neural Network (DO-ResNet) model to concurrently estimate the ocean subsurface temperature (OST) and ocean subsurface salinity (OSS) in the tropical Western Pacific using multi-source remote sensing data, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height anomaly(SSHA), sea surface wind (SSW), and geographical information (including longitude (LON) and latitude (LAT)). In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE) and coefficient of determination (R²) were employed to evaluate the model's performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating OST (OSS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. Under the data conditions considered in this study, DO-ResNet demonstrates superior performance relative to the extreme gradient boosting model, random forest model, and artificial neural network model. Additionally, this study evaluates the model’s accuracy by comparing its estimations of OST and OSS across different depths with Argo-derived data, demonstrating the model's ability to effectively capture the most spatial features, and by comparing NRMSE across different depths and seasons, the model displays strong seasonal adaptability. In conclusion, this research introduces a novel artificial intelligence technique for estimating OST and OSS in the tropical Western Pacific Ocean.