Reconstructing 3D ocean subsurface salinity (OSS) from T–S mapping via a data-driven deep learning model

Abstract

Despite the increasing volume of oceanographic data, the available ocean salinity data remains patently insufficient which limits studying ocean dynamics, climate change and the calculation of salinity-related ocean elements. Considering that traditional salinity reconstruction methods often suffer from various factors such as additional constraints, priori physical assumptions and large of specific regression coefficients, a generative adversarial networks (GANs) based deep learning (DL) framework is proposed to directly construct a near real-time, high-resolution daily three-dimensional ocean subsurface salinity (3D OSS) dataset from a data-driven perspective in this study. Four models with different structural combinations are designed in the China’s marginal seas. Experimental results demonstrate that the models can successfully reconstruct the high-precision and high-resolution 3D OSS on a daily scale in 12 depth levels (from 2 m to 200 m). The asymmetric inception-3DGAN model with all enhanced structures has the highest accuracy, the average root-mean-squared error (RMSE) is 0.135psu, the average coefficient of determination (R2) is 0.5641 and the percent bias is 0.436%. Comparing with model without enhanced structures, the average RMSE is decreased by 22.41% when adding all the enhancement structures. Besides, temporal and spatial error analysis are also conducted to evaluate the models’ performance from different aspects. Finally, the model results are used to analyze common ocean elements, such as water mass properties, dynamic fields and geostrophic velocity fields, demonstrating that the 3D OSS dataset construction approach proposed in this study not only provides some new insights into ocean observations, but also has high application values.