Knowledge-Informed Deep Learning Model for Subsurface Thermohaline Reconstruction From Satellite Observations

Abstract

3-D ocean temperature and salinity data are the basis for studying ocean dynamic processes and warming. Satellite remote sensing observations on the ocean surface are abundant and full-coverage, while in situ observations in the ocean interior are very sparse and unevenly distributed. Currently, the remote sensing inversion models of temperature and salinity in the ocean interior are unable to learn both global and local detail information, and modeling layer-by-layer blocks the connection between vertical depth levels, resulting in poor accuracy. In this study, we proposed a novel clustering-guided and knowledge-distillation network (CGKDN) model based on the ocean knowledge-driven model. The model introduced K-means clustering for the partitions of ocean processes, knowledge distillation (KD) fusing global and local detail information, and adaptive depth gradient loss linking the vertical depth dimension, which enhanced the interpretability and accuracy of the model. Comparison of the reconstructions with the existing major publicly available datasets through the validation of 10% EN4 in situ profile observations from 2001 to 2020 reveals that the reconstructions are more accurate. Concretely, the average root mean square error (RMSE) (°C) across time-series and vertical levels of CGKDN/Institute of Atmospheric Physics (IAP)/OCEAN5 ocean analysis-reanalysis (ORAS5)/deep ocean remote sensing (DORS) ocean subsurface temperature (OST) is 0.590/0.598/0.690/0.723, and the average RMSE (PSU) of CGKDN/IAP/ORAS5 ocean subsurface salinity (OSS) is 0.101/0.103/0.106, respectively. Furthermore, the downscaled quarter-degree reconstructions present more mesoscale detail signals, consistent with the ARMOR3D data. This study not only improves the estimation accuracy of subsurface temperature and salinity but also serves the study of ocean interior dynamic processes and variabilities and provides valuable references for reconstructing other ocean subsurface physical variables.