Temperature inversion of mesoscale eddies from satellite observations in the South China Sea

Abstract

As a common oceanic phenomenon, Mesoscale eddies play a significant role in oceanic heat exchange and energy transfer in the South China Sea. However, satellite remote sensing is primarily utilized for measuring surface ocean data, posing challenges in detecting subsurface eddies. This study proposes a data-driven deep learning approach to address the deficiency of subsurface oceanic data. This method leverages satellite surface data to establish a mapping model between surface satellite data and subsurface temperature. The proposed Unet-Eddy model can successfully invert the threedimensional temperature of mesoscale eddies at depths of 0-1000 m. Experimental results demonstrate the potential of this approach in inverting the three-dimensional subsurface temperature of mesoscale eddies and partially compensating for the shortage of internal oceanic data.