A Convolutional Neural Network Using Surface Data to Predict Subsurface Temperatures in the Pacific Ocean

This paper proposes a convolutional neural network (CNN) method to estimate subsurface temperature (ST) in the Pacific Ocean from a suite of satellite remote sensing measurements. These include sea surface temperature(SST), sea surface height (SSH), and sea surface salinity (SSS). We propose using the multisource sea surface parameters to establish a monthly CNN model to reconstruct the ocean subsurface temperature (ST) and use Argo data for accurate validation. The results show that the CNN can accurately estimate the ST of the Pacific Ocean by using the model. We trained the model for 12 months. The most prominent months are January, April, July, and October with average mean square error (MSE) values of 0.2659, 0.3129, 0.5318, and 0.5160, and the average coefficients of determination (R²) were 0.968, 0.971, 0.949, and 0.967, respectively. This study improves the accuracy of ST estimation and the good results based on reanalysis indicate that the model is promising to be applied to satellite observations.

Abstract licence: CC BY-NC-ND

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