Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites

Hao Cheng,Jiagen Li,Liang Sun

doi:10.3390/w13030388

Abstract

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

Highlights

The datasets provided by satellite remote sensing have promoted ocean research.these datasets are mostly confined to the sea surface because it is difficult for electromagnetic radiation to reach the interior of the ocean [1]
We introduced a backpropagation neural network (BPNN) method to estimate the internal temperature structure of the North Pacific Ocean (NPO) based on sea surface parameters (SSH, sea surface temperature (SST), sea surface salinity (SSS), sea surface wind (SSW) and sea surface velocity (SSV))
The estimated temperature field at each depth was verified by the observed temperature field and the accuracy of the estimation by the model was determined using the mean square error (MSE) and R2 values

Summary

Introduction

The datasets provided by satellite remote sensing have promoted ocean research These datasets are mostly confined to the sea surface because it is difficult for electromagnetic radiation to reach the interior of the ocean [1]. We need to obtain more information about the interior of the ocean because most of the important oceanographic phenomena exist below the surface and these phenomena are useful in studying both the characteristics of the ocean and global climate change [5,6,7,8]. The air–sea interactions caused by the thermal difference between the land and the sea can affect regional and global large-scale circulation systems, leading to disastrous weather and climate events, such as storm surges [18,19,20,21] and super typhoons [22,23,24]. Model results have shown that warming of the upper ocean may be associated with some climate events, such as

Results

Discussion

Conclusion