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.

[1]  Antonio J. Busalacchi,et al.  The roles of vertical mixing, solar radiation, and wind stress in a model simulation of the sea surface temperature seasonal cycle in the tropical Pacific Ocean , 1994 .

[2]  Hua Su,et al.  Retrieving Ocean Subsurface Temperature Using a Satellite‐Based Geographically Weighted Regression Model , 2018, Journal of Geophysical Research: Oceans.

[3]  Thomas M. Smith,et al.  Daily High-Resolution-Blended Analyses for Sea Surface Temperature , 2007 .

[4]  Fan Wang,et al.  Deep-reaching thermocline mixing in the equatorial pacific cold tongue , 2016, Nature Communications.

[5]  V. V. Efimov,et al.  Seasonal instability of Pacific sea-surface-temperature anomalies , 1997 .

[6]  Peter M. Atkinson,et al.  Looking back and looking forwards: Historical and future trends in sea surface temperature (SST) in the Indo-Pacific region from 1982 to 2100 , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Gilles Reverdin,et al.  Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and -2 , 2000 .

[8]  Xiao‐Hai Yan,et al.  Estimation of subsurface temperature anomaly in the Indian Ocean during recent global surface warming hiatus from satellite measurements: A support vector machine approach , 2015 .

[9]  Wei Zhou,et al.  Development of a global gridded Argo data set with Barnes successive corrections: A NEW GLOBAL GRIDDED ARGO DATA SET , 2017 .

[10]  Hua Su,et al.  Retrieving Temperature Anomaly in the Global Subsurface and Deeper Ocean From Satellite Observations , 2018 .

[11]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[12]  Wenhui Xu,et al.  The strong El Niño of 2015/16 and its dominant impacts on global and China's climate , 2016, Journal of Meteorological Research.

[13]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[14]  Masayoshi Ishii,et al.  Steric sea level changes estimated from historical ocean subsurface temperature and salinity analyses , 2006 .

[15]  A. Timmermann,et al.  Increasing frequency of extreme El Niño events due to greenhouse warming , 2014 .

[16]  Kevin E. Trenberth,et al.  Distinctive climate signals in reanalysis of global ocean heat content , 2013 .

[17]  W. Timothy Liu,et al.  Estimation of Subsurface Temperature Anomaly in the North Atlantic Using a Self-Organizing Map Neural Network , 2012 .

[18]  Gilles Larnicol,et al.  Combining Argo and remote-sensing data to estimate the ocean three-dimensional temperature fields—a first approach based on simulated observations , 2004 .

[19]  Hua Su,et al.  Subsurface temperature estimation from remote sensing data using a clustering-neural network method , 2019, Remote Sensing of Environment.

[20]  Bin Wang,et al.  Formation Mechanism for 2015/16 Super El Niño , 2017, Scientific Reports.

[21]  M. M. Ali,et al.  Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach , 2004 .

[22]  Makarand Deo,et al.  Prediction of Sea Surface Temperature by Combining Numerical and Neural Techniques , 2016 .

[23]  Hidekatsu Yamazaki,et al.  A Method to Estimate Three-Dimensional Thermal Structure from Satellite Altimetry Data , 2009 .

[24]  Ruiqiang Ding,et al.  Decadal and seasonal dependence of North Pacific sea surface temperature persistence , 2009 .

[25]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[26]  Agus Santoso,et al.  Increased variability of eastern Pacific El Niño under greenhouse warming , 2018, Nature.

[27]  Hua Su,et al.  Estimating Subsurface Thermohaline Structure of the Global Ocean Using Surface Remote Sensing Observations , 2019, Remote. Sens..