This study proposed a neural-network-based model to estimate the ocean vertical water temperature from the surface temperature in the northwest Pacific Ocean. The performance of the model and the sources of errors were assessed using the Gridded Argo dataset including 576 stations with 26 vertical levels from surface (0 m)–2,000 m over the period of 2007–2009. The parameter selection, model building, stability of the neural network were also investigated. According to the results, the averaged root mean square error (RMSE) of estimated temperature was 0.7378 °C and the correlation coefficient R was 0.9967. More than 67% of the estimates from the four selected months (January, April, July and October) lay within ± 0.5 °C. When counting with errors lower than ± 1°C, the lowest percentage was 83%.
1. Nardelli B.B. and Santoleri R.: Methods for the reconstruction of vertical profiles from surface data: Multivariate analyses residual GEM and variable temporal signals in the North Pacific Ocean, J. Atmos. Ocean. Technol., 22 (11), 1762-1781, 2005.
2. Swain D., Ali M.M., Weller R.A.: Estimation of mixed layer depth from surface parameters, J. Mar. Res., 64, 745-758, 2006.
3. Ballabrera-Poy J., Mourre B., Garcia-Ladona E., et al.: Linear and non-linear T-S models for the eastern North Atlantic from Argo data: Role of surface salinity observations, Deep- Sea Res. I, 56, 1605-1614, 2009.
4. AVISO: Ssalto/Duacs User handbook: (M)SLA and (M) ADT Near-Real Time and Delayed Time Product. Ref: CLS-DOS-NT-O6-034, 39-41, 2012.
5. Cressie N.: The Origins of Kriging, Math. Geol., 22(3), 239-252, 1990.
6. MyOcean: MyOcean catalogue of products v2.1: the ocean in one click, 2012.
7. Elsken T.: Even on finite test sets smaller nets may perform better, Neural Netw., 10(2), 369-385, 1997.
8. Dombi G.W.; Nandi P.; Saxe J.M., et al.: Prediction of rib fracture injury outcome by an artificial neural network. J. Trauma, 39(5), 915-921, 1995.
9. Li Z.D. and Sun W.: A new method of calculate weights of attributes in spectral clustering Algorithms, IEEE: Int. Conf. Inf. Technol. Comput. Eng. Manag. Sci., 8-60, 2011.