Table of Contents
International Journal of Oceanography
Volume 2009 (2009), Article ID 167239, 13 pages
Research Article

Forecasts of Tropical Pacific Sea Surface Temperatures by Neural Networks and Support Vector Regression

1Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada V6T 1Z1
2Department of Earth and Ocean Sciences, University of British Columbia, 6339 Stores Road, Vancouver, BC, Canada V6T 1Z4

Received 24 May 2009; Accepted 28 September 2009

Academic Editor: Lakshmi Kantha

Copyright © 2009 Silvestre Aguilar-Martinez and William W. Hsieh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Two nonlinear regression methods, Bayesian neural network (BNN) and support vector regression (SVR), and linear regression (LR), were used to forecast the tropical Pacific sea surface temperature (SST) anomalies at lead times ranging from 3 to 15 months, using sea level pressure (SLP) and SST as predictors. Datasets for 1950–2005 and 1980–2005 were studied, with the latter period having the warm water volume (WWV) above the isotherm integrated across the equatorial Pacific available as an extra predictor. The forecasts indicated that the nonlinear structure is mainly present in the second PCA (principal component analysis) mode of the SST field. Overall, improvements in forecast skills by the nonlinear models over LR were modest. Although SVR has two structural advantages over neural network models, namely (a) no multiple minima in the optimization process and (b) an error norm robust to outliers in the data, it did not give better overall forecasts than BNN. Addition of WWV as an extra predictor generally increased the forecast skills slightly; however, the influence of WWV on SST anomalies in the tropical Pacific appears to be linear.