World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

Robust Nonlinear Canonical Correlation Analysis: Application to Seasonal Climate Forecasting : Volume 15, Issue 1 (27/02/2008)

By Cannon, A. J.

Click here to view

Book Id: WPLBN0003985694
Format Type: PDF Article :
File Size: Pages 12
Reproduction Date: 2015

Title: Robust Nonlinear Canonical Correlation Analysis: Application to Seasonal Climate Forecasting : Volume 15, Issue 1 (27/02/2008)  
Author: Cannon, A. J.
Volume: Vol. 15, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Hsieh, W. W., & Cannon, A. J. (2008). Robust Nonlinear Canonical Correlation Analysis: Application to Seasonal Climate Forecasting : Volume 15, Issue 1 (27/02/2008). Retrieved from

Description: Meteorological Service of Canada, Environment Canada, 201-401 Burrard Street, Vancouver, BC V6C 3S5, Canada. Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae).

Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.

Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

Barnett, T P. and Preisendorfer, R.: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis, Mon. Weather Rev., 115, 1825–1850, 1987.; Barnston, A G. and Ropelewski, C F.: Prediction of ENSO episodes using canonical correlation-analysis, J. Climate, 5, 1316–1345, 1992.; Bishop, C M.: Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 504 pp., 1995.; Cannon, A J.: Nonlinear principal predictor analysis: Application to the Lorenz system, J. Climate, 19, 579–589, 2006.; Glahn, H R.: Canonical correlation and its relationship to discriminant analysis and multiple regression, J. Atmos. Sci., 25, 23–31, 1968.; Hanson, S J. and Burr, D J.: Minkowski-r back-propagation: Learning in connectionist models with non-Euclidean error signals, Neural Information Processing Systems, American Institute of Physics, 348–357, 1988.; Hou, Z. and Koh, T S.: Image denoising using robust regression, IEEE Signal Proc. Lett., 11, 243–246, 2004.; Hsieh, W W.: Nonlinear canonical correlation analysis by neural networks, Neural Networks, 13, 1095–1105, 2000.; Hsieh, W W.: Nonlinear canonical correlation analysis of the tropical Pacific climate variability using a neural network approach, J. Climate, 14, 2528–2539, 2001.; Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, 1996.; Lai, P L. and Fyfe, C.: A neural implementation of canonical correlation analysis, Neural Networks, 12, 1391–1397, 1999.; Lai, P L. and Fyfe, C.: Kernel and nonlinear canonical correlation analysis, Int. J. Neural. Syst., 10, 365–377, 2000.; Lax, D A.: Robust estimators of scale: Finite-sample performance in long-tailed symmetric distributions, J. Am. Stat. Assoc., 80, 736–741, 1985.; McPhaden, M J., Zhang, X B., Hendon, H H., and Wheeler, M C.: Large scale dynamics and MJO forcing of ENSO variability, Geophys. Res. Lett., 33, L16702, doi:10.1029/2006GL026786, 2006.; Melzer, T., Reiter, M., and Bischof, H.: Appearance models based on kernel canonical correlation analysis, Pattern Recogn., 36, 1961–1971, 2003.; Monahan, A H.: Nonlinear principal component analysis: Tropical Indo-Pacific sea surface temperature and sea level pressure, J. Climate, 14, 219–233, 2001.; Panayiotis, C A., Charalambous, C., and Martzoukos, S H.: Robust artificial neural networks for pricing of European options, Computational Economics, 27, 329–351, 2006.; Shabbar, A. and Barnston, A G.: Skill of seasonal climate forecasts in Canada using canonical correlation analysis, Mon. Weather Rev., 124, 2370–2385, 1996.; Shawe-Taylor, J. and Cristianini, N.: Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, 2004.; Smith, T M. and Reynolds, R W.: Improved extended reconstruction of SST (1854–1997), J. Climate, 17, 2466–2477, 2004.; Suykens, J. A K., Van~Gestel, T., De~Brabanter, J., De~Moor, B., and Vandewalle, J.: Least Squares Support Vector Machines, World Scientific, Singapore, 318 pp., 2002.; von Storch, H. and Zwiers, F W.: Statistical Analysis in Climate Research, Cambridge University Press, 484 pp., 1999.; Wilcox, R R.: Introduction to Robust Estimation and Hypothesis Testing, 2nd. Ed., Academic Press, 608 pp., 2004.; Wu, A. and Hsieh, W W.: Nonlinear canonical correlation analysis of the tropical Pacific wind stress and sea surface temperature, Clim. Dynam., 19, 713–722, 2002.; Wu, A. and Hsieh, W W.: Nonlinear interdecadal changes of the El Niño-Southern Oscillation, Clim. Dynam., 21


Click To View

Additional Books

  • A Comparison of Assimilation Results fro... (by )
  • Impulse Exchange at the Surface of the O... (by )
  • Critical Behavior in Earthquake Energy D... (by )
  • Size Distribution and Structure of Barch... (by )
  • Emergent Behavior in a Coupled Economic ... (by )
  • Response of an Ocean-atmosphere Climate ... (by )
  • On the Origin of Time-dependent Behaviou... (by )
  • Corrigendum to Number-average Size Model... (by )
  • Power Law Statistics of Force and Acoust... (by )
  • Recurrent Frequency-size Distribution of... (by )
  • Using Sparse Regularization for Multires... (by )
  • Calculation of the Parameter Deflection ... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Fair are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.