• Allen, D., 1974: The relationship between variable selection and data augmentation and a method for prediction. Technometrics,16, 125–127.

  • Barnston, A. G., and H. M. van den Dool, 1993: A degeneracy in cross-validation skill in regression-based forecasts. J. Climate,6, 963–977.

  • ——, and Coauthors, 1994: Long-lead seasonal forecasting—Where do we stand? Bull. Amer. Meteor. Soc.,75, 2097–2114.

  • ——, M. H. Glantz, and Y. He, 1999: Predictive skill of statistical and dynamical climate models in SST forecasts during the 1997–98 El Niño episode and the 1998 La Niña onset. Bull. Amer. Meteor. Soc.,80, 217–243.

  • Beran, R. J., 1997: Diagnosing bootstrap success. Ann. Inst. Stat. Math.,49, 1–24.

  • Bishop, C. M., 1995: Neural Networks for Pattern Recognition. Clarendon Press, 482 pp.

  • Cichocki, A., and R. Unbehauen, 1993: Neural Networks for Optimization and Signal Processing. John Wiley and Sons, 526 pp.

  • Davison, A. C., and D. V. Hinkley, 1997: Bootstrap Methods and their Application. Cambridge University Press, 582 pp.

  • Efron, B., 1992: Jackknife-after-bootstrap standard errors and influence functions. J. Roy. Stat. Soc.,B54, 83–127.

  • ——, and R. J. Tibshirani, 1993: An Introduction to the Bootstrap. Chapman & Hall, 436 pp.

  • Enfield, D. B., 1989: El Niño, past and present. Rev. Geophys.,27, 159–187.

  • Golub, G. H., M. Heath, and G. Wahba, 1979: Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics,21, 215–223.

  • Goswami, P., and Srividya, 1996: A novel neural network design for long range prediction of rainfall pattern. Curr. Sci.,70, 447–457.

  • Haber, E., and D. W. Oldenburg, 2000: A GCV based method for nonlinear ill-posed problems. Comput. Geosci.,4, 41–63.

  • Hastenrath, S., L. Greischar, and J. van Heerden, 1995: Prediction of the summer rainfall over South Africa. J. Climate,8, 1511–1518.

  • Keppenne, C. L., and M. Ghil, 1992: Adaptive filtering and prediction of the Southern Oscillation Index. J. Geophys. Res.,97, 20 449–20 454.

  • Knaff, J. A., and C. W. Landsea, 1997: An El Niño–Southern Oscillation climatology and persistence (CLIPER) forecasting scheme. Wea. Forecasting,12, 633–652.

  • Michaelsen, J., 1987: Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor.,26, 1589–1600.

  • Navone, H. D., and H. A. Ceccatto, 1994: Predicting Indian monsoon rainfall—A neural network approach. Climate Dyn.,10, 305–312.

  • Penland, C., 1989: Random forcing and forecasting using Principal Oscillation Pattern analysis. Mon. Wea. Rev.,117, 2165–2185.

  • ——, and T. Magorian, 1993: Prediction of Niño 3 sea surface temperatures using linear inverse modeling. J. Climate,6, 1067–1076.

  • ——, L. Matrosova, K. Weickmann, and C. Smith, 1999: Forecast of Tropical SSTs using linear inverse modeling (LIM). Exp. Long-Lead Forecast Bull.,8, 38–41.

  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analysis using optimum interpolation. J. Climate,7, 929–948.

  • Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev.,115, 1606–1626.

  • Saunders, A., M. Ghil, and J. D. Neelin, 1999: Forecasts of Niño 3 SST anomalies and SOI based on singular spectrum analysis combined with the maximum entropy method. Exp. Long-Lead Forecast Bull.,8, 39–41.

  • Smith, T. M., R. W. Reynolds, R. E. Livezey, and D. C. Stokes, 1996:Reconstruction of historical sea surface temperatures using orthogonal functions. J. Climate,9, 1403–1420.

  • Syu, H., and J. D. Neelin, 1999: Prediction of NINO3 SST anomaly in a hybrid coupled model with a piggy-back data assimilation initialization. Exp. Long-Lead Forecast Bull.,8, 10–12.

  • Tang, B., W. W. Hsieh, A. H. Monahan, and F. T. Tangang, 2000: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. J. Climate,13, 287–293.

  • Tangang, F. T., W. W. Hsieh, and B. Tang, 1998a: Forecasting the regional sea surface temperature of the tropical Pacific by neural network models, with wind stress and sea level pressure as predictors. J. Geophys. Res.,103, 7511–7522.

  • ——, B. Tang, A. H. Monahan, and W. W. Hsieh, 1998b: Forecasting ENSO events: A neural network–extended EOF approach. J. Climate,11, 29–41.

  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc.,78, 2771–2777.

  • Wahba, G., 1990: Spline Models for Observational Data. SIAM, 169 pp.

  • Ward, M. N., and C. K. Folland, 1991: Prediction of seasonal rainfall in the north Nordeste of Brazil using eigenvectors of sea-surface temperature. Int. J. Climatol.,11, 711–743.

  • Weare, B. C., and J. S. Nasstrom, 1982: Examples of extended empirical orthogonal function analysis. Mon. Wea. Rev.,110, 481–485.

  • Woodruff, S. D., R. J. Slutz, R. L. Jenne, and P. M. Steurer, 1987: A comprehensive ocean–atmosphere data set. Bull. Amer. Meteor. Soc.,68, 1239–1250.

  • Yuval, 2000: Neural network training for prediction of climatological time series, regularized by minimization of the generalized cross-validation function. Mon. Wea. Rev.,128, 1456–1473.

  • Zebiak, S. E., M. A. Cane, and D. Chen, 1999: Forecast of Tropical Pacific SST using a simple ocean–atmosphere dynamical model. Exp. Long-Lead Forecast Bull.,8, 1–5.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 116 116 103
PDF Downloads 11 11 0

Enhancement and Error Estimation of Neural Network Prediction of Niño-3.4 SST Anomalies

View More View Less
  • 1 Department of Earth and Ocean Sciences, University of British Columbia, Vancouver, British Columbia, Canada
© Get Permissions
Restricted access

Abstract

A procedure to enhance neural network (NN) predictions of tropical Pacific sea surface temperature anomalies and calculating their estimated errors is presented. A simple linear correction enables more accurate predictions of warm and cold events but can result in introduction of larger errors in other cases. The prediction error estimates aid recognizing erroneously magnified anomalies and are used to sort the predictions into El Niño, La Niña, and neutral states. The error estimation process is based on bootstrap resamplings of the data and construction of a large number of bootstrap prediction replicas. A statistic calculated on the set of bootstrap replicas that corresponds to each of the actual predictions is used to estimate the prediction’s errors. The method is demonstrated on NN prediction of the Niño-3.4 index.

Corresponding author address: Yuval, Dept. of Earth and Ocean Sciences, University of British Columbia, 1461-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada.

Email: yuval@ocgy.ubc.ca

Abstract

A procedure to enhance neural network (NN) predictions of tropical Pacific sea surface temperature anomalies and calculating their estimated errors is presented. A simple linear correction enables more accurate predictions of warm and cold events but can result in introduction of larger errors in other cases. The prediction error estimates aid recognizing erroneously magnified anomalies and are used to sort the predictions into El Niño, La Niña, and neutral states. The error estimation process is based on bootstrap resamplings of the data and construction of a large number of bootstrap prediction replicas. A statistic calculated on the set of bootstrap replicas that corresponds to each of the actual predictions is used to estimate the prediction’s errors. The method is demonstrated on NN prediction of the Niño-3.4 index.

Corresponding author address: Yuval, Dept. of Earth and Ocean Sciences, University of British Columbia, 1461-6270 University Boulevard, Vancouver, BC V6T 1Z4, Canada.

Email: yuval@ocgy.ubc.ca

Save