Forecasting regional sea surface temperatures in the tropical Pacific by neural network models, with wind stress and sea level pressure as predictors

Journal of Geophysical Research: Oceans

Volumn 103, Issue 3334, 1998, Pages 7511-7522

  Tangang, Fredolin T ^a,b   Hsieh, William W ^a   Tang, Benyang ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORK; SEA LEVEL PRESSURE; SEA SURFACE TEMPERATURE; WIND STRESS;

(TROPICAL); PACIFIC;

EID: 0032522032     PISSN: None     EISSN: 21699291     Source Type: Journal    
DOI: 10.1029/97jc03414     Document Type: Article

References (30)

1. Balmaseda, M.A., M.K. Davey, and D.L. Anderson, Decadal and seasonal dependence of ENSO prediction skill, J. Clim., 8, 2705-2715, 1995.
2. Barnett, T.P., Prediction of the EL Niño of 1982-83, Mon. Weather Rev., 112, 1043-1047, 1984.
3. Barnston, A., et al., Long-lead seasonal forecasts: Where do we stand?, Bull. Am. Meteorol. Soc., 75, 2097-2114, 1994.
4. Barnston, A., and C.F. Ropelewski, Prediction of ENSO episodes using canonical correlation analysis. J. Clim., S, 1316-1345, 1992.
5. Chauvin, Y., Generalization performance of overtrained backpropagation, in Proceedings of Eurasip Workshop on Neural Networks, edited by L.B. Almedia and C.J. Wellekens, pp. 46-55, Springer-Verlag, New York, 1989.
6. Chen, D., S.E. Zebiak, A.J. Busalacchi, and M.A. Cane, An improved procedure for El Niño forecasting: Implications for predictability, Science, 269, 1699-1702, 1995.
7. Derr, V.E., and R.J. Slutz, Prediction of El Niño events in the Pacific by means of neural networks, AI Appl., 8(2), 51-63, 1994.
8. Graham, N.E., J. Michaelsen, and T.P. Barnett, An investigation of the El Niño-Southern Oscillation cycle with statistical models, 1, Predictor field characteristics, J. Geophys. Res., 92(C13), 14,251-14,270, 1987a.
9. Graham, N.E., J. Michaelsen, and T.P. Barnett, An investigation of the El Niño-Southern Oscillation cycle with statistical models, 2, Model results, J. Geophys. Res., 92(C13), 14,271-14,290, 1987b.
10. Grieger, B., and M. Latif, Reconstruction of the El Niño attractor with neural networks, Clim. Dyn., 10, 267-276, 1994.
11. Kolen, J.I., and J.B. Pollack, Backpropagation is sensitive to initial conditions, in Advances in Neural Information Processing System, vol. 3, edited by R. Lippmannn et al., pp. 860-867, Morgan Kaufmann, San Francisco. Calif., 1991.
12. Latif, M., et al., A review of ENSO prediction studies, Clim. Dyn., 9, 167-179, 1994.
13. Michaelsen, J., Cross-validation in statistical climate forecast models, J. Clim. Appl. Meteorol., 26, 1589-1600, 1987.
14. Rasmusson, E.M., and T.H. Carpenter, Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation / El Nino, Mon. Weather Rev., 110, 354-384, 1982.
15. Reed, R., Pruning algorithm: A survey, IEEE Trans. Neural Networks, 4(5), 740-747, 1993.
16. Reynolds, R.W., and T.M. Smith, Improved global sea surface temperature analyses using optimum interpolation, J. Clim., 7, 929-948, 1994.
17. Ropelewski, C.F., and M.S. Halpert, North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO), Mon. Weather Rev., 114, 2352-2362, 1987.
18. Smith, T.M., R.W. Reynolds, R.E. Livezey, and D.C. Stokes, Reconstruction of historical sea surface temperatures using empirical orthogonal functions, J. Clim., 9, 1403-1420, 1996.
19. Tang, B., Periods of linear development of the ENSO cycle and POP forecast experiment, J. Clim., 8, 682-691, 1995.
20. Tang, B., G.M. Flato, and G. Holloway, A study of Arctic sea ice and sea-level pressure using POP and neural network methods, Atmos. Ocean, 32, 507-529, 1994.
21. Tangang, F.T., B. Tang, A.H. Monahan, and W.W. Hsieh, Forecasting ENSO events: A neural network-extended EOF approach, J. Clim., 11, 29-41, 1998.
22. Tangang, F.T., W.W. Hsieh, and B. Tang, Forecasting the equatorial sea surface temperatures by neural network models, Clim. Dyn., 13, 135-147, 1997.
23. Trenberth, K., and T.J. Hoar, The 1990-1995 El Niño-Southern Oscillation event: Longest on record, Geophys. Res. Lett., 23, 57-60, 1996.
24. Trenberth, K., W. Large, and J. Olson, The effective drag coefficient for evaluating wind stress over the ocean, J. dim., 2, 1507-1516, 1989.
25. Wang, B., Interdecadal changes in El Niño onset in the last four decades, J. Clim., 8, 267-285, 1995.
26. Weigend, A.S., Time series analysis and prediction, in Mathematical Perspectives on Neural Networks, edited by Smolensky et al., pp. 395-449, Erlbaum, Hillsdale, N.J., 1996.
27. Weigend, A.S., D.E. Rumelhart, and B.A. Hubermann, Generalization by weight-elimination with application to forecasting, in Advances in Neural Information Processing System, vol. 3, edited by R. Lippmann et al., pp. 875-882, Morgan Kaufmann, San Francisco, Calif., 1991.
28. Woodruff, S.D., S.J. Lubker, K. Wolter, S.J. Worley, and J.D. Elms, Comprehensive Ocean-Atmosphere Data Set (COADS) Release la: 1980-92, Earth Syst. Monitor, 4(1), 1-8, 1993.
29. Xu,L., S. Klasa, and A. Yuille, Recents advances on techniques of statics feed-forward networks with supervised learning, Int. J. Neural Syst., 3(3), 253-290, 1992.
30. Yasunari, T., Impact of Indian monsoon on the coupled atmosphere/ocean system in the tropical Pacific, Meteorol. Atmos. Phys., 44, 29-41, 1990.