Application of neural network principal components to climate data

Journal of Atmospheric and Oceanic Technology

Volumn 21, Issue 2, 2004, Pages 149-158

  Ali, Ahmed Haider ^a  

^a RAYTHEON COMPANY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; MAPS; MATHEMATICAL MODELS; OCEAN ENGINEERING; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING;

CLIMATE DATA; PRINCIPAL COMPONENTS (PC);

CLIMATOLOGY;

ARTIFICIAL NEURAL NETWORK; CLIMATE CHANGE; DATA PROCESSING; DATA SET; PRINCIPAL COMPONENT ANALYSIS;

EID: 1842782319     PISSN: 07390572     EISSN: None     Source Type: Journal    
DOI: 10.1175/1520-0426(2004)021<0149:AONNPC>2.0.CO;2     Document Type: Article

References (23)

1. Baldwin, M. P., 2001: Annular modes in global surface pressure. Geophys. Res. Lett., 28, 4115-4118.
2. Barnett, T. P., 1977: The principal time and space scales of the Pacific trade wind fields. J. Atmos. Sci., 34, 221-236.
3. Diamantaras, K. I., and S. Y. Kung, 1996: Principal Component Neutral Networks Theory and Applications. John Wiley & Sons, 255 pp.
4. Földiák, P., 1989: Adaptive network for optimal linear feature extraction. Proc. Int. Joint Conf. on Neural Networks, Washington, DC, IEEE, 401-406.
5. Gong, D., and S. Wang, 1999: Definition of Antarctic oscillation index. Geophys. Res. Lett., 26, 1601-1604.
6. Hebb, D. O., 1949: The Organization of Behavior. John Wiley & Sons, 335 pp.
7. Hsieh, W. W., 2001: Nonlinear principal component analysis by neural networks. Tellus, 53, 599-615.
8. Hsieh, W. W., 2004: Nonlinear multivariate and time series analysis by neural network methods. Rev. Geophys., in press.
9. Hsieh, W. W., and B. Tang, 1998: Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Amer. Meteor. Soc., 79, 1855-1870.
10. Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Re-analysis Project. Bull. Amer. Meteor. Soc., 77, 437-471.
11. Kelly, K. A., 1988: Comment on "Empirical orthogonal function analysis of advanced very high resolution radiometer surface temperature patterns in the Santa Barbara Channel" by G. S. E. Lagerloef and R. L. Bernstein. J. Geophys. Res., 93C, 15 753-15 754.
12. Kung, S. Y., and K. I. Diamantaras, 1990: A neural network learning algorithm for adaptive principal component extraction (APEX). Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Albuquerque, NM, IEEE, 861-864.
13. Lough, J. M., 1986: Tropical Atlantic sea surface temperatures and rainfall variations in sub-Saharan Africa. Mon. Wea. Rev., 114, 561-570.
14. McCulloch, W. S., and W. Pitts, 1943: A logical calculus of the ideas immanent in the nervous activity. Bull. Math. Biophys., 5, 115-120.
15. Monahan, A. H., 2001: Nonlinear principal component analysis: Tropical Indo-Pacific sea surface temperature and sea level pressure. J. Climate, 14, 219-233.
16. Newell, R. E., and B. C. Weare, 1976: Ocean temperatures and large scale atmospheric variations. Nature, 262, 40-41.
17. Oja, E., 1982: A simplified neural model as a principal component analyzer. J. Math. Biol., 15, 267-273.
18. Preisendorfer, R. W., 1988: Principal Component Analysis in Meteorology and Oceanography. Elsevier, 425 pp.
19. Rogers, J. C., and H. Van Loon, 1982: Spatial variability of sea level pressure and 500-mb height anomalies over the Southern Hemisphere. Mon. Wea. Rev., 110, 1375-1392.
20. Rubner, J., and P. Tavan, 1989: A self-organizing network for principal-component analysis. Europhys. Lett., 10, 693-698.
21. Suykens, J. A. K., J. Vandewalle, and B. L. De Moor, 1996: Artificial Neural Networks for Modeling and Control of Non-Linear Systems. Kluwer Academic, 235 pp.
22. Tangang, F. T., B. Tang, A. H. Monahan, and W. W. Hsieh, 1998: Forecasting ENSO events: A neural network-extended EOF approach. J. Climate, 11, 29-41.
23. Thompson, D. W. J., and M. J. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 1000-1016.