An ACE-based Nonlinear Extension to Traditional Empirical Orthogonal Function Analysis

Klaus Baggesen Hilger, Allan Aasbjerg Nielsen, Ole Andersen, Per Knudsen

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    Abstract

    This paper shows the application of the empirical orthogonal unctions/principal component transformation on global sea surface height and temperature data from 1996 and 1997. A nonlinear correlation analysis of the transformed data is proposed and performed by applying the alternating conditional expectations algorithm. New canonical variates are found that indicate that the highest correlation between ocean temperature and height is associated with the build-up of the El Niño during the last half of 1997.
    Original languageEnglish
    Title of host publicationProceedings of MultiTemp2001 Workshop, Trento, Italy
    Publication date2001
    Publication statusPublished - 2001
    EventAnalysis of Multi-Temporal Remote Sensing Images - MultiTemp2001, Trento, Italy
    Duration: 1 Jan 2001 → …

    Conference

    ConferenceAnalysis of Multi-Temporal Remote Sensing Images
    CityMultiTemp2001, Trento, Italy
    Period01/01/2001 → …

    Cite this

    Hilger, K. B., Nielsen, A. A., Andersen, O., & Knudsen, P. (2001). An ACE-based Nonlinear Extension to Traditional Empirical Orthogonal Function Analysis. In Proceedings of MultiTemp2001 Workshop, Trento, Italy