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    Interpretation of Extended Empirical Orthogonal Function (EEOF) Analysis

    Source: Monthly Weather Review:;1993:;volume( 121 ):;issue: 009::page 2631
    Author:
    Chen, Jeng-Ming
    ,
    Harr, Patrick A.
    DOI: 10.1175/1520-0493(1993)121<2631:IOEEOF>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Application of an empirical orthogonal function (EOF) analysis to a data matrix that contains two or more variable fields has been referred to as extended EOF (EEOF) analysis. Coherence between individual features contained within one EEOF has been implied to represent interrelationships between the fields (in the case of a combination of different variables) or propagating features (in the case of the same field at different times). However, caution must be exercised in the interpretation of interrelationships within one EEOF because the derivation of the EEOFs is based on the optimization of the variance of every EEOF as an entity and may not indicate correlations among substructures within one EEOF. These types of problems associated with interpretation of EEOF analyses are highlighted through an analytic example and application to a dataset with known statistical properties. Although other multivariate analysis techniques such as singular value decomposition and canonical correlation analysis are being used with more frequency, it is important to highlight potential difficulties associated with the EEOF technique that has been an integral analysis tool in meteorological research.
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      Interpretation of Extended Empirical Orthogonal Function (EEOF) Analysis

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4203141
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    contributor authorChen, Jeng-Ming
    contributor authorHarr, Patrick A.
    date accessioned2017-06-09T16:09:36Z
    date available2017-06-09T16:09:36Z
    date copyright1993/09/01
    date issued1993
    identifier issn0027-0644
    identifier otherams-62268.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4203141
    description abstractApplication of an empirical orthogonal function (EOF) analysis to a data matrix that contains two or more variable fields has been referred to as extended EOF (EEOF) analysis. Coherence between individual features contained within one EEOF has been implied to represent interrelationships between the fields (in the case of a combination of different variables) or propagating features (in the case of the same field at different times). However, caution must be exercised in the interpretation of interrelationships within one EEOF because the derivation of the EEOFs is based on the optimization of the variance of every EEOF as an entity and may not indicate correlations among substructures within one EEOF. These types of problems associated with interpretation of EEOF analyses are highlighted through an analytic example and application to a dataset with known statistical properties. Although other multivariate analysis techniques such as singular value decomposition and canonical correlation analysis are being used with more frequency, it is important to highlight potential difficulties associated with the EEOF technique that has been an integral analysis tool in meteorological research.
    publisherAmerican Meteorological Society
    titleInterpretation of Extended Empirical Orthogonal Function (EEOF) Analysis
    typeJournal Paper
    journal volume121
    journal issue9
    journal titleMonthly Weather Review
    identifier doi10.1175/1520-0493(1993)121<2631:IOEEOF>2.0.CO;2
    journal fristpage2631
    journal lastpage2636
    treeMonthly Weather Review:;1993:;volume( 121 ):;issue: 009
    contenttypeFulltext
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