Discovery of Climate Indices Using Clustering

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Discovery of Climate Indices Using Clustering (2003)

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by Michael Steinbach , Pang-ning Tan

Venue:	In Proc. of the 9th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining
Citations:	36 - 12 self

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@INPROCEEDINGS{Steinbach03discoveryof,
    author = {Michael Steinbach and Pang-ning Tan},
    title = {Discovery of Climate Indices Using Clustering},
    booktitle = {In Proc. of the 9th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining},
    year = {2003},
    pages = {24--27}
}

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Abstract

To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earth’s oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eigenvalue analysis techniques, such as principal components analysis (PCA) and singular value decomposition (SVD), to discover climate indices. However, eigenvalue techniques are only useful for finding a few of the strongest signals. Furthermore, they impose a condition that all discovered signals must be orthogonal to each other, making it difficult to attach a physical interpretation to them. This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions. Some of these centroids correspond to known climate indices and provide a validation of our methodology; other centroids are variants of known indices that may provide better predictive power for some land areas; and still other indices may represent potentially new Earth science phenomena. Finally, we show that cluster based indices generally outperform SVD derived indices, both in terms of area weighted correlation and direct correlation with the known indices.

Keyphrases

climate index climate index using clustering time series known index earth scientist eigenvalue analysis technique singular value decomposition physical interpretation earth ocean predictive power homogeneous behavior centroid correspond direct correlation new earth science phenomenon land climate principal component analysis alternative clustering-based methodology eigenvalue technique land area

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