Correlation study of time-varying multivariate climate data sets

Authors:
Jeffrey Sukharev;Chaoli Wang;Kwan-Liu Ma;Andrew T. Wittenberg
Affiliations:
University of California, Davis, USA;University of California, Davis, USA;University of California, Davis, USA;National Oceanic and Atmospheric Administration, USA
Venue:
PACIFICVIS '09 Proceedings of the 2009 IEEE Pacific Visualization Symposium
Year:
2009

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Abstract

We present a correlation study of time-varying multivariate volumetric data sets. In most scientific disciplines, to test hypotheses and discover insights, scientists are interested in looking for connections among different variables, or among different spatial locations within a data field. In response, we propose a suite of techniques to analyze the correlations in time-varying multivariate data. Various temporal curves are utilized to organize the data and capture the temporal behaviors. To reveal patterns and find connections, we perform data clustering and segmentation using the k-means clustering and graph partitioning algorithms. We study the correlation structure of a single or a pair of variables using pointwise correlation coefficients and canonical correlation analysis. We demonstrate our approach using results on time-varying multivariate climate data sets.