Correlation study of time-varying multivariate climate data sets

We present a correlation study of time-varying multivariate volu-metric data sets. In most scientific disciplines, to test hypothe-ses 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 con-nections, 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 point-wise correlation coefficients and canonical correlation analysis. We demonstrate our approach using results on time-varying multivari-ate climate data sets