Mining Massive Earth Science Data Sets for Large Scale Structure
Amy Braverman, Eric J. Fetzer · 2003
The traditional way to look for large scale structure in very large observational or model generated data sets is to examine maps of means and standard deviations of parameters of interest on a coarse spatio-temporal grid. This approach is popular because it is easy to implement and understand, but unfortunately it throws away almost all of the distributional information in the data. Moreover, maps are computed for individual parameters of interest, and therefore do not retain information about relationships among two or more parameters. In this work, we use a modified data compression algorithm to produce multivariate distribution estimates for each grid cell. The algorithms optimally mediates between data reduction and fidelity loss using information-theoretic principles. Changes in these distribution estimates over time, space and resolution reflect large scale data structure. This is the basis for a data mining algorithm that characterizes those changes using a pseudo-metric for the distance between distributions. We demonstrate using data from the Atmospheric Infrared Sounder (AIRS) on board NASA’s Aqua satellite.