NASA's Atmospheric Infrared Sounder (AIRS) has been collecting large quantities of remote sensing data about the vertical structure of Earth's atmosphere since AIRS was launched aboard the Aqua spacecraft in mid-2002. These data pose a classic problem in the analysis of massive data sets: how do we understand the relationships among fine-scale phenomena within their global context? We answer that question here by partitioning the data on a coarse spatio-temporal grid, and estimating the multivariate distribution of the data within each grid cell. Then, we look for patterns in the evolution of those distributions as functions of space and time, and ultimately we tie them back to physical phenomena generating the data sets. Quantifying this evolution is challenging because the data are high dimensional, and the distributions are complex. We attack the problem using a distance between distributions as a measure of similarity among grid cells' data and therefore as a measure of similarity between the underlying physical processes. We close with a look at the physical implications of our findings for climate studies. This represents joint research with Eric Fetzer, Brian Kahn, and Joao Teixeira.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.