DMS Statistics and Data Science Seminar

Speaker: Prof. Bo Li (Department of Statistics and Data Science, Washington University in St. Louis)

Title: Spatially Varying Changepoint Detection with Application to Mapping the Impact of the Mount Pinatubo Eruption

Abstract: Significant events such as volcanic eruptions can exert global and long-lasting impacts on climate. These impacts, however, are not uniform across space and time. Motivated by the need to understand how the 1991 Mt. Pinatubo eruption influenced global and regional climate, we propose a Bayesian framework to simultaneously detect and estimate spatially varying temporal changepoints. Our approach accounts for the diffusive nature of volcanic effects and leverages spatial correlation. We then extend the changepoint detection problem to large-scale spherical spatiotemporal data and develop a scalable method for global applications. The framework enables Gibbs sampling for changepoints within MCMC, offering greater computational efficiency than the Metropolis–Hastings algorithm. To address the high dimensionality of global data, we incorporate spherical harmonic transformations, which further substantially reduce computational burden while preserving accuracy. We demonstrate the effectiveness of our method using both simulated datasets and real data on stratospheric aerosol optical depth and surface temperature to detect and estimate changepoints associated with the Mt. Pinatubo eruption.

Host: Roberto Molinari 

SDS seminar’s website: https://auburn.edu/cosam/datascienceseminar/index.htm