Space-Time Dynamical Modeling of Aerosol Transport

• Activity Number: 101
• Type: Invited
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: ENAR
• Author(s): Catherine Calder*+ and Candace Berrett and Tao Shi
• Companies: The Ohio State University and Brigham Young University and The Ohio State University
• Keywords: Bayesian statistics ; Computer models ; Spatial statistics

Abstract

Kernel-based dynamic models for space-time data offer a flexible and descriptive framework for studying atmospheric processes. Nonstationary and anisotropic covariance structures can be readily accommodated by allowing kernel parameters to vary over space and time. In addition, dimension reduction strategies make model fitting computationally feasible for large datasets. Fitting these models to data derived from instruments onboard satellites, which often contain significant amounts of missingness due to cloud cover and retrieval errors, can be difficult. In this talk, we discuss an approach to overcome the challenges of missing satellite-derived data by supplementing an analysis with output from a computer model which contains valuable information about the space-time dependence structure of the process of interest. We illustrate our approach through a case study of aerosol optical depth across mainland Southeast Asia. In addition, we introduce a graphical approach to selecting the amount of dimension reduction in a dynamic space-time model.