Online Program Home
My Program

Abstract Details

Activity Number: 249 - The Climate Program at SAMSI
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #304287 Presentation
Title: Ice Model Calibration Using Semi-Continuous Spatial Data
Author(s): Won Chang* and Alex Konomi and Yawen Guan and Murali Haran and Georgios Karagiannis
Companies: University of Cincinnati and University of Cincinnati and North Carolina State University and Penn State University and Durham University
Keywords: computer model calibration; computer model emulation; semi-continuous data; principal component analysis; West Antarctic ice sheet; large spatial data

Rapid changes in the West Antarctic ice sheet caused by human activities can lead to significant environmental impacts. Ice sheet models provide a useful tool for projecting the evolution of future Arctic and Antarctic ice sheets, but they often need to be properly calibrated due to the presence of highly uncertain input parameters. Calibrating an ice sheet model is often challenging because the relevant model output and observational data take the form of semi-continuous spatial data, with a point mass at zero and a right-skewed continuous distribution for positive values. Here we introduce a hierarchical latent variable model that can efficiently handle semi-continuous spatial data using a mixture distribution approach. To overcome challenges due to high-dimensionality we use likelihood-based generalized principal component analysis to impose low-dimensional structures on the latent variables for spatial dependence. We demonstrate that our proposed reduced-dimension method can successfully overcome the aforementioned challenges in the example of calibrating PSU-3D ice model.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program