STAMPS@CMU presents:

Statistical Methods for Ice Sheet Model Calibration

by Murali Haran (Department of Statistics, Pennsylvania State University)

Online webinar November 13, 2020 at 1:30-2:30 PM EST.
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Abstract
In this talk I will consider the scientifically challenging task of understanding the past and projecting the future dynamics of the Antarctic ice sheet; this ice sheet is of particular interest as its melting may lead to drastic sea level rise. The scientific questions lead to the following statistical and computational question: How do we combine information from noisy observations of an ice sheet with a physical model of the ice sheet to learn about the parameters governing the dynamics of the ice sheet? I will discuss two classes of methods: (i) approaches that perform inference based on an emulator, which is a stochastic approximation of the ice sheet model, and (ii) an inferential approach based on a heavily parallelized sequential Monte Carlo algorithm. I will explain how the choice of method depends on the particulars of the questions we are trying to answer, the data we use, and the complexity of the ice sheet model we work with. This talk is based on joint work with Ben Lee (George Mason U.), Won Chang (U of Cincinnati), Klaus Keller, Rob Fuller, Dave Pollard, and Patrick Applegate (Penn State Geosciences).

Bio

Murali Haran is Professor and Head of the Department of Statistics at Penn State University. He has a PhD in Statistics from the University of Minnesota, and a BS in Computer Science (with minors in Statistics, Mathematics and Film Studies) from Carnegie Mellon University. His research interests are in Monte Carlo algorithms, spatial models, the statistical analysis of complex computer models, and interdisciplinary research in climate science and infectious diseases.