Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation

Yawen Guan, Christian Sampson, J. Derek Tucker, Won Chang, Anirban Mondal, Murali Haran, Deborah Sulsky

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online.

Original languageEnglish (US)
Pages (from-to)444-463
Number of pages20
JournalJournal of Agricultural, Biological, and Environmental Statistics
Issue number3
StatePublished - Sep 15 2019

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • General Environmental Science
  • Agricultural and Biological Sciences (miscellaneous)
  • General Agricultural and Biological Sciences
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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