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

  • Yawen GuanEmail author
  • Christian Sampson
  • J. Derek Tucker
  • Won Chang
  • Anirban Mondal
  • Murali Haran
  • Deborah Sulsky


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.


Arctic sea ice Calibration Emulation Gaussian process Image registration 



This material was based upon work partially supported by the National Science Foundation under Grant DMS-1638521 to the Statistical and Applied Mathematical Sciences Institute and by the National Oceanic and Atmospheric Administration under Grant NA15OAR4310165 to the University of New Mexico. MH was also partially supported by NSF-DMS1418090 and through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, U.S. Department of Energy, or the United States Government.


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Copyright information

© International Biometric Society 2019

Authors and Affiliations

  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.The Statistical and Applied Mathematical Sciences InstituteDurhamUSA
  3. 3.The University of North Carolina at Chapel HillChapel HillUSA
  4. 4.Sandia National LaboratoriesAlbuquerqueUSA
  5. 5.University of CincinnatiCincinnatiUSA
  6. 6.Case Western Reserve UniversityClevelandUSA
  7. 7.Pennsylvania State UniversityUniversity ParkUSA
  8. 8.University of New MexicoAlbuquerqueUSA

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