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

Author

Listed:
  • Yawen Guan

    (North Carolina State University
    The Statistical and Applied Mathematical Sciences Institute)

  • Christian Sampson

    (The Statistical and Applied Mathematical Sciences Institute
    The University of North Carolina at Chapel Hill)

  • J. Derek Tucker

    (Sandia National Laboratories)

  • Won Chang

    (University of Cincinnati)

  • Anirban Mondal

    (Case Western Reserve University)

  • Murali Haran

    (Pennsylvania State University)

  • Deborah Sulsky

    (University of New Mexico)

Abstract

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.

Suggested Citation

  • Yawen Guan & Christian Sampson & J. Derek Tucker & Won Chang & Anirban Mondal & Murali Haran & Deborah Sulsky, 2019. "Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 444-463, September.
    • Handle: RePEc:spr:jagbes:v:24:y:2019:i:3:d:10.1007_s13253-019-00353-7
    DOI: 10.1007/s13253-019-00353-7
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    References listed on IDEAS

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    1. Tucker, J. Derek & Wu, Wei & Srivastava, Anuj, 2013. "Generative models for functional data using phase and amplitude separation," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 50-66.
    2. S. Conti & J. P. Gosling & J. E. Oakley & A. O'Hagan, 2009. "Gaussian process emulation of dynamic computer codes," Biometrika, Biometrika Trust, vol. 96(3), pages 663-676.
    3. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    4. Jeremy Oakley, 2002. "Bayesian inference for the uncertainty distribution of computer model outputs," Biometrika, Biometrika Trust, vol. 89(4), pages 769-784, December.
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    Cited by:

    1. Suman Majumder & Yawen Guan & Brian J. Reich & Susan O’Neill & Ana G. Rappold, 2021. "Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire $$\hbox {PM}_{2.5}$$ PM 2.5 Concentration Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(1), pages 23-44, March.
    2. Dorit Hammerling & Brian J. Reich, 2019. "Guest Editors’ Introduction to the Special Issue on “Climate and the Earth System”," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 395-397, September.

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