Model Calibration Using Warping Metrics: With Application to Sea Ice Deformation in MPM-ice

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Christian Sampson
University of North Carolina in Chapel Hill
Seminar Date: 
25. February 2019 - 11:30 - 12:00
Lecture room, Ground Floor, NERSC

Sea Ice is a critical component of earth’s climate system and mediates a broad range of physical processes in the Arctic, such as ocean-atmosphere interaction. Increased warming in the Arctic has drastically changed sea ice dynamics in the region and the ice composition, thinner and more first year ice, and an increased marginal ice zone. Accurate representation of lead formation is now more important than ever for the calculation of important climatological variables, such as oceanic heat flux. It is also of considerable importance for operational reasons, as the Arctic opens to increased commercial traffic. MPM-ice is a large-scale, lagrangian sea ice model which uses the novel material point method to solve the sea ice momentum equation. This model also allows for the explicit representation of lead position and geometry through the use of the Elastic-Decohesive Rheology [1]. The simulated leads can be partially misaligned or misshapen when compared to observational data, obtained using RGPS imagery. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for sharp linear features like leads. We have developed 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 [2]. These techniques align geometric features between model and data using a boundary preserving diffeomorphism. This work can also have application to other physical models which produce coherent structures.

[1] D. Sulsky, K. Peterson, Toward a new elastic–decohesive model of Arctic sea ice, Physica D: Nonlinear Phenomena, Volume 240, Issue 20, 2011.

[2] Computer model calibration based on image warping metrics: an application for sea ice deformation, Y. Guan; C. Sampson; J. D. Tucker; W. Chang; A. Mondal; M. Haran; D. Sulsky, Journal of Agricultural, Biological, and Environmental Statistics, in press 2019.