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Activity Number: 561 - Small Data, Big Impact
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract #307275 Presentation
Title: Sea Ice Computer Model Calibration Using Space Filling Curves
Author(s): Derek Tucker* and Joel Upston and Deborah Sulsky
Companies: Sandia National Laboratories and University of New Mexico and University of New Mexico
Keywords: Arctic Sea Ice; Bayesian Estimation; Functional Data Anlaysis; Model Calibration; Space filling Curves

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. The simulated features such as ice cracks can be misaligned and misshapen when compared to the small amount of observational data. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to the experimental data. Traditional calibration methods based on generalized least-square metrics are do not account for both phase and amplitude separation of linear features such as sea ice cracks. Through the use of space filling curves we present a statistical emulation and calibration framework that accounts for ice crack misalignment and misshapenness between the observation and model data. This method uses the optimal 1-D alignment of model output with observed features which provides a proper distance for calibration. We will compare our method to current calibration metrics on simulated data and real Arctic Sea ice observations.

Authors who are presenting talks have a * after their name.

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