Applied Seminar: Joel Upston
Monday, November 25, 2019 -
3:30pm to 4:30pm
MPM Sea Ice Model Calibration using 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 Material Point Method (MPM) Sea Ice Model uses an elastic decohesive constitutive model to help model ice dynamics and fractures in the ice. 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.
Contact Name: Jacob Schroder