Date of Award

Spring 1-1-2017

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Applied Mathematics

First Advisor

William Kleiber

Second Advisor

Jem Corcoran

Third Advisor

Manuel Lladser

Abstract

Geomagnetic storms play a significant role in space weather physics and have the potential to impact our daily lives. Widespread impacts of space weather physics can include power grid outages, air traffic rerouting, and disruption of GPS signals. The Lyon-Fedder-Mobarry global magnetosphere–ionosphere coupled model (LFM-MIX) is a computer model used at the Center for Integrated Space Weather Modeling (CISM) to study Sun-Earth interactions by simulating geomagnetic storms. LFM-MIX uses solar wind observations to perform a magnetohydrodynamic (MHD) simulation of the magnetosphere (LFM) and couples it with an electrostatic model of the ionosphere (MIX). Given a set of input parameters and solar wind data, LFM-MIX numerically solves the MHD equations and outputs a large bivariate spatiotemporal field of ionospheric energy and flux. These input parameters are unknown and we focus on quantifying them. The currently available methods are insufficient for our data set due to its high dimensionality, thus we develop our own method based on statistical calibration. Here, statistical calibration refers to the process of fitting a model to observed data by adjusting the input parameters. Our approach, which we call feature-based calibration, involves calculating some goodness of fit criterion between model output and observed data, then predicting its value over the entire feasible parameter space and locating the minimum of the predicted surface. We apply this approach to several goodness of fit criteria based on different defining features of the data.

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