Date of Award
Master of Science
Department of Engineering Physics
William F. Bailey, PhD
Linear prediction filtering techniques have been used in studying the coupling processes between the solar wind and magnetosphere. The magnetosphere is a complex, dynamic system with at least two independent coupling methods for energy input, driven and unloading. Linear models were built and tested on the Bargatze data set, consisting of over 70 days of geomagnetic indices and solar wind data ordered in 34 intervals of increasing geomagnetic activity. Linear filtering techniques employing single-and multiple-input, autoregressive models predicted values of the magnetic index AL from solar wind data. The impulse response curves of the AL-coupling function groups showed amplitude peaks at 25 and 70 minutes, confirming results in previous studies. The separate peaks indicate responses corresponding to the driven and unloading time scales. The average correlation coefficients generated between predicted AL values and the measured values of AL were 0.665, 0.738, and 0.793 for single, dual, and triple input models, respectively.
DTIC Accession Number
Borst, Carter N., "Application of Autoregressive Moving Average Linear Prediction Filters to the Characterization of Solar Wind-Magnetosphere Coupling" (1996). Theses and Dissertations. 6127.