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Remote sensing electric fields in the magnetosphere using field line resonances

thesis
posted on 2025-05-09, 02:23 authored by Liam Warden
Remote sensing the electric field of resonant ultra low frequency (ULF) waves in near-Earth space is a useful diagnostic tool, particularly for wave-particle interaction studies in the magnetosphere. An extensively used model described in Ozeke et al. (2009) maps field line resonant (FLR) ULF waves from ground magnetic fields to magnetospheric electric fields to obtain the ratio e/b. However, many simplifications about ULF wave physics and the ionosphere were made in the model which has only been compared with a single resonance data interval. In this thesis, three data-driven investigations of the e/b approach for remote sensing are described. These are empirical estimates of e/b, empirical estimates of latitudinal resonance widths and statistical analyses of FLR parameters in the Ozeke et al. (2009) model and resonant wave field amplitudes. One hundred and six (106) FLR intervals obtained from the Van Allen Probe A spacecraft and associated ground magnetometer arrays were used. The model did not estimate the known electric field spectral amplitudes of the majority of the events. Empirical estimates of e/b were found to be most suitable when obtained using a spectral method. The accuracy of latitudinal resonance width estimates were found to depend on the method used and the data pre-processing. An amplitude-division method using data pre-processed with a boxcar window function or frequency-domain exponential taper were most suitable for resonance width estimates. A cross correlation and multiple linear regression analysis showed that combinations of ULF wave parameters do not adequately explain the variance in magnetospheric electric field spectral amplitudes from a data set of 63 FLR intervals. Topics for future work include extending resonance parameter space, obtaining ionospheric measurements of ULF waves, and determining the details of ULF wave mode mix in ground magnetic field data.

History

Year awarded

2023

Thesis category

  • Doctoral Degree

Degree

Doctor of Philosophy (PhD)

Supervisors

Waters, Colin (University of Newcastle); Sciffer, Murray (University of Newcastle)

Language

  • en, English

College/Research Centre

College of Engineering, Science and Environment

School

School of Information and Physical Sciences

Rights statement

Copyright 2023 Liam Warden

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