HR: 1340h
AN: SM43A-1216    [Abstracts]
TI: Probability Distributions of Wavelet Coefficients of the Ground-Based Magnetometer Data for Storm and Quiet Times
AU: * Maslova, I
EM: inga@cc.usu.edu
AF: Mathematics and Statistics Department, Utah State University, 3900 Old Main Hill, Logan, UT 84322-3900 United States
AU: Kokoszka, P
EM: Piotr.Kokoszka@usu.edu
AF: Mathematics and Statistics Department, Utah State University, 3900 Old Main Hill, Logan, UT 84322-3900 United States
AU: Zhu, L
EM: zhu@cc.usu.edu
AF: Center for Atmospheric and Space Sciences, Utah State University, 4405 Old Main Hill, Logan, UT 84322-4405 United States
AU: Sojka, J J
EM: fasojka@sojka.cass.usu.edu
AF: Center for Atmospheric and Space Sciences, Utah State University, 4405 Old Main Hill, Logan, UT 84322-4405 United States
AB: The ground-based magnetometer network has long been a powerful tool for monitoring and observing the variations of the currents flowing in the magnetosphere-ionosphere (M-I) system. These current variations directly reflect the response of the M-I system to the solar wind driver and are closely connected to various nonlinear dynamic processes in the M-I system, including storms and substorms. Due to the multiscale and nonlinear natures of the M-I current system, the time series of magnetometer data are non-stationary and their frequency behavior changes over time. They are therefore not amenable to traditional time domain or spectral (Fourier) analysis. In recent years, various new mathematical techniques have been developed to analyze magnetometer data and the wavelet technique has stood out as being particularly relevant. In order to correctly make statistical inference based on wavelet analysis, the wavelet coefficient distributions of magnetometer data must be examined. In this work, we applied the discrete wavelet transform to the 1 min magnetometer data from March 2001 to April 2001, and then used various statistical techniques to analyze the probability distributions of the wavelet coefficients and estimate their tail indexes. It is found that the distributions of the wavelet coefficients of the magnetometer data for both storm and quiet times are highly non-normal and can be classified as being heavy tailed and the tail index values are centered around 2. This means that the probability of exceptionally large wavelet coefficients is much higher than implied by the standard statistical theory based on the normal distribution. It is also found that the tail indexes for storm times are on average smaller than those of quiet times, which reflects the stronger impulsive and non-stationary features in magnetometer data during storm times.
DE: 2778 Ring current
DE: 2788 Magnetic storms and substorms (7954)
DE: 3270 Time series analysis (1872, 4277, 4475)
DE: 3280 Wavelet transform (3255, 4455)
SC: SPA-Magnetospheric Physics [SM]
MN: Fall Meeting 2005