Nonlinear fluctuation analysis for a set of 41 magnetic clouds measured by the Advanced Composition Explorer (ACE) spacecraft

• Main Authors: A. Ojeda González, W. D. Gonzalez, O. Mendes, M. O. Domingues, R. R. Rosa
• Format: Article
• Language: English
• Published: Copernicus Publications 2014-10-01
• Series: Nonlinear Processes in Geophysics
• Online Access: http://www.nonlin-processes-geophys.net/21/1059/2014/npg-21-1059-2014.pdf

The statistical distribution of values in the signal and the autocorrelations (interpreted as the memory or persistence) between values are attributes of a time series. The autocorrelation function values are positive in a time series with persistence, while they are negative in a time series with anti-persistence. The persistence of values with respect to each other can be strong, weak, or nonexistent. A strong correlation implies a "memory" of previous values in the time series. The long-range persistence in time series could be studied using semivariograms, rescaled range, detrended fluctuation analysis and Fourier spectral analysis, respectively. In this work, persistence analysis is to study interplanetary magnetic field (IMF) time series. We use data from the IMF components with a time resolution of 16 s. Time intervals corresponding to distinct processes around 41 magnetic clouds (MCs) in the period between March 1998 and December 2003 were selected. In this exploratory study, the purpose of this selection is to deal with the cases presenting the three periods: plasma sheath, MC, and post-MC. We calculated one exponent of persistence (e.g., α, β, <i>Hu</i>, <i>Ha</i>) over the previous three time intervals. The persistence exponent values increased inside cloud regions, and it was possible to select the following threshold values: &alpha;_(<i>j</i>) = 1.392, <i>Ha</i>_(<i>j</i>) = 0.327, and <i>Hu</i>_(<i>j</i>) = 0.875. These values are useful as another test to evaluate the quality of the identification. If the cloud is well structured, then the persistence exponent values exceed thresholds. In 80.5% of the cases studied, these tools were able to separate the region of the cloud from neighboring regions. The Hausdorff exponent (<i>Ha</i>) provides the best results.