Investigating dynamical complexity in the magnetosphere using various entropy measures

Abstract

[1] The complex system of the Earth's magnetosphere corresponds to an open spatially extended nonequilibrium (input-output) dynamical system. The nonextensive Tsallis entropy has been recently introduced as an appropriate information measure to investigate dynamical complexity in the magnetosphere. The method has been employed for analyzing D st time series and gave promising results, detecting the complexity dissimilarity among different physiological and pathological magnetospheric states (i.e., prestorm activity and intense magnetic storms, respectively). This paper explores the applicability and effectiveness of a variety of computable entropy measures (e.g., block entropy, Kolmogorov entropy, T complexity, and approximate entropy) to the investigation of dynamical complexity in the magnetosphere. We show that as the magnetic storm approaches there is clear evidence of significant lower complexity in the magnetosphere. The observed higher degree of organization of the system agrees with that inferred previously, from an independent linear fractal spectral analysis based on wavelet transforms. This convergence between nonlinear and linear analyses provides a more reliable detection of the transition from the quiet time to the storm time magnetosphere, thus showing evidence that the occurrence of an intense magnetic storm is imminent. More precisely, we claim that our results suggest an important principle: significant complexity decrease and accession of persistency in D st time series can be confirmed as the magnetic storm approaches, which can be used as diagnostic tools for the magnetospheric injury (global instability). Overall, approximate entropy and Tsallis entropy yield superior results for detecting dynamical complexity changes in the magnetosphere in comparison to the other entropy measures presented herein. Ultimately, the analysis tools developed in the course of this study for the treatment of D st index can provide convenience for space weather applications.Citation: Balasis, G., I. A. Daglis, C. Papadimitriou, M. Kalimeri, A. Anastasiadis, and K. Eftaxias (2009), Investigating dynamical complexity in the magnetosphere using various entropy measures,

Wavelet-based multiscale analysis of geomagnetic disturbance

“…This indicates the formation of a new dynamical phase considerably more ordered than the background state. This result is in accordance with the entropy decrease shown by Balasis et al (2008Balasis et al ( , 2009. A transition from a random to a correlated state was actually observed and discussed during the active periods of storm in the Dst index and the SYM-H index (Wanliss, 2005a;Wanliss and Dobias, 2007).…”

“…The scaling exponent β is related to the Hurst exponent, H : β = 2H + 1 with 0 < H < 1 (1 < β < 3) for the fBm random field model (Heneghan and McDarby, 2000). The exponent H characterizes the persistent/antipersistent properties of the signal (Balasis et al, , 2008(Balasis et al, , 2009(Balasis et al, , 2011). The range 0 < H < 0.5 (1 < β < 2) indicates antipersistency, which means that if the fluctuations increase in a period, it is likely to decrease in the interval immediately following and vice versa.…”

Quantifying Dynamical Complexity of Magnetic Storms and Solar Flares via Nonextensive Tsallis Entropy

“…In a series of papers, examining the applicability of various information measures to investigate dynamical complexity in the magnetosphere, it has been found that the application of Tsallis entropy to the analysis of the D st time series yields superior results for detecting dynamical complexity changes associated with magnetic storms in comparison to other entropy measures [12,51,52].…”

“…It was argued that the intermittency of the turbulent fluctuations should be related physically to the nonextensive character of the interplanetary medium counting for nonlocal interactions via the entropy generalization [9,10]. Moreover, the nonextensive Tsallis entropy has been recently introduced [11][12][13][14] as an appropriate information measure to investigate dynamical complexity in the magnetosphere. The method has been employed for analyzing D st time series and gave promising results, detecting the complexity dissimilarity among different physiological and pathological magnetospheric states (i.e., pre-storm activity and intense magnetic storms, respectively).…”

“…The reader is referred to [6] for a complete list of publications (∼3500) related to the topic. Among the works listed there are papers on the heliosphere [7,8], solar wind [9,10] and magnetospheric dynamics [11][12][13][14].…”