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Detection of dynamical complexity changes in Dst time series using entropy concepts and
 

Summary: Detection of dynamical complexity changes in
Dst time series using entropy concepts and
rescaled range analysis
Georgios Balasis, Ioannis A. Daglis, Anastasios Anastasiadis and Konstantinos
Eftaxias
Abstract Using an array of diagnostic tools including entropy concepts and rescaled
range analysis, we establish that the Dst index time series exhibits long-range cor-
relations, and that the underlying stochastic process can be modeled as fractional
Brownian motion. We show the emergence of two distinct patterns in the Earth's
magnetosphere: (1) a pattern associated with the intense magnetic storms, which is
characterized by a higher degree of organization (i.e., lower complexity or higher
predictability for the system) and persistent behavior, and (2) a pattern associated
with normal periods, which is characterized by a lower degree of organization (i.e.,
higher complexity or lower predictability for the system) and anti-persistent behav-
ior.
1 Introduction
Studies of entropy provide physical insight into space plasma transport, intermit-
tency, turbulence, and information flow in the heliosphere and magnetosphere (Wing
and Johnson, 2010). Moreover, entropy-based information theory can be used to
characterize the dynamics of the Earth's magnetosphere (Balasis et al., 2008, 2009;

  

Source: Anastasiadis, Anastasios - Institute for Space Applications and Remote Sensing, National Observatory of Athens

 

Collections: Physics