Extreme events and long-range correlations in space weather

Abstract

Space weather is driven by the solar wind and many geospace storms and substorms are natural hazards with considerable societal impact. The dynamical and statistical features of these events are complicated because of the turbulent nature of their driver, the solar wind. Large-scale data sets of geospace storms and substorms are analysed for this study of the inherent statistical characteristics of extreme events in geospace. The detrended fluctuation analysis, based on the autocorrelation functions, is used and yields scaling behavior representing long-term correlations. The scaling function is represented by two exponents, arising due mainly to the presence of the largely coherent internal dynamics of the magnetosphere and the turbulent nature of the solar wind driver

Latitudinal variation of stochastic properties of the geomagnetic field

“…The origin of such an intermittent and multifractal character has been interpreted and modeled in terms of dynamical changes and turbulent dynamics (Wanliss et al, 2005;Anh et al, 2007;Yu et al, 2010;Sharma and Veeramani, 2011).…”

Deterministic dynamics of the magnetosphere: results of the 0–1 test

“…Such driven nonequilibrium systems exhibit global features that allow dynamical modelling and at the same time show multiscale features (Consolini and Chang, 2001;Ukhorskiy et al, 2004;Zelenyi and Milovanov, 2004). Also the magnetosphere exhibits long-range correlations (Sharma and Veeramani, 2011), and for such systems the 0-1 test has limitations in providing the detailed features (Hu et al, 2005). Thus, while the results of the 0-1 test by themselves may not be conclusive, it provides additional evidence of the deterministic aspects of the magnetosphere.…”

Complexity in nature and data-enabled science: The Earth's magnetosphere

“…The interplay between the global and multiscale behavior is described below in the case of the Earth's magnetosphere. These studies require the use many techniques, such as phase space reconstruction [3,4,19,22], mutual information functions [23,24], and detrended fluctuation analysis [24,25].…”

“…In order to analyze the correlations in AL data sets with 1-hr and 5-min averages were used [24]. The hourly averaged AL for the period 1978-1988, which covers a typical 11-year solar cycle and 5 min averaged data for 1978 were used to compare the scaling properties.…”