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
Earth's magnetosphere exhibits both global and multiscale behavior and the extensive database from spacecraft and ground-based measurements has been used to develop dynamical models. The dominant feature of the magnetosphere is its large scale or global behavior, as is evident during substorms. The dynamical features can be modeled using the techniques of phase space reconstruction and this has led to near real-time forecasts of space weather. The multiscale nature limits the predictability of such dynamical models and this is quantified in terms of conditional probabilities. The combination of the dynamical and probabilistic descriptions provides a comprehensive approach to the modeling of Earth's magnetosphere. The spatial structure of the magnetosphere is modeled using distributed data from ground magnetometer stations and can be used to make local forecasts.
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