Physics – Geophysics
Scientific paper
Apr 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011jgra..11604220r&link_type=abstract
Journal of Geophysical Research, Volume 116, Issue A4, CiteID A04220
Physics
Geophysics
Interplanetary Physics: Coronal Mass Ejections (4305, 7513), Mathematical Geophysics: Probabilistic Forecasting (3238, 4315), Mathematical Geophysics: Time Series Analysis (1872, 1988, 4277, 4475), Mathematical Geophysics: Spectral Analysis (3205, 3280, 4319), Space Weather: Solar Effects
Scientific paper
The purpose of this paper is to investigate the statistical properties of high-speed coronal mass ejections (fast CMEs), which play a major role in Space Weather. We study the cumulative distribution of the initial CME speeds applying a new, advanced statistical method based on the scaling properties of averages of maximal speeds selected in time intervals of fixed sizes. This method allows us for the first time to obtain a systematic statistical description of the fast CME speeds. Using this method, we identify a self-similar (power law) high-speed portion of the spectrum of the speed maxima in the range of speeds from about 700 km/s to 2000 km/s. This self-similar range of the speed distribution provides a meaningful definition of “the fast” CMEs and indicates that these CMEs are produced by a process that is the same across the range of scales. The investigation of the temporal behavior of the fast CME events indicates that the time intervals between fast CMEs are not independent, i.e., fast CMEs arrive in clusters. We characterize the fast CMEs clustering by the exponent $\theta$ called the extremal index, which is the inverse of the averaged number of CMEs per cluster. An independent correlation analysis of the tail of the CME distribution confirms and further quantifies the temporal dependence among the fast CME events. To illustrate the predictive capabilities of the method, we identify clusters in the time series of CMEs with speeds greater than 1000 km/s and calculate their statistical characteristics such as the size and duration of the clusters. The method used in this paper can be applied to many other extreme geophysical events.
Feynman Joan
Ruzmaikin Aleksandr
Stoev Stilian A.
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