Astronomy and Astrophysics – Astrophysics
Scientific paper
Dec 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010agufmsh43a1809f&link_type=abstract
American Geophysical Union, Fall Meeting 2010, abstract #SH43A-1809
Astronomy and Astrophysics
Astrophysics
[7513] Solar Physics, Astrophysics, And Astronomy / Coronal Mass Ejections, [7924] Space Weather / Forecasting, [7974] Space Weather / Solar Effects
Scientific paper
In many geophysical phenomena such as hurricanes, the solar energetic particle events and the coronal mass ejections we discuss here, the knowledge of the tails of the probability distribution functions is critical to our understanding and forecasting of the most hazardous extreme events. In addition, often the events appear to cluster making †he statistical samplings based on independent events incorrect. The probability distributions of extreme events are not Gaussian but are characterized by extended high intensity tails. In practice, it has become common to estimate the shape of the tail using an empirical fit to a known distribution function, such as a log-normal. These empirical estimates are often challenged because the validity of the basic mathematics of statistics underlying them has not been demonstrated. Recently new statistical techniques have been developed that allow us to test the validity of our empirical methods. We apply one of the new methods based on the use of scaling properties of the data maxima to the distribution of speeds of coronal mass ejections (CMEs). We show that the distribution function of the fast CME speeds asymptotically follows the power-law extreme value distribution and that the intervals between fast CMEs do not follow the exponential distribution expected for independent random events but cluster in time. This work was supported by the Jet Propulsion Laboratory of the California Institute of Technology, under a contract with the National Aeronautics and Space Administration.
Feynman Joan
Ruzmaikin Aleksandr
Stoev Stilian
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