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| Titel |
New Insights into the Estimation of Extreme Geomagnetic Storm Occurrences |
| VerfasserIn |
Alexis Ruffenach, Hugo Winter, Benoit Lavraud, Pietro Bernardara |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250146443
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| Publikation (Nr.) |
 EGU/EGU2017-10470.pdf |
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| Zusammenfassung |
| Space weather events such as intense geomagnetic storms are major disturbances of the
near-Earth environment that may lead to serious impacts on our modern society. As such, it is
of great importance to estimate their probability, and in particular that of extreme events. One
approach largely used in statistical sciences for extreme events probability estimates is
Extreme Value Analysis (EVA). Using this rigorous statistical framework, estimations of
the occurrence of extreme geomagnetic storms are performed here based on the
most relevant global parameters related to geomagnetic storms, such as ground
parameters (e.g. geomagnetic Dst and aa indexes), and space parameters related to the
characteristics of Coronal Mass Ejections (CME) (velocity, southward magnetic
field component, electric field). Using our fitted model, we estimate the annual
probability of a Carrington-type event (Dst = -850nT) to be on the order of 10−3, with a
lower limit of the uncertainties on the return period of ∼500 years. Our estimate is
significantly higher than that of most past studies, which typically had a return period of
a few 100 years at maximum. Thus precautions are required when extrapolating
intense values. Currently, the complexity of the processes and the length of available
data inevitably leads to significant uncertainties in return period estimates for the
occurrence of extreme geomagnetic storms. However, our application of extreme value
models for extrapolating into the tail of the distribution provides a mathematically
justified framework for the estimation of extreme return periods, thereby enabling the
determination of more accurate estimates and reduced associated uncertainties. |
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