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| Titel |
Predicting geomagnetic storms from solar-wind data using time-delay neural networks |
| VerfasserIn |
H. Gleisner, H. Lundstedt, P. Wintoft |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 14, no. 7 ; Nr. 14, no. 7, S.679-686 |
| Datensatznummer |
250012349
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| Publikation (Nr.) |
 copernicus.org/angeo-14-679-1996.pdf |
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| Zusammenfassung |
| We have used time-delay feed-forward neural
networks to compute the geomagnetic-activity index Dst one
hour ahead from a temporal sequence of solar-wind data. The input data include
solar-wind density n, velocity V and the southward component Bz
of the interplanetary magnetic field. Dst is not included in
the input data. The networks implement an explicit functional relationship
between the solar wind and the geomagnetic disturbance, including both direct
and time-delayed non-linear relations. In this study we especially consider the
influence of varying the temporal size of the input-data sequence. The networks
are trained on data covering 6600 h, and tested on data covering 2100 h. It is
found that the initial and main phases of geomagnetic storms are well predicted,
almost independent of the length of the input-data sequence. However, to predict
the recovery phase, we have to use up to 20 h of solar-wind input data. The
recovery phase is mainly governed by the ring-current loss processes, and is
very much dependent on the ring-current history, and thus also the solar-wind
history. With due consideration of the time history when optimizing the
networks, we can reproduce 84% of the Dst variance. |
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