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Titel |
Estimating the geoeffectiveness of halo CMEs from associated solar and IP parameters using neural networks |
VerfasserIn |
J. Uwamahoro, L. A. McKinnell, J. B. Habarulema |
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 ; 30, no. 6 ; Nr. 30, no. 6 (2012-06-12), S.963-972 |
Datensatznummer |
250017235
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Publikation (Nr.) |
copernicus.org/angeo-30-963-2012.pdf |
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Zusammenfassung |
Estimating the geoeffectiveness of solar events is of significant
importance for space weather modelling and prediction. This paper describes
the development of a neural network-based model for estimating the
probability occurrence of geomagnetic storms following halo coronal mass
ejection (CME) and related interplanetary (IP) events. This model
incorporates both solar and IP variable inputs that characterize geoeffective
halo CMEs. Solar inputs include numeric values of the halo CME angular width
(AW), the CME speed (Vcme), and the comprehensive flare index (cfi), which
represents the flaring activity associated with halo CMEs. IP parameters used
as inputs are the numeric peak values of the solar wind speed (Vsw) and the
southward Z-component of the interplanetary magnetic field (IMF) or Bs. IP
inputs were considered within a 5-day time window after a halo CME eruption.
The neural network (NN) model training and testing data sets were constructed
based on 1202 halo CMEs (both full and partial halo and their properties)
observed between 1997 and 2006. The performance of the developed NN model was
tested using a validation data set (not part of the training data set) covering
the years 2000 and 2005. Under the condition of halo CME occurrence, this
model could capture 100% of the subsequent intense geomagnetic storms
(Dst ≤ −100 nT). For moderate storms (−100 < Dst ≤ −50), the model is
successful up to 75%. This model's estimate of the storm occurrence rate
from halo CMEs is estimated at a probability of 86%. |
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