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Ann. Geophys., 20, 947-951, 2002
www.ann-geophys.net/20/947/2002/
© European Geosciences Union 2002
Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: effects of recurring substorms
M. Fukata1, S. Taguchi1, T. Okuzawa1, and T. Obara2
1Dept. of Information and Communication Engineering, University of Electro-Communications, Chofu, 182-8585, Japan
2Communications Research Laboratory, Koganei, 184-8795, Japan
Correspondence to: S. Taguchi (taguchi@ice.uec.ac.jp)
Abstract. During the recovery phase
of geomagnetic storms, the flux of relativistic (>2 MeV) electrons at
geosynchronous orbits is enhanced. This enhancement reaches a level that can
cause devastating damage to instruments on satellites. To predict these
temporal variations, we have developed neural network models that predict the
flux for the period 1–12 h ahead. The electron-flux data obtained during
storms, from the Space Environment Monitor on board a Geostationary
Meteorological Satellite, were used to construct the model. Various
combinations of the input parameters AL, SAL,
Dst and SDst were tested (where S
denotes the summation from the time of the minimum Dst). It was found
that the model, including SAL as one
of the input parameters, can provide some measure of relativistic electron-flux
prediction at geosynchronous orbit during the recovery phase. We suggest from
this result that the relativistic electron-flux enhancement during the recovery
phase is associated with recurring substorms after Dst minimum and their
accumulation effect.
Key words. Magnetospheric physics
(energetic particles, trapped; magnetospheric configuration and dynamics;
storms and substorms)
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