Neural network prediction of relativistic electrons at geosynchronous orbit during the storm recovery phase: Effects of recurring substorms

M. Fukata, S. Taguchi, T. Okuzawa, T. Obara

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

During the recovery phase of geomagnetic storms, the flux of relativistic (>2MeV) 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, ∑ AL, Dst and ∑ Dst were tested (where ∑ denotes the summation from the time of the minimum Dst). It was found that the model, including ∑ AL 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.

Original languageEnglish
Pages (from-to)947-951
Number of pages5
JournalAnnales Geophysicae
Volume20
Issue number7
DOIs
Publication statusPublished - 2002
Externally publishedYes

Keywords

  • Magnetospheric physics (energetic particles, trapped; magnetospheric configuration and dynamics; storms and substorms)

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Geology
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)
  • Space and Planetary Science

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