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

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

Jul 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002angeo..20..947f&link_type=abstract

Annales Geophysicae, vol. 20, Issue 7, pp.947-951

Mathematics

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3

Scientific paper

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, S

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