Forecasting auroral electrojet activity from solar wind input with neural networks

Details Forecasting auroral electrojet activity from solar wind input with neural networks Forecasting auroral electrojet activity from solar wind input with neural networks

May 1999

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1999georl..26.1353w&link_type=abstract

Geophysical Research Letters, Volume 26, Issue 10, p. 1353-1356

Physics

20

Magnetospheric Physics: Forecasting, Ionosphere: Auroral Ionosphere, Ionosphere: Current Systems, Magnetospheric Physics: Storms And Substorms

Scientific paper

Neural networks are developed for reconstructing the chaotic attractor in the nonlinear dynamics of the solar wind driven, coupled magnetosphere-ionosphere (MI) system. Two new methods which improve predictive ability are considered: a gating method which accounts for different levels of activity and a preconditioning algorithm which allows the network to ignore very short time fluctuations during training. The two networks are constructed using the Bargatze et al. [1985] substorm database that contains solar wind speed and interplanetary magnetic field (IMF) along with ionospheric electrojet index, AL. Both networks are found to produce improvements in predictability, and the significance of the performance increase of the gated network is demonstrated using the bootstrap model testing method.

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