Response of the auroral electrojets to the solar wind modeled with neural networks

Details Response of the auroral electrojets to the solar wind modeled with neural networks Response of the auroral electrojets to the solar wind modeled with neural networks

Jul 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997jgr...10214269g&link_type=abstract

Journal of Geophysical Research, Volume 102, Issue A7, p. 14269-14278

Physics

30

Magnetospheric Physics: Solar Wind/Magnetosphere Interactions

Scientific paper

The dissipative processes in the Earth's magnetosphere, such as the ring current and the auroral electrojets, depend on both the external solar wind forcing and factors internal to the magnetosphere. Previous studies have shown that artificial neural networks are able to compute the ring current index Dst very accurately from only solar wind data. In this study, we use neural networks to model the response of the auroral electrojets to the solar wind conditions. The solar wind input to the networks consist of 5-min averaged data from the Earth-orbiting spacecraft IMP 8, while the output is the auroral electrojet index AE. The relationships between the solar wind and the AE index, as modeled by the neural networks, are investigated in a parameter study. The relative importance of individual solar wind variables is studied, as well as the abilities of various coupling functions. It is shown that the use of individual solar wind variables as input to a neural network is superior to the use of corresponding coupling functions. The nonlinear neural networks are related to earlier linear techniques, and the abilities of linear networks (linear filters) are compared to those of nonlinear networks. It is found that a nonlinear network with n, V, By, and Bz as input during 100 min can account for 76% of the variance (r~0.87) in the AE index. No influence of Bx is found. With the coupling function p1/2V2Bs as input to a nonlinear network, 71% of the AE index variance is predicted. These results are averaged over a large test set (~330hours) of data not used to train the networks. The test data are from 1973-1974 and include a diverse set of conditions, ranging from almost quiet to exceptionally disturbed.

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