Prediction of the Dst index from solar wind parameters by a neural network method
© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences. 2002
Received: 1 June 2001
Accepted: 17 October 2002
Published: 21 June 2014
Using the Elman-type neural network technique, operational models are constructed that predict the Dst index two hours in advance. The input data consist of real-time solar wind velocity, density, and magnetic field data obtained by the Advanced Composition Explorer (ACE) spacecraft since May 1998 (http://www2.crl.go.jp/uk/uk223/service/nnw/index.html). During the period from February to October 1998, eleven storms occurred with minimum Dst values below -80 nT. For ten of these storms the differences between the predicted minimum Dst and the minimum Dst calculated from ground-based magnetometer data were less than 23%. For the remaining one storm (beginning on 19 October 1998) the difference was 48%. The discrepancy is likely to stem from a imperfect correlation between the solar wind parameters near ACE and those near the earth. While the IMF Bz remains to be the most important parameter, other parameters do have their effects. For instance, Dst appears to be enhanced when the azimuthal direction of IMF is toward the sun. A trapezoid-shaped increase in the solar wind density enhances the main phase Dst by almost 10% compared with the case of no density increase. Velocity effects appear to be stronger than the density effects. Our operational models have, in principle, no limitations in applicability with respect to storm intensity.