Magnetosphere: Prediction of the Dst index from solar wind parameters by a neural network method

Abstract

Using Elman-type neural network technique, operational models are constructed that predict the Dst index 2 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. 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 minimum Dst calculated from ground-based magnetometer data were less than 23 percent. For the remaining one storm (beginning on 19 October 1998) the difference was 48 percent. 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 B(sub z) 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 percent compared with the case of no density increase. Velocity effects appear to be stronger than the density effects. Operational models have, in principle, no limitations in applicability with respect to storm intensity.