Skip to main content


You are viewing the new article page. Let us know what you think. Return to old version

Article | Open | Published:

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


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 ( 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.


  1. Akasofu, S.-I., Predicting Geomagnetic Storms as a Space Weather Project, Geophys. Monogr. Ser., 125, edited by P. Song, H. J. Singer, and G. L. Siscoe, AGU Washington DC, 329–337, 2001.

  2. Blanchard, G. T. and R. L. McPherron, A bimodal representation of the response function relating the solar wind electric field to the AL index, Solar-Terrestrial Prediction Proc., vol. 2, pp. 479–486, U. S. Dept. of Commerce, Boulder, Colo., 1992.

  3. Burton, R. K., R. L. McPherron, and C. T. Russel, An empirical relationship between interplanetary conditions and Dst, J. Geophys. Res., 80, 4204–4214, 1975.

  4. Ebihara, Y. and M. Ejiri, Modeling of solar wind control of the ring current buildup: A case study of the magnetic storms in April 1997, Geophys. Res. Lett., 25, 3751–3754, 1998.

  5. Ebihara, Y. and M. Ejiri, Simulation study on fundamental properties of the storm-time ring current, J. Geophys. Res., 105, 15,843–15,859, 2000.

  6. Elman, J. L., Finding structure in time, Cognitive Sci., 14, 179, 1990.

  7. Feldstein, Y. I., Modeling of the magnetic field of magnetospheric ring current as a function of interplanetary medium parameters, Space Sci. Rev., 59, 83–165, 1992.

  8. Iyemori, T., H. Maeda, and T. Kamei, Impulse response of geomagnetic indices to interplanetary magnetic field, J. Geomag. Geoelectr., 31, 1–9, 1979.

  9. Kamide, Y. and N. Fukushima, Analysis of magnetic storms with DR-indices for equatorial ring current field, Rep. Ionoshere Space Res. Japan, 25, 125–162, 1971.

  10. Klimas, A. J., D. Vassiliadis, and D. N. Baker, Data-derived analogues of the magnetospheric dynamics, J. Geophys. Res., 102, 26,993–27,009, 1997.

  11. Klimas, A. J., D. Vassiliadis, and D. N. Baker, Dst index prediction using data-derived analogues of the magnetospheric dynamics, J. Geophys. Res., 103, 20,435–20,447, 1998.

  12. Kugblenu, S., S. Taguchi, and T. Okuzawa, Prediction of the geomagnetic storm associated Dst index using an artificial NN algorithm, Earth Planet Sci., 51, 307–313, 1999.

  13. Lundstedt, H. and P. Wintoft, Prediction of geomagnetic storms from solar wind data with the use of a neural network, Ann. Geophys., 12, 19–24, 1994.

  14. McComas, D. J., S. J. Bame, P. Barker, W. C. Feldman, J. L. Phillips, P. Riley, and J. W. Griffee, Solar wind electron proton alpha (SWEPAM) for the advanced composition explorer, Space Sci. Rev., 86, 563–612, 1998.

  15. O’Brien, T. P. and R. L. McPherron, An empirical phase space analysis of ring current dynamics: Solar wind control of injection and decay, J. Geophys. Res., 105, 7707–7719, 2000.

  16. Smith, C. W., J. L’Heureux, N. F. Ness, M. H. Acuna, L. F. Burlaga, and J. Scheifele, The ACE magnetic fields experiment, Space Sci. Rev., 86, 613–631, 1998.

  17. Wu, J.-G. and H. Lundstedt, Geomagnetic storm predictions from solar wind data with the use of dynamic neural networks, J. Geophys. Res., 102, 14,255–14,268, 1997a.

  18. Wu, J.-G. and H. Lundstedt, Neural network modeling of solar wind-magnetosphere interaction, J. Geophys. Res., 102, 14,457–14,466, 1997b.

  19. Zwickl, R. D., K. A. Dogget, S. Sahm, W. P. Barrett, R. N. Grubb, T. R. Detman, V. J. Raben, C. W. Smith, P. Riley, R. E. Gold, R. A. Mewaldt, and T. Maruyama, The NOAA real-time solar-wind (RTSW) system using ACE data, Space Sci. Rev., 86, 633–648, 1998.

Download references

Author information

Correspondence to Shigeaki Watanabe.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark


  • Solar Wind
  • Magnetic Storm
  • Solar Wind Velocity
  • Solar Wind Parameter
  • Solar Wind Density