| Geomagnetic storms and substorms develop
under strong control of the solar wind. This is demonstrated by the fact that
the geomagnetic activity indices Dst and AE can be predicted from
the solar wind alone. A consequence of the strong control by a common source is
that substorm and storm indices tend to be highly correlated. However, a part of
this correlation is likely to be an effect of internal magnetospheric processes,
such as a ring-current modulation of the solar wind-AE relation.
The present work extends previous studies of nonlinear AE
predictions from the solar wind. It is examined whether the AE
predictions are modulated by the Dst index.This is accomplished by
comparing neural network predictions from Dst and the solar wind, with
predictions from the solar wind alone. Two conclusions are reached: (1) with an
optimal set of solar-wind data available, the AE predictions are not
markedly improved by the Dst input, but (2) the AE predictions are
improved by Dst if less than, or other than, the optimum solar-wind data
are available to the net. It appears that the solar wind-AE relation
described by an optimized neural net is not significantly modified by the
magnetosphere's Dst state. When the solar wind alone is used to predict AE,
the correlation between predicted and observed AE is 0.86, while the
prediction residual is nearly uncorrelated to Dst. Further, the finding
that Dst can partly compensate for missing information on the solar wind,
is of potential importance in operational forecasting where gaps in the stream
of real time solar-wind data are a common occurrence.
Key words. Magnetospheric physics (solar wind ·
magnetosphere interactions; storms and substorms) |