Ionospheric foF2 storm forecasting using neural networks

The ionosphere shows a large degree of variability on time scales from hour s to the solar cycle length. This variation is associated with magnetospher ic storms, the Earth's rotation, the season, and the level of solar activit y. To make accurate predictions of key ionospheric parameters all. these va riations must be considered. Neural networks, which are data driven non-lin ear models, are very useful for such tasks. In this work we examine if the F2 layer plasma frequency, foF2, at a single ionospheric station can be pre dicted 1 to 24 hours in advance by using information of past foF2 observati ons, magnetospheric activity, and time as inputs to neural networks. Partic ular attention has been paid to periods when great geomagnetic storms were in progress with the aim to develop a successful ionospheric storm forecast ing tool. (C) 2000 Elsevier Science Ltd. All rights reserved