Long-term trends in f0 F2 over Grahamstown using Neural Networks

Abstract

Many authors have claimed to have found long-term trends in f0 F2 , or the lack thereof, for different stations. Such investigations usually involve gross assumptions about the variation of f0 F2 with solar activity in order to isolate the long-term trend, and the variation with magnetic activity is often ignored completely. This work describes two techniques that make use of Neural Networks to isolate long-term variations from variations due to season, local time, solar and magnetic activity. The techniques are applied to f0 F2 data from Grahamstown, South Africa (26 E, 33 S). The maximum long-term change is shown to be extremely linear, and negative for most hours and days.

The maximum percentage change tends to occur in summer in the afternoon, but is noticeably dependent on solar activity. The effect of magnetic activity on the percentage change is not marked.