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.
Highlights
The ionospheric quantity ƒ0F2 is well known to vary with season; diurnally; and with solar activity and magnetic activity
The choice of two months for F2 and two days for A16 was based on the results of an independent investigation in which Neural Network (NN) were trained with input variables of different lengths, the optimum length being chosen as that length which produced the minimum rms error (Williscroft and Poole, 1996)
The calculated uncertainty in the evaluation of ƒ0F2 from the NNs varies slightly with the input parameters, but is of the order of 0.03 MHz, well below the long-term changes made evident by this investigation
Summary
The ionospheric quantity ƒ0F2 is well known to vary with season (day number, DN); diurnally (hour LT, HR); and with solar activity and magnetic activity. We need to consider a fifth variation, which we can call Long-Term Trend (LTT). We approached the problem using two techniques
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