Ionospheric variability density profile studies and electron with neural networks

Previously, the authors used Neural Networks (NNs) to predict the maximum electron density in the ionosphere over Grahamstown, South Africa (33.3 deg S, 26.5 deg E). Day number, hour, a 2 month running mean sunspot number (R2), and a 2-day running mean magnetic index (A16) were used as inputs to the NN. Two further applications of NNs are discussed in this paper. It discusses the use of NNs to quantify and describe the variability of ionospheric parameters. The parameter foF2 is used by way of illustration. It is suggested that variability can be well described in terms of a predictable, average response that depends on external variables such as latitude, longitude, day number, local time, sunspot number and magnetic activity, and a part that is unpredictable in the short term but nevertheless can be described in terms of a standard deviation. The need to be specific about the values of the external variables is stressed, and is illustrated with reference to the highly non-linear response of the ionosphere to combined seasonal and magnetic influences. The lack of a simple relationship between the average, predictable variability and the unpredictable part is illustrated. It is shown that NNs can provide an elegant method of quantifying variability with respect to any external parameter without the need to divide the data into categories for independent analysis. (Author)