Neural networks (NNs) have been used to solve many problems associated with geophysics. This paper describes a new application of NNs to determine optimum parameters for the prediction of foF2, as well as the first application of NNs to the prediction of foF2. We have trained several NNs to predict the noon value of foF2 at Grahamstown (33°S, 26°E) using season, solar activity and magnetic activity as input data, taken over one sunspot cycle (1973–1983). Using the criterion that the best indices of solar and magnetic activity are the ones that give the lowest rms error between predicted and measured foF2, we have determined optimum averaging lengths for these indices. Our optimum index for solar activity is a two month running mean of daily sunspot number, in contrast to the value of 1 year recommended by the International Reference Ionosphere (IRI). Our determination for magnetic activity is a running mean of ak of two days. A neural net trained with these optimum indices can predict the daily noon value of foF2 in Grahamstown with an rms error of .95 MHz. The rms error of the monthly average is .48 MHz.

Several papers have been written about the observed relationship between ground based magnetometer pulsation measurements and simultaneous oscillations of Doppler frequency shifts in ionospherically reflected radio frequency echoes. In this paper we derive the mechanisms which relate these observations for the vertical incidence case at low to mid latitudes. We investigate the effects of oscillating electric and magnetic fields at ionospheric heights on the phase path of the reflected radio wave, which gives rise to the Doppler shifts. We identify three main mechanisms at work in the ionosphere, which are applicable at all latitudes; however, our numerical computations do not take account of certain high‐latitude effects. By quantifying the electric and magnetic pulsation fields for a partially reflected downcoming Alfvén wave, we derive a quantitative phase and amplitude relationship between the rate of change of phase path on Doppler velocity and the pulsation magnetic field measured on the ground. A surprising result is that the dominant mechanism is not necessarily the vertical component of bulk plasma movement, as has been previously suggested. In many cases, the dominant mechanism is compression and rarefaction of plasma frozen onto the field lines as they oscillate under the action of the field‐aligned component of the pulsation magnetic field.

[1] The use of neural networks (NNs) has been employed in this work to develop a global model of the ionospheric F 2 region critical frequency, f o F 2 . The main principle behind our approach has been to utilize parameters other than simple geographic coordinates, on which f o F 2 is known to depend, and to exploit the ability of NNs to establish and model this nonlinear relationship for predictive purposes. The f o F 2 data used in the training of the NNs were obtained from 59 ionospheric stations across the globe at various times from 1964 to 1986, on the basis of availability. To test the success of this approach, one NN (NN1) was trained without data from 13 stations, selected for their geographic remoteness, which could then be used to validate the predictions of the NN for those remote coordinates. These stations were subsequently included in our final NN (NN2). The input parameters consisted of day number (day of the year), universal time, solar activity, magnetic activity, geographic latitude, angle of meridian relative to subsolar point, magnetic dip angle, magnetic declination, and solar zenith angle. Comparisons between f o F 2 values determined using NNs and the International Reference Ionosphere (IRI) model (from Union Radio Scientifique Internationale (URSI) and International Radio Consultative Committee (CCIR) coefficients) with observed values are given with their root-mean-square (RMS) error differences for test stations. The results from NN2 are used to produce the global behavior of hourly values of f o F 2 and are compared with the IRI model using URSI and CCIR coefficients. The results obtained (i.e., RMS error differences), which compare favorably with the IRI models, justify this technique for global f o F 2 modeling.

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In this paper we refine the earlier work (Poole et al., 1988) in which we derived three mechanisms relating geomagnetic pulsations to simultaneous Doppler velocity oscillations in a vertically incident, ionospherically reflected radio wave. We show that the generally held belief that field‐aligned electron velocities associated with the corresponding currents can be ignored is unfounded in the case of magnetic pulsations. These field‐aligned velocities contribute significantly to two of the three mechanisms identified in the earlier work. Diagrams are presented to compare our results with those previously obtained by us and other workers using various simplifying assumptions.