On the global model for f_oF₂ using neural networks

Abstract

[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.

A new global model for the ionospheric F2 peak height for radio wave propagation

“…A new approach for global ionospheric parameters prediction based on neural network (NN) technique is studied by Altinay et al (1997), Wintoft and Cander (1999), Kumluca et al (1999), Tulunay et al (2000), McKinnell and Poole (2001), Poole and Poole (2002), Oyeyemi and Poole (2004), and Oyeyemi et al (2005). Xenos (2002) demonstrated the NN technique for single station modelling and regional mapping of M(3000)F2 in the European region.…”

Using a neural network to make operational forecasts of ionospheric variations and storms at Kokubunji, Japan

“…In particular, multi-perceptron neural networks using the error back propagation algorithm are very useful for modelling complicated phenomena because of their ability of non-linear function approximation (Rumelhart et al, 1986). Recently, they are applied to predicting the F-layer parameters in South Africa (McKinnell and Poole, 2004), short-term prediction of foF2 (Wintoft and Cander, 1999), short-term prediction of foF2 in Russia (Barkhatov and Renynov, 2004), temporal and spatial forecasting (Tulunay et al, 1999), foF2 storm forecasting in Europe (Wintoft and Cander, 2000), a global model for foF2 (Oyeyemi et al, 2005a), and global forecasting up to 5 hours in advance (Oyeyemi et al, 2005b). However, there is as yet no practical method to predict daily variations or to forecast ionospheric storms specific to the Japan's area.…”

Regional reference total electron content model over Japan based on neural network mapping techniques

“…and observed ionospheric parameters (foF2, h'F2, hmF2, etc. ) (Williscroft and Poole, 1996;McKinnell and Poole, 2004;Oyeyemi et al, 2005), short-term forecasting of ionospheric conditions (Altinay et al, 1997;Cander et al, 1998;Kumluca et al, 1999;Wintoft and Cander, 2000;Poole and McKinnell, 2000;Oyeyemi et al, 2006), and long-term trend analyses (Poole and Poole, 2002;Yue et al, 2006). Because of the input-output mapping features of NNs, they could be used to generate reference ionospheric models for possible incorporation into the IRI (McKinnell and Friedrich, 2007).…”