Investigation of the relationship between f0f2 and the TEC using single station neural network models

Abstract

The relationship between the ionospheric critical plasma frequencies (f0F2) and GNSS-TEC (Global Navigation Satellite System –Total Electron Content) measurements was investigated using an artificial neural network method. About 20 pairs of ionosonde-GNSS receiver stations from 2000 to 2016 were used. Results from this work indicate that the relationship between f₀F2 and TEC is mostly affected by the seasons, followed by the level of solar activity, and then the local time. Geomagnetic activity was the least significant of the factors investigated. The relationship between f₀F2 and TEC was also shown to exhibit spatial variation; the variation is less conspicuous for closely located stations. Single station models predicted the f₀F2 more accurately at their particular localities and clearly overestimated values of the f₀F2 ionosonde observations when used at different localities. This finding indicates that model predictions are better (in terms of reduced prediction errors) for the stations for which they are developed than for a different station. Our result visibly point out that models developed for a particular station cannot be effectively applied in another station located farther apart in space. The new approach described in this study represents an important contribution in space weather prediction.