Estimation of Ionospheric Critical Plasma Frequencies From GNSS-TEC Measurements Using Artificial Neural Networks

This paper describes a new neural network-based approach to estimate ionospheric critical plasma frequencies (f(0)F2) from Global Navigation Satellite Systems (GNSS)-vertical total electron content (TEC) measurements. The motivation for this work is to provide a method that is realistic and accurate for using GNSS receivers (which are far more commonly available than ionosondes) to acquire f(0)F2 data. Neural networks were employed to train vertical TEC and corresponding f(0)F2 observations respectively obtained from closely located GNSS receivers and ionosondes in various parts of the globe. Available data from 52 pairs of ionosonde-GNSS receiver stations for the 17-year period from 2000 to 2016 were used. Results from this work indicate that the relationship between f(0)F2 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 f(0)F2 and TEC was also shown to exhibit spatial variation; the variation is less conspicuous for closely located stations. The results also show that there is a good correlation between the f(0)F2 and TEC parameters. The f(0)F2/TEC ratio was generally observed to be lower during enhanced ionospheric ionizations in the day time and higher during reduced ionospheric ionizations in the nights and early mornings. The analysis of errors shows that the model developed in this work (known as the NNT2F2 model) can be used to estimate the f(0)F2 from GNSS-TEC measurements with accuracies of less than 1 MHz. The new approach described in this paper to obtain f(0)F2 based on GNSS-TEC data represents an important contribution in space weather prediction. Plain Language Summary Ionospheric critical plasma frequency (known as f(0)F2 for short) represents the value of radio frequency below which radio signals are reflected by the ionosphere. It is therefore an important information for radio communicators to be able to understand the paths of their radio propagation between transmitters and receivers; f(0)F2 is usually derived from ionosondes/digisondes that are expensive and sparsely located across the globe. On the other hand, Global Navigation Satellite Systems receivers have been used to measure the ionospheric TEC (total electron content), and they are much more abundantly located across the globe. This research presents a new method that is based on the application of artificial neural networks to derive f(0)F2 from TEC. It offers a computer program that can be used on Global Navigation Satellite Systems receivers to derive f(0)F2 values from TEC measurements. This therefore makes f(0)F2 data to be much more spatially available.