The first regional total electron content (TEC) model over the entire African region (known as AfriTEC model) using empirical observations is developed and presented. Artificial neural networks were used to train TEC observations obtained from Global Positioning System receivers, both on ground and onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate satellites for the African region from years 2000 to 2017. The neural network training was implemented using inputs that enabled the networks to learn diurnal variations, seasonal variations, spatial variations, and variations that are connected with the level of solar activity, for quiet geomagnetic conditions (−20 nT ≤ Dst ≤ 20 nT). The effectiveness of three solar activity indices (sunspot number, solar radio flux at 10.7‐cm wavelength [F10.7], and solar ultraviolet [UV] flux at 1 AU) for the neural network trainings was tested. The F10.7 and UV were more effective, and the F10.7 was used as it gave the least errors on the validation data set used. Equatorial anomaly simulations show a reduced occurrence during the June solstice season. The distance of separation between the anomaly crests is typically in the range from about 11.5 ± 1.0° to 16.0 ± 1.0°. The separation is observed to widen as solar activity levels increase. During the December solstice, the anomaly region shifts southwards of the equinox locations; in year 2012, the trough shifted by about 1.5° and the southern crest shifted by over 2.5°.

The Sun is the major driver of space weather events, and as a result, most applications requiring modeling/forecasting of space weather phenomena depend largely on the activities of Sun. Accurate modeling of solar activity parameters like the sunspot number (SSN) is therefore considered significant for the quantitative modeling of space weather phenomena. Sunspot number forecasts are applied in ionospheric models like the International Reference Ionosphere model and in several other projects requiring prediction of space weather phenomena. A method called Hybrid Regression‐Neural Network that combines regression analysis and neural network learning is used for forecasting the SSN. Considering the geomagnetic Ap index during the end of the previous cycle (known as the precursor Ap index) as a reliable measurement, we predict the end of solar cycle 24 to be in March 2020 (±7 months), with monthly SSN 5.4 (±5.5). Using an estimated value of precursor Ap index as 5.6 nT for solar cycle 25, we predict the maximum SSN to be 122.1 (±18.2) in January 2025 (±6 months) and the minimum to be 6.0 (±5.5) in April 2031 (±5 months). We found from the model that on changing the assumed value of precursor Ap index (5.6 nT) by ±1 nT, the predicted peak of solar cycle 25 changes by about 11 sunspots for every 1‐nT change in the assumed precursor Ap index.

It has been shown in ionospheric research that modelling total electron content (TEC) during storm conditions is a big challenge. In this study, TEC modelling was performed over Sutherland (32.38°S, 20.81°E, 41.09°S geomagnetic), South Africa, during storm conditions, using a combination of empirical orthogonal function (EOF) and regression analyses techniques. The neural network (NN) technique was also applied to the same TEC data set, and its output was compared with TEC modeled using the EOF model. TEC was derived from GPS observations, and a geomagnetic storm was defined for Dst≤−50 nT. The hour of the day and the day number of the year, F_10.7p and A indices, were chosen as inputs for the modeling techniques to take into account diurnal and seasonal variation of TEC, solar, and geomagnetic activities, respectively. Both EOF and NN models were developed using GPS TEC data for storm days counted from 1999 to 2013 and tested on different storms. For interpolation, the EOF and NN models were validated on storms that occurred during high and low solar activity periods (storms of 2000 and 2006), while for extrapolation the validation was done for the storms of 2014 and 2015, identified based on the provisional Dst index data. A comparison of the modeled TEC with the observed TEC showed that both EOF and NN models perform well for storms with nonsignificant ionospheric TEC response and storms that occurred during period of low solar activity. For storms with significant TEC response, TEC magnitude is well captured during the nighttime and early morning, but short‐term features, TEC enhancement, and depression are not sufficiently captured by the models. Statistically, the NN model performs 12.79% better than the EOF model on average, over all storm periods considered. Furthermore, it has been shown that the EOF and NN models developed for a specific station can be used to estimate TEC over other locations within a latitudinal and longitudinal coverage of 8.7° and 10.6°, respectively. This is an important result as it reduces the data dimensionality problem for computational purposes.