While the formation of equatorial electrojet (EEJ) and its temporal variation is believed to be fairly well understood, the longitudinal variability at all local times is still unknown. This paper presents a case and statistical study of the longitudinal variability of dayside EEJ for all local times using ground-based observations. We found EEJ is stronger in the west American sector and decreases from west to east longitudinal sectors. We also confirm the presence of significant longitudinal difference in the dusk sector pre-reversal drift, using the ion velocity meter (IVM) instrument onboard the C/NOFS satellite, with stronger pre-reversal drift in the west American sector compared to the African sector. Previous satellite observations have shown that the African sector is home to stronger and year-round ionospheric bubbles/irregularities compared to the American and Asian sectors. This study's results raises the question if the vertical drift, which is believed to be the main cause for the enhancement of Rayleigh–Taylor (RT) instability growth rate, is stronger in the American sector and weaker in the African sector – why are the occurrence and amplitude of equatorial irregularities stronger in the African sector?

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.

This paper presents the development of a storm‐time total electron content (TEC) model over the African sector for the first time. The storm criterion used was |Dst| ≥ 50 nT and Kp ≥ 4. We have utilized Global Positioning System (GPS) observations from 2000 to 2018 from about 252 receivers over the African continent and surroundings within spatial coverage of 40°S–40°N latitude and 25°W–60°E longitude. To increase data coverage in areas devoid of ground‐based instrumentation including oceans, we used the available radio occultation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) TEC from 2008 to 2018. The model is based on artificial neural networks which are used to learn the relationship between TEC and the corresponding physical/geophysical input parameters representing factors which influence ionospheric variability. An important result from this effort was the inclusion of the time history of the geomagnetic activity indicators which improved TEC modeling by about 5% and 12% in middle and low latitudes, respectively. Overall, the model performs comparatively well with, and sometimes better than, the earlier single station modeling efforts even during quiet conditions. Given that this is a storm‐time model, this result is encouraging since it is challenging to model ionospheric parameters during geomagnetically disturbed conditions. Statistically, the average root‐mean‐square error (RMSE) between modeled and GPS TEC is 5.5 TECU (percentage error = 30.3%) and 5.0 TECU (percentage error = 30.4%) for the Southern and Northern Hemisphere midlatitudes respectively compared to 7.5 TECU (percentage error = 22.0%) in low latitudes.