Abstract

Ionospheric models play a crucial role in understanding, prediction, and mitigation of the effects of ionospheric variability on a wide range of technological and scientific applications relying on space-based services. Conversely, the models need to be routinely updated with newer datasets and specifications to account for the regional discrepancies in the changing ionospheric conditions due to various dominant localized physical and chemical processes. Although there have been ongoing improvements to the extensively utilized empirical model known as the International Reference Ionosphere (IRI), the newly emerged version (IRI-2020) needs to undergo global testing. In this research, we carried out diurnal and seasonal variations in GPS-TEC and the assessment of some ionospheric models such as IRI-2016 and its recently updated version (IRI-2020), alongside the NeQuick-2 model at 2 stations each in the East, West and South in the equatorial and low-latitude African longitudes during different phases of solar cycles 24–25 (2016 – 2021). Also, we carried out statistical analysis between GPS-TEC and the ionospheric models using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), in order to show the model with the best forecasting capability in the African region. Diurnal, seasonal and solar cycle variations in GPS-TEC, NeQuick-2, IRI-2016 and IRI-2020 were observed, showing higher magnitudes in the West, followed by the East in close range and least in the Southern sector of the African longitudes. TEC Variations in some sectors in the African longitudes show a consistent trend in this research. More importantly, there are observed regional differences within the African longitudes owing to the wider coverage of landmass in the equatorial and low latitudes. However, TEC variations in the Northern sector of Africa are not included in the present research. From our observation, NeQuick-2 and IRI-2016 models either underestimate or overestimate GPS-TEC during different phases of the solar cycles at the three sectors in the African longitudes, whereas IRI-2020 shows mostly underestimating characteristics at the three sectors irrespective of solar activity conditions during the study period. Nevertheless, the underestimation or overestimation of NeQuick-2 and IRI-2016, and the underestimation of IRI-2020 are reflected in the RMSE and MAE values. Regrettably, the predictions from IRI-2020 model are not satisfactory at any of the three sectors in the African longitudes and prompt attention of the modeling community for further investigations towards possible refinements in the model specifications.

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