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The ionospheric dynamics is highly influenced by the solar radiation. During a solar eclipse, the moon occults the solar radiation from reaching the ionosphere, which may drastically affect the variability of the ionosphere. The variability of total electron content (TEC) observed by dual frequency Global Positioning System (GPS) receivers has made it possible to study effects of solar eclipse on the ionosphere. Total eclipse occurred on November 03, 2013, and the maximum amplitude was visible at Owiny in northern Uganda. Ionospheric behavior during this eclipse was analysed by using TEC data archived at Mbarara (MBAR), Malindi (MAL2), Eldoret (MOIU), and Kigali University (NURK) International GPS Satellite (IGS) stations. TEC variations of four consecutive days were used to study instantaneous changes of TEC during the eclipse event. The results generally show TEC decrease at the four stations. However, a maximum perturbation amplitude of ≥20 TECU was observed at MAL2 (18:00–20:00 UT) which is further south of the equator than the other stations. TEC enhancement and depletion were observed during the totality of the eclipse at MOIU, MBAR, NURK, and MAL2 (13:00–15:00 UT). This study found out that the ionospheric TEC over East Africa was modified by wave-like energy and momentum transport and obscuration of the solar disc due to the total solar eclipse.

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Abstract. In this paper, an empirical total electron content (TEC) model and trends in the TEC over the African low-latitude region are presented. GPS-derived TEC data from Malindi, Kenya (geographic coordinates 40.194∘ E, 2.996∘ S), and global ionospheric maps (GIMs) were used. We employed an empirical orthogonal function (EOF) analysis method together with least-squares regression to model the TEC. The EOF-based TEC model was validated through comparisons with GIMs, the GPS-derived TEC and the TEC derived from the International Reference Ionosphere 2016 (IRI-2016) model for selected quiet and storm conditions. The single-station EOF-based TEC model over Malindi satisfactorily reproduced the known diurnal, semiannual and annual variations in the TEC. Comparison of the EOF-based TEC model results with the TEC derived from the IRI-2016 model showed that the EOF-based model predicted the TEC over Malindi with fewer errors than the IRI-2016. For the selected storms, the EOF-based TEC model simulated the storm time TEC response over Malindi better than the IRI-2016. In the case of the regional model, the EOF-based TEC model was able to reproduce the TEC characteristics in the equatorial ionization anomaly region. The EOF-based TEC model was then used as a background for estimating TEC trends. A latitudinal dependence in the trends was observed over the African low-latitude region.

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