Mapping of the Ionospheric Total Electron Content over the East African Low–Latitude Region

Abstract

Ionospheric behaviour is often monitored using total electron content (TEC) measurements from Global Navigation Satellite System (GNSS) receivers. However, the GNSS receivers are sparse over the East African low-latitude region, resulting in spatio-temporal gaps. In an attempt to address this challenge, the possibility of constructing East African ionospheric maps using Kriging method was investigated in this study. Prior to constructing the maps, the behaviour of the low-latitude ionosphere over this region was characterised using TEC data for 2014 and 2018 during quiet and disturbed conditions. The results show that the highest values of TEC exist between 10:00 UT and 13:00 UT and the minimum values exist at around 03:00 UT. The seasonal trend of TEC reveals that the highest values of TEC occur during March equinox and the lowest during June solstice. The post-sunset TEC often show enhancements between 18:00 UT and 21:00 UT. The enhancements were more pronounced in 2014 than in 2018, especially during the equinoctial months for stations at the crest of the equatorial ionisation anomaly region. These enhancements occurred about an hour earlier at MAL2 than at ADIS during March and September, 2014. The spatial variation of TEC over the East African region was analysed by fitting different semivariogram models. The results reveal that the best semivariogram that describes the spatial variation of TEC over the East African low-latitude region is the Gaussian model. Further analysis shows that the spatial variation of TEC is larger in the meridional direction than in the zonal direction. The Gaussian semivariogram model was then used to construct the TEC maps over the region. The TEC maps were validated using TEC derived from GNSS receivers. The results show very high positive correlation with correlation coefficients between 0.9360 and 0.9970. This suggests that the TEC maps generated by the Kriging interpolation method can be used as good estimates of the GPS-derived TEC at the unmeasured grid points over the region. Therefore, the maps can be used to fill the gaps that exist in the TEC data over the region.