Two dimensional imaging of the ionospheric total electron content over East Africa

Abstract

Total Electron Content (TEC) is an important parameter for monitoring the effects of space weather. It is often obtained from a network of Global Positioning System (GPS) receivers. However, the GPS receivers are unevenly distributed in East Africa resulting in limited TEC data in some areas. In an attempt to address this challenge, a hybrid approach involving Finite Element Method (FEM) together with Ensemble Kalman Filter (EnKF) technique was used to construct TEC maps from the limited TEC observations. These maps offer crucial, detailed spatial information regarding TEC variations in the ionosphere. This information is valuable for applications such as global navigation, space weather monitoring, and scientific research. Before constructing the maps, the characteristics of TEC over the region during quiet and disturbed conditions for the high (2014) and low (2018) solar activity years were studied. Studying TEC variation before generating maps is important as it provides valuable insights into the behavior of the ionosphere, helps in constructing accurate TEC maps and supports various applications that rely on precise and uninterrupted ionospheric information. The results revealed that maximum TEC over East Africa occurred between 12:00 and 13:00 UT, while minimum TEC occurred at about 03:30 UT. The highest TEC values were observed in March equinox and the lowest in June solstice for both years. After sunset, TEC enhancements were observed between 18:00 UT and 21:00 UT especially in 2014 and enhancements at the crest of the anomaly occurred roughly an hour earlier than at the trough. The day maximum TEC values decreased with increasing magnetic latitude. More TEC fluctuations were observed in the Equatorial Ionization Anomaly (EIA) region during storm days, reflecting the disturbances in the ionosphere caused by geomagnetic storms. To construct the maps, an observation model was developed using the FEM, by making assumptions about the relationship between the measured data, the ionospheric TEC distribution, and the additive noise. Prior information was then integrated into the model and a tuning parameter was introduced to address data limitations. By running multiple Kalman filters in parallel, TEC images were generated through weighted averaging. The TEC maps clearly captured the diurnal variations in TEC over East Africa. An expected rise in TEC during the daytime and a decrease during the nighttime, indicating the influence of solar radiation on the ionosphere was shown. Furthermore, the maps revealed that TEC levels were highest during equinox months and lowest during solstice months, demonstrating a distinct seasonal trend. In addition, significant asymmetries related to equinoxes and solstices were observed. The TEC maps also provided valuable information about the response of the ionosphere to geomagnetic storms. During geomagnetic storm events, significant deviations in TEC values were observed, indicating disturbances in the ionospheric TEC. The maps were validated against selected GPS receivers. The results showed a strong positive correlation coefficient ranging between 0.98 and 0.99. This suggests that the constructed TEC maps align well with actual measured data and can be used as a good tool for predicting TEC over the East African low-latitude region.