Comparative analysis of the GNSS-TEC methods for determining effective height of the ionosphere

The ionosphere over the low latitude region, where East Africa falls, is associated with high perturbations arising from numerous phenomenon such as Equatorial Ionization Anomaly (EIA), plasma bubbles, electrostatic instabilities etc. All these together, make difficult to determine the actual values of important parameters used to characterize the ionosphere of the region in order to mitigate its negative contribution to technology. One such parameter is the effective height which is used among others to map the Total electron Content (TEC) over a given region, in argumentation, calibration of Global Navigation Satellite System (GNSS) observations etc. value of the effective height of a place is got by radiosounders installed in a given location. However, these sounders are not readily available in many places, especially East Africa where the few that are available are currently malfunctioning and also their ionospheric tial coverage is very limited. Alternative techniques that use TEC from GNSS receivers to estimate effective height of the ionosphere have been suggested and employed in different places, giving results that in some instances conflict with each other. This study has done comparative analysis of the suggested GNSS-TEC techniques to determine their reliability over low latitude region of East Africa and has found out that some of them do not give consistent results over the region. To supplement the existing methods, a new method is proposed that relates IRI TEC to TEC obtained from the difference between sTEC recorded simultaneously by a given receiver ground and the receiver on board Swarm satellite when swarm is over head the ground receiver. The results obtained using the new gested technique gives consistent results over East African region which is in agreement with two other techniques among the four methods discussed. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.