An investigation of a new artificial neural network-based TEC model using ground-based GPS and COSMIC-2 measurements over low latitudes

Abstract

Mapping the ionosphere over low-latitude regions with a high accuracy is a challenging task due to the complex nature of the ionospheric structure. In this study, the artificial neural network (ANN) technique was used to investigate the determination of the total electron content (TEC) in low latitude regions using measurements from 187 ground-based International global navigation satellite system (GNSS) Service (IGS) stations and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2) mission in the period from 1 October 2019 to 30 September 2021. To reduce the systematic discrepancies between the space-borne and ground-based observational systems, the ANN technique was also utilized to calibrate the COSMIC-2 derived TECs to the ground level. The RMSE of the calibrated COSMIC-2 TECs was 1.78 TECU. This, in comparison with the RMSE value of 4.10 TECU for the uncalibrated COSMIC-2 TECs, demonstrated 57% improvement. It is found that the performance of the new model was strongly associated with the value of TEC and low-latitude ionospheric structure, such as the equatorial ionization anomaly (EIA). Compared with the IRI-2016 model, the Technical University of Catalonia (UPC) rapid global ionospheric maps (GIMs) (UQRG), the Center for Orbit Determination in Europe (CODE) final GIMs (CODG), and the Chinese Academy of Sciences (CAS) final GIMs (CASG), the new model outperformed the IRI-2016 model and was competitive with the GIMs. All results suggest that the new model developed using both ground-based and space-borne measurements together with the ANN technique can model the ionospheric structure, such as the EIA. Therefore, the new model developed may become a good reference for the studies of the structure and variation of the ionosphere over low latitudes.