Abstract
The global ionosphere map (GIM) is not capable of serving precise positioning and navigation for single frequency receivers in Australia due to sparse International GNSS Service (IGS) stations located in the vast land. This study proposes an approach to represent Australian total electron content (TEC) using the spherical cap harmonic analysis (SCHA) and artificial neural network (ANN). The new Australian TEC maps are released with an interval of 15 min for longitude and latitude in 0.5° × 0.5°. The validation results show that the Australian Ionospheric Maps (AIMs) well represent the hourly and seasonally ionospheric electrodynamic features over the Australian continent; the accuracy of the AIMs improves remarkably compared to the GIM and the model built only by the SCHA. The residual of the AIM is inversely proportional to the level of solar radiation. During the equinoxes and solstices in a solar minimum year, the residuals are 2.16, 1.57, 1.68, and 1.98 total electron content units (TECUs, 1 TECU = 1016 electron/m2), respectively. Furthermore, the AIM has a strong capability in capturing the adequate electrodynamic evolutions of the traveling ionospheric disturbances under severe geomagnetic storms. The results demonstrate that the ANN-aided SCHA method is an effective approach for mapping and investigating the TEC maps over Australia.
Highlights
It is well known that the ionospheric delay effect is one of the major error sources for global navigation satellite system (GNSS) radio signal propagation
The long-term dual-frequency observations obtained from the Australian Region GPS Network (ARGN) network are used to develop the Australian total electron content (TEC) model by the spherical cap harmonic analysis method optimized by the artificial neural network technique, and the performances of the Australian TEC model are evaluated over large spatial-temporal scales and under quiet-disturbed space weather conditions compared to the global ionosphere map (GIM)
The promising results indicate the GNSS stations of the ARGN network are sufficient to develop an Australian ionospheric model for mapping TEC using the spherical cap harmonic analysis (SCHA) method, and the artificial neural network (ANN) technique could improve the accuracy of TEC mapping approximately 8–10%, compared to the Australian TEC model built by only the SCHA method
Summary
It is well known that the ionospheric delay effect is one of the major error sources for global navigation satellite system (GNSS) radio signal propagation. With the growing demands of high-accuracy GNSS services, the traditional global ionospheric models with low spatial-temporal resolutions cannot meet the requirement for regional applications [6]. In this condition, some regional TEC models with higher mapping accuracy are developed using the observations derived from regional dense GNSS stations. A spherical cap harmonic model was developed with a data set from 40 GNSS stations for mapping and predicting TEC in China [7], this model maps regional TEC values with higher accuracy and has a powerful capability in predicting ionospheric variations based on spectrum analysis and least-squares collocation. The obtained results further confirm the usefulness of the SCHA method for building near-real-time regional maps as well as its expanding potential application in other ionospheric parameters modeling
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