Ionospheric variability is influenced by many factors, such as solar radiation, neutral atmosphere composition, and geomagnetic disturbances. Mainly characterized by the total electron content(TEC) and electron densit...Ionospheric variability is influenced by many factors, such as solar radiation, neutral atmosphere composition, and geomagnetic disturbances. Mainly characterized by the total electron content(TEC) and electron density, the climatology of the ionosphere features temporal and spatial changes. Establishing a multivariant regression model helps substantially in better understanding the ionosphere characteristics and their long-term variability. In this paper, an improvement of the existing ionosphere multivariate linear fitting regression model is proposed and investigated using data from both the ionosonde and the global ionosphere map(GIM) derived from groundbased Global Navigation Satellite System(GNSS) observations. The proposed method gives more consideration to the impact of the solar activity and adds modeling of the annual periodic fluctuations and half-year periodic fluctuations for the F10.7 index. The improved model is verified to have a better correlation with the real observations and can help reduce the calculation uncertainty.Moreover, the proposed model is used to evaluate the fitting accuracy of the GIMs produced by five authorized data analysis centers from the International GNSS Service(IGS). The results show that there is a fixing hole in the North America region for the GIM model where the correlation between the GIM and the proposed model always returns lower values compared to other places.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 61521091, 61771030, 61301087)supported by 2011 Collaborative Innovation Center of Geospatial Technology。
文摘Ionospheric variability is influenced by many factors, such as solar radiation, neutral atmosphere composition, and geomagnetic disturbances. Mainly characterized by the total electron content(TEC) and electron density, the climatology of the ionosphere features temporal and spatial changes. Establishing a multivariant regression model helps substantially in better understanding the ionosphere characteristics and their long-term variability. In this paper, an improvement of the existing ionosphere multivariate linear fitting regression model is proposed and investigated using data from both the ionosonde and the global ionosphere map(GIM) derived from groundbased Global Navigation Satellite System(GNSS) observations. The proposed method gives more consideration to the impact of the solar activity and adds modeling of the annual periodic fluctuations and half-year periodic fluctuations for the F10.7 index. The improved model is verified to have a better correlation with the real observations and can help reduce the calculation uncertainty.Moreover, the proposed model is used to evaluate the fitting accuracy of the GIMs produced by five authorized data analysis centers from the International GNSS Service(IGS). The results show that there is a fixing hole in the North America region for the GIM model where the correlation between the GIM and the proposed model always returns lower values compared to other places.
