文章基本信息

标题：Ground GNSS Station Selection to Generate the Global Ionosphere Maps Using the Information Content
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作者：Sicheng Wang ; Sixun Huang ; Hanxian Fang 等
期刊名称：Space Weather
印刷版ISSN：1542-7390
出版年度：2022
卷号：20
期号：1
页码：1-12
DOI：10.1029/2020SW002675
语种：English
出版社：American Geophysical Union
摘要：More and more satellite constellations and ground receivers are available all over the world, and they can provide rich ionospheric remote sensing data sources. Due to the fact that different analysis centers use different generation techniques and different ground stations, their routinely released Global Ionosphere Maps (GIMs) have some differences. To study the problems of how many stations and which stations should be optimally chosen to generate the GIMs, a step-by-step iterative method based on the information content is proposed under the framework of 3DVAR data assimilation. This method adopts the information content to qualitatively evaluate the information contribution of a measurement to the reconstructed GIM, and selects the ground station with a maximum information content in every iteration. About 500 ground receivers from the International GNSS Services networks and Multi-GNSS Experiment networks are collected from DOY 001 to DOY 120 in 2018 to demonstrate the feasibility of this method. The results show that the information contribution from the first 100 selected stations is over 70%, and the information contribution from the first 200 chosen stations is up to 87%. When the station selection criterion is taken as 95% of the total information content, nearly 300 stations are needed to reconstruct each GIM. Moreover, the quantitative information contribution from GPS, GLONASS, GALILEO, and BEIDOU satellite constellations is preliminarily analyzed.