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Global Monitoring of Geomagnetic Storm‐Induced Ionosphere Anomalies Using 3‐D Ionospheric Modeling With Multi‐GNSS and COSMIC Measurements
Author(s) -
Cheng Na,
Song Shuli,
Jiao Guoqiang,
Jin Xulei,
Li Wei
Publication year - 2021
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1029/2020rs007074
Subject(s) - ionosphere , gnss applications , space weather , global positioning system , remote sensing , geomagnetic storm , glonass , geology , meteorology , geophysics , earth's magnetic field , environmental science , geodesy , computer science , geography , physics , telecommunications , magnetic field , quantum mechanics
Ionosphere is a signicant component of the solar‐terrestrial space environment. Geomagnetic storm induces global ionospheric disturbances, severely affect radio communications and human space activities, e.g., earth observation, deep space exploration, and space weather monitoring and prediction. Monitoring ionospheric anomalies are critical to improve the performance of Global Navigation Satellite System (GNSS) positioning, navigation and timing (PNT) and provide early‐warning of disaster service during the extreme space weather event. The spatial and temporal variation of ionospheric electron density (IED) is utilized to characterize the ionospheric anomalies respond to storm. Thus, the global‐scale three‐dimensional (3‐D) ionospheric model is constructed by computerized ionospheric omography (CIT) technique combine multi‐GNSS(GPS/GLONASS/BDS/Galileo) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements. As ground‐based multi‐GNSS and space‐based Low Earth Orbit (LEO)/GNSS observation networks expand gradually, massive measurements are employed in ionospheric inversion in high temporal‐spatial resolution. Hence, the Open Multi‐Processing (OpenMP) parallel computing method is applied to improve the efciency of imaging 3‐D ionosphere. The processing time reduces to within 10 mins, thus 3‐D ionospheric model updates in near real‐time is achievable. Moreover, the 3‐D IED model is applied to monitor the ionosphere dynamically during storms and the storm‐induced ionospheric anomalies are observed. This contribution suggests our reconstructed 3‐D model is capable of reecting the ionospheric anomalies during storm in global‐scale, also it reveals the characteristics of the ionosphere respond to the storm and its evolution.