Advanced GNSS for Ionospheric Sounding and Disturbances Monitoring

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Upper Atmosphere".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1179

Special Issue Editors


E-Mail Website
Guest Editor
GNSS Research Center, Wuhan University, Wuhan 430079, China
Interests: broadcast ionospheric models; GNSS ionospheric modeling; signal bias estimation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
Interests: GNSS precise point positioning (PPP); tropospheric parameter estimation; low Earth orbit (LEO) navigation augmentation
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: GNSS theory and algorithm; ionospheric model based on artificial intelligence; GNSS broadcasting ionospheric model; geomagnetic storms; ionospheric disturbances; GNSS precise point positioning; cycle slip detection

Special Issue Information

Dear Colleagues,

The ionosphere is an important part of the Earth’s upper atmosphere. Many space weather events, such as solar wind, solar flare, coronal mass ejection, and geomagnetic storm, can cause ionospheric disturbances. Natural disasters, such as earthquakes, volcanic eruptions, tsunamis, and typhoons, can also trigger ionospheric distrubances. The global navigation satellite system (GNSS) is a well-established ionospheric observing system which can accurately sense ionospheric total electron content, ionosphric density, ionospheric storms, travelling ionospheric disturbances, sudden ionospheric disturbances, and so on. The advanced GNSS has heralded a new era of ionospheric sounding and severe space weather monitoring. The effective monitoring and timely forecasting of severe space weather events are expected to help prevent disasters and protect lives on our planet. To take advantage of the advanced GNSS technique, this Special Issue mainly focuses on papers that address a series of topics, including (but not limited to) the following:

  • Advanced GNSS ionoshperic sounding and data processing;
  • The data mining of ionospheric products;
  • The monitoring of ionospheric disturbances using GNSS techniques;
  • Severe space weather events forecasting;
  • Ionospheric disturbances triggered by solar wind, solar flare, and cornal mass ejection;
  • Ionospheric disturbances triggered by earthquakes, volcanic eruptions, tsunamis, and typhoons.

Miscellaneous interdisciplinary studies and new applications in ionosphere, atmosphere, meteorology, and climatology fields are also welcome.

Dr. Qiang Zhang
Dr. Guangxing Wang
Dr. Xing Su
Guest Editors

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Keywords

  • GNSS
  • severe space weather event forecasting
  • ionospheric modelling and monitoring
  • ionospheric disturbances
  • ionospheric total electron content
  • ionosphric density
  • ionospheric storm
  • travelling ionospheric disturbances
  • miscellaneous applications

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Published Papers (1 paper)

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Research

13 pages, 6096 KiB  
Article
Enhanced Forecasting of Global Ionospheric Vertical Total Electron Content Maps Using Deep Learning Methods
by Yang Lin, Hanxian Fang, Die Duan, Hongtao Huang, Chao Xiao and Ganming Ren
Atmosphere 2024, 15(11), 1319; https://doi.org/10.3390/atmos15111319 - 2 Nov 2024
Viewed by 790
Abstract
The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI [...] Read more.
The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI forecasting methods, termed the Global Ionospheric Maps Forecast Enhancement Model (GIMs-FEM). The results indicated that by extracting the GIM features from existing forecasts and incorporating additional proxies for geomagnetic and solar activity, the GIMs-FEM provided stable and reliable forecasting outcomes. Compared to the original forecasting models, the overall model error was reduced by approximately 15–17% on the test dataset. Furthermore, we analyzed the model’s performance under different solar activity conditions and seasons. Additionally, the RMSE for the C1pg model ranged from 0.98 TECu in the solar minimum year (2019) to 6.91 TECu in the solar maximum year (2014), while the enhanced GIMs (C1pg) model ranged from 0.91 to 5.75 TECu, respectively. Under varying solar activity conditions, the RMSE of GIMs-FEM for C1pg (C2pg) ranged from 0.98 to 6.91 TECu (0.96 to 7.26 TECu). Seasonally, the GIMs-FEM model performed best in the summer, with the lowest RMSE of 1.9 TECu, and showed the highest error in the autumn, with an RMSE of 2.52 TECu. Full article
(This article belongs to the Special Issue Advanced GNSS for Ionospheric Sounding and Disturbances Monitoring)
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