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Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650093, China
Interests: Ionospheric modelling; space weather monitoring; seismic–ionospheric effect; deep learning

Prof. Dr. Jinyun Guo

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College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266510, China
Interests: GNSS TEC; ionosphere anomaly; seismology–ionosphere coupling; GNSS atmosphere; space geodesy
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Dr. Dongsheng Zhao

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Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China
Interests: GNSS; Ionospheric scintillation; positioning and navigation

Dr. Craig M. Hancock

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School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: GNSS; ionospheric delay; digital construction; geospatial
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Special Issue Information

Dear Colleagues,

The ionosphere reflects and modifies radio waves used for communication and the Global Navigation Satellite System (GNSS), highlighting the need for a comprehensive understanding of its variation. It contains day-to-day spatial/temporal variabilities and small-scale plasma disturbances, e.g., traveling ionospheric disturbance, ionospheric irregularities, and plasma bubbles. Hence, the development of high-accuracy ionospheric models remains challenging.

Rapid development of the cutting-edge multi-GNSS systems and low-orbit radio occultation systems, e.g., COSMIC-1/2, FY-3C/D, massive ground and spaceborne geodetic observations, provides unprecedented opportunities for high-accuracy ionospheric monitoring, modeling, and other associated applications. Taking advantage of these multi-source data, this Special Issue aims to present new studies on these topics, including (but not limited to):

Ground and spaceborne GNSS observation processing.
Ionospheric monitoring.
Ionospheric modeling.
Ionospheric irregularity and scintillation.
Ionospheric response to severe space events and natural disasters, e.g., earthquakes, typhoons, tsunamis, etc.
Other ionosphere-associated studies.

Dr. Wang Li
Prof. Dr. Jinyun Guo
Dr. Dongsheng Zhao
Dr. Craig M. Hancock
Guest Editors

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Keywords

GNSS
ionospheric monitoring
ionospheric modeling
ionospheric irregularities
ionospheric scintillation
ionospheric radio occultation
geomagnetic storm
ionospheric response to natural disasters

Published Papers (2 papers)

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Research

Open AccessArticle

An Ionospheric Total Electron Content Model with a Storm Option over Japan Based on a Multi-Layer Perceptron Neural Network

Wang Li

and

Xuequn Wu

Atmosphere 2023, 14(4), 634; https://doi.org/10.3390/atmos14040634 - 27 Mar 2023

Viewed by 283

Abstract

Ionospheric delay has a severe effect on reducing the accuracy of positioning and navigation of single-frequency receivers. Therefore, it is necessary to construct a precise regional ionospheric model for real-time Global Navigation Satellite System (GNSS) applications. The total electron contents (TECs) of 839 GNSS stations affiliated with the GPS Earth Observation Network were used to build a Japanese ionospheric model (JIM) based on a multi-layer perceptron neural network. During quiet space conditions, the correlation coefficient between the targets and the predictions of the JIM was about 0.98, and the root-mean square error (RMSE) of TEC residuals was ~1.5TECU, while under severe space events, the correlation coefficient increased to 0.99, and the corresponding RMSE dropped to 0.96 TECU. Moreover, the JIM model successfully reconstructed the two-dimensional (time vs latitude) TEC maps, and the TEC maps had evident hourly and seasonal variations. Most of TEC residuals accumulated between universal time 01–06 with an averaged magnitude of 1-2TECU. Furthermore, the JIM model had a perfect prediction performance under various kinds of complex space environments. In the quiet days, the prediction accuracy of the JIM was nearly equal to the global ionosphere map (GIM), and in some moments, the JIM was more competitive than the GIM. In the disturbed days, the RMSEs of TEC residuals were proportional to the solar wind speed and were inversely proportional to the geomagnetic Dst value. The maximum RMSE of the JIM was lower than 2TECU, while the corresponding RMSEs for the IRI and TIE-GCM exceeded 5TECU. Full article

(This article belongs to the Special Issue Observation and Modeling of Ionosphere: Recent Trends, Current Progress and Future Directions)

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Open AccessArticle

Integrated Analysis of Lithosphere-Atmosphere-Ionospheric Coupling Associated with the 2021 M_w 7.2 Haiti Earthquake

and

Atmosphere 2023, 14(2), 347; https://doi.org/10.3390/atmos14020347 - 09 Feb 2023

Cited by 1 | Viewed by 811

Abstract

The search for Earthquake (EQ) precursors in the ionosphere and atmosphere from satellite data has provided significant information about the upcoming main shock. This study presents the abnormal atmospheric and ionospheric perturbations associated with the M_w 7.2 Haiti EQ on 14 August 2021 at geographical coordinates (18° N, 73° W) and shallow hypocentral depth of 10 km from the data of permanent Global Navigation Satellite System (GNSS) stations near the epicenter, followed by Swarm satellites data. The total vertical electron (VTEC) anomalies occur within a 5-day window before the main shock in the analysis of nearby operation stations, followed by Swarm (A and C satellites) ionospheric anomalies in the same 5-day window before the main shock. Moreover, the geomagnetic activities are completely quiet within 10 days before and 10 days after the main shock. Similarly, the atmospheric parameters endorse the EQ anomalies within 5 days before the main shock day. The evolution of gases from the lithosphere at the epicentral region possessed significant atmospheric and ionospheric perturbations within the EQ preparation period of 5-day before the main shock under the hypothesis of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC). Full article

(This article belongs to the Special Issue Observation and Modeling of Ionosphere: Recent Trends, Current Progress and Future Directions)

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