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Global Ionosphere Modeling Based on GNSS, Satellite Altimetry, Radio Occultation, and DORIS Data Considering Ionospheric Variation

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Journal of Geophysical Research: Space Physics
Authors:
Jun Chen at Minjiang University
Jun Chen
Xiaodong Ren at Wuhan University
Xiaodong Ren
Pengxin Yang at Wuhan University
Pengxin Yang
Guozhen Xu at Wuhan University
Guozhen Xu
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Abstract and Figures

The accuracy of ionospheric models estimated by ground‐based multiple global navigation satellite system ionospheric data over regions with sparse tracking stations is not ideal. To improve the accuracy of the estimated ionospheric model, different types of ionospheric data with different combinations were employed for previous studies. However, the ionospheric observational ranges for different types of ionospheric data are not the same. In this study, the accuracy of ionospheric maps generated by ground‐based ionospheric data (ground‐based strategy) and ground‐based ionospheric data combined with data provided by other geodetic measurements normalized by the single‐layer normalization method (multi‐source strategy) were studied. The results showed that the main differences between the ionospheric models estimated by the two strategies occur for data taken over the ocean, which mainly range from −1 to 0 total electron content unit (TECU). When assessed using Jason‐3 vertical total electron content data, the mean root mean square (RMS) value of the ionospheric model estimated by the multi‐source strategy was 5.03 TECU, which is approximately 15% smaller than that estimated by the ground‐based strategy. The maximum reduction in results using the multisource strategy was approximately 25% over different latitudes compared with that of the ground‐based strategy. Furthermore, the self‐consistency evaluation method was employed for evaluation. The results showed that the RMS of the ionospheric model estimated by the multi‐source strategy was 2.41 TECU, which is 3.60% better than that of the ground‐based strategy. The maximum reduction was 15% on different days.
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1. Introduction
The accuracy of the global ionosphere map (GIM) generated by the global navigation satellite system (GNSS)
ionospheric data is approximately 2–8 TECU (total electron content unit) over the past one and a half solar
cycles (Hernández-Pajares etal.,2009,2017; Li etal.,2021; Rovira-Garcia etal.,2019). With the development
of multi-GNSS, scholars have studied the performance of ionospheric models estimated by multi-GNSS iono-
spheric data either on a regional or global scale (Li etal.,2015; Zhang etal.,2015). However, compared with the
ionospheric models estimated by GNSS ionospheric data, the improvement of ionospheric models estimated by
multi-GNSS ionospheric data is limited due to the lack of data over regions with sparse tracking stations.
To improve the accuracy of the ionospheric model, different types of ionospheric data have been employed over
the past decade. Todorova etal.(2008) generated GIMs using GNSS and dual-frequency altimeter data provided
by the Jason-2 satellite (satellite altimetry data) to improve the data coverage over the ocean. Since then, scholars
have investigated the performance of GIMs generated by different approaches. The first approach uses the combi-
nation of ground-based GNSS data, radio occultation data, and satellite altimetry data (Alizadeh etal.,2011).
The weight of different kinds of ionospheric data was given as constant. In addition, the least-squares method
was applied for estimation. The difference of observational range was neglected. The second approach uses
the combination of ground-based GNSS data, ionospheric empirical models, radio occultation data, satellite
altimetry data, and very long baseline interferometry (VLBI) data (Dettmering, Heinkelmann, & Schmidt,2011;
Dettmering, Schmidt, etal.,2011). The iterative maximum likelihood variance component estimation was used
to calculate the weight of different kinds of ionospheric data and the coefficients of B-splines for modeling. The
offsets for different ionospheric data relative to ground-based GNSS data were treated as constants. The third
approach uses the combination of ground-based GNSS data, radio occultation data, and satellite altimetry data
Abstract The accuracy of ionospheric models estimated by ground-based multiple global navigation
satellite system ionospheric data over regions with sparse tracking stations is not ideal. To improve the accuracy
of the estimated ionospheric model, different types of ionospheric data with different combinations were
employed for previous studies. However, the ionospheric observational ranges for different types of ionospheric
data are not the same. In this study, the accuracy of ionospheric maps generated by ground-based ionospheric
data (ground-based strategy) and ground-based ionospheric data combined with data provided by other geodetic
measurements normalized by the single-layer normalizationmethod (multi-source strategy) were studied. The
results showed that the main differences between the ionospheric models estimated by the two strategies occur
for data taken over the ocean, which mainly range from −1 to 0 total electron content unit (TECU). When
assessed using Jason-3 vertical total electron content data, the mean root mean square (RMS) value of the
ionospheric model estimated by the multi-source strategy was 5.03 TECU, which is approximately 15% smaller
than that estimated by the ground-based strategy. The maximum reduction in results using the multisource
strategy was approximately 25% over different latitudes compared with that of the ground-based strategy.
Furthermore, the self-consistency evaluationmethod was employed for evaluation. The results showed that the
RMS of the ionospheric model estimated by the multi-source strategy was 2.41 TECU, which is 3.60% better
than that of the ground-based strategy. The maximum reduction was 15% on different days.
CHEN ETAL.
© 2023. American Geophysical Union.
All Rights Reserved.
Global Ionosphere Modeling Based on GNSS, Satellite
Altimetry, Radio Occultation, and DORIS Data Considering
Ionospheric Variation
Jun Chen1,2 , Xiaodong Ren2 , Pengxin Yang2, Guozhen Xu2, Liangke Huang3 , Si Xiong4, and
Xiaohong Zhang2,5
1Department of Surveying and Mapping Engineering, Minjiang University, Fuzhou, China, 2School of Geodesy and
Geomatics, Wuhan University, Wuhan, China, 3College of Geomatic and Geoinformatics, Guilin University of Technology,
Guilin, China, 4School of Resources and Environmental Science and Engineering, Hubei University of Science and
Technology, Xianning, China, 5Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, China
Key Points:
• The single-layer normalization method
is employed to make multi-source data
have the same observational range
compared with those of ground-based
global navigation satellite system data
• The distribution of multi-source data
has better coverage, especially over
the oceanic region
• The main differences between the
ionospheric models estimated by
the ground-based strategy and the
multi-source strategy occur over the
ocean
Correspondence to:
X. Ren,
renxiaodongfly@gmail.com
Citation:
Chen, J., Ren, X., Yang, P., Xu, G.,
Huang, L., Xiong, S., & Zhang, X.
(2023). Global ionosphere modeling
based on GNSS, satellite altimetry,
radio occultation, and DORIS data
considering ionospheric variation.
Journal of Geophysical Research: Space
Physics, 128, e2023JA031514. https://doi.
org/10.1029/2023JA031514
Received 22 MAR 2023
Accepted 4 SEP 2023
Author Contributions:
Conceptualization: Jun Chen
Data curation: Pengxin Yang, Guozhen
Xu, Liangke Huang, Si Xiong
Formal analysis: Jun Chen, Pengxin Yang
Investigation: Jun Chen
Methodology: Xiaodong Ren
Software: Jun Chen, Xiaodong Ren, Si
Xiong
Supervision: Xiaohong Zhang
Validation: Jun Chen, Xiaodong Ren,
Si Xiong
Visualization: Guozhen Xu
Writing – original draft: Jun Chen
Writing – review & editing: Xiaodong
Ren, Liangke Huang, Xiaohong Zhang
10.1029/2023JA031514
RESEARCH ARTICLE
1 of 14
... Fortunately, Low Earth Orbit (LEO) satellite constellations can provide navigation enhancement services in the future. The accuracy of the estimated ionospheric model combined with LEO data can be improved (Chen et al., 2023;Ren, Zhang, et al., 2020) since the ionospheric data provided by LEO satellites can better cover the region with sparse ground-based GNSS stations. Further, if LEO satellites and globally distributed continuously operating reference stations can provide real-time observations in the future, there will be a large amount of data. ...
... The estimated differential carrier-phase hardware delays are used to correct the delays in real-time ionospheric observation, obtaining bias-free real-time ionospheric observation. The detailed modeling method and processing steps for ionosphere modeling augmented by available LEO satellite data for the current stage can be found in Chen et al. (2023). ...
Method and Validation of Real‐Time Global Ionosphere Modeling Constraint by Multi‐Source GNSS/LEO Data
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... Given the above, we conclude that the TEC agreement between the JPL-GIM and the Jason altimeter measurements is acceptable, in agreement with other products (Li et al. 2023), and comparable with errors obtained by other GIM methodologies (Lay et al. 2022;Chen et al. 2023). ...
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The Jet Propulsion Laboratory (JPL) develops JPL-GIM, a software for generating global ionospheric maps (GIMs) of total electron content (TEC) using measurements from multiple Global Navigation Satellite System (GNSS) constellations. Within this overview paper, we delve into the current state and the most recent enhancements of JPL-GIM. An adaptable Kalman filter provides maps with user-defined temporal and spatial resolutions, while concurrently delivering essential covariance data for uncertainty assessment. Furthermore, multiple shell models offer a versatile framework to balance accuracy and computational efficiency. We present the five operational JPL GIM products (JPLG, JPRG, JPLI, JPLD, JPRT), highlighting JPLG and JPRG, our products routinely delivered to the International GNSS Service (IGS), and introduce a new near-real-time product (JPRT). As an added demonstration of JPL-GIM’s capabilities, we present a very-high-resolution (2-minute, multi-GNSS, 1000-station) configuration to showcase JPL-GIM’s ability to resolve long-lasting effects of the 2022 Hunga Tonga-Hunga Ha’apai eruption. Validations using independent datasets confirm the accurate reproduction of ionospheric variations across all latitudinal bands.
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