Electron Density Reconstruction by Ionospheric Tomography From the Combination of GNSS and Upcoming LEO Constellations

Ren, Xiaodong; Mei, Dengkui; Zhang, Xiaohong; Freeshah, Mohamed; Xiong, Si

doi:10.1029/2020ja029074

Cited by 10 publications

(7 citation statements)

References 46 publications

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“…The ionosphere as the ionized part of the atmosphere has crucial implications for telecommunication systems, satellite navigation users, and power facilities (Board & National Research Council, 2013). High‐accuracy ionospheric models, including two‐dimensional and three‐dimensional ionospheric models, are still being developed (Ren, Mei et al., 2021; Ren, Zhang et al., 2021). The ionospheric predictions are of essential importance for certain scientific research and technological applications.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation

et al. 2022

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The application of deep learning technology to ionospheric prediction has become a new research hotspot. However, there are still some gaps, such as the prediction effect with different input solar and geomagnetic activity parameters, and the forecast accuracy with different prediction methods as well as the validation of long period data results, to be filled. We developed an ionospheric long short‐term memory network (Ion‐LSTM) with multiple input parameters to predict the global ionospheric total electron content (TEC). Two solutions with different ionospheric data based on Ion‐LSTM were assessed, namely spherical harmonic coefficients (SHC) and vertical TEC (VTEC) prediction solution. The results show two solutions, both perform well in accuracy and stability. The input of the geomagnetic activity index improves the prediction effect of the model in the storm period. For the 1‐ and 2‐day‐predicted global ionospheric maps (GIMs) from 2015 to 2020, the root mean square error (RMSE) of SHC prediction solution is 1.69 TECU and 1.84 TECU while that of the VTEC prediction solution is 1.70 TECU and 1.84 TECU, respectively. Over 70% of the absolute residuals are within 3 TECU in high solar activity and over 96% in low solar activity. Further, by comparing the predicted results between Ion‐LSTM and conventional methods (e.g., Center for Orbit Determination in Europe (CODE) predicted GIMs), the evaluation results show that the RMSE of Ion‐LSTM is 0.7 TECU lower than that of CODE predicted GIMs under different solar and geomagnetic activities. Additionally, the accuracy of the Ion‐LSTM prediction results decreases slightly as the input time span increases.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation

et al. 2022

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“…Ren, Mei, et al. (2021) showed that the improvement of ionospheric tomography is not as significant as that of the SLN method. In terms of the SLN method, it is easier to process since it only needs the NeQuick2 model to calculate the ratio value that depends on the solar activity index.…”

Section: Discussionmentioning

confidence: 99%

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

Chen,

Ren,

Yang

et al. 2023

JGR Space Physics

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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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“…Based on simulated data, it has been demonstrated that LEO‐based ionospheric observations have better coverage and a higher density of IPPs in polar and ocean regions (Ren, Zhang, et al., 2020). Besides, the rays of LEO satellites flying rapidly had better geometric distributions than ground‐based GNSS rays, which can be used to reduce the number of voxels with no observations (Ren et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Due to the fact that GNSS receivers are usually positioned in an imbalanced manner, with high data coverage over land but not over ocean areas, there are still regions in the ionospheric inversion that are not crossed by GNSS rays. These regions devoid of rays lead to data gaps (Jayawardena et al., 2016; Nesterov & Kunitsyn, 2011; Ren et al., 2021; Schmidt et al., 2008). The incompleteness of input data results in the typical ill‐posed problem of the tomographic system (Nesterov & Kunitsyn, 2011; Wen et al., 2008; Wen, Liu, & Tang, 2010; Yao, Tang, et al., 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Generally, there are a finite number of satellites available for a single LEO mission. Ren et al (2021) explored the performances and benefits of incorporating the LEO-based ionospheric data for global 3D ionospheric tomography through simulation experiments. However, it is unknown whether combining ground-based ionospheric data with real space-borne ionospheric data from a dozen LEO satellites will enhance tomographic performance.…”

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confidence: 99%

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Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

et al. 2023

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Global monitoring and exploration of ionospheric space weather are highly demanded for accurate and precise representations of electron density (F. S. Prol, Kodikara, et al., 2021). There have been well-established techniques for measuring ionospheric electron density (IED), exemplified by ionosondes, incoherent scatter radars, and in situ probes (Peng et al., 2021;Smirnov et al., 2021). However, such measurements are typically constrained to a single place (Norberg et al., 2018;Pryse, 2003). Computerized ionospheric tomography (CIT), first proposed by Austen et al. (1988), has developed into a new approach for imaging the IED and comprehending ionospheric plasma dynamics. Although the Navy Navigational Satellite System's polar-orbiting satellites at an altitude of approximately 1,100 km can produce two-dimensional ionospheric images via radio tomography, these satellites can only be monitored via a series of ground receivers aligned in longitude but separated in latitude (Fremouw et al., 1992;Leitinger et al., 1997;Pryse & Kersley, 1992). Following that, it was demonstrated how to reconstruct the three-dimensional ionospheric structures using high-orbit satellites, specifically the global navigation satellite

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Electron Density Reconstruction by Ionospheric Tomography From the Combination of GNSS and Upcoming LEO Constellations

Abstract: The global navigation satellite system (GNSS) is characterized by continuous and global coverage at low cost, high temporal and spatial resolution, and high accuracy, thus being widely used as a significant tool for global or regional ionospheric modeling and monitoring (

Cited by 10 publications

References 46 publications

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation

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

Global Three‐Dimensional Ionospheric Tomography by Combination of Ground‐Based and Space‐Borne GNSS Data

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