A new global TEC empirical model based on fusing multi-source data

Feng, Jiagui; Zhang, Ting; Li, Wang; Zhao, Zhenzhen; Han, Baomin; Wang, Qing

doi:10.1007/s10291-022-01355-8

Cited by 14 publications

(7 citation statements)

References 42 publications

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“…As we know, a strong geomagnetic storm can easily trigger large‐scale ionospheric perturbations driven by prompt penetration of the eastward electric field, thermospheric composition change, neutral winds, etc. (Feng et al., 2023; Li et al., 2022; Nava et al., 2016; Qian et al., 2012). The storm‐effect ionospheric change and plasma fluctuation driven by volcanic eruption could have interfered with each other.…”

Section: Discussionmentioning

confidence: 99%

Asymmetric Ionospheric Fluctuations Over the Circum‐Pacific Regions Following the January 2022 Tonga Volcanic Eruption

Li,

Zhu,

Feng

et al. 2023

Space Weather

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The Hunga Tonga‐Hunga Ha'apai volcanic eruption on 15 January 2022 had a significant impact on the ionosphere‐thermosphere system, resulting in large‐scale ionospheric irregularities with longitudinal and latitudinal asymmetries. Multiple instruments recorded these irregularities, indicating the propagation of a westward wave at an average velocity of 354 ± 8 m/s, which led to plasma irregularities of 0.2 TECu/min. Conversely, an eastward‐propagating wave was detected on the Pacific's east coast, traveling at a speed of 348 ± 6 m/s, with a corresponding decrease in plasma fluctuations to 0.1 TECu/min. In Asia, noticeable plasma irregularities appeared within a few hours after the eruption, and the maximum speed exceeded 1,100 m/s, which cannot be explained by the acoustic wave model. There was also a significant latitudinal asymmetry of ionospheric disturbances in the Asian‐Oceania sector, with the plasma density around Oceania depleted by 2–3 orders of magnitude within the altitudes of ∼150–575 km, while the ion density over Asia was enhanced by 1–2 orders of magnitude, and was uplifted ∼50 km. The plasma temperature was proportional to ion density, indicating the ion temperature reduced ∼500 K and increased 100–200 K around Oceania and Asia, respectively. The equatorial electric field, vertical E × B drifts and thermospheric O/N2 density ratio also fluctuated significantly following the eruption, indicating the redistribution of charged particles due to the magnetic field mapping effect, which was the main contributor to the asymmetries observed.

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Section: Discussionmentioning

confidence: 99%

Asymmetric Ionospheric Fluctuations Over the Circum‐Pacific Regions Following the January 2022 Tonga Volcanic Eruption

Li,

Zhu,

Feng

et al. 2023

Space Weather

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“…Meanwhile, Feng et al. (2023) employed spherical harmonic (SH) to fuse multi‐source TEC data, for example, satellite altimetry data, occultation data, and TEC value provided by the IRI‐2016 model, considering ionospheric anomalies by using the nonlinear least‐squares method. Moreover, Ren et al.…”

Section: Introductionmentioning

confidence: 99%

“…The different TEC values relative to ground-based GNSS data were estimated synchronously in modeling. Meanwhile, Feng et al (2023) employed spherical harmonic (SH) to fuse multi-source TEC data, for example, satellite altimetry data, occultation data, and TEC value provided by the IRI-2016 model, considering ionospheric anomalies by using the nonlinear least-squares method. Moreover, Ren et al (2020) presented the performance results of an estimated global ionospheric model augmented by an LEO constellation with 192 satellites using simulated data for the first time.…”

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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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“…IGS uses observations from more than 500 TEC stations and sophisticated data processing methods to fill data gaps, mainly in the ocean area, to obtain a complete global IGS‐TEC map. However, the global TEC empirical model established with the TEC grid data of IGS as the background has poor prediction accuracy in ocean areas, and its ability to describe some ionospheric anomalies is insufficient (Feng et al., 2022; Li et al., 2019). The main reason is that the deployments of GNSS receivers are currently limited to continent, however, 70% of the Earth's surface covered by oceans has not yet been explored for consecutive and effective ionospheric measurements.…”

Section: Introductionmentioning

confidence: 99%

Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN

Zhou

et al. 2023

Space Weather

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Total electron content (TEC) is a quantitative measurement of ionospheric columnar electron content within the distance from satellite to ground receiving station. The TEC is not only the key characteristic of ionospheric morphology, but also generally utilized in ionospheric correction related to precise positioning, navigation and radio wave science. Since 1998, the Massachusetts Institute of Technology (MIT) has been collecting and processing TEC observations with high spatial and temporal resolution (MIT-TEC) from more than 6,000 GPS observatories around the world (Vierinen et al., 2016). Due to the limited coverage of GPS receiver stations that measure TEC data (A. J. Mannucci et al., 1998), the original MIT-TEC map is incomplete, limiting the study in ocean areas. The missing portion of the MIT-TEC data is about 52% of the world. Over the past few decades, Global Navigation Satellite System (GNSS) has become one of the main technical means of earth ionosphere detection due to its advantages of all-weather, receiver's easy-to-deploy and high precision (Davies, 1990;A. Mannucci et al., 1999). Since the establishment of the Ionospheric Working Group by International GNSS Services (IGS) in 1998, the Global Ionospheric TEC Grid (IGS-TEC), as an important part of the GNSS precision products released Abstract Timely, reliable and comprehensive global observation information is essential for space weather research. However, limited observation technology hinders the consecutive global coverage of observation data. For the integrity and continuity of the global observation data, deep learning can obtain a global Ionospheric total electron content (TEC) map by fusing multi-source TEC maps. Different from the previous methods, in the study, a deep learning hybrid model (RFGAN) based on Dual-Discriminator Conditional Generative Adversarial Network (DDcGAN) and Free-Form Image Inpainting with Gated Convolution (Deepfill v2) is proposed to fuse the Massachusetts Institute of Technology (MIT)-TEC, International Global Navigation Satellite System TEC (IGS-TEC) and altimetry satellite TEC. Throughout the RFGAN structure, we use an autoencoder model with gated convolution to inpaint the missing parts of MIT-TEC and altimetry satellite TEC. Meanwhile, DDcGAN fuses the inpainted MIT-TEC (MIT'-TEC) and IGS-TEC to get a global TEC map with high accuracy. To a certain extent, we inpainted the ocean area of MIT-TEC through RFGAN. At the same time, RFGAN keeps the consistency of RFGAN-TEC and MIT-TEC in the continent area. Our proposed deep learning hybrid model can be easily extended and widely applied to other fields of space science, especially in addressing observational data loss and multi-source data fusion.Plain Language Summary Ionospheric total electron content (TEC) maps are very important for satellite navigation, shortwave communications and space weather research. There are many TEC map data from different sources, and their TEC show significant difference. Massachusetts Institute of Technology (MIT)-TEC and altimetry ...

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A new global TEC empirical model based on fusing multi-source data

Cited by 14 publications

References 42 publications

Asymmetric Ionospheric Fluctuations Over the Circum‐Pacific Regions Following the January 2022 Tonga Volcanic Eruption

Asymmetric Ionospheric Fluctuations Over the Circum‐Pacific Regions Following the January 2022 Tonga Volcanic Eruption

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

Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN

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