Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

Zhang, Wen; Huo, Xingliang; Yuan, Yunbin; Li, Zishen; Wang, Ningbo

doi:10.3390/rs13194002

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…Universe 2022, 8, 98 2 of 21 relative variations. Zhang et al [12] investigated different strategies for using single-station TEC data (over a network) to update IRI model.…”

Section: Sibnet-siberian Network Of Gnss Receiversmentioning

confidence: 99%

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MITIGATOR: GNSS-Based System for Remote Sensing of Ionospheric Absolute Total Electron Content

et al. 2022

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Monitoring the Earth’s ionosphere is an important, fundamental and applied problem. Global Navigation Satellite Systems (GNSS) provide a way of measuring the ionospheric total electron content (TEC), but real-time single-station absolute TEC measurements are still a problem. This study describes a single-station system to measure the absolute TEC, based on the GNSS – MITIGATOR (MonITorInG the Absolute TOtal electRon content) system. The latter enables real-time measurements for the absolute TEC and its derivatives in time and in space to be obtained. The system is implemented by using JAVAD receivers. The convergence time and the run-mode retention time is ~8 h. We provide potential methods for using the system to estimate the critical frequency of the ionosphere, foF2, at oblique paths in the Siberian region. The developed tool could be useful for supporting real-time multi-instrumental ionosphere monitoring or for compensating for the ionospheric errors of radio equipment.

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“…Universe 2022, 8, 98 2 of 21 relative variations. Zhang et al [12] investigated different strategies for using single-station TEC data (over a network) to update IRI model.…”

Section: Sibnet-siberian Network Of Gnss Receiversmentioning

confidence: 99%

“…Magsi et al [11] described a system based on one receiver, but the data do not involve the absolute TEC, only its relative variations. Zhang et al [12] investigated different strategies for using single-station TEC data (over a network) to update IRI model.…”

Section: Introductionmentioning

confidence: 99%

MITIGATOR: GNSS-Based System for Remote Sensing of Ionospheric Absolute Total Electron Content

et al. 2022

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“…In the absence of measurement conditions or other extreme cases, TEC can be calculated by physical models [10,11], mathematical models [12,13], and empirical models [14,15]. Among them, the international reference ionosphere (IRI) is the most common empirical model and is also a well-known recommended international standard, which is often used as a comparison model to verify other models [16,17]. The IRI model has been steadily improved with newer data and better modeling techniques by the joint working group of the Committee on Space Research and the International Union of Radio Science (URSI) [18].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map

Liu

Wang²,

Yang³

et al. 2022

Remote Sensing

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In order to achieve the high-accuracy prediction of the total electron content (TEC) of the regional ionosphere for supporting the application of satellite navigation, positioning, measurement, and controlling, we proposed a modeling method based on machine learning (ML) and use this method to establish an empirical prediction model of TEC for parts of Europe. The model has three main characteristics: (1) The principal component analysis (PCA) is used to separate TEC’s temporal and spatial variation characteristics and to establish its corresponding map, (2) the solar activity parameters of the 12-month mean flux of the solar radio waves at 10.7 cm (F10.712) and the 12-month mean sunspot number (R12) are introduced into the temporal map as independent variables to reflect the temporal variation characteristics of TEC, and (3) The modified Kriging spatial interpolation method is used to achieve the spatial reconstruction of TEC. Finally, the regression learning method is used to determine the coefficients and harmonic numbers of the model by using the root mean square error (RMSE) and its relative value (RRMSE) as the evaluation standard. Specially, the modeling process is easy to understand, and the determined model parameters are interpretable. The statistical results show that the monthly mean values of TEC predicted by the proposed model in this paper are highly consistent with the observed values curve of TEC, and the RRMSE of the predicted results is 12.76%. Furthermore, comparing the proposed model with the IRI model, it can be found that the prediction accuracy of TEC by the proposed model is much higher than that of the IRI model either with CCIR or URSI coefficients, and the improvement is 38.63% and 35.79%, respectively.

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