Disturbances in the Ionosphere and Distortion of Radio Wave Characteristics That Accompanied the Super Typhoon Lekima Event of 4–12 August 2019

Zheng, Yu; Chernogor, L. F.; Гармаш, К. П.; Guo, Qiang; Rozumenko, V. T.; Luo, Y.

doi:10.1029/2022ja030553

Cited by 10 publications

(13 citation statements)

References 77 publications

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“…When the wave path and satellite line of sight are parallel to each other, the integration of wavefronts cancels the wave phases and thus results in minimal amplitudes (Bagiya et al, 2017). The amplitude of disturbances also shows differences under different methods (Zheng et al, 2022), the variations in the electron density are 3%-19%, while the disturbances in this study are less than 1%. This difference is most likely due to the "smoothing out" (Xu et al, 2019).…”

Section: Discussionmentioning

confidence: 56%

Characterization of Ionospheric Disturbances Following Multiple Typhoons Using GPS‐Derived TEC

Jin

2022

JGR Space Physics

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Typhoon is one of the major hazards in ocean coastal areas, but traditional techniques are inadequate to monitor typhoons due to limited or high‐cost observations, like radio sounding and meteorological radar. Previous studies have found that typhoons can cause ionospheric disturbances, but the relationship and characteristics are still unclear. In this paper, about 400 stations observations of the Global Positioning System (GPS) network in Taiwan are used to extract ionospheric disturbances during multiple typhoons. The detailed characteristics of the ionospheric disturbances are investigated using a fourth‐order Butterworth filter following the 2016 Nepartak, 2019 Lekima, 2019 Mitag, and 2020 Hagupit typhoons. The results show that significant ionospheric disturbances were observed during the typhoons, and the larger disturbances are mostly located 400–1200 km far from the typhoon eye. The estimated horizontal propagation velocity of the ionospheric disturbances is about 127–194 m/s. The locations of the ionospheric disturbances between the typhoon eye and the landfall site are related to the typhoon path. The azimuth distribution of the ionospheric disturbance around the typhoon eye is affected by the GPS elevation angles. At 500–700 km from the typhoon eye, the mean ionospheric disturbances are 0.17 TECU (TEC Unit) and 0.15 TECU for super typhoon Nepartak and Lekima, and 0.13 TECU and 0.18 TECU for typhoon Mitag and Hagupit. The higher the intensity of the typhoon is, the greater the magnitude of the ionospheric disturbance is.

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Section: Discussionmentioning

confidence: 56%

Characterization of Ionospheric Disturbances Following Multiple Typhoons Using GPS‐Derived TEC

Jin

2022

JGR Space Physics

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“…(2020), and Zheng et al. (2022). Figure 2 shows schematic presentation of the great‐circle propagation paths used for the observation of solar eclipse effects.…”

Section: Instrumentation and Techniquesmentioning

confidence: 96%

“…The variations in the HF radio wave characteristics were observed by the multifrequency multiple-path coherent radio system at the Harbin Engineering University, PRC, described by , , Guo et al (2020), andZheng et al (2022). Figure 2 shows schematic presentation of the great-circle propagation paths used for the observation of solar eclipse effects.…”

Section: Instrumentation and Techniquesmentioning

confidence: 99%

Some Features of the Ionospheric Radio Wave Characteristics Over China Observed During the Solar Eclipse of 21 June 2020

et al. 2022

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Solar eclipses give rise to a number of nonstationary processes in the Earth-atmosphere-ionosphere-magnetosphere system (Chernogor, 2013) when the parameters of all components of this system and their geophysical fields change. The electron density and electron and ion temperatures show a decrease, whereas the plasma transport and energetic particle precipitation from the magnetosphere are activated, and plasma instabilities and wave processes of different nature are generated in the atmosphere (see Chernogor, 2013).During the course of solar eclipses, the parameters of radio channels through the atmosphere and ionosphere are subjected to considerable changes practically in all frequency bands, which inevitably cause degradation of signals, especially of those in the VLF, LF, MF, and HF bands.Hundreds of scientific papers are concerned with the ionospheric disturbances that accompany solar eclipses.

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“…Considering the importance of accurately reflecting the distribution of electron content in the ionosphere for improving applications such as satellite communication, navigation, positioning [6], measurement, and disaster prevention [7,8], TEC observation, research, and modeling have always been a hot topic of the exploration for decades. TEC can be directly measured by the ionospheric sounder or indirectly inverted by carrier phase delay during the transmission of the radio signal [9].…”

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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Disturbances in the Ionosphere and Distortion of Radio Wave Characteristics That Accompanied the Super Typhoon Lekima Event of 4–12 August 2019

Cited by 10 publications

References 77 publications

Characterization of Ionospheric Disturbances Following Multiple Typhoons Using GPS‐Derived TEC

Characterization of Ionospheric Disturbances Following Multiple Typhoons Using GPS‐Derived TEC

Some Features of the Ionospheric Radio Wave Characteristics Over China Observed During the Solar Eclipse of 21 June 2020

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

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