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Search Results (1,249)

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11 pages, 5116 KiB  
Article
A Study on the Predawn Ionospheric Heating Effect and Its Main Controlling Factors
by Zhenzhang Tang, Huijun Le, Libo Liu, Yiding Chen, Ruilong Zhang, Wenbo Li and Wendong Liu
Remote Sens. 2024, 16(20), 3809; https://doi.org/10.3390/rs16203809 (registering DOI) - 13 Oct 2024
Abstract
We proposed, for the first time, that the angle between the horizontal projection of the magnetic field line and the sunrise line (AMFS) is a crucial factor that controls predawn heating. Through quantitative analysis, we determined that both the AMFS and the length [...] Read more.
We proposed, for the first time, that the angle between the horizontal projection of the magnetic field line and the sunrise line (AMFS) is a crucial factor that controls predawn heating. Through quantitative analysis, we determined that both the AMFS and the length of the magnetic field line (LMF) significantly affect predawn heating. We found that an increased AMFS intensifies predawn heating, while an increased LMF counteracts it. Our research indicates that the optimal conditions for peak predawn heating occur at an AMFS of approximately 30 degrees and an LMF of about 4000 km, where the effect surpasses 400 K. Additionally, we observed that the effects of both the AMFS and the LMF on predawn heating exhibit a saturation effect. This study provides a clearer understanding of the factors driving predawn ionospheric heating, with implications for topside ionosphere research. Full article
Show Figures

Figure 1

Figure 1
<p>The left panel shows the ion temperature error, with the median error of the model of 58.6 K. The right panel is the count bin figure. The black line represents the fitting line between the observed and the model results, with a slope of 1.0041.</p>
Full article ">Figure 2
<p>(<b>a</b>–<b>c</b>) These diagrams depict the global ion temperature distribution at specific UT during the June solstice and the March equinox at an altitude of 600 km and with the F107 of 140. The white line represents the sunrise line at the 200 km altitude, where photoelectrons are generated. The black line shows the horizontal projection of the magnetic field line at the sunrise line’s altitude. (<b>d</b>) This graph defines and explains the calculation of the AMFS. The magenta line denotes the sunrise line, while the black line denotes the magnetic field line’s projection. M1 and M2 are the points of identical magnetic latitudes in the northern and southern hemispheres, respectively. R1 and R2 mark the sunrise line’s position at these latitudes. A1 and A2 are the calculation points for the predawn heating in each hemisphere.</p>
Full article ">Figure 3
<p>The graph shows the relationship between the AMFS and the ion temperature between the northern and southern hemispheres, with the LMF values ranging from 2000 to 10,000 km and a span of plus or minus 10% of the center LMF value. The red line represents the fitting line, with k being the slope of the fitting line. The blue line is the error bar chart, every point is plotted for every 5 degrees increase in AMFS, from 5 degrees to 50 degrees, and each point has a range of plus or minus 2.5 degrees.</p>
Full article ">Figure 4
<p>The relationship between the LMF and the ion temperature difference between the northern and southern hemispheres at different AMFS values. The other descriptions are similar to <a href="#remotesensing-16-03809-f003" class="html-fig">Figure 3</a>. The blue line is the error bar chart, every point is plotted for every 500 km increase in LMF, from 2000 km to 10,000 km, and each point has a range of plus or minus 10%.</p>
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<p>The combined effect of the LMF and the AMFS on predawn heating.</p>
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14 pages, 2587 KiB  
Article
Prediction of Ionospheric Scintillations Using Machine Learning Techniques during Solar Cycle 24 across the Equatorial Anomaly
by Sebwato Nasurudiin, Akimasa Yoshikawa, Ahmed Elsaid and Ayman Mahrous
Atmosphere 2024, 15(10), 1213; https://doi.org/10.3390/atmos15101213 - 11 Oct 2024
Abstract
Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models [...] Read more.
Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models for the prediction of ionospheric scintillation events at the equatorial anomaly during the maximum and minimum phases of solar cycle 24. The models developed in this study are the Random Forest (RF) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm. The models take inputs based on the solar wind parameters obtained from the OMNI Web database from the years 2010–2017 and Pc5 wave power obtained from the Bear Island (BJN) magnetometer station. We retrieved data from the Scintillation Network and Decision Aid (SCINDA) receiver in Egypt from which the S4 index was computed to quantify amplitude scintillations that were utilized as the target in the model development. Out-of-sample model testing was performed to evaluate the prediction accuracy of the models on unseen data after training. The similarity between the observed and predicted scintillation events, quantified by the R2 score, was 0.66 and 0.74 for the RF and XGBoost models, respectively. The corresponding Root Mean Square Errors (RMSEs) associated with the models were 0.01 and 0.01 for the RF and XGBoost models, respectively. The similarity in error shows that the XGBoost model is a good and preferred choice for the prediction of ionospheric scintillation events at the equatorial anomaly. With these results, we recommend the use of ensemble learning techniques for the study of the ionospheric scintillation phenomenon. Full article
(This article belongs to the Section Planetary Atmospheres)
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Figure 1

Figure 1
<p>A schematic diagram of the RF model used to predict S4 events. To improve the precision of scintillation event predictions, the data are split up into decision trees, and the overall choice of each tree is combined.</p>
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<p>Pc5 pulsations extracted from the BJN station together with the S4 from the SCINDA station. From top to bottom: (<b>a</b>) Pc5 pulsations extracted from BJN station; (<b>b</b>) S4 from the SCINDA station; (<b>c</b>) wavelet-based analysis of the Pc5 pulsation; (<b>d</b>) wavelet-based analysis of the S4.</p>
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<p>Statistical correlation of Pc5 events and S4 and their correlation with the Kp index. The Kp index is represented by the line plot and the blue and orange graphs represent the Pc5 and S4 events respectively.</p>
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<p>Linear regression plot of the ML models during the training phase; (<b>a</b>) the RF model; (<b>b</b>) the XGBoost model.</p>
Full article ">Figure 4 Cont.
<p>Linear regression plot of the ML models during the training phase; (<b>a</b>) the RF model; (<b>b</b>) the XGBoost model.</p>
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<p>Regression metric of the out-of-sample testing phase of the models; (<b>a</b>) RF model; (<b>b</b>) XGBOST model.</p>
Full article ">Figure 5 Cont.
<p>Regression metric of the out-of-sample testing phase of the models; (<b>a</b>) RF model; (<b>b</b>) XGBOST model.</p>
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15 pages, 3438 KiB  
Communication
Galileo and BeiDou AltBOC Signals and Their Perspectives for Ionospheric TEC Studies
by Chuanfu Chen, Ilya Pavlov, Artem Padokhin, Yury Yasyukevich, Vladislav Demyanov, Ekaterina Danilchuk and Artem Vesnin
Sensors 2024, 24(19), 6472; https://doi.org/10.3390/s24196472 - 8 Oct 2024
Abstract
For decades, GNSS code measurements were much noisier than phase ones, limiting their applicability to ionospheric total electron content (TEC) studies. Ultra-wideband AltBOC signals changed the situation. This study revisits the Galileo E5 and BeiDou B2 AltBOC signals and their potential applications in [...] Read more.
For decades, GNSS code measurements were much noisier than phase ones, limiting their applicability to ionospheric total electron content (TEC) studies. Ultra-wideband AltBOC signals changed the situation. This study revisits the Galileo E5 and BeiDou B2 AltBOC signals and their potential applications in TEC estimation. We found that TEC noises are comparable for the single-frequency AltBOC phase-code combination and those of the dual-frequency legacy BPSK/QPSK phase combination, while single-frequency BPSK/QPSK TEC noises are much higher. A two-week high-rate measurement campaign at the ACRG receiver revealed a mean 100 sec TEC RMS (used as the noise proxy) of 0.26 TECU, 0.15 TECU, and 0.09 TECU for the BeiDou B2(a+b) AltBOC signal and satellite elevations 0–30°, 30–60°, and 60–90°, correspondingly, and 0.22 TECU, 0.14 TECU, and 0.09 TECU for the legacy B1/B3 dual-frequency phase combination. The Galileo E5(a+b) AltBOC signal corresponding values were 0.25 TECU, 0.14 TECU, and 0.09 TECU; for the legacy signals’ phase combination, the values were 0.19 TECU, 0.13 TECU, and 0.08 TECU. The AltBOC (for both BeiDou and Galileo) SNR exceeds those of BPSK/QPSK by 7.5 dB-Hz in undisturbed conditions. Radio frequency interference (the 28 August 2022 and 9 May 2024 Solar Radio Burst events in our study) decreased the AltBOC SNR 5 dB-Hz more against QPSK SNR, but, due to the higher initial SNR, the threshold for the loss of the lock was never broken. Today, we have enough BeiDou and Galileo satellites that transmit AltBOC signals for a reliable single-frequency vTEC estimation. This study provides new insights and evidence for using Galileo and BeiDou AltBOC signals in high-precision ionospheric monitoring. Full article
(This article belongs to the Special Issue Advances in GNSS Signal Processing and Navigation)
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Figure 1

Figure 1
<p>Autocorrelation functions for BPSK, QPSK, and AltBOC signals.</p>
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<p>Pseudorange noises for BPSK, QPSK, and AltBOC signals.</p>
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<p>Slant TEC (<b>a</b>), SNR (<b>b</b>), and TEC RMS (<b>c</b>) for the ACRG-BeiDou C24 pass on 28 February 2024. In panels (<b>a</b>,<b>c</b>), the green, orange, blue, and red lines correspond to L2L5, L8C8, L2C2, and C2C5 combinations, correspondingly. In panel (<b>b</b>), the orange, green, and blue solid lines correspond to S8, S5, and S2 observables, correspondingly. The purple line in panel (<b>b</b>) shows the satellite’s elevation angle.</p>
Full article ">Figure 4
<p>Slant TEC (<b>a</b>), SNR (<b>b</b>), and TEC RMS (<b>c</b>) for the ACRG-Galileo E05 pass on 28 February 2024. In panels (<b>a</b>,<b>c</b>), the green, orange, blue, and red lines correspond to L1L5, L8C8, L1C1, and C1C5 combinations, correspondingly. In panel (<b>b</b>), the orange, green, and blue solid lines correspond to S8, S5, and S1 observables, correspondingly. The purple line in panel (<b>b</b>) shows the satellite’s elevation angle.</p>
Full article ">Figure 5
<p>Absolute vertical TEC estimates at SGPO station with single-frequency combination of AltBOC signals from Galileo and BeiDou satellites (orange line) and from dual-frequency phase combination of non-AltBOC signals from GPS, GLONASS, Galileo, and BeiDou satellites (blue line).</p>
Full article ">Figure 6
<p>Signal-to-noise ratio (SNR) from the Galileo E34 (<b>a</b>) and BeiDou C44 (<b>b</b>) satellites observed at the SGPO station on 28 August 2022, alongside the corresponding solar radio flux at 610 MHz (black curves). In panel (<b>a</b>), the red line shows the SNR S8X of E5(a+b) signal, the green line represents the SNR S7X of E5b sideband, the orange line—SNR S5X of E5a sideband, and the blue line—the SNR S1X of E1 signal; and, in panel (<b>b</b>), the red line shows the SNR S8X of B2(a+b) signal, the green line—the SNR S7Z of B2b sideband, the orange line—SNR S5X of B2a sideband, and the blue line—the SNR S2I of B1I signal.</p>
Full article ">Figure 7
<p>Signal-to-noise ratio (SNR) from the Galileo E10 (<b>a</b>) and BeiDou C23 (<b>b</b>) satellites observed at the ACRG station on 9 May 2024, alongside the corresponding solar radio flux at 1415 MHz (black curves). In panel (<b>a</b>), the red line shows the SNR S8X of E5(a+b) signal, the green line—the SNR S7X of E5b sideband, the orange line—SNR S5X of E5a sideband, and the blue line—the SNR S1X of E1 signal; and, in panel (<b>b</b>), the red line shows the SNR S8X of B2(a+b) signal, the green line—the SNR S7Z of B2b sideband, the orange line—SNR S5X of B2a sideband, and the blue line—the SNR S2I of B1I signal.</p>
Full article ">Figure 7 Cont.
<p>Signal-to-noise ratio (SNR) from the Galileo E10 (<b>a</b>) and BeiDou C23 (<b>b</b>) satellites observed at the ACRG station on 9 May 2024, alongside the corresponding solar radio flux at 1415 MHz (black curves). In panel (<b>a</b>), the red line shows the SNR S8X of E5(a+b) signal, the green line—the SNR S7X of E5b sideband, the orange line—SNR S5X of E5a sideband, and the blue line—the SNR S1X of E1 signal; and, in panel (<b>b</b>), the red line shows the SNR S8X of B2(a+b) signal, the green line—the SNR S7Z of B2b sideband, the orange line—SNR S5X of B2a sideband, and the blue line—the SNR S2I of B1I signal.</p>
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21 pages, 8606 KiB  
Article
Design of a High-Power Nanosecond Electromagnetic Pulse Radiation System for Verifying Spaceborne Detectors
by Tianchi Zhang, Zongxiang Li, Changjiao Duan, Lihua Wang, Yongli Wei, Kejie Li, Xin Li and Baofeng Cao
Sensors 2024, 24(19), 6406; https://doi.org/10.3390/s24196406 - 2 Oct 2024
Abstract
The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which [...] Read more.
The Spaceborne Global Lightning Location Network (SGLLN) serves the purpose of identifying transient lightning events occurring beneath the ionosphere, playing a significant role in detecting and warning of disaster weather events. To ensure the effective functioning of the wideband electromagnetic pulse detector, which is a crucial component of the SGLLN, it must be tested and verified with specific signals. However, the inherent randomness and unpredictability of lightning occurrences pose challenges to this requirement. Consequently, a high-power electromagnetic pulse radiation system with a 20 m aperture reflector is designed. This system is capable of emitting nanosecond electromagnetic pulse signals under pre-set spatial and temporal conditions, providing a controlled environment for assessing the detection capabilities of SGLLN. In the design phase, an exponentially TEM feed antenna has been designed firstly based on the principle of high-gain radiation. The feed antenna adopts a pulser-integrated design to mitigate insulation risks, and it is equipped with an asymmetric protective loading to reduce reflected energy by 85.7%. Moreover, an innovative assessment method for gain loss, based on the principle of Love’s equivalence, is proposed to quantify the impact of feed antenna on the radiation field. During the experimental phase, a specialized E-field sensor is used in the far-field experiment at a distance of 400 m. The measurements indicate that at this distance, the signal has a peak field strength of 2.2 kV/m, a rise time of 1.9 ns, and a pulse half-width of 2.5 ns. Additionally, the beamwidth in the time domain is less than 10°. At an altitude of 500 km, the spaceborne detector records a signal with a peak field strength of approximately 10 mV/m. Particularly, this signal transformed into a nonlinear frequency-modulated signal in the microsecond range across its frequency spectrum, which is consistent with the law of radio wave propagation in the ionosphere. This study offers a stable and robust radiation source for verifying spaceborne detectors and establishes an empirical foundation for investigating the impact of the ionosphere on signal propagation characteristics. Full article
(This article belongs to the Section Electronic Sensors)
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Figure 1

Figure 1
<p>The time-domain gain of TEM horn antenna.</p>
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<p>Schematic diagram of the simulation layout.</p>
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<p>Simulation results of the far-field probe.</p>
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<p>Diagram of the TEM horn antenna structure.</p>
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<p>Diagram of the pulse source structure.</p>
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<p>The output waveforms of the pulse source at different charging voltages.</p>
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<p>Diagram of the pulser-integrated antenna.</p>
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<p>Simulation results for the VSWR.</p>
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<p>Simulation results of the radiation system: (<b>a</b>) Surface current; (<b>b</b>) Reflected voltage.</p>
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<p>The simulation results at 100 MHz: (<b>a</b>) Radiation pattern excited by the feed antenna (obstruction present); (<b>b</b>) Radiation pattern excited by the equivalent field source (no obstruction).</p>
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<p>The circuit schematic of the E-field sensor.</p>
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<p>The photos of the measuring equipment: (<b>a</b>) E-field sensor; (<b>b</b>) Photoelectric converter.</p>
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<p>The diagram of sensor calibration experiment configuration.</p>
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<p>Calibrated sensitivity curve of the E-field Sensor.</p>
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<p>The diagram of the measurement system.</p>
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<p>The photograph of the electromagnetic pulse radiation system.</p>
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<p>Radiating signal in the far-field region at a distance of 400 m: (<b>a</b>) Time-domain waveform; (<b>b</b>) Frequency spectrum.</p>
Full article ">Figure 17 Cont.
<p>Radiating signal in the far-field region at a distance of 400 m: (<b>a</b>) Time-domain waveform; (<b>b</b>) Frequency spectrum.</p>
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<p>Time-domain radiation patterns and partially selected signal waveforms: (<b>a</b>) E-plane; (<b>b</b>) H-plane.</p>
Full article ">Figure 18 Cont.
<p>Time-domain radiation patterns and partially selected signal waveforms: (<b>a</b>) E-plane; (<b>b</b>) H-plane.</p>
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<p>Radiating signal at 500 km satellite orbit: (<b>a</b>) Time-domain waveforms; (<b>b</b>) Time–frequency analysis.</p>
Full article ">Figure A1
<p>Altitude profiles of electron density.</p>
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18 pages, 1578 KiB  
Review
The Generation of Seismogenic Anomalous Electric Fields in the Lower Atmosphere, and Its Application to Very-High-Frequency and Very-Low-Frequency/Low-Frequency Emissions: A Review
by Masashi Hayakawa, Yasuhide Hobara, Koichiro Michimoto and Alexander P. Nickolaenko
Atmosphere 2024, 15(10), 1173; https://doi.org/10.3390/atmos15101173 - 30 Sep 2024
Abstract
The purpose of this paper is, first of all, to review the previous works on the seismic (or earthquake (EQ)-related) direct current (DC) (or quasi-stationary) electric fields in the lower atmosphere, which is likely to be generated by the conductivity current flowing in [...] Read more.
The purpose of this paper is, first of all, to review the previous works on the seismic (or earthquake (EQ)-related) direct current (DC) (or quasi-stationary) electric fields in the lower atmosphere, which is likely to be generated by the conductivity current flowing in the closed atmosphere–ionosphere electric circuit during the preparation phase of an EQ. The current source is electromotive force (EMF) caused by upward convective transport and the gravitational sedimentation of radon and charged aerosols injected into the atmosphere by soil gasses during the course of the intensification of seismic processes. The theoretical calculations predict that pre-EQ DC electric field enhancement in the atmosphere can reach the breakdown value at the altitudes 2–6 km, suggesting the generation of a peculiar seismic-related thundercloud. Then, we propose to apply this theoretical inference to the observational results of seismogenic VHF (very high frequency) and VLF/LF (very low frequency/low frequency) natural radio emissions. The formation of such a peculiar layer initiates numerous chaotic electrical discharges within this region, leading to the generation of VHF electromagnetic radiation. Earlier works on VHF seismogenic radiation performed in Greece have been compared with the theoretical estimates, and showed a good agreement in the frequency range and intensity. The same idea can also be applied, for the first time, to seismogenic VLF/LF lightning discharges, which is completely the same mechanism with conventional cloud-to-ground lightning discharges. In fact, such seismogenic VLF/LF lightning discharges have been observed to appear before an EQ. So, we conclude in this review that both seismogenic VHF radiation and VLF/LF lightning discharges are regarded as indirect evidence of the generation of anomalous electric fields in the lowest atmosphere due to the emanation of radioactive radon and charged aerosols during the preparation phase of EQs. Finally, we have addressed the most fundamental issue of whether VHF and VLF/LF radiation reported in earlier works is either of atmospheric origin (as proposed in this paper) or of lithospheric origin as the result of microfracturing in the EQ fault region, which has long been hypothesized. This paper will raise a question regarding this hypothesis of lithospheric origin by proposing an alternative atmospheric origin outlined in this review. Also, the data on seismogenic electromagnetic radiation and its inference on perturbations in the lower atmosphere will be suggested to be extensively integrated in future lithosphere–atmosphere–ionosphere coupling (LAIC) studies. Full article
(This article belongs to the Section Upper Atmosphere)
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Figure 1

Figure 1
<p>Height (z) and spatial (r) dependence of the vertical component of the electric field amplitude E<sub>z</sub>(r,z) relative to its breakdown value E<sub>k</sub>(z). The following parameters are chosen in the computations: from the top to the bottom, (<b>a</b>) H<sub>c</sub> = 2 km, u<sub>0</sub> = 3.3 × 10<sup>−2</sup> m/s; (<b>b</b>) H<sub>c</sub> = 5 km, u<sub>0</sub> = 3.2 × 10<sup>−2</sup> m/s; and (<b>c</b>) H<sub>c</sub> = 6 km, u<sub>0</sub> = 3.2 × 10<sup>−2</sup> m/s. See the details in [<a href="#B74-atmosphere-15-01173" class="html-bibr">74</a>].</p>
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<p>The theoretical frequency spectrum of electromagnetic radiation at a distance of 300 km from the EQ epicenter (black curve), and the two vertical bars (at two frequencies of 41 and 53 MHz) denote the experimental data. See [<a href="#B73-atmosphere-15-01173" class="html-bibr">73</a>] for the details on the physical parameters used in the computation.</p>
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<p>(<b>a</b>) A conventional atmospheric lightning discharge (in the case of -CG lightning discharge), and (<b>b</b>) a peculiar seismogenic thundercloud due to pre-EQ activity (emanation of radon and charged aerosols), and the similar lightning discharge.</p>
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<p>The temporal evolution of seismogenic lightning discharges detected by a Taiwanese lightning network as observed for the 1999 Chi-chi EQ. Source: Tsai et al. (2006) [<a href="#B98-atmosphere-15-01173" class="html-bibr">98</a>].</p>
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14 pages, 3241 KiB  
Article
Modeling the Effect of Ionospheric Electron Density Profile and Its Inhomogeneities on Sprite Halos
by Jinbo Zhang, Jiawei Niu, Zhibin Xie, Yajun Wang, Xiaolong Li and Qilin Zhang
Atmosphere 2024, 15(10), 1169; https://doi.org/10.3390/atmos15101169 - 30 Sep 2024
Abstract
Sprite halos are diffuse glow discharges in the D-region ionosphere triggered by the quasi-electrostatic (QES) fields of lightning discharges. A three-dimensional (3D) QES model is adopted to investigate the effect of ionospheric electron density on sprite halos. The electron density is described by [...] Read more.
Sprite halos are diffuse glow discharges in the D-region ionosphere triggered by the quasi-electrostatic (QES) fields of lightning discharges. A three-dimensional (3D) QES model is adopted to investigate the effect of ionospheric electron density on sprite halos. The electron density is described by an exponential formula, parameterized by reference height (h’) and sharpness (β), and the local inhomogeneity has a Gaussian density distribution. Simulation results indicate that the reference height and steepness of the nighttime electron density affect the penetration altitudes and amplitudes of normalized electric fields, as well as the altitudes and intensities of the corresponding sprite halos optical emissions. A comparison of the daytime and nighttime conditions demonstrates that the daytime electron density profile is not favorable for generating sprite halos emissions. Furthermore, the pre-existing electron density inhomogeneities lead to enhanced local electric fields and optical emissions, potentially offering a plausible explanation for the horizontal displacement between sprites and their parent lightning, as well as their clustering. Full article
(This article belongs to the Special Issue Impact of Thunderstorms on the Upper Atmosphere)
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Figure 1

Figure 1
<p>The 3D QES heating model coordinate system. The electron density increases exponentially above 60 km, and the D-region ionosphere of simulated space is shown in blue. By default, the positive and the negative charge with a Gaussian spherically distribution is centered at 10 km and 5 km, respectively.</p>
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<p>(<b>a</b>) Conductivity profiles <span class="html-italic">σ</span> = <span class="html-italic">σ<sub>i</sub></span> + <span class="html-italic">σ<sub>e</sub></span>, considering the typical nighttime electron density profile, (<b>b</b>) electron mobility <span class="html-italic">μ</span><sub>e</sub> is a nonlinear function of the reduced electric field <span class="html-italic">E/N</span>, (<b>c</b>) ionization rate <span class="html-italic">ν</span><sub>i</sub> and attachment rates <span class="html-italic">ν<sub>a</sub></span>, the vertical dashed line is the conventional breakdown field <span class="html-italic">E<sub>k</sub></span> = 3.2 × 10<sup>6</sup><span class="html-italic">N/N</span><sub>0</sub>. (<b>d</b>) optical excitation rates <span class="html-italic">v<sub>k</sub></span> for different bands.</p>
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<p>Effects of the ionospheric height parameter <span class="html-italic">h’</span> for the nighttime ionospheric electron density profile. (<b>a</b>) Altitude electron density <span class="html-italic">N</span><sub>e</sub> profiles with the same <span class="html-italic">β</span> = 0.5 km<sup>−1</sup> and different <span class="html-italic">h’</span> = 81~87 km. Altitude profiles of (<b>b</b>) the normalized electric field <span class="html-italic">E/E<sub>k</sub></span> and (<b>c</b>) optical emission intensities (N2 1P) directly above the charge center at the end of the discharge (<span class="html-italic">t</span> = 1 ms) for different electron density profiles. (<b>d</b>) The time-averaged optical emission intensity of N<sub>2</sub> 1P over a time of 2 ms viewed along the <span class="html-italic">x</span>-axis direction. The red dashed line indicates the 80 km altitude. In all simulated cases, it was assumed that a charge of 150 C was removed by +CG lightning discharge from 10 km altitude in 1 ms (the same below).</p>
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<p>Same as in <a href="#atmosphere-15-01169-f003" class="html-fig">Figure 3</a> but for the nighttime ionospheric electron density profile with the same <span class="html-italic">h’</span> = 85 km and different ionospheric sharpness parameter <span class="html-italic">β</span> = 0.3~0.9 km<sup>−1</sup>. (<b>a</b>) Altitude electron density <span class="html-italic">N</span><sub>e</sub> profiles with the same <span class="html-italic">h’</span> = 85 km and different <span class="html-italic">β</span> = 0.3~0.9 km<sup>−1</sup>. Altitude profiles of (<b>b</b>) the normalized electric field <span class="html-italic">E/E<sub>k</sub></span> and (<b>c</b>) optical emission intensities (N2 1P) directly above the charge center at the end of the discharge (<span class="html-italic">t</span> = 1 ms) for different electron density profiles. (<b>d</b>) The time-averaged optical emission intensity of N<sub>2</sub> 1P over a time of 2 ms viewed along the <span class="html-italic">x</span>-axis direction.</p>
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<p>Comparison of the effects of the ionospheric electron density profiles between daytime and nighttime. (<b>a</b>) Altitude electron density profiles, for nighttime conditions <span class="html-italic">h’</span> = 85 km and <span class="html-italic">β</span> = 0.5 km (red solid line), while for daytime conditions <span class="html-italic">h’</span> = 72 km and <span class="html-italic">β</span> = 0.3 km (blue dashed line). (<b>b</b>) Vertical distribution of the normalized electric field <span class="html-italic">E/E<sub>k</sub></span> in a cross-section of the domain at <span class="html-italic">x</span> = 0 km at the end of the discharge (<span class="html-italic">t</span> = 1 ms). (<b>c</b>) Altitude profiles of the normalized electric field <span class="html-italic">E/E<sub>k</sub></span> directly above the charge center over a time of 2 ms after a +CG discharge. (<b>d</b>) The time-averaged optical emission intensity of N<sub>2</sub> 1P over a 2 ms duration viewed along the <span class="html-italic">x</span>-axis direction.</p>
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<p>Effects of an ionospheric electron density inhomogeneity. (<b>a</b>–<b>d</b>) The altitude distribution of the electron density without and with ionospheric inhomogeneity for different horizontal shifts ranging from 0 to 20 km (<span class="html-italic">d</span> = 0~20 km) from the thundercloud charge center. (<b>e</b>–<b>h</b>) Vertical distribution of the normalized electric field <span class="html-italic">E/E<sub>k</sub></span> in a cross-section of the domain at <span class="html-italic">x</span> = 0 km at the end of the discharge (<span class="html-italic">t</span> = 1 ms) for different electron density profiles. (<b>i</b>–<b>l</b>) The time-averaged optical emission intensity of N<sub>2</sub> 1P over a time of 2 ms viewed along the <span class="html-italic">x</span>-axis direction.</p>
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<p>Same as in <a href="#atmosphere-15-01169-f006" class="html-fig">Figure 6</a> but for multiple ionospheric electron density inhomogeneities distributed in the lower ionosphere. (<b>a</b>) The altitude distribution of the electron density with multiple inhomogeneities. (<b>b</b>) Vertical distribution of the normalized electric field <span class="html-italic">E/E<sub>k</sub></span> in a cross-section of the domain at <span class="html-italic">x</span> = 0 km, at the end of the discharge (<span class="html-italic">t</span> = 1 ms). (<b>c</b>) The time-averaged optical emission intensity of N2 1P over a time of 2 ms viewed along the <span class="html-italic">x</span>-axis direction.</p>
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11 pages, 2455 KiB  
Communication
Efficient Fourth-Order PSTD Algorithm with Moving Window for Long-Distance EMP Propagation
by Yongli Wei, Baofeng Cao, Zongxiang Li, Tianchi Zhang, Changjiao Duan, Xiao Li, Xiaoqiang Li and Peng Li
Sensors 2024, 24(19), 6317; https://doi.org/10.3390/s24196317 - 29 Sep 2024
Abstract
Satellite-borne electromagnetic pulse (EMP) detection technology plays an important role in military reconnaissance, space environment monitoring, and early warning of natural disasters. However, the complex ionosphere greatly distorts the waveform during propagation and poses a challenge to EMP detection. Therefore, it is necessary [...] Read more.
Satellite-borne electromagnetic pulse (EMP) detection technology plays an important role in military reconnaissance, space environment monitoring, and early warning of natural disasters. However, the complex ionosphere greatly distorts the waveform during propagation and poses a challenge to EMP detection. Therefore, it is necessary to conduct theoretical research on EMP propagation in the ionosphere. Conventional second-order pseudo-spectral time-domain (PSTD-2) algorithm has difficulties in keeping the stability and accuracy of waveforms in calculations over hundreds of kilometers of propagation. To overcome the difficulties, a fourth-order PSTD algorithm incorporating the moving window technique (MWPSTD-4) is proposed. In the numerical examples, the performance of MWPSTD-4 is compared with PSTD-4 and PSTD-2 in the long-distance propagation of EMP. The results show that the MWPSTD-4 improves efficiency while guaranteeing accuracy and is suitable for large-scale electromagnetic field simulation. The proposed method provides a basic algorithm to eliminate the numerical dispersion interference for calculating the long-distance propagation of EMP in complex spaces and is helpful for the design and calibration of satellite-borne EMP detectors. Full article
(This article belongs to the Section Physical Sensors)
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<p>Illustration of moving window concept.</p>
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<p>Comparison of the free-space numerical phase velocity errors associated with the PSTD-2 and PSTD-4 methods.</p>
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<p>Gaussian waveforms after propagating 8 km.</p>
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<p>Correlation between CPU time and length of moving window.</p>
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<p>Waveforms after propagating the same distance at different time steps.</p>
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12 pages, 4527 KiB  
Article
Observation of Post-Sunset Equatorial Plasma Bubbles with BDS Geostationary Satellites over South China
by Guanyi Ma, Jinghua Li, Jiangtao Fan, Qingtao Wan, Takashi Maruyama, Liang Dong, Yang Gao, Le Zhang and Dong Wang
Remote Sens. 2024, 16(18), 3521; https://doi.org/10.3390/rs16183521 - 23 Sep 2024
Abstract
An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and [...] Read more.
An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and on the ground. This paper presents a newly established GNSS ionospheric observation network (GION) around the north equatorial ionization anomaly (EIA) crest in south China, which has a longitudinal coverage of ∼30° from 94°E to 124°E. The measurement with signals from geostationary earth orbit (GEO) satellites of the BeiDou navigation satellite system (BDS) is capable of separating the temporal and spatial variations of the ionosphere. A temporal fluctuation of TEC (TFT) parameter is proposed to characterize EPBs. The longitude of the EPBs’ generation can be located with TFT variations in the time–longitude dimension. It is found that the post-sunset EPBs have a high degree of longitudinal variability. They generally show a quasiperiodic feature, indicating their association with atmospheric gravity wave activities. Wave-like structures with different scale sizes can co-exist in the same night. Full article
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<p>IPPs at a shell height of 400 km observed by the GION receivers of the BDS, GPS, and GAL at 11:30 UT on 23 February 2024. A cutoff angle of 30° is applied. The asterisk in magnenta shows the GNSS receiver’s position.</p>
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<p>EPBs detected on 6 October 2023. (<b>a</b>) vTEC, (<b>b</b>) TFTg and (<b>c</b>) ROTIg in the left are from observation by BDG 2. (<b>d</b>) vTEC, (<b>e</b>) TFTg and (<b>f</b>) ROTIg in the right are from observation by BDG 3.</p>
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<p>EPBs observed on 8 September 2023. (<b>a</b>) TFTg, (<b>b</b>) ROTIg and (<b>c</b>) ROTIi as a function of longitude and time.</p>
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<p>EPBs observed on 6 October 2023. (<b>a</b>) TFTg, (<b>b</b>) ROTIg and (<b>c</b>) ROTIi as a function of longitude and time.</p>
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<p>EPBs observed on 23 February 2024. (<b>a</b>) TFTg, (<b>b</b>) ROTIg and (<b>c</b>) ROTIi as a function of longitude and time.</p>
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<p>Longitude Coverage of EPBs’ Daily Occurrence.</p>
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<p>Daily number of EPB generations.</p>
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<p>Monthly generation rate of EPBs in 5 belts in the range of [97.5°E, 122.5°E].</p>
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37 pages, 11393 KiB  
Article
Optimizing Deep Learning Models with Improved BWO for TEC Prediction
by Yi Chen, Haijun Liu, Weifeng Shan, Yuan Yao, Lili Xing, Haoran Wang and Kunpeng Zhang
Biomimetics 2024, 9(9), 575; https://doi.org/10.3390/biomimetics9090575 - 22 Sep 2024
Abstract
The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding [...] Read more.
The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO. Full article
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<p>Flowchart of FAMBWO.</p>
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<p>The convergence behavior of FAMBWO.</p>
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<p>The convergence behavior of FAMBWO.</p>
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<p>Convergence curves of different algorithms on the unimodal functions.</p>
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<p>Convergence curves of different algorithms on the multimodal functions.</p>
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<p>Convergence curves of different algorithms on the multimodal functions.</p>
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<p>Convergence curves of different algorithms on composition functions with 30 dimensions.</p>
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<p>Convergence curves of different algorithms on composition functions with 100 dimensions.</p>
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<p>Convergence curves of different algorithms on composition functions with 100 dimensions.</p>
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<p>The raw and processed TEC: the upper shows the raw TEC, the middle shows the first-order difference, and the bottom shows the normalized TEC.</p>
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<p>Schematic diagram of sample-making process.</p>
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<p>MA-BiLSTM model structure.</p>
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<p>Flowchart of FAMBWO-MA-BiLSTM.</p>
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<p>Comparison of prediction errors among 4 frameworks.</p>
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20 pages, 6931 KiB  
Article
Swarm Investigation of Ultra-Low-Frequency (ULF) Pulsation and Plasma Irregularity Signatures Potentially Associated with Geophysical Activity
by Georgios Balasis, Angelo De Santis, Constantinos Papadimitriou, Adamantia Zoe Boutsi, Gianfranco Cianchini, Omiros Giannakis, Stelios M. Potirakis and Mioara Mandea
Remote Sens. 2024, 16(18), 3506; https://doi.org/10.3390/rs16183506 - 21 Sep 2024
Abstract
Launched on 22 November 2013, Swarm is the fourth in a series of pioneering Earth Explorer missions and also the European Space Agency’s (ESA’s) first constellation to advance our understanding of the Earth’s magnetic field and the near-Earth electromagnetic environment. Swarm provides an [...] Read more.
Launched on 22 November 2013, Swarm is the fourth in a series of pioneering Earth Explorer missions and also the European Space Agency’s (ESA’s) first constellation to advance our understanding of the Earth’s magnetic field and the near-Earth electromagnetic environment. Swarm provides an ideal platform in the topside ionosphere for observing ultra-low-frequency (ULF) waves, as well as equatorial spread-F (ESF) events or plasma bubbles, and, thus, offers an excellent opportunity for space weather studies. For this purpose, a specialized time–frequency analysis (TFA) toolbox has been developed for deriving continuous pulsations (Pc), namely Pc1 (0.2–5 Hz) and Pc3 (22–100 mHz), as well as ionospheric plasma irregularity distribution maps. In this methodological paper, we focus on the ULF pulsation and ESF activity observed by Swarm satellites during a time interval centered around the occurrence of the 24 August 2016 Central Italy M6 earthquake. Due to the Swarm orbit’s proximity to the earthquake epicenter, i.e., a few hours before the earthquake occurred, data from the mission may offer a variety of interesting observations around the time of the earthquake event. These observations could be associated with the occurrence of this geophysical event. Most notably, we observed an electron density perturbation occurring 6 h prior to the earthquake. This perturbation was detected when the satellites were flying above Italy. Full article
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<p>VirES web interface (<a href="https://vires.services" target="_blank">https://vires.services</a>, accessed on 6 September 2024): globe view and 3D visualization of Swarm orbit on 23 August 2016 from 19:14 to 20:00 coordinated universal time (UTC). Swarm A’s track is depicted in blue, Swarm C’s track is depicted in green, and Swarm B’s track is in red. The red star shows the epicenter of the August 2016 Central Italy earthquake. Swarm A and C flew above the epicenter 6 h prior to the occurrence of the earthquake.</p>
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<p>On the 23rd of August, 2016, at 22:00 UTC, the Dst index reached −73 nT, indicating a moderate magnetic storm. The red star shows the time of occurrence of the earthquake.</p>
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<p>TFA tool environment. From left to right: the user interface, the backend of the tool and the plotter. Here, the plots show the magnetic field B, the frequencies (corresponding to Pc 3–4 ULF waves), and the magnetic latitudes, as measured by Swarm A, C, and B, respectively, on 4 June 2014.</p>
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<p>The four distinct categories of signals encountered in the Swarm time series, as seen in the wavelet domain using the TFA tool: “Pc3 ULF wave events” (top left), “ESF signature events” (top right), “Artificial noise” (bottom left), and “Background noise” (bottom right).</p>
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<p>Swarm TFA plot for a full satellite track on 23 August 2016 from 17:41 to 18:27 UTC, before the Central Italy earthquake that occurred on 24 August 2016 at 1:36 UTC. In this track, Swarm A and C fly over Turkey (c.f. <a href="#remotesensing-16-03506-f0A1" class="html-fig">Figure A1</a>). (From left to right) Swarm B, A, and C, showing the filtered series of the magnetic field magnitude (top panels), their corresponding wavelet spectra for the joined Pc3 and Pc4 range (middle panels), and a composite plot of the measured electron density (green line) and their location at magnetic latitude (blue line) from −90° to +90° (bottom panels). Please note that, at the bottom of these plots, we provide information on universal time (UT), the geographic longitude, and the magnetic local time (MLT). Apart from the elevated wavelet power of the magnetic field over the north and south poles, no other activity is observed for this track. (Please note, for this and the following tracks, electron density data for Swarm B are unavailable).</p>
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<p>Asin <a href="#remotesensing-16-03506-f005" class="html-fig">Figure 5</a>, for a Swarm track on 23 August 2016 from 19:14 to 20:00 UTC, approximately 6 h before the earthquake. This is the track that Swarm A and C satellites fly over Italy (c.f. <a href="#remotesensing-16-03506-f001" class="html-fig">Figure 1</a>). Elevated power in the wavelet spectra of the magnetic field is observed in (at least) four distinct areas (including the poles), with more prominent activity for Swarm A and C. Simultaneously, perturbations of electron density are observed at low latitudes only for Swarm C, which was flying closer to the earthquake epicenter than Swarm A. The red star denotes the coordinates of the earthquake epicenter. A question that naturally arises is whether the observed “peculiarity” (i.e., the perturbation seen in the electron density measurements of Swarm C) could be linked to the occurrence of the forthcoming geophysical extreme event.</p>
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<p>As in <a href="#remotesensing-16-03506-f005" class="html-fig">Figure 5</a>, for a Swarm track on 23 August 2016 from 20:46 to 21:32 UTC, approximately 4.5 h before the earthquake. However, Swarm A and C satellites fly over the Atlantic ocean (c.f. <a href="#remotesensing-16-03506-f0A2" class="html-fig">Figure A2</a>). For Swarm A and C, we observe simultaneous post-sunset perturbations both for the magnetic field and the electron density data at low latitudes. The latter indicates ESF activity. Please note that this track is close temporally (c.f. 30 min before) to the time of the Dst peak value of the magnetic storm shown in <a href="#remotesensing-16-03506-f002" class="html-fig">Figure 2</a>. As before, we observe increased activity of the magnetic field at auroral latitudes.</p>
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<p>As in <a href="#remotesensing-16-03506-f005" class="html-fig">Figure 5</a>, for a Swarm track on 23 August 2016 from 22:19 to 23:06 UTC. This track is closer to the time of the earthquake; however, the satellites fly at different distances from the epicenter (c.f. <a href="#remotesensing-16-03506-f0A3" class="html-fig">Figure A3</a>). Elevated magnetic field activity is observed only at the poles, while no activity is observed at low latitudes.</p>
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<p>VirES web interface (<a href="https://vires.services" target="_blank">https://vires.services</a>, accessed on 6 September 2024): globe view with a 3D visualization of Swarm’s orbit on 23 August 2016 from 17:41 to 18:27 UTC. Swarm satellites flew above Turkey before capturing an irregular signal (next track, <a href="#remotesensing-16-03506-f001" class="html-fig">Figure 1</a>) possibly associated with the occurrence of the August 2016 earthquake in Italy. Swarm A’s track is depicted in blue, Swarm C’s track is depicted in green, and Swarm B’s track is depicted in red.</p>
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<p>VirES web interface (<a href="https://vires.services" target="_blank">https://vires.services</a>, accessed on 6 September 2024): globe view and 3D visualization of Swarm’s orbit on 23 August 2016 from 20:46 to 21:32 UTC. Swarm satellites flew past Portugal after capturing an irregular signal (previous track, <a href="#remotesensing-16-03506-f001" class="html-fig">Figure 1</a>) possibly associated with the occurrence of the August 2016 earthquake in Italy. Swarm A’s track is depicted in blue, Swarm C’s track is depicted in green, and Swarm B’s track is depicted in red.</p>
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<p>VirES web interface (<a href="https://vires.services" target="_blank">https://vires.services</a>, accessed on 6 September 2024): globe view and 3D visualization of Swarm’s orbit on 23 August 2016 from 22:19 to 23:06 UTC. Swarm satellites flew over the Atlantic ocean after capturing an irregular signal (previous track, <a href="#remotesensing-16-03506-f0A2" class="html-fig">Figure A2</a>) possibly associated with the occurrence of the August 2016 earthquake in Italy. Swarm A’s track is depicted in blue, Swarm C’s track is depicted in green, and Swarm B’s track is depicted in red.</p>
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<p>Swarm TFA plot for a full satellite track on 23 August 2016 from 13:02 to 13:49 UTC, before the Central Italy earthquake that occurred on 24 August 2016 at 1:36 UTC. From left to right: Swarm B, A, and C, showing the filtered series of the magnetic field magnitude data (top panels), their corresponding wavelet spectra for the joined Pc3 and Pc4 range (middle panels), and a composite plot of the measured electron density data (green line) and their location at magnetic latitude (blue line) from −90° to +90° (bottom panels). Please note that, at the bottom of these plots, we provide information on UT, geographic longitude, and magnetic local time (MLT).</p>
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<p>Swarm TFA plot for a full satellite track on 23 August 2016 from 14:35 to 15:22 UTC, before the Central Italy earthquake that occurred on 24 August 2016 at 1:36 UTC. From left to right: Swarm B, A, and C, showing the filtered series of the magnetic field magnitude data (top panels), their corresponding wavelet spectra for the joined Pc3 and Pc4 range (middle panels), and a composite plot of the measured electron density data (green line) and their location at magnetic latitude (blue line) from −90° to +90° (bottom panels). Please note that, at the bottom of these plots, we provide information on UT, geographic longitude, and magnetic local time (MLT).</p>
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<p>Swarm TFA plot for a full satellite track on 23 August 2016 from 16:08 to 16:55 UTC, before the Central Italy earthquake that occurred on 24 August 2016 at 1:36 UTC. From left to right: Swarm B, A, and C, showing the filtered series of the magnetic field magnitude data (top panels), their corresponding wavelet spectra for the joined Pc3 and Pc4 range (middle panels), and a composite plot of the measured electron density data (green line) and their location at magnetic latitude (blue line) from −90° to +90° (bottom panels). Please note that, at the bottom of these plots, we provide information on UT, geographic longitude, and magnetic local time (MLT).</p>
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<p>Swarm TFA plot for a full satellite track from 23:52 UTC on 23 August 2016 to 00:58 UTC on 24 August 2016, before the Central Italy earthquake that occurred on 24 August 2016 at 1:36 UTC. From left to right: Swarm B, A, and C, showing the filtered series of the magnetic field magnitude data (top panels), their corresponding wavelet spectra for the joined Pc3 and Pc4 range (middle panels), and a composite plot of the measured electron density data (green line) and their location at magnetic latitude (blue line) from −90° to +90° (bottom panels). Please note that, at the bottom of these plots, we provide information on UT, geographic longitude, and magnetic local time (MLT).</p>
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<p>Swarm TFA plot for a full satellite track on 24 August 2016 from 01:25 UTC to 02:12 UTC, before the Central Italy earthquake that occurred on 24 August 2016 at 1:36 UTC. From left to right: Swarm B, A, and C, showing the filtered series of the magnetic field magnitude data (top panels), their corresponding wavelet spectra for the joined Pc3 and Pc4 range (middle panels), and a composite plot of the measured electron density data (green line) and their location at magnetic latitude (blue line) from −90° to +90° (bottom panels). Please note that, at the bottom of these plots, we provide information on UT, geographic longitude, and magnetic local time (MLT).</p>
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<p>Swarm TFA plot for a full satellite track on 24 August 2016 from 02:59 UTC to 03:46 UTC, before the Central Italy earthquake that occurred on 24 August 2016 at 1:36 UTC. From left to right: Swarm B, A, and C, showing the filtered series of the magnetic field magnitude data (top panels), their corresponding wavelet spectra for the joined Pc3 and Pc4 range (middle panels), and a composite plot of the measured electron density data (green line) and their location at magnetic latitude (blue line) from −90° to +90° (bottom panels). Please note that, at the bottom of these plots, we provide information on UT, geographic longitude, and magnetic local time (MLT).</p>
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12 pages, 3994 KiB  
Article
Possible Identification of Precursor ELF Signals on Recent EQs That Occurred Close to the Recording Station
by Ioannis Contopoulos, Janusz Mlynarczyk, Jerzy Kubisz and Vasilis Tritakis
Atmosphere 2024, 15(9), 1134; https://doi.org/10.3390/atmos15091134 - 19 Sep 2024
Abstract
The Lithospheric–Atmospheric–Ionospheric Coupling (LAIC) mechanism stands as the leading model for the prediction of seismic activities. It consists of a cascade of physical processes that are initiated days before a major earthquake. The onset is marked by the discharge of ionized gases, such [...] Read more.
The Lithospheric–Atmospheric–Ionospheric Coupling (LAIC) mechanism stands as the leading model for the prediction of seismic activities. It consists of a cascade of physical processes that are initiated days before a major earthquake. The onset is marked by the discharge of ionized gases, such as radon, through subterranean fissures that develop in the lead-up to the quake. This discharge augments the ionization at the lower atmospheric layers, instigating disturbances that extend from the Earth’s surface to the lower ionosphere. A critical component of the LAIC sequence involves the distinctive perturbations of Extremely Low Electromagnetic Frequencies (ELF) within the Schumann Resonances (SR) spectrum of 2 to 50 Hz, detectable days ahead of the seismic event. Our study examines 10 earthquakes that transpired over a span of 3.5 months—averaging nearly three quakes monthly—which concurrently generated 45 discernible potential precursor seismic signals. Notably, each earthquake originated in Southern Greece, within a radius of 30 to 250 km from the observatory on Mount Parnon. Our research seeks to resolve two important issues. The first concerns the association between specific ELF signals and individual earthquakes—a question of significant importance in seismogenic regions like Greece, where earthquakes occur frequently. The second inquiry concerns the parameters that determine the detectability of an earthquake by a given station, including the requisite proximity and magnitude. Initial findings suggest that SR signals can be reliably linked to a particular earthquake if the observatory is situated within the earthquake’s preparatory zone. Conversely, outside this zone, the correlation becomes indeterminate. Additionally, we observe a differentiation in SR signals based on whether the earthquake took place over land or offshore. The latter category exhibits unique signal behaviors, potentially attributable to the water layers above the epicenter acting as a barrier to the ascending gases, thereby affecting the atmospheric–ionospheric ionization process. Full article
(This article belongs to the Section Upper Atmosphere)
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<p>The Mediterranean Ridge and the Hellenic Trench around Crete.</p>
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<p>Typical ELF recordings 0–50 Hz, raw data (upper panels) and associated Fourier spectra (low panels), received by both the Greek (<b>left</b>) and the Polish (<b>right</b>) systems in our observing site on Mount Parnon. Schumann’s resonances around 7, 14, 21, and 28 Hz are evident in both spectra. PSD stands for “Power Spectral Density” in the Greek system recording (<b>left</b>). ASD stands for “Amplitude Spectral Density” in the Polish system recording (<b>right</b>). B stands for the magnetic field value (in pT). N–S denotes recordings from the North–South oriented coil.</p>
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<p>Typical strong perturbations in the raw data and spectra are considered potential precursor seismic signals. Simultaneous measurements were received by the Greek (blue) and the Polish (red) systems, respectively.</p>
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<p>Greek peninsula with the recording station of Parnon (red pin in the center of the circles) and the 10 EQs of <a href="#atmosphere-15-01134-t001" class="html-table">Table 1</a> (yellow pins) shown. The color circles delimit the preparation areas around the station if the EQ epicenter lies at the position of the station itself. The outer red circle corresponds to an EQ of 6 Richter while the inner blue circle corresponds to an EQ of 4 Richter. Overall, the shown circles delimit zones of EQs = 6, 5.5, 5.0, 4.5 and 4 Richter from the outer to the inner circle, respectively.</p>
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<p>The first (<b>left</b>) and the last (<b>right</b>) recorded potential precursor signals before the EQ at Leonidion (no.1 in <a href="#atmosphere-15-01134-t001" class="html-table">Table 1</a>). E–W denotes recordings from the East–West oriented coil.</p>
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<p>The last 3 successive signals before the 5.6 R EQ in the Kyparissia Gulf (no. 7 in <a href="#atmosphere-15-01134-t001" class="html-table">Table 1</a>). The spectrum of these signals differs from the spectrum of the signals in <a href="#atmosphere-15-01134-f005" class="html-fig">Figure 5</a> (they show two characteristic excesses at the boundaries of the normal precursor frequency area of 20–25 Hz).</p>
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18 pages, 17888 KiB  
Article
Morphological Features of Severe Ionospheric Weather Associated with Typhoon Doksuri in 2023
by Wang Li, Fangsong Yang, Jiayi Yang, Renzhong Zhang, Juan Lin, Dongsheng Zhao and Craig M. Hancock
Remote Sens. 2024, 16(18), 3375; https://doi.org/10.3390/rs16183375 - 11 Sep 2024
Abstract
The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using [...] Read more.
The atmospheric gravity waves (AGWs) generated by severe typhoons can facilitate the transfer of energy from the troposphere to the ionosphere, resulting in medium-scale traveling ionospheric disturbances (MSTIDs). However, the complex three-dimensional nature of MSTIDs over oceanic regions presents challenges for detection using ground-based Global Navigation Satellite System (GNSS) networks. This study employs a hybrid approach combining space-based and ground-based techniques to investigate the spatiotemporal characteristics of ionospheric perturbations during Typhoon Doksuri. Plane maps depict significant plasma fluctuations extending outward from the typhoon’s gale wind zone on 24 July, reaching distances of up to 1800 km from the typhoon’s center, while space weather conditions remained relatively calm. These ionospheric perturbations propagated at velocities between 173 m/s and 337 m/s, consistent with AGW features and associated propagation speeds. Vertical mapping reveals that energy originating from Typhoon Doksuri propagated upward through a 500 km layer, resulting in substantial enhancements of plasma density and temperature in the topside ionosphere. Notably, the topside horizontal density gradient was 1.5 to 2 times greater than that observed in the bottom-side ionosphere. Both modeling and observational data convincingly demonstrate that the weak background winds favored the generation of AGWs associated with Typhoon Doksuri, influencing the development of distinct MSTIDs. Full article
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<p>The path of Typhoon Doksuri from July 20 to 28, 2023 (<b>a</b>), along with variations in central pressure (<b>b</b>) and average speed (<b>c</b>). The triangle denotes the location of GNSS receivers.</p>
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<p>Fluctuations in proton density (<b>a</b>), temperature (<b>b</b>), and speed (<b>c</b>) of solar wind, as well as Dst (<b>d</b>) and Kp (<b>e</b>), throughout the progression of Typhoon Doksuri.</p>
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<p>Plasma irregularities over the stations PTAG, CHEN, and HKCL in the period of DOY 203–208.</p>
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<p>Plasma irregularities calculated by GPS data from CHEN on DOY 205.</p>
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<p>Temporal-distance profile of STEC fluctuations within a 1800 km radius from the typhoon’s center on DOY 205.</p>
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<p>Spatial dynamic maps of plasma irregularities on DOY 205.</p>
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<p>Changes in electron density and electron temperature, derived from Swarm-A on DOY 200–206, 2023. The dashed circles signify distances of 700 km and 2000 km away from the typhoon’s eye, and the stars signify the typhoon’s eye at 09 UT on DOY 202–206.</p>
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<p>Ground tracks of plasma profiles from COSMIC-2 on DOY 205 and the corresponding changes of plasma density gradient.</p>
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<p>Topography along the trajectory of Typhoon Doksuri on DOY 205, where the red region with a radius of 700 km indicates the influence of gale-force winds.</p>
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<p>Time-latitudinal variations of (<b>a</b>) TEC and (<b>e</b>) thermospheric O/N<sub>2</sub> ratio during DOY 202–207 simulated by the Thermosphere-Ionosphere-Electrodynamics General Circulation Model, along with observational data, including (<b>b</b>) Global Navigation Satellite System-TEC, (<b>c</b>,<b>d</b>) Zonal and Meridional winds (121°E, 23°N) simulated by the Horizontal Wind Model 2014 empirical model on DOY 205, and (<b>f</b>) temporal variation of the equatorial electrojet estimated by the difference between DLH and PHU. Additionally, (<b>g</b>–<b>l</b>) showcase changes in the thermospheric O/N<sub>2</sub> ratio within a longitudinal range of 110–140°E, as derived from the Global Ultraviolet Imager (GUVI) on TIMED satellite.</p>
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15 pages, 5121 KiB  
Article
Regional Spatial Mean of Ionospheric Irregularities Based on K-Means Clustering of ROTI Maps
by Yenca Migoya-Orué, Oladipo E. Abe and Sandro Radicella
Atmosphere 2024, 15(9), 1098; https://doi.org/10.3390/atmos15091098 - 9 Sep 2024
Abstract
In this paper, we investigate and propose the application of an unsupervised machine learning clustering method to characterize the spatial and temporal distribution of ionospheric plasma irregularities over the Western African equatorial region. The ordinary Kriging algorithm was used to interpolate the rate [...] Read more.
In this paper, we investigate and propose the application of an unsupervised machine learning clustering method to characterize the spatial and temporal distribution of ionospheric plasma irregularities over the Western African equatorial region. The ordinary Kriging algorithm was used to interpolate the rate of change of the total electron content (TEC) index (ROTI) over gridded 0.5° by 0.5° latitude and longitude regional maps in order to simulate the level of ionospheric plasma irregularities in a quasi-real-time scenario. K-means was used to obtain a spatial mean index through an optimal stratification of regional post-processed ROTI maps. The results obtained could be adapted by appropriate K-means algorithms to a real-time scenario, as has been performed for other applications. This method could allow us to monitor plasma irregularities in real time over the African region and, therefore, lead to the possibility of mitigating their effects on satellite-based location systems in the said region. Full article
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<p>Map of geographic locations of GNSS ground-based stations used.</p>
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<p>Solar wind plasma speed, IMF-Bz, and Dst parameters for days enclosing 2nd October 2013 storm.</p>
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<p>An example of irregularities during disturbed conditions on 2 October 2013 (DOY 275), as indicated by ROT/ROTI in FUTY (<b>left</b>) and NKLG (<b>right</b>) stations. The different colors lines represent the different visible satellites.</p>
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<p>ROTI values at IPP (<b>left</b>) during disturbed conditions on 2 October 2013 (DOY 275) at 2000 UT and ROTI map after Kriging interpolation (<b>right</b>). Empty red circles in both maps represent the GNSS ground-based stations used.</p>
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<p>Hourly regional ROTI maps (<b>left</b>) and Kriging variance maps (<b>right</b>) during a geomagnetically disturbed day (DOY 275 of the year 2013). The yellow–red areas in the maps on the left indicate the presence of nighttime irregularities at 1900, 2000, 2100, and 2200 UT.</p>
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<p>Hourly regional ROTI maps (<b>left</b>) and Kriging variance maps (<b>right</b>) during a geomagnetic quiet day (DOY 301 of the year 2013). The yellow–red areas in the maps on the left indicate the presence of nighttime irregularities at 1900, 2000, 2100, and 2200 UT.</p>
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<p>Histogram of the ROTI variance values after Kriging interpolation corresponding to all maps every 15 min on day 301, 2013. The mean, standard deviation, and 99% and 75% percentile values are included.</p>
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<p>ROTI maps projected on West African map on DOY 275 (disturbed day) at 20:20 UT (<b>left</b>) and its stratification in clusters (<b>right</b>).</p>
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<p>ROTI projected over African map for a quiet day (DOY 301, 2013) at 22:20 UT (<b>left</b>) and its stratification in clusters (<b>right</b>).</p>
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24 pages, 10714 KiB  
Article
A Potential Link between Space Weather and Atmospheric Parameters Variations: A Case Study of November 2021 Geomagnetic Storm
by Mauro Regi, Alessandro Piscini, Patrizia Francia, Marcello De Lauretis, Gianluca Redaelli and Giuseppina Carnevale
Remote Sens. 2024, 16(17), 3318; https://doi.org/10.3390/rs16173318 - 7 Sep 2024
Abstract
On 4 November 2021, during the rising phase of solar cycle 25, an intense geomagnetic storm (Kp = 8−) occurred. The effects of this storm on the outer magnetospheric region up to the ionospheric heights have already been examined in previous investigations. This [...] Read more.
On 4 November 2021, during the rising phase of solar cycle 25, an intense geomagnetic storm (Kp = 8−) occurred. The effects of this storm on the outer magnetospheric region up to the ionospheric heights have already been examined in previous investigations. This work is focused on the analysis of the solar wind conditions before and during the geomagnetic storm, the high-latitude electrodynamics conditions, estimated through empirical models, and the response of the atmosphere in both hemispheres, based on parameters from the ECMWF ERA5 atmospheric reanalysis dataset. Our investigations are also supported by counter-test analysis and Monte Carlo tests. We find, for both hemispheres, a significant correspondence, within 1–2 days, between high-latitude electrodynamics variations and changes in the temperature, specific humidity, and meridional and zonal winds, in both the troposphere and stratosphere. The results indicate that, in the complex solar wind–atmosphere relationship, a significant role might be played by the intensification of the polar cap potential. We also study the reciprocal relation between the ionospheric Joule heating, calculated from a model, and two adiabatic invariants used in the analysis of solar wind turbulence. Full article
(This article belongs to the Section Earth Observation Data)
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<p>(From top to bottom) The interplanetary parameter conditions <math display="inline"><semantics> <msub> <mi>B</mi> <mi>Y</mi> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>B</mi> <mi>Z</mi> </msub> </semantics></math> (panels <b>a1</b>–<b>a3</b>), <math display="inline"><semantics> <msub> <mi>V</mi> <mrow> <mi>S</mi> <mi>W</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>n</mi> <mrow> <mi>S</mi> <mi>W</mi> </mrow> </msub> </semantics></math> (panels <b>b1</b>–<b>b3</b>), used by the W05 model; the hemispheric average in the northern hemisphere of polar cap potential <span class="html-italic">E</span> (panels <b>c1</b>–<b>c3</b>) and FACs intensities <span class="html-italic">F</span> (panels <b>d1</b>–<b>d3</b>) for both positive (black curves) and negative (blue curves) components, as well as the <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (red curves). The same quantities for the southern hemisphere are very similar to those shown here and are not reported for simplicity; the geomagnetic activity indices (panels <b>e1</b>–<b>e3</b>) Dst (black curves) and Kp (red segments) during 3–6 November for the three years 2019, 2020 and 2021.</p>
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<p>Relative temperature deviation <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative specific humidity <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>Q</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative zonal wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>U</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative meridional wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>V</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the northern hemisphere: in these panels, the tropopause is indicated by the horizontal line. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of the moving correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of the Dst index are reported in all panels as vertical dashed lines; the considered tropospheric altitude range of 3–11 km in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Results of the counter-test analysis conducted for the time interval 3–6 November 2020. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line) and height average atmospheric parameters (green line) and filtered (black line) at tropospheric heights for T (panels <b>a1</b>–<b>a3</b>), Q (panels <b>c1</b>–<b>c3</b>), U (panels <b>e1</b>–<b>e3</b>), and V (panels <b>g1</b>–<b>g3</b>). The result of moving the correlation analysis among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with filtered height average atmospheric parameters (panels <b>b1</b>–<b>b3</b>,<b>d1</b>–<b>d3</b>,<b>f1</b>–<b>f3</b>,<b>h1</b>–<b>h3</b>). Horizontal red lines in panels <b>b</b>,<b>d</b>,<b>f</b>,<b>h</b> represent the 95% confidence interval.</p>
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<p>Relative temperature deviation <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>T</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>T</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative specific humidity <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>Q</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>Q</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>Q</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative zonal wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>U</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>U</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>U</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Relative meridional wind <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>V</mi> </mrow> </semantics></math> during 3–6 November 2021 (panels <b>a</b>–<b>c</b>) and the time-filtered version <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mrow> </semantics></math> (panels <b>d</b>–<b>f</b>) as a function of time and height, for three different geographic latitudes in the southern hemisphere: in these panels, the tropopause is indicated by horizontal lines. Electric field potential difference <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> (blue line in panels <b>g</b>–<b>i</b>) in the southern hemisphere and height average <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (magenta line) and filtered <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line) atmospheric parameters at tropospheric heights. The result of moving the correlation analysis (panels <b>j</b>–<b>l</b>) among <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> (green line) and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> with <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> (black line). The SSC and minimum value of Dst index are reported in all panels as vertical dashed lines; the tropospheric altitude range of 3–11 km considered in computing <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <mi>V</mi> </mfenced> </semantics></math> and then <math display="inline"><semantics> <mfenced separators="" open="&#x2329;" close="&#x232A;"> <mo>Δ</mo> <msub> <mi>V</mi> <mi>f</mi> </msub> </mfenced> </semantics></math> is marked by horizontal dashed lines in panels (<b>d</b>–<b>f</b>). Horizontal red lines in panels (<b>j</b>–<b>l</b>) represent the 95% confidence interval.</p>
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<p>Cross-covariance analysis results among filtered averaged atmospheric parameters (from top to bottom) of T, Q, U and V and for the troposphere (left columns) and stratosphere (right columns) and in both the southern and northern hemispheres: in each panel, the results for the disturbed (black curves) and quiet (gray curves) time intervals are reported, together with the 99% confidence threshold (red dashed curves) estimated through the MC test. The selected maximum and reliable correlations are marked with green triangles (see text for details).</p>
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<p>The latitudinal dependence of the absolute value of the correlation between <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>E</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics></math>. Horizontal dashed lines indicate the correlation threshold for different confidence levels. The vertical line indicates the average geographic latitude of the predicted auroral electron flux maximum.</p>
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<p>Comparison between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>C</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>a</b>) and between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>R</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>c</b>) during 2–5 November 2021. Logarithmic of normalized absolute value cross-wavelet spectrum (<math display="inline"><semantics> <msub> <mi>W</mi> <mrow> <mi>x</mi> <mi>y</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> </semantics></math>, color scales) computed between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>C</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>b</b>) and between <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>R</mi> </msub> </semantics></math> and <span class="html-italic">J</span> (panel <b>d</b>). The 95% significance thresholds are highlighted with white curves in panels b and d, while the cones of influence are reported in light blue. Geomagnetic activity index SYM-H (panel <b>e</b>). The vertical dashed lines in all panels refer to the beginning of the SSC (<math display="inline"><semantics> <msub> <mi>t</mi> <mrow> <mi>S</mi> <mi>S</mi> <mi>C</mi> </mrow> </msub> </semantics></math>), and the first (<math display="inline"><semantics> <msub> <mi>t</mi> <mn>1</mn> </msub> </semantics></math>) and second (<math display="inline"><semantics> <msub> <mi>t</mi> <mn>2</mn> </msub> </semantics></math>) minima of the main phase of the storm.</p>
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18 pages, 5626 KiB  
Article
Improving GNSS-IR Sea Surface Height Accuracy Based on a New Ionospheric Stratified Elevation Angle Correction Model
by Jiadi Zhu, Wei Zheng, Yifan Shen, Keke Xu and Hebing Zhang
Remote Sens. 2024, 16(17), 3270; https://doi.org/10.3390/rs16173270 - 3 Sep 2024
Viewed by 176
Abstract
Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. [...] Read more.
Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) sea surface altimetry is a method of inferring sea surface height based on the signal-to-noise ratio of satellite signals. It enables the retrieval of sea surface height variations with high precision. However, navigation satellite signals are influenced by the ionosphere during propagation, leading to deviations in the measured values of satellite elevation angles from their true values, which significantly affects the accuracy of GNSS-IR sea surface altimetry. Based on this, the contents of this paper are as follows: Firstly, a new ionospheric stratified elevation angle correction model (ISEACM) was developed by integrating the International Reference Ionosphere Model (IRI) and ray tracing methods. This model aims to improve the accuracy of GNSS-IR sea surface altimetry by correcting the ionospheric refraction effects on satellite elevation angles. Secondly, four GNSS stations (TAR0, PTLD, GOM1, and TPW2) were selected globally, and the corrected sea surface height values obtained using ISEACM were compared with observed values from tide gauge stations. The calculated average Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) were 0.20 m and 0.83, respectively, indicating the effectiveness of ISEACM in sea surface height retrieval. Thirdly, a comparative analysis was conducted between sea surface height retrieval before and after correction using ISEACM. The optimal RMSE and PCC values with tide gauge station observations were 0.15 m and 0.90, respectively, representing a 20.00% improvement in RMSE and a 4.00% improvement in correlation coefficient compared to traditional GNSS-IR retrieval heights. These experimental results demonstrate that correction with ISEACM can effectively enhance the precision of GNSS-IR sea surface altimetry, which is crucial for accurate sea surface height measurements. Full article
(This article belongs to the Special Issue SoOP-Reflectometry or GNSS-Reflectometry: Theory and Applications)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The electron density map of the United States region; (<b>b</b>) the electron density map of the Spain region; (<b>c</b>) the electron density surface distribution map of the Australia region.</p>
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<p>The flowchart of the new ionospheric stratified elevation angle correction model.</p>
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<p>Basic principle diagram of GNSS-IR sea surface height measurement.</p>
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<p>Distribution of GNSS stations and Fresnel reflection regions diagram.</p>
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<p>Diagram of angle correction quantities for ionospheric stratified elevation angle correction model stations: (<b>a</b>) The angular correction for TAR0 station; (<b>b</b>) The angular correction for PTLD station; (<b>c</b>) The angular correction for GOM1 station; (<b>d</b>) The angular correction for TPW2 station.</p>
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<p>Comparison between the inverted height sequence values after correction using the ionospheric stratified elevation angle correction model and the observed tidal sequence values: (<b>a</b>) TAR0 station; (<b>b</b>) PTLD station; (<b>c</b>) GOM1 station; (<b>d</b>) TPW2 station.</p>
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<p>Pearson correlation coefficient between the inverted height sequence after correction using the ionospheric stratified elevation angle correction model and the observed tidal sequence value: (<b>a</b>) Image of the correlation coefficient for TAR0; (<b>b</b>) Image of the correlation coefficient for PTLD; (<b>c</b>) Image of the correlation coefficient for GOM1; (<b>d</b>) Image of the correlation coefficient for TPW2.</p>
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<p>Comparison of height inversion sequences and tidal observation sequences before and after correction by the ionospheric stratified elevation angle correction model for four stations: (<b>a</b>) TAR0 station; (<b>b</b>) PTLD station; (<b>c</b>) GOM1 station; (<b>d</b>) TPW2 station.</p>
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<p>Comparison of residual plots between height inversion sequences and tidal observation sequences before and after correction by the ionospheric stratified elevation angle correction model for four stations: (<b>a</b>) Residual image of TAR0; (<b>b</b>) Residual image of PTLD; (<b>c</b>) Residual image of GOM1; (<b>d</b>) Residual image of TPW2.</p>
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