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27 pages, 36855 KiB  
Article
Evaluation and Anomaly Detection Methods for Broadcast Ephemeris Time Series in the BeiDou Navigation Satellite System
by Jiawei Cai, Jianwen Li, Shengda Xie and Hao Jin
Sensors 2024, 24(24), 8003; https://doi.org/10.3390/s24248003 (registering DOI) - 14 Dec 2024
Viewed by 386
Abstract
Broadcast ephemeris data are essential for the precision and reliability of the BeiDou Navigation Satellite System (BDS) but are highly susceptible to anomalies caused by various interference factors, such as ionospheric and tropospheric effects, solar radiation pressure, and satellite clock biases. Traditional threshold-based [...] Read more.
Broadcast ephemeris data are essential for the precision and reliability of the BeiDou Navigation Satellite System (BDS) but are highly susceptible to anomalies caused by various interference factors, such as ionospheric and tropospheric effects, solar radiation pressure, and satellite clock biases. Traditional threshold-based methods and manual review processes are often insufficient for detecting these complex anomalies, especially considering the distinct characteristics of different satellite types. To address these limitations, this study proposes an automated anomaly detection method using the IF-TEA-LSTM model. By transforming broadcast ephemeris data into multivariate time series and integrating anomaly score sequences, the model enhances detection robustness through data integrity assessments and stationarity tests. Evaluation results show that the IF-TEA-LSTM model reduces the RMSE by up to 20.80% for orbital parameters and improves clock deviation prediction accuracy for MEO satellites by 68.37% in short-term forecasts, outperforming baseline models. This method significantly enhances anomaly detection accuracy across GEO, IGSO, and MEO satellite orbits, demonstrating its superiority in long-term data processing and its capacity to improve the reliability of satellite operations within the BDS. Full article
(This article belongs to the Special Issue Advances in GNSS Signal Processing and Navigation)
Show Figures

Figure 1

Figure 1
<p>Schematic diagram of incremental updates for window scrolling.</p>
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<p>Flowchart for constructing anomaly score forest clusters.</p>
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<p>LSTM unit.</p>
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<p>LSTM.</p>
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<p>Anomaly detection framework based on IF-TEA-LSTM.</p>
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<p>M300 RPO receiver main unit.</p>
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<p>M300 RPO receiver antenna.</p>
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<p>Comparative analysis of the five parameter sets (<math display="inline"><semantics> <msqrt> <mi>A</mi> </msqrt> </semantics></math>, <span class="html-italic">e</span>, <math display="inline"><semantics> <msub> <mi>i</mi> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <mo>Ω</mo> </semantics></math>, <math display="inline"><semantics> <mi>ω</mi> </semantics></math>) for MEO and IGSO orbits, along with GEO orbit parameters, based on hourly sampling. The five subplots on the left, from top to bottom, represent the parameters <math display="inline"><semantics> <msqrt> <mi>A</mi> </msqrt> </semantics></math>, <span class="html-italic">e</span>, <math display="inline"><semantics> <msub> <mi>i</mi> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <mo>Ω</mo> </semantics></math>, and <math display="inline"><semantics> <mi>ω</mi> </semantics></math> for MEO and IGSO orbits, while the right side corresponds to GEO. The differences between the two orbit types are prominently highlighted.</p>
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<p>Visualization of the distribution of 10 broadcast ephemeris parameters for the C26 satellite with hourly sampling. From top to bottom, left to right, the parameters are <math display="inline"><semantics> <msub> <mi>M</mi> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>n</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mover accent="true"> <mo>Ω</mo> <mo>˙</mo> </mover> </semantics></math>, <math display="inline"><semantics> <mi>IDOT</mi> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>u</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>u</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> </semantics></math>.</p>
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<p>BDS broadcast ephemeris stability test results.</p>
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<p>Broadcast ephemeris time-series difference distribution fitting results. As shown in the figure, subplots (<b>a</b>–<b>i</b>) respectively represent the normal distribution curves of the nine parameters, which correspond to <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>n</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mover accent="true"> <mo>Ω</mo> <mo>˙</mo> </mover> </semantics></math>, <math display="inline"><semantics> <mi>IDOT</mi> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>u</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>u</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> </semantics></math>.</p>
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<p>The figure demonstrates the monitoring thresholds of <math display="inline"><semantics> <msqrt> <mi>A</mi> </msqrt> </semantics></math>, <span class="html-italic">e</span>, <math display="inline"><semantics> <msub> <mi>i</mi> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <mo>Ω</mo> </semantics></math>, <math display="inline"><semantics> <mi>ω</mi> </semantics></math>, <math display="inline"><semantics> <msub> <mi>M</mi> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>n</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mover accent="true"> <mo>Ω</mo> <mo>˙</mo> </mover> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>D</mi> <mi>O</mi> <mi>T</mi> </mrow> </semantics></math> in subplots (<b>a</b>–<b>i</b>). In <a href="#sensors-24-08003-f012" class="html-fig">Figure 12</a>, <a href="#sensors-24-08003-f013" class="html-fig">Figure 13</a> and <a href="#sensors-24-08003-f014" class="html-fig">Figure 14</a> presented within the text, ‘MT’ refers to the monitoring threshold.</p>
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<p>Subplots (<b>a</b>–<b>c</b>) show the distribution of differences for different parameter pairs, with <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>r</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>u</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>u</mi> <mi>s</mi> </mrow> </msub> </semantics></math>, and <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>c</mi> </mrow> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> </semantics></math> paired according to their respective threshold ranges.</p>
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<p>The monitoring thresholds for <math display="inline"><semantics> <msub> <mi>f</mi> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>f</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>f</mi> <mn>2</mn> </msub> </semantics></math> are shown in subfigures (<b>a</b>–<b>c</b>), respectively, with <math display="inline"><semantics> <msub> <mi>f</mi> <mn>2</mn> </msub> </semantics></math> remaining constant at 0.</p>
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<p>Correlation analysis of BDS broadcast ephemeris parameters.</p>
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<p>Comparisonof predicted orbital parameter results with actual measurements for selected satellites. The red line indicates predicted values, while the blue line indicates actual values. The dataset has been processed for outlier detection using robust Methods and iForest. Subfigure (<b>a</b>) represents LSTM, subfigure (<b>b</b>) represents A-LSTM, subfigure (<b>c</b>) represents TE-LSTM, and subfigure (<b>d</b>) represents IF-TEA-LSTM.</p>
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<p>Prediction accuracy and performance of <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>n</mi> </mrow> </semantics></math> under different reference frames. The six subplots (<b>a</b>–<b>f</b>) represent this parameter under the following forecast time horizons: (<b>a</b>) 24 h forecast, 1 h interval; (<b>b</b>) 96 h forecast, 1 h interval; (<b>c</b>) 7 d forecast, 1 h interval; (<b>d</b>) 15 d forecast, 1 h interval; (<b>e</b>) 30 d forecast, 1 h interval; (<b>f</b>) 90 d forecast, 1 h interval. The subplot distribution in <a href="#sensors-24-08003-f018" class="html-fig">Figure 18</a>, <a href="#sensors-24-08003-f019" class="html-fig">Figure 19</a>, <a href="#sensors-24-08003-f020" class="html-fig">Figure 20</a> and <a href="#sensors-24-08003-f021" class="html-fig">Figure 21</a> follows the same structure.</p>
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<p>Prediction accuracy and performance of <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>D</mi> <mi>O</mi> <mi>T</mi> </mrow> </semantics></math> under different reference frames. The six subplots (<b>a</b>–<b>f</b>) show the parameter for forecast periods of 24 h, 96 h, 7 days, 15 days, 30 days, and 90 days, each with a 1-h interval.</p>
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<p>Prediction accuracy and performance of <math display="inline"><semantics> <mover accent="true"> <mo>Ω</mo> <mo>˙</mo> </mover> </semantics></math> under different reference frames. The six subplots (<b>a</b>–<b>f</b>) show the parameter for forecast periods of 24 h, 96 h, 7 days, 15 days, 30 days, and 90 days, each with a 1-hour interval.</p>
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<p>Prediction accuracy and performance of <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> </semantics></math> under different reference frames. The six subplots (<b>a</b>–<b>f</b>) show the parameter for forecast periods of 24 h, 96 h, 7 days, 15 days, 30 days, and 90 days, each with a 1-hour interval.</p>
Full article ">Figure 21
<p>Prediction accuracy and performance of <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>c</mi> </mrow> </msub> </semantics></math> under different reference frames. The six subplots (<b>a</b>–<b>f</b>) show the parameter for forecast periods of 24 h, 96 h, 7 days, 15 days, 30 days, and 90 days, each with a 1-hour interval.</p>
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<p>Analysis of the distribution characteristics of the five major anomaly parameters under different orbit types. Subplot (<b>a</b>) illustrates the distribution characteristics of the <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>n</mi> </mrow> </semantics></math> parameter across GEO, IGSO, and MEO orbit types; subplot (<b>b</b>) shows the distribution of the <math display="inline"><semantics> <mover accent="true"> <mo>Ω</mo> <mo>˙</mo> </mover> </semantics></math> parameter under the same orbit types; subplot (<b>c</b>) depicts the distribution characteristics of the <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>D</mi> <mi>O</mi> <mi>T</mi> </mrow> </semantics></math> parameter for GEO, IGSO, and MEO; subplot (<b>d</b>) highlights the distribution of the <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>c</mi> </mrow> </msub> </semantics></math> parameter across the three orbit types; and subplot (<b>e</b>) presents the distribution characteristics of the <math display="inline"><semantics> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>s</mi> </mrow> </msub> </semantics></math> parameter for GEO, IGSO, and MEO.</p>
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<p>Comparison chart of clock bias parameter prediction results and actual results for some satellites. Subplot (<b>a</b>) represents the comparison between the prediction results before and after differencing, while subplot (<b>b</b>) represents the anomalies detected by IF-TEA-LSTM.</p>
Full article ">
22 pages, 1684 KiB  
Article
Evaluating Ionospheric Total Electron Content (TEC) Variations as Precursors to Seismic Activity: Insights from the 2024 Noto Peninsula and Nichinan Earthquakes of Japan
by Karan Nayak, Rosendo Romero-Andrade, Gopal Sharma, Charbeth López-Urías, Manuel Edwiges Trejo-Soto and Ana Isela Vidal-Vega
Atmosphere 2024, 15(12), 1492; https://doi.org/10.3390/atmos15121492 (registering DOI) - 14 Dec 2024
Viewed by 185
Abstract
This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and [...] Read more.
This study provides a comprehensive investigation into ionospheric perturbations associated with the Mw 7.5 earthquake on the Noto Peninsula in January 2024, utilizing data from the International GNSS Service (IGS) network. Focusing on Total Electron Content (TEC), the analysis incorporates spatial mapping and temporal pattern assessments over a 30-day period before the earthquake. The time series for TEC at the closest station to the epicenter, USUD, reveals a localized decline, with a significant negative anomaly exceeding 5 TECU observed 22 and 23 days before the earthquake, highlighting the potential of TEC variations as seismic precursors. Similar patterns were observed at a nearby station, MIZU, strengthening the case for a seismogenic origin. Positive anomalies were linked to intense space weather episodes, while the most notable negative anomalies occurred under geomagnetically calm conditions, further supporting their seismic association. Using Kriging interpolation, the anomaly zone was shown to closely align with the earthquake’s epicenter. To assess the consistency of TEC anomalies in different seismic events, the study also examines the Mw 7.1 Nichinan earthquake in August 2024. The results reveal a prominent negative anomaly, reinforcing the reliability of TEC depletions in seismic precursor detection. Additionally, spatial correlation analysis of Pearson correlation across both events demonstrates that TEC coherence diminishes with increasing distance, with pronounced correlation decay beyond 1000–1600 km. This spatial decay, consistent with Dobrovolsky’s earthquake preparation area, strengthens the association between TEC anomalies and seismic activity. This research highlights the complex relationship between ionospheric anomalies and seismic events, underscoring the value of TEC analysis as tool for earthquake precursor detection. The findings significantly enhance our understanding of ionospheric dynamics related to seismic events, advocating for a comprehensive, multi-station approach in future earthquake prediction efforts. Full article
20 pages, 8224 KiB  
Article
Statistical Analysis of the Occurrence of Ionospheric Scintillations at the Low-Latitude Sanya Station During 2004–2021
by Bo Xiong, Changhao Yu, Xiaolin Li, Yuxiao Li, Lianhuan Hu, Yuqing Wang, Lingxiao Du and Yuxin Wang
Remote Sens. 2024, 16(24), 4668; https://doi.org/10.3390/rs16244668 (registering DOI) - 13 Dec 2024
Viewed by 307
Abstract
The ionosphere of the Earth often becomes turbulent and develops electron density irregularities that can cause rapid and random changes in the amplitude and phase of radio signals, which is known as ionospheric scintillation. In this study, the statistical behavior of global navigation [...] Read more.
The ionosphere of the Earth often becomes turbulent and develops electron density irregularities that can cause rapid and random changes in the amplitude and phase of radio signals, which is known as ionospheric scintillation. In this study, the statistical behavior of global navigation satellite system (GNSS) ionospheric amplitude scintillation of varying intensities over the Chinese low-latitude station in Sanya (18.34°N, 109.62°E; magnetic latitude: 7.61°N) has been investigated with respect to its dependence on solar activity, seasons, local time (LT), and geomagnetic activity during the period from July 2004 to December 2021. A detailed study on the solar activity dependence of scintillation occurrence shows that the occurrence rates of strong and moderate scintillations significantly increase with enhanced solar activity, but weak amplitude scintillations do not entirely conform to this characteristic. In terms of seasonal dependence, the scintillations in Sanya from 2004 to 2021 mainly occurred during equinoxes and exhibit a distinct equinoctial asymmetry. This asymmetry is characterized by a higher occurrence rate in autumn than in spring during the years 2007, 2011, and from 2017 to 2021, while in other years, the pattern is reversed, with a higher occurrence rate in spring than in autumn. Regarding LT dependence, scintillations are predominantly observed during 19:30–23:30 LT, with a notable persistence beyond midnight during years of high solar activity. Furthermore, geomagnetic disturbances have been observed to promote weak scintillations at 20:00 LT during the autumn and winter of 2014, and from 20:00 LT to 01:00 LT the next day in the latter half of 2013. In contrast, during the spring and autumn of most other years with high solar activity, these disturbances have been found to inhibit weak scintillations from 20:00 LT to midnight. The promoting/inhibiting effect of geomagnetic disturbances on ionospheric scintillation is not solely influenced by electric field disturbances but is to some extent jointly controlled by a variety of factors including solar activity, season, and LT. Full article
Show Figures

Figure 1

Figure 1
<p>The distribution of the occurrence rates of scintillations with different intensities from 2004 to 2009, as a function of year, month, and LT.</p>
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<p>The distribution of the occurrence rates of scintillations with different intensities from 2010 to 2015, as a function of year, month, and LT.</p>
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<p>The distribution of the occurrence rates of scintillations with different intensities from 2016 to 2021, as a function of year, month, and LT.</p>
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<p>The distribution of the occurrence rates of scintillations of different intensities in Sanya from 2004 to 2021, as influenced by solar activity.</p>
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<p>The distribution of the occurrence rates of weak scintillations in Sanya from 2004 to 2013, as a function of season and LT, with the dashed line dividing the characteristics of LT distribution on the left and seasonal distribution on the right.</p>
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<p>The distribution of the occurrence rates of weak scintillations in Sanya from 2014 to 2021, as a function of season and LT, with the dashed line dividing the characteristics of LT distribution on the left and seasonal distribution on the right.</p>
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<p>The distribution of the occurrence rates of moderate scintillations in Sanya from 2004 to 2013, as a function of season and LT, with the dashed line dividing the characteristics of LT distribution on the left and seasonal distribution on the right.</p>
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<p>The distribution of the occurrence rates of moderate scintillations in Sanya from 2014 to 2021, as a function of season and LT, with the dashed line dividing the characteristics of LT distribution on the left and seasonal distribution on the right.</p>
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<p>The distribution of the occurrence rates of strong scintillations in Sanya from 2004 to 2013, as a function of season and LT, with the dashed line dividing the characteristics of LT distribution on the left and seasonal distribution on the right.</p>
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<p>The distribution of the occurrence rates of strong scintillations in Sanya from 2014 to 2021, as a function of season and LT, with the dashed line dividing the characteristics of LT distribution on the left and seasonal distribution on the right.</p>
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<p>The distribution of the EAI for different intensities of scintillations in Sanya from 2004 to 2021.</p>
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<p>The distribution of the occurrence rates for different intensities of scintillations with solar activity, season, and geomagnetic activity in Sanya between 19:00 LT and 03:00 LT the next day from 2004 to 2021.</p>
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<p>The distribution of the occurrence rates of weak scintillations with geomagnetic activity in Sanya between 19:00 LT and 01:00 LT the next day from 2004 to 2021.</p>
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<p>The distribution of the occurrence rates of moderate scintillations with geomagnetic activity in Sanya between 19:00 LT and 01:00 LT the next day from 2004 to 2021.</p>
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<p>The distribution of the occurrence rates of strong scintillations with geomagnetic activity in Sanya between 19:00 LT and 01:00 LT the next day from 2004 to 2021.</p>
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<p>The impact of geomagnetic disturbances on the distribution of occurrence rates of scintillations of different intensities in Sanya between 19:00 LT and 01:00 LT the next day from 2004 to 2021.</p>
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20 pages, 8899 KiB  
Article
Evaluation of Satellite-Derived Atmospheric Temperature and Humidity Profiles and Their Application as Precursors to Severe Convective Precipitation
by Zhaokai Song, Weihua Bai, Yuanjie Zhang, Yuqi Wang, Xiaoze Xu and Jialing Xin
Remote Sens. 2024, 16(24), 4638; https://doi.org/10.3390/rs16244638 - 11 Dec 2024
Viewed by 301
Abstract
This study evaluated the reliability of satellite-derived atmospheric temperature and humidity profiles derived from occultations of Fengyun-3D (FY-3D), the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the Meteorological Operational Satellite program (METOP), and the microwave observations of NOAA Polar Orbital Environmental [...] Read more.
This study evaluated the reliability of satellite-derived atmospheric temperature and humidity profiles derived from occultations of Fengyun-3D (FY-3D), the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the Meteorological Operational Satellite program (METOP), and the microwave observations of NOAA Polar Orbital Environmental Satellites (POES) using various conventional sounding datasets from 2020 to 2021. Satellite-derived profiles were also used to explore the precursors of severe convective precipitations in terms of the atmospheric boundary layer (ABL) characteristics and convective parameters. It was found that the satellite-derived temperature profiles exhibited high accuracy, with RMSEs from 0.75 K to 2.68 K, generally increasing with the latitude and decreasing with the altitude. Among these satellite-derived profile sources, the COSMIC-2-derived temperature profiles showed the highest accuracy in the middle- and low-latitude regions, while the METOP series had the best performance in high-latitude regions. Comparatively, the satellite-derived relative humidity profiles had lower accuracy, with RMSEs from 13.72% to 24.73%, basically increasing with latitude. The METOP-derived humidity profiles were overall the most reliable among the different data sources. The ABL temperature and humidity structures from these satellite-derived profiles showed different characteristics between severe precipitation and non-precipitation regions and could reflect the evolution of ABL characteristics during a severe convective precipitation event. Furthermore, some convective parameters calculated from the satellite-derived profiles showed significant and rapid changes before the severe precipitation, indicating the feasibility of using satellite-derived temperature and humidity profiles as precursors to severe convective precipitation. Full article
Show Figures

Figure 1

Figure 1
<p>Profile examples of the three occultation datasets: (<b>a</b>) COSMIC-2, (<b>b</b>) METOP, (<b>c</b>) FY-3D. The orange points represent the original profile data, while the red points indicate the profile data interpolated to fixed pressure levels.</p>
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<p>Distribution of all datasets at one certain hour (12:00 UTC, 4 January 2021).</p>
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<p>Global horizontal distribution of each satellite profile dataset in July 2021: (<b>a</b>) FY-3D, (<b>b</b>) COSMIC-2, (<b>c</b>) METOP, and (<b>d</b>) POES.</p>
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<p>Vertical distribution of each satellite profile dataset in July 2021: (<b>a</b>) COSMIC-2, (<b>b</b>) METOP, and (<b>c</b>) FY-3D. The vertical axis represents pressure, while the horizontal axis indicates the total number of observations within different pressure ranges over an entire month.</p>
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<p>Density estimation scatter plot of RMSE/MBE calculated from satellite-derived temperature profiles and matching sounding profiles as a function of distance. The horizontal axis represents the distance between the satellite profiles and the matched sounding profiles. The horizontal axis represents the RMSE (<b>a</b>–<b>d</b>) and MBE (<b>e</b>–<b>h</b>) between the four types of satellite profiles and matching sounding profiles, with units in Kelvin (K).</p>
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<p>Density estimation scatter plot of RMSE/MBE calculated from satellite-derived humidity profiles and matching sounding profiles as a function of distance. The horizontal axis represents the distance between the satellite profiles and the matched sounding profiles. The horizontal axis represents the RMSE (<b>a</b>–<b>d</b>) and MBE (<b>e</b>–<b>h</b>) between the four types of satellite profiles and matching sounding profiles, with units in %.</p>
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<p>Comparison between four types of satellite-derived temperature profiles: (<b>a</b>) FY-3D, (<b>b</b>) POES, (<b>c</b>) METOP, (<b>d</b>) COSMIC-2 and radiosonde profiles at various latitude regions and altitudes. Solid lines represent the mean bias error (MBE, unit: K), dashed lines represent the root mean square error (RMSE, unit: K), the vertical axis represents the pressure levels (unit: hPa), and the three colors represent the low-, mid-, and high-latitude regions, respectively, while the black vertical dashed line indicates the zero value.</p>
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<p>Comparison between four types of satellite-derived humidity profiles: (<b>a</b>) FY-3D, (<b>b</b>) POES, (<b>c</b>) METOP, (<b>d</b>) COSMIC-2 and radiosonde profiles at various latitude regions and altitudes. Solid lines represent the mean bias error (MBE, unit: %), dashed lines represent the root mean square error (RMSE, unit: %), the vertical axis represents the pressure levels (unit: hPa), and the three colors represent the low-, mid-, and high-latitude regions, respectively, while the black vertical dashed line indicates the zero value.</p>
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<p>Kernel density estimation plots for the four types of satellite-derived temperature profiles: (<b>a1</b>–<b>c1</b>) POES, (<b>a2</b>–<b>c2</b>) FY-3D, (<b>a3</b>–<b>c3</b>) METOP, (<b>a4</b>,<b>b4</b>) COSMIC-2 and radiosonde profiles. Both the horizontal and vertical axes represent temperature (unit: Kelvin). The bold <b>R</b> represents the results obtained through a significance test at the 0.05 level. The straight line has a slope of 1, and the shading of the fill reflects the data probability in different areas. Results are presented from left to right for low-, mid-, and high-latitude regions.</p>
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<p>Kernel density estimation plots for the four types of satellite-derived humidity profiles: (<b>a1</b>–<b>c1</b>) POES, (<b>a2</b>–<b>c2</b>) FY-3D, (<b>a3</b>–<b>c3</b>) METOP, (<b>a4</b>,<b>b4</b>) COSMIC-2 and radiosonde profiles. Both the horizontal and vertical axes represent relative humidity (unit: %). The bold <b>R</b> represents the results obtained through a significance test at the 0.05 level. Results are presented from left to right for low-, mid-, and high-latitude regions.</p>
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<p>The precipitation rate variation for the selected area from 00:00 to 22:00 on July 20 is shown in (<b>a</b>–<b>l</b>).</p>
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<p>POES-derived temperature and humidity profiles in the precipitation area (<b>a</b>–<b>c</b>) and non-precipitation area (<b>d</b>–<b>f</b>) at 03:00 UTC on 20 July 2021. Corresponding precipitation rates (<b>g</b>) are given in mm/h. The red stars indicate the locations of the profile observations. Left to right in (<b>a</b>–<b>c</b>) represent the dewpoint temperature profiles (K) and temperature profiles (K), potential temperature profiles (K), and specific humidity profiles (g/kg).</p>
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<p>Precipitation rates (mm/h) in the selected precipitation area (rectangular box in the figure), showing (<b>a</b>–<b>c</b>) the period before precipitation, (<b>d</b>–<b>f</b>) during precipitation, and (<b>g</b>–<b>i</b>) after precipitation. Different colored stars represent the profile observation locations from different datasets. The red stars indicate the locations of the profile observations.</p>
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<p>Temperature and humidity profiles in the selected area showing (<b>a</b>–<b>c</b>) before precipitation, (<b>d</b>–<b>f</b>) during precipitation, and (<b>g</b>–<b>i</b>) after precipitation. The numerical values in the top right corner indicate the average precipitation rate in the area, in mm/h. From left to right in (<b>a</b>–<b>c</b>) represents the dewpoint temperature profiles (K) and temperature profiles (K), potential temperature profiles (K), and specific humidity profiles (g/kg).</p>
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<p>The average precipitation rate (mm/h) of the selected area varies with time (<b>a</b>) and the error bars of convective parameters during the precipitation process: (<b>b</b>) MUCAPE (J/kg), (<b>c</b>) MUCIN (J/kg), (<b>d</b>) LCL (km), (<b>e</b>) LFC (km), (<b>f</b>) K_index (K), (<b>g</b>) Lift (K), (<b>h</b>) Si (K), (<b>i</b>) lapse_rate (K/km), (<b>j</b>) Static_stability (K/hPa), (<b>k</b>) Moist_static_energy (J/kg), (<b>l</b>) RH. Each blue box represents the interquartile range, with the upper edge corresponding to the 75th percentile, the line inside the box indicating the 50th percentile, and the lower edge representing the 25th percentile. The red dots represent the mean value of the data, while the red crosses represent outliers.</p>
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14 pages, 8940 KiB  
Article
Some Effects of the Shiveluch Volcano Eruption of the 10 April 2023 on Atmospheric Electricity and the Ionosphere
by Sergey Smirnov, Sergey Pulinets and Vasily Bychkov
Atmosphere 2024, 15(12), 1467; https://doi.org/10.3390/atmos15121467 - 9 Dec 2024
Viewed by 380
Abstract
The full range of effects of strong volcanic eruptions on the electrical characteristics of the atmosphere is not yet fully understood. On the 10 April 2023, the largest eruption in recent decades of the Shiveluch volcano in Kamchatka occurred. At the same time, [...] Read more.
The full range of effects of strong volcanic eruptions on the electrical characteristics of the atmosphere is not yet fully understood. On the 10 April 2023, the largest eruption in recent decades of the Shiveluch volcano in Kamchatka occurred. At the same time, a sharp increase in electron concentration was observed in the F layer of the ionosphere above the volcano. Simultaneously, at a distance of 450 km from the volcano, an intense anomaly was observed in the vertical component of the electric field potential gradient in the surface atmosphere. At this distance, the anomaly could not have been caused by a space charge of volcanic ash. The article examines the atmospheric–electrical effects of a volcanic eruption and proposes a physical mechanism for these phenomena. The formation of strong electric field positive jump as result of volcano eruption was confirmed by the consecutive Shiveluch volcano eruption on the 18 August 2024. Full article
(This article belongs to the Special Issue Feature Papers in Upper Atmosphere (2nd Edition))
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<p>The boundaries of the distribution of ash as a result of the eruption of Shiveluch Volcano on the 10 April 2023 are marked in purple [<a href="#B18-atmosphere-15-01467" class="html-bibr">18</a>].</p>
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<p>Electrical and meteorological parameters of the atmosphere registered on the 10 April at Paratunka Observatory during the eruption of the Shiveluch volcano (moment 13:15—red arrow). (<b>a</b>) Potential gradient; (<b>b</b>) air conductivity caused by positive ions (red line) and negative ions (blue line); (<b>c</b>) precipitation.</p>
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<p>Meteorological parameters of the atmosphere registered on the 10 April at Paratunka observatory during the eruption of the Shiveluch volcano (13:15—red arrow) (<b>a</b>) wind speed; (<b>b</b>) air humidity.</p>
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<p>Ionograms registered on the 10 April 2023 at: (<b>a</b>) 13:00 UTC; (<b>b</b>) 13:15 UTC; (<b>c</b>) 13:30 UTC.</p>
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<p>Variations in the ionospheric parameters during the time interval from the 6 to the 16 April 2023 (<b>a</b>) h’F; (<b>b</b>) foF2 (blue—current parameter value, red—running median); (<b>c</b>) ΔfoF2; (<b>d</b>)—Dst index; (<b>e</b>) GIM TEC variations over the Paratunka Observatory’s location (blue—current parameter value, red—running median); (<b>f</b>) GIM TEC variations over the Shiveluch volcano’s location (blue—current parameter value, red—running median); (<b>g</b>) ΔTEC for Shiveluch data; (<b>h</b>) Dst index. All data are in the local time.</p>
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<p>(<b>a</b>) Counting rate of pulsed electromagnetic VLF radiation within the frequency range 3–30 kHz recorded by the VLF receiver at the Paratunka Observatory; (<b>b</b>) recording of explosive microseims at the seismic station BDR on the 10 April [<a href="#B19-atmosphere-15-01467" class="html-bibr">19</a>]. The lower panel shows the consecutive images from the Himawari 9 geostationary satellite [<a href="#B20-atmosphere-15-01467" class="html-bibr">20</a>]. From left to right: 10 April 13:10 UTC; 14:20 UTC; 15:50 UTC; 11 April 00:00 UTC. Red circles denote the Shiveluch volcano caldera position and the volcanic ash spread.</p>
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<p>(<b>a</b>) Legend of the signals observed by the L-array; (<b>b</b>) ionograms registered by one of the channels of the L-array [<a href="#B21-atmosphere-15-01467" class="html-bibr">21</a>].</p>
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<p>Schematic presentation of the oblique reflection trace formation on the ionogram image.</p>
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<p>Variation in the vertical potential gradient (PG) of the atmospheric electric field registered on the 18 August 2024 at Paratunka Observatory with a 1 s time resolution. Purple lines show the range between +200 V/m and −200 V/m.</p>
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<p>(<b>a</b>) Variations in positive (red) and negative (blue) conductivities registered on the 18 August 2024 at Paratunka Observatory; (<b>b</b>) sum of the positive and negative conductivities.</p>
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<p>Examples of vertical ionograms in the beginning at 02:45 UT (<b>a</b>) and the end at 04:15 UT (<b>b</b>) of the anomalous reflection effect on the 18 August 2024. The anomalous sporadic E layer formed at 120 km of altitude is circled with an oval in (<b>b</b>).</p>
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21 pages, 3865 KiB  
Article
Magnetosphere–Ionosphere Conjugate Harang Discontinuity and Sub-Auroral Polarization Streams (SAPS) Phenomena Observed by Multipoint Satellites
by Ildiko Horvath and Brian C. Lovell
Atmosphere 2024, 15(12), 1462; https://doi.org/10.3390/atmos15121462 - 7 Dec 2024
Viewed by 347
Abstract
It is well understood that near midnight, the Harang Discontinuity separates the auroral duskside eastward electrojet (EEJ) and dawnside westward electrojet (WEJ) and associated plasma flows driven by enhanced magnetospheric convections via Magnetosphere–Ionosphere (M–I) coupling. There are conflicting reports regarding the significance of [...] Read more.
It is well understood that near midnight, the Harang Discontinuity separates the auroral duskside eastward electrojet (EEJ) and dawnside westward electrojet (WEJ) and associated plasma flows driven by enhanced magnetospheric convections via Magnetosphere–Ionosphere (M–I) coupling. There are conflicting reports regarding the significance of Region1 (R1) and R2 currents and the enhancement of Sub-Auroral Polarization Streams (SAPS) in the Harang region. We investigate the M–I conjugate Harang and SAPS phenomena using multipoint satellite observations. Results show the inner-magnetosphere (1) Harang region at midnight (between the plasmapause and the closed/open field-line boundary) with (2) a strong SAPS electric field (EX ≈ 30 mV/m; in magnitude) in a fast-time voltage generator (VGFT) near the plasmapause and the topside ionosphere (3) Harang Discontinuity (where R1 and R2 currents flow along) with (4) an enhanced SAPS flow (~1800 m/s) in the underlying VGFT system (requiring no R2 currents). From these (1–4) findings we conclude (i) the significance of both R1 and R2 currents in the observed M–I conjugate Harang phenomenon’s development, (ii) the different development of the reversing EEJ–WEJ compared to the regular auroral EEJ and WEJ in the topside ionosphere R1–R2 system, and (iii) the R2 currents’ absence in the enhanced SAPS flow newly formed in the VGFT system. Full article
(This article belongs to the Special Issue Coupling between Plasmasphere and Upper Atmosphere)
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<p>The schematic diagram modified from <a href="#atmosphere-15-01462-f001" class="html-fig">Figure 1</a> of Koskinen and Pulkkinen [<a href="#B13-atmosphere-15-01462" class="html-bibr">13</a>] shows the Harang Discontinuity (HD; in red), appearing as an eastward/westward electrojet (EEJ/WEJ) reversal (shaded interval in yellow), and the various currents and E×B drifts in the auroral zone.</p>
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<p>Illustrating Event 1 observed by TH-E in the inner magnetosphere. (<b>a</b>) The orbit plots depict the earthward and tailward edges of the Harang region, (<b>b</b>) the schematic diagram of the auroral scenario along with the (<b>c</b>) actual TH-E footprints and substorm onset locations (symbol stars), and (<b>d</b>) the Harang region’s E field and plasma environment.</p>
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<p>Illustrating Event 2 observed by TH-D in the inner magnetosphere. (<b>a</b>) The orbit plots depict the earthward and tailward edges of the Harang region; (<b>b</b>) the schematic diagram of the auroral scenario along with (<b>c</b>) the actual TH-D footprints and substorm onset locations (symbol stars); and (<b>d</b>) the Harang region’s E field and plasma environment.</p>
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<p>Illustrating the Harang region’s inner-magnetosphere plasma environment, the THEMIS line plots depict the signatures of short circuiting, TPBL and EMIC waves, and the hot zone during (<b>a</b>) Event 1 and (<b>b</b>) Event 2. As indicated by ∗10<sup>16</sup>, the flux values are multiplied by 10<sup>16</sup> for better presentations.</p>
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<p>Illustrating Event 1 observed by DMSP F17 in the topside ionosphere. (<b>a</b>) The schematic diagram of the auroral scenario is shown, along with the underlying polar convection and auroral precipitation pattern, where the F17 passes are plotted with the SAPS, EEJ, WEJ, and substorm onset locations, and (<b>b</b>) the F17 line plots depict the Harang region’s plasma environment and underlying currents. As indicated by ∗10<sup>3</sup>, the Ne values are multiplied by 10<sup>3</sup> for a better presentation.</p>
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<p>Illustrating Event 2 observed by DMSP F18 in the topside ionosphere. (<b>a</b>) The schematic diagram of the auroral scenario is shown, along with the underlying polar convection and auroral precipitation pattern, where the F18 passes are plotted with the SAPS, EEJ, WEJ, and substorm onset locations, and (<b>b</b>) the F18 line plots depict the Harang region’s plasma environment and underlying currents. As indicated by ∗10<sup>3</sup>, the Ne values are multiplied by 10<sup>3</sup> for a better presentation.</p>
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<p>Illustrating SAR arc development in Event 1 observed over Gillam, near the TH-E-observed SAPS location and substorm onset locations; the REGO image shows a series of SAR arc events occurring during the magnetotail-reconnection-related particle injections unfolding during a series of substorms (marked by the symbol stars in colors).</p>
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<p>Illustrating SAR arc development in Event 2 observed over Gillam, away from the TH-D-observed SAPS location but close to one of the substorm onset locations; the REGO image shows a series of SAR arc events occurring during the magnetotail-reconnection-related particle injections unfolding during a series of substorms (marked by the symbol stars in colors).</p>
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28 pages, 5473 KiB  
Article
Sensitivity of Band-Pass Filtered In Situ Low-Earth Orbit and Ground-Based Ionosphere Observations to Lithosphere–Atmosphere–Ionosphere Coupling Over the Aegean Sea: Spectral Analysis of Two-Year Ionospheric Data Series
by Wojciech Jarmołowski, Anna Belehaki and Paweł Wielgosz
Sensors 2024, 24(23), 7795; https://doi.org/10.3390/s24237795 - 5 Dec 2024
Viewed by 409
Abstract
This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they [...] Read more.
This study demonstrates a rich complexity of the time–frequency ionospheric signal spectrum, dependent on the measurement type and platform. Different phenomena contributing to satellite-derived and ground-derived geophysical data that only selected signal bands can be potentially sensitive to seismicity over time, and they are applicable in lithosphere–atmosphere–ionosphere coupling (LAIC) studies. In this study, satellite-derived and ground-derived ionospheric observations are filtered by a Fourier-based band-pass filter, and an experimental selection of potentially sensitive frequency bands has been carried out. This work focuses on band-pass filtered ionospheric observations and seismic activity in the region of the Aegean Sea over a two-year time period (2020–2021), with particular focus on the entire system of tectonic plate junctions, which are suspected to be a potential source of ionospheric disturbances distributed over hundreds of kilometers. The temporal evolution of seismicity power in the Aegean region is represented by the record of earthquakes characterized by M ≥ 4.5, used for the estimation of cumulative seismic energy. The ionospheric response to LAIC is explored in three data types: short inspections of in situ electron density (Ne) over a tectonic plate boundary by Swarm satellites, stationary determination of three Ne density profile parameters by the Athens Digisonde station AT138 (maximum frequency of the F2 layer: foF2; maximum frequency of the sporadic E layer: foEs; and frequency spread: ff), and stationary measure of vertical total electron content (VTEC) interpolated from a UPC-IonSAT Quarter-of-an-hour time resolution Rapid Global ionospheric map (UQRG) near Athens. The spectrograms are made with the use of short-term Fourier transform (STFT). These frequency bands in the spectrograms, which show a notable coincidence with seismicity, are filtered out and compared to cumulative seismic energy in the Aegean Sea, to the geomagnetic Dst index, to sunspot number (SN), and to the solar radio flux (F10.7). In the case of Swarm, STFT allows for precise removal of long-wavelength Ne signals related to specific latitudes. The application of STFT to time series of ionospheric parameters from the Digisonde station and GIM VTEC is crucial in the removal of seasonal signals and strong diurnal and semi-diurnal signal components. The time series formed from experimentally selected wavebands of different ionospheric observations reveal a moderate but notable correlation with the seismic activity, higher than with any solar radiation parameter in 8 out of 12 cases. The correlation coefficient must be treated relatively and with caution here, as we have not determined the shift between seismic and ionospheric events, as this process requires more data. However, it can be observed from the spectrograms that some weak signals from selected frequencies are candidates to be related to seismic processes. Full article
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<p>Selection of Swarm B and C tracks in the last quarter of 2020 together with the epicenter of earthquakes that occurred at that time in the Aegean Sea and neighboring regions. The tectonic plate boundaries are also presented in this map.</p>
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<p>Example residual Swarm Ne data (<b>upper right</b>), example spectrogram of suspected co-seismic Ne disturbance detected by Swarm B (<b>lower right</b>), and Swarm PSD sampled at 35 s wave period with tectonic plate boundaries (<b>left</b>).</p>
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<p>Critical frequency of (<b>a</b>) F2 layer (foF2), (<b>b</b>) sporadic E layer (foEs), and (<b>c</b>) spread frequency (ff) from Athens Digisonde (black) and their 90-day trends estimated by DFT (red) in 2020/2021. Data gaps are also ignored in further correlation analysis.</p>
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<p>The VTEC interpolated from UQRG GIM near Athens (38° N and 24° E) (black) and its 90-day trend (red) in 2020/2021.</p>
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<p>Maxima of PSD of Swarm B Ne disturbances in (<b>a</b>) 2020 and (<b>b</b>) 2021 (arbitrary scaling, blue narrow line) together with earthquakes in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). Max PSD have calculated the 20-day moving average (blue bold line). The earthquakes have calculated an indicator of seismicity (black bold line). The Dst index is plotted as an orange line. The sunspot number is represented by a yellow area plot. The solar radio flux is shown as a red line. The time periods indicated with red horizontal lines cover earthquake groups presented geographically in <a href="#sensors-24-07795-f007" class="html-fig">Figure 7</a>. Green horizontal lines denote periods of higher seismicity.</p>
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<p>Maxima of PSD of Swarm C Ne disturbances in (<b>a</b>) 2020 and (<b>b</b>) 2021 (arbitrary scaling, blue narrow line) together with earthquakes in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). Max PSD have calculated the 20-day moving average (blue bold line). The earthquakes have calculated an indicator of seismicity (black bold line). The Dst index is plotted as an orange line. The sunspot number is represented by a yellow area plot. The solar radio flux is shown as a red line. The time periods indicated with red horizontal lines cover earthquake groups presented geographically in <a href="#sensors-24-07795-f008" class="html-fig">Figure 8</a>. Green horizontal lines denote periods of higher seismicity.</p>
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<p>Geographical location of earthquakes occurring in selected periods in 2020 ((<b>a</b>–<b>f</b>) present earthquake groups indicated in <a href="#sensors-24-07795-f005" class="html-fig">Figure 5</a>a and <a href="#sensors-24-07795-f006" class="html-fig">Figure 6</a>a by red horizontal lines), together with Swarm B (green) and Swarm C (red) tracks at the same time.</p>
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<p>Geographical location of earthquakes occurring in selected periods in 2021 ((<b>a</b>–<b>f</b>) present earthquake groups indicated by red horizontal lines in <a href="#sensors-24-07795-f005" class="html-fig">Figure 5</a>b and <a href="#sensors-24-07795-f006" class="html-fig">Figure 6</a>b), together with Swarm B (green) and Swarm C (red) tracks at the same time.</p>
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<p>STFT analysis and band-pass filtering of foF2 parameter from Athens Digisonde in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (10–6 days) calculated using a 20-day window. The foF2 is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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<p>STFT analysis and band-pass filtering of foEs parameter from Athens Digisonde in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (10–6 days) calculated using a 20-day window. The foEs is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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<p>STFT analysis and band-pass filtering of ff parameter from Athens Digisonde in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (6–10 days) calculated using a 20-day window. The ff is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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<p>STFT analysis and band-pass filtering of VTEC interpolated near Athens from UQRG in 2020 (<b>a</b>,<b>b</b>) and in 2021 (<b>c</b>,<b>d</b>). Subfigures (<b>a</b>,<b>c</b>) are spectrograms of high-pass filtered (90 days) signal, whereas (<b>b</b>,<b>d</b>) show standard deviation of band-pass filtered signal (10–6 days) calculated using a 20-day window. VTEC is compared to the Dst index (orange), sunspot number (yellow area), solar radio flux (red), and magnitudes and depths of the earthquakes occurring in the Aegean region (magnitude multiplied by 10—black stems with dots, depth—black stems with circles). The earthquakes have a calculated indicator of seismicity (black bold line).</p>
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18 pages, 1898 KiB  
Article
Improving Performance of Uncombined PPP-AR Model with Ambiguity Constraints
by Yichen Liu, Urs Hugentobler and Bingbing Duan
Remote Sens. 2024, 16(23), 4537; https://doi.org/10.3390/rs16234537 - 3 Dec 2024
Viewed by 541
Abstract
With the advancement of multi-frequency and multi-constellation GNSS signals and the introduction of observable-specific bias (OSB) products, the uncombined precise point positioning (PPP) model has grown more prevalent. However, this model faces challenges due to the large number of estimated parameters, resulting in [...] Read more.
With the advancement of multi-frequency and multi-constellation GNSS signals and the introduction of observable-specific bias (OSB) products, the uncombined precise point positioning (PPP) model has grown more prevalent. However, this model faces challenges due to the large number of estimated parameters, resulting in strong correlations between state parameters, such as clock errors, ionospheric delays, and hardware biases. This can slow down the convergence time and impede ambiguity resolution. We propose two methods to improve the triple-frequency uncombined PPP-AR model by integrating ambiguity constraints. The first approach makes use of the resolved ambiguities from dual-frequency ionosphere-free combined PPP-AR processing and incorporates them as constraints into triple-frequency uncombined PPP-AR processing. While this approach requires the implementation of two filters, increasing computational demands and thereby limiting its feasibility for real-time applications, it effectively reduces parameter correlations and facilitates ambiguity resolution in post-processing. The second approach incorporates fixed extra-wide-lane (EWL) and wide-lane (WL) ambiguities directly, allowing for rapid convergence, and is well suited for real-time processing. Results show that, compared to the uncombined PPP-AR model, integrating N1 and N2 constraints reduces averaged convergence time from 8.2 to 6.4 min horizontally and 13.9 to 10.7 min vertically in the float solution. On the other hand, integrating EWL and WL ambiguity constraints reduces the horizontal convergence to 5.9 min in the float solution and to 4.6 min for horizontal and 9.7 min for vertical convergence in the fixed solution. Both methods significantly enhance the ambiguity resolution in the uncombined triple-frequency PPP model, increasing the validated fixing rate from approximately 80% to 89%. Full article
(This article belongs to the Special Issue Multi-GNSS Precise Point Positioning (MGPPP))
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<p>The distribution of the IGS sites used in the processing.</p>
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<p>Float position errors and their formal uncertainty of the uncombined models in east (red), north (blue), and up (black) components during the first hour. The shaded area represents the error bar. (<b>a</b>) Triple-Uncomb model, (<b>b</b>) Triple-Uncomb+N1&amp;N2 model, (<b>c</b>) Triple-Uncomb+EWL&amp;WL model.</p>
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<p>Histogram of the values of the ambiguity constraints in two models in experiment GODS, DOY 301, 0:00–4:00. (<b>a</b>) N1&amp;N2 ambiguity constraints, (<b>b</b>) EWL&amp;WL ambiguity constraints.</p>
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<p>Horizontal scatter plot of position errors with RMS values during initial 20 min in all experiments. The blue, orange, and green dots represent float, fixed, and validated fixed solutions, respectively. (<b>a</b>) Dual-IF model, (<b>b</b>) Triple-Uncomb model, (<b>c</b>) Triple-TCAR model, (<b>d</b>) Triple-Uncomb+N1&amp;N2 model, (<b>e</b>) Triple-Uncomb+EWL&amp;WL model.</p>
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<p>Cumulative distribution of the number of the converged experiments according to the horizontal convergence time. The black and red dotted lines denote the 95th and 68th percentile.</p>
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<p>Averaged positioning error over time in horizontal and vertical components during the first hour. Blue, red, yellow, purple, and green lines represent Dual-IF, Triple-Uncomb, Triple-TCAR, Triple-Uncomb+N1&amp;N2, and Triple-Uncomb+EWL&amp;WL models, respectively. Shaded area represents the uncertainty of the solution. Blue horizontal line denotes the convergence level. (<b>a</b>) Horizontal float positioning error, (<b>b</b>) horizontal fixed positioning error, (<b>c</b>) vertical float positioning error, (<b>d</b>) vertical fixed positioning error.</p>
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<p>The averaged convergence time for float and fixed solution among five models. Convergence time statistics align with the results in <a href="#remotesensing-16-04537-f006" class="html-fig">Figure 6</a>. (<b>a</b>) Horizontal component. (<b>b</b>) Vertical component.</p>
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<p>Averaged fixing rate and validated fixing rate for five models for all experiments. The fixing rate and validated fixing rate are both averaged over all experiments.</p>
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20 pages, 14857 KiB  
Article
Modification of IPI Method for Extraction of Short-Term and Imminent OLR Anomalies and Case Study of Two Large Earthquakes
by Maoning Feng, Pan Xiong, Weixi Tian, Yue Liu, Changhui Ju, Cheng Song and Yongxian Zhang
Geosciences 2024, 14(12), 325; https://doi.org/10.3390/geosciences14120325 - 1 Dec 2024
Viewed by 491
Abstract
The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which [...] Read more.
The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies. Full article
(This article belongs to the Special Issue Earthquake Hazard Modelling)
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<p>A schematic diagram of the preprocessing process.</p>
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<p>A schematic diagram of the scope of the study area.</p>
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<p>A schematic diagram of the time window.</p>
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<p>A schematic diagram of the extent of the Ridgecrest earthquake study area. In the left figure, the red star indicates the epicenter, blue triangles mean cities, and gray lines mean faults. The red line labeled “GF” indicates the Garlock Fault, and the red circle labeled “ECSZ” represents the Eastern California Shear Zone. In the top-right figure, the blue dashed line represents the Ridgecrest Research Area, the yellow circle indicates Ridgecrest eq, and the red lines show the Plate Boundaries.</p>
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<p>Annual variation curve of total OLR residual value of Ridgecrest earthquake in study area of Ridgecrest earthquake (2018–2022).</p>
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<p>Evolution of OLR anomaly extracted by MIPI method prior to Ridgecrest earthquake. Purple five-pointed star indicates day before earthquake, and black five-pointed star indicates day of earthquake, red circle marked the different group of anomalies and the red number is their numbering.</p>
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<p>Ridgecrest earthquake short-term and imminent anomaly and fault location map on day of earthquake.</p>
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<p>A schematic diagram of the extent of the Maduo earthquake study area. In the left figure, the red star indicates the epicenter, blue triangles mean cities, and gray lines mean faults. The red line indicates the Maduo–Gande Fault. In the top-right figure, the blue dashed line represents the Maduo Research Area, the red circle indicates the Maduo eq, and the red lines show the Plate Boundaries.</p>
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<p>Evolution map of anomalies prior to Maduo earthquake. A purple five-pointed star indicates the day before the earthquake, and a black five-pointed star indicates the day of the earthquake, red circle marked the different group of anomalies and the red number is their numbering.</p>
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<p>Maduo earthquake short-term and imminent anomaly and fault location map on day of earthquake.</p>
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25 pages, 6462 KiB  
Article
Unusual Sunrise and Sunset Terminator Variations in the Behavior of Sub-Ionospheric VLF Phase and Amplitude Signals Prior to the Mw7.8 Turkey Syria Earthquake of 6 February 2023
by Mohammed Y. Boudjada, Pier F. Biagi, Hans U. Eichelberger, Giovanni Nico, Konrad Schwingenschuh, Patrick H. M. Galopeau, Maria Solovieva, Michael Contadakis, Valery Denisenko, Helmut Lammer, Wolfgang Voller and Franz Giner
Remote Sens. 2024, 16(23), 4448; https://doi.org/10.3390/rs16234448 - 27 Nov 2024
Viewed by 377
Abstract
We report on the recent earthquakes (EQs) that occurred, with the main shock on 6 February 2023, principally in the central southern part of Turkey and northwestern Syria. This region is predisposed to earthquakes because of the tectonic plate movements between Anatolian, Arabian, [...] Read more.
We report on the recent earthquakes (EQs) that occurred, with the main shock on 6 February 2023, principally in the central southern part of Turkey and northwestern Syria. This region is predisposed to earthquakes because of the tectonic plate movements between Anatolian, Arabian, and African plates. The seismic epicenter was localized at 37.08°E and 37.17°N with depth in the order of 10 km and magnitude Mw7.8. We use Graz’s very-low-frequency VLF facility (15.43°E, 47.06°N) to investigate the amplitude variation in the Denizköy VLF transmitter, localized in the Didim district of Aydin Province in the western part of the Anatolian region in Turkey. Denizköy VLF transmitter is known as Bafa transmitter (27.31°E, 37.40°N), radiating at a frequency of 26.7 kHz under the callsign TBB. This signal is detected daily by the Graz facility with an appropriate signal-to-noise ratio, predominantly during night observations. We study in this analysis the variations of TBB amplitude and phase signals as detected by the Graz facility two weeks before the earthquake occurrence. It is essential to note that the TBB VLF transmitter station and the Graz facility are included in the preparation seismic area, as derived from the Dobrovolsky relationship. We have applied the multi-terminators method (MTM), revealing anomalies occurring at sunset and sunrise terminator occasions and derived from the amplitude and the phase. Minima and maxima of the TBB signal are linked to three terminators, i.e., Graz facility, TBB transmitter, and EQ epicenter, by considering the MTM method. We show that the significant anomalies are those linked to the EQ epicenter. This leads us to make evident the precursor seismic anomaly, which appears more than one week (i.e., 27 January 2023) before EQ occurrence. They can be considered the trace, the sign, and the residue of the sub-ionospheric propagation of the TBB transmitter signal disturbed along its ray path above the preparation EQ zone. We find that the sunrise–sunset anomalies are associated with tectonic regions. One is associated with the Arabian–African tectonic plates with latitudinal stresses in the south–north direction, and the second with the African–Anatolian tectonic plates with longitudinal stresses in the east–west direction. The terminator time shift anomalies prior to EQ are probably due to the lowering (i.e., minima) and raising (i.e., maxima) of the ionospheric electron density generated by atmospheric gravity waves. Full article
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<p>The seismic preparation zone (golden arrows) as derived from the Dobrovolsky relationship. The radius of the preparation area is found to be equal to 2260 km, which is bigger than the distances between the EQ epicenter, the TBB transmitter station, and the Graz facility.</p>
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<p>The amplitude variations in the TBB transmitter signal are shown from 29 January 2023 (029 DOY) to 6 February 2023 (037 DOY), the day of the earthquake occurrence. The horizontal and vertical axes indicate, respectively, the observation time in days of the year 2023 and the amplitude level in dB. The green and magenta vertical dashed lines correspond, respectively, to the terminators of the Graz facility and TBB transmitter. EQ event on 6 February 2023 (037 DOY) is designed by a dotted vertical line.</p>
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<p>The phase variations of the TBB transmitter signal are shown from 29 January 2023 (029 DOY) to 6 February 2023 (037 DOY). The horizontal and vertical axes indicate the observation time in days of the year 2023 and the phase in degrees. Like in <a href="#remotesensing-16-04448-f001" class="html-fig">Figure 1</a>, the green and violet vertical dashed lines correspond to the Graz facility and TBB transmitter station terminator times. The EQ event on 6 February 2023 (037 DOY) is designed by a dotted vertical line.</p>
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<p>The multi-terminators method, applied in this study, leads to an estimate of the terminator time shift of the amplitude (left panel in <a href="#remotesensing-16-04448-f004" class="html-fig">Figure 4</a>) and the phase (right panel in <a href="#remotesensing-16-04448-f004" class="html-fig">Figure 4</a>) at sunrise and sunset as recorded on 2 February 2023 (033 DOY). We consider the two terminators, one at the Graz facility (green vertical dashed line) and the other at the TBB transmitter station (violet vertical dashed line). The particular features observed around both terminators for the investigated period, from 23 January (023 DOY) to 6 February 2023 (037 DOY), are stored. Minima and maxima variations are, respectively, designed by red and blue dashed boxes. Note the phase jumps (right-panel in <a href="#remotesensing-16-04448-f004" class="html-fig">Figure 4</a>) at 33.2 DOY from −180° to +180° due to the reverse of the wave ellipticity axis around the TBB sunrise terminator. The main spectral features in <a href="#remotesensing-16-04448-f004" class="html-fig">Figure 4</a> are listed in <a href="#remotesensing-16-04448-t001" class="html-table">Table 1</a>.</p>
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<p>The minima and maxima of TBB amplitudes as recorded by the Graz facility at sunrise (first and second panels) and sunset (third and fourth panels). The horizontal and vertical axes indicate DOY 2023, and the terminator times are expressed in hours, respectively. The period starts on 23 January 2023 (023 DOY) and ends on 8 February 2023 (039 DOY). The earthquake day (037 DOY) is designed by the vertical red dashed line. The green, magenta, and black color lines indicate the sunrise or sunset terminator variations, respectively, at the Graz VLF facility (47.03°N, 15.46°E), TBB transmitter station (37.40°N, 27.31°E), and the earthquake epicenter (37.17°N, 37.08°E). The blue circles design selected minima and maxima anomalies as reported in <a href="#remotesensing-16-04448-t004" class="html-table">Table 4</a>.</p>
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<p>Like in <a href="#remotesensing-16-04448-f005" class="html-fig">Figure 5</a>, but in the case of minima and maxima associated with the phase of the TBB transmitter signal, respectively, as recorded by the Graz facility at sunrise (two upper panels) and sunset (two lower panels) terminators. The blue circles design selected minima and maxima anomalies as reported in <a href="#remotesensing-16-04448-t004" class="html-table">Table 4</a>.</p>
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<p>Anomaly locations taking into consideration sunrise and sunset terminators for minima and maxima of amplitude (first two panels with red color points) and phase (second two panels with green color points). The black points indicate Graz Facility (15.46°E, 47.03°N), TBB transmitter station (27.31°E, 37.40°N), and EQ epicenter (37.08°E, 37.17°N).</p>
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<p>Geographical locations of the four bands as listed in <a href="#remotesensing-16-04448-t007" class="html-table">Table 7</a>. The horizontal and vertical axes indicate, respectively, the geographical longitude and latitude. The black points indicate Graz Facility (15.46°E, 47.03°N), TBB transmitter station (27.31°E, 37.40°N), and EQ epicenter (37.08°E, 37.17°N). The color points design the amplitude minimum anomalies (red color, Band_Am_Min1), the amplitude maximum anomalies (green color, Band_Am_Min2), the phase minimum anomalies (blue color, Band_Ph_Min1), and the phase maximum anomalies (magenta color, Band_Am_Min1). Each point is linked to the corresponding observation date (e.g., 24/01 to 24 January 2023).</p>
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<p>Geographical locations of the sub-areas that belong to the preparation earthquake zone. The sub-areas that are specific to given bands are shown with red, green, blue, and magenta colors corresponding to the amplitude minimum anomalies (Band_Am_Min1), the amplitude maximum anomalies (Band_Am_Min2), the phase minimum anomalies (Band_Ph_Min1) and the phase maximum anomalies (Band_Am_Min1). Latitudinal and longitudinal expansions are displayed, respectively, in the left and right panels.</p>
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14 pages, 3774 KiB  
Article
Locating Strong Electromagnetic Pulses Recorded by a Single Satellite with Cluster Analysis and Worldwide Lightning Location Network Observations
by Zongxiang Li, Baofeng Cao, Wenjuan Zhang, Xiaoqiang Li, Xiong Zhang, Yongli Wei, Xiao Li, Changjiao Duan and Peng Li
Remote Sens. 2024, 16(23), 4442; https://doi.org/10.3390/rs16234442 - 27 Nov 2024
Viewed by 453
Abstract
The integration of satellite-borne and ground-based global lightning location networks offers a better perspective to study lightning processes and their evolutionary characteristics within thunderstorm clouds, thereby bolstering the predictive capabilities for severe weather phenomena. Currently, the satellite-borne network is in the preliminary testing [...] Read more.
The integration of satellite-borne and ground-based global lightning location networks offers a better perspective to study lightning processes and their evolutionary characteristics within thunderstorm clouds, thereby bolstering the predictive capabilities for severe weather phenomena. Currently, the satellite-borne network is in the preliminary testing phase with a single satellite. The geographic locations of single-satellite detection events primarily rely on synchronous information from coincident ground-based network events; this method is called synchronous locating (SCL). However, variations in detection-frequency bands and system capabilities prevent this method from accurately locating more than a mere 10% of events. To address this limitation, this paper introduces a cluster-analysis-based strategy, utilizing the observations from the Worldwide Lightning Location Network (WWLLN), termed the cluster analysis locating (CAL) method. The CAL method’s performance, influenced by the density-based spatial clustering of applications with noise (DBSCAN), the K-means, and the mean shift algorithms, is examined. Subsequently, an advanced version, mean shift denoised (MSDN)-CAL, is proposed, demonstrating marked improvements in location accuracy and reliability over the other CAL methods. The satellite-borne wideband electromagnetic pulse detector (WEMPD), orbiting at an altitude of approximately 500 km with a 97.5° inclination, captured 1061 strong electromagnetic pulses (EMPs). Among these, trans-ionospheric single pulses (TISPs) and trans-ionospheric pulse pairs (TIPPs) constituted 21.30% and 78.70%, respectively. Using the MSDN-CAL method successfully determines the geographic locations for 81.15% (861 out of 1061) of the events. This success rate represents an approximate eightfold enhancement over the SCL method. The arithmetic mean, geometric mean, and standard deviation of the two-dimensional range deviation of the locating results between the MSDN-CAL method versus the WWLLN-SCL (or the Guangdong-Hong Kong-Macao Lightning Location System (GHMLLS)-SCL) method are 51.06 (176.26) km, 16.17 (92.53) km, and 100.95 (174.79) km, respectively. Furthermore, it has been possible to estimate the occurrence altitudes for 81.92% (684 out of 835) of the TIPP events. The altitude deviations, as determined by comparing them with the GHMLLS-SCL method’s locating results, exhibit an arithmetic mean of 2.08 km, a geometric mean of 1.30 km, and a standard deviation of 2.26 km. The outcomes of this research establish a foundation for deeper investigation into the origins of various event types, their seasonal variations, and their geographical distribution patterns. Moreover, they pave the way for utilizing a single satellite to measure global surface reflectance, thus contributing valuable data for meteorological and atmospheric studies. Full article
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<p>Schematic of the space-borne WEMPD signal detection.</p>
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<p>Geolocation of single-satellite detection events via the CAL method.</p>
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<p>Strong EMPs captured by the WEMPD. Panels (<b>a</b>,<b>b</b>) display the time-frequency distributions for the TISP and TIPP events, respectively. Panels (<b>c</b>,<b>d</b>) illustrate the time–frequency distributions of the aforementioned events following a dechirping process. The TISP event was located in Central Africa at coordinates (6.0349 N, 17.3659 E) and occurred at 4 August 2023 19:10:05.319101706 (LT), with <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>T</mi> <mi>E</mi> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>d</mi> <mi>e</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>22.38</mn> <mtext> </mtext> <mi>TECU</mi> </mrow> </semantics></math>. The TIPP event is located in the coastal area of Colombia at coordinates (6.0349 N, 17.3659 E), and the occurrence time is 6 April 2023 05:13:12.483972000 (LT), with <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>T</mi> <mi>E</mi> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>d</mi> <mi>e</mi> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>14.15</mn> <mtext> </mtext> <mi>TECU</mi> </mrow> </semantics></math>.</p>
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<p>Statistical histograms of the time discrepancy (<math display="inline"><semantics> <mrow> <mo>∆</mo> <mi>t</mi> </mrow> </semantics></math>) between the corrected ‘occurrence’ times of the WEMPD-detected events, accounting for distance and ionospheric effects, and the event timestamps recorded by the ground-based lightning location networks. Panel (<b>a</b>) represents the comparison with the WWLLN, while panel (<b>b</b>) corresponds to the GHMLNS.</p>
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<p>Evaluation of comprehensive scores for the three CAL methods under varying parameter selections. Panel (<b>a</b>) represents the K-means-CAL; panel (<b>b</b>) illustrates the MS-CAL; panels (<b>c</b>) and (<b>d</b>) correspond to the DBSCAN-CAL. Note: the comprehensive score refers to the mean of the arithmetic mean, geometric mean, and standard deviation of the TDRD of the locating results between the CAL method and the WWLLN-SCL method.</p>
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<p>Statistical histograms of the TDRD of the locating results between the four CAL methods and the WWLLN-SCL method. Panel (<b>a</b>) presents the DBSCAN-CAL; panel (<b>b</b>) shows the K-means-CAL; panel (<b>c</b>) corresponds to MS-CAL; and panel (<b>d</b>) represents the MSDN-CAL. The figure presents 86 samples, which correspond to the successful determination of geographical locations using the CAL method out of a total of 90 events. These events are characterized by a time difference (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math>) of less than 100 μs between the WEMPD and the GHMLL.</p>
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<p>Statistical histograms of the differences of the three-dimensional geographical coordinates of the TIPP events’ locating results between the MSDN-CAL method and the GHMLL-SCL method. (<b>a</b>) TDRD; (<b>b</b>) AD. The figure presents 17 samples, which correspond to the successful determination of geographical locations using the MSDN-CAL method out of a total of 19 events. These events are characterized by a time difference (<math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>t</mi> </mrow> </semantics></math>) of less than 100 microseconds between the WEMPD and the GHMLL.</p>
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22 pages, 15337 KiB  
Article
BDS-3/GNSS Undifferenced Pseudorange and Phase Time-Variant Mixed OSB Considering the Receiver Time-Variant Biases and Its Benefit on Multi-Frequency PPP
by Guoqiang Jiao, Ke Su, Min Fan, Yuze Yang and Huaquan Hu
Remote Sens. 2024, 16(23), 4433; https://doi.org/10.3390/rs16234433 - 27 Nov 2024
Viewed by 341
Abstract
The legacy Global Navigation Satellite System (GNSS) satellite clock offsets obtained by the dual-frequency undifferenced (UD) ionospheric-free (IF) model absorb the code and phase time-variant hardware delays, which leads to the inconsistency of the precise satellite clock estimated by different frequencies. The dissimilarity [...] Read more.
The legacy Global Navigation Satellite System (GNSS) satellite clock offsets obtained by the dual-frequency undifferenced (UD) ionospheric-free (IF) model absorb the code and phase time-variant hardware delays, which leads to the inconsistency of the precise satellite clock estimated by different frequencies. The dissimilarity of the satellite clock offsets generated by different frequencies is called the inter-frequency clock bias (IFCB). Estimates of the IFCB typically employ epoch-differenced (ED) geometry-free ionosphere-free (GFIF) observations from global networks. However, this method has certain theoretical flaws by ignoring the receiver time-variant biases. We proposed a new undifferenced model coupled with satellite clock offsets, and further converted the IFCB into the code and phase time-variant mixed observable-specific signal bias (OSB) to overcome the defects of the traditional model and simplify the bias correction process of multi-frequency precise point positioning (PPP). The new model not only improves the mixed OSB performance, but also avoids the negative impact of the receiver time-variant biases on the satellite mixed OSB estimation. The STD and RMS of the original OSB can be improved by 7.5–60.9% and 9.4–66.1%, and that of ED OSB (it can reflect noise levels) can be improved by 50.0–87.5% and 60.0–88.9%, respectively. Similarly, the corresponding PPP performance for using new mixed OSB is better than that of using the traditional IFCB products. Thus, the proposed pseudorange and phase time-variant mixed OSB concept and the new undifferenced model coupled with satellite clock offsets are reliable, applicable, and effective in multi-frequency PPP. Full article
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<p>Distribution of the selected GNSS tracking stations for satellite mixed OSB estimation.</p>
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<p>Multi-GNSS pseudorange and phase time-variant mixed OSB service system.</p>
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<p>GPS L5 mixed OSB on DOY 011, 2021.</p>
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<p>The BDS-3 B1C and B2a mixed OSB on DOY 011, 2021.</p>
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<p>The Galileo E5b, E5, and E6 mixed OSB on DOY 011, 2021.</p>
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<p>Amplitudes of the mixed OSB of GPS L5, BDS-3 B1C, B2a, and Galileo E5b, E5, and E6 signals on DOY 011, 2021.</p>
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<p>Pseudorange and phase time-variant mixed epoch-differenced (ED) OSB for GPS L5 signal on DOY 011, 2021.</p>
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<p>Pseudorange and phase time-variant mixed ED OSB for BDS-3 B1C and B2a signals on DOY 011, 2021.</p>
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<p>Daily satellite pseudorange and phase time-variant mixed OSB for Galileo E5b, E5, and E6 signals on DOY 011, 2021.</p>
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<p>Daily receiver pseudorange and phase time-variant mixed OSB for GPS L5, BDS-3 B1C and B2a, and Galileo E5b, E5, and E6 signals on DOY 011, 2021.</p>
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<p>Daily JAVAD TRE-3 receiver pseudorange and phase time-variant mixed OSB at BDS-3 B2a and Galileo E6 signals on DOY 011, 2021.</p>
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<p>Positioning error of the GPS L1 + L2 + L5, BDS-3 B1I + B3I + B2a, BDS-3 B1I + B3I + B1C + B2a, and Galileo E1 + E5a + E5b + E5 + E6 multi-frequency PPP models on DOY 011, 2021.</p>
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<p>Positioning error of the GPS L1 + L2 + L5, BDS-3 B1I + B3I + B2a, BDS-3 B1I + B3I + B1C + B2a, and Galileo E1 + E5a + E5b + E5 + E6 multi-frequency PPP models on DOY 011, 2021.</p>
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<p>Phase residuals of the GPS L1 + L2 + L5, BDS-3 B1I + B3I + B2a, BDS-3 B1I + B3I + B1C + B2a, and Galileo E1 + E5a + E5b + E5 + E6 multi-frequency PPP on DOY 011, 2021.</p>
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<p>Phase residuals of the GPS L1 + L2 + L5, BDS-3 B1I + B3I + B2a, BDS-3 B1I + B3I + B1C + B2a, and Galileo E1 + E5a + E5b + E5 + E6 multi-frequency PPP on DOY 011, 2021.</p>
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<p>Boxplot of the convergence time for GPS L1 + L2 + L5, BDS-3 B1I + B3I + B2a, BDS-3 B1I + B3I + B1C + B2a, and Galileo E1 + E5a + E5b + E5 + E6 multi-frequency PPP models.</p>
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<p>Boxplot of the positioning accuracy for GPS L1 + L2 + L5, BDS-3 B1I + B3I + B2a, BDS-3 B1I + B3I + B1C + B2a, and Galileo E1 + E5a + E5b + E5 + E6 multi-frequency PPP models.</p>
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19 pages, 4245 KiB  
Technical Note
Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
by Rui Yan, Jianping Huang, Jian Lin, Qiao Wang, Zhenxia Zhang, Yanyan Yang, Wei Chu, Dapeng Liu, Song Xu, Hengxin Lu, Weixing Pu, Lu Wang, Na Zhou, Wenjing Li, Qiao Tan and Zeren Zhima
Remote Sens. 2024, 16(23), 4426; https://doi.org/10.3390/rs16234426 - 26 Nov 2024
Viewed by 304
Abstract
The China Seismo-Electromagnetic Satellite (CSES-01) is the first satellite of the space-based observational platform for the earthquake (EQ) monitoring system in China. It aims to monitor the ionospheric disturbances related to EQ activities by acquiring global electromagnetic fields, ionospheric plasma, energy particles, etc., [...] Read more.
The China Seismo-Electromagnetic Satellite (CSES-01) is the first satellite of the space-based observational platform for the earthquake (EQ) monitoring system in China. It aims to monitor the ionospheric disturbances related to EQ activities by acquiring global electromagnetic fields, ionospheric plasma, energy particles, etc., opening a new path for innovative explorations of EQ prediction. This study analyzed 47 shallow strong EQ cases (Ms ≥ 7 and depth ≤ 100 km) recorded by CSES-01 from its launch in February 2018 to February 2023. The results show that: (1) For the majority (90%) of shallow strong EQs, at least one payload onboard CSES-01 recorded discernible abnormal signals before the mainshocks, and for over 65% of EQs, two or three payloads simultaneously recorded ionospheric disturbances; (2) the majority of anomalies recorded by different payloads onboard CSES-01 predominantly manifest within one week before or on the mainshock day, or occasionally about 11–15 days or 20–25 days before the mainshock; (3) typically, the abnormal signal detected by CSES-01 does not directly appear overhead the epicenter, but rather hundreds of kilometers away from the epicenter, and more preferably toward the equatorward direction; (4) the anomaly recognition rate of each payload differs, with the highest rate reaching more than 70% for the Electric Field Detector (EFD), Search-Coil Magnetometer (SCM), and Langmuir Probe (LAP); (5) for the different parameters analyzed in this study, the plasma density from LAP, and electromagnetic field in the ULF band recorded by EFD and SCM, and energetic electrons from the High-Energy Particle Package (HEPP) show a relatively high occurrence of abnormal phenomena during the EQ time. Although CSES-01 has recorded prominent ionospheric anomalies for a significant portion of EQ cases, it is still challenging to accurately extract and confirm the real seismic precursor signals by relying solely on a single satellite. The combination of seismology, electromagnetism, geodesy, geochemistry, and other multidisciplinary means is needed in the future’s exploration to get infinitely closer to addressing the global challenge of EQ prediction. Full article
(This article belongs to the Section Environmental Remote Sensing)
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<p>The layout of the payloads onboard CSES-01.</p>
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<p>The flowchart of data processing.</p>
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<p>The distribution of global shallow, strong EQs with a depth ≤ 100 km and Ms ≥ 7 occurred from February 2018 to February 2023.</p>
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<p>The orbit trajectories of CSES-01 data selected for the Türkiye EQs on 6 February 2023. The red stars represent the epicenters, and the magenta dashed range shows the potential influential zone (10° × 10°). The blue curves illustrate the ascending orbit (nightside) trajectories of the CSES-01 satellite. The black sector boundary is the boundary of an enlarged image.</p>
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<p>The disturbances in <span class="html-italic">Ne</span> and <span class="html-italic">N<sub>O<b>+</b></sub></span> over the influential area of the Türkiye EQ from 11 January to 9 February. From the top panel to the bottom panel: The space weather indexes (<span class="html-italic">Kp</span> and <span class="html-italic">Dst</span>), <span class="html-italic">Ne</span>, the change amplitude (%) of <span class="html-italic">Ne</span>, <span class="html-italic">N<sub>O+</sub></span>, and the change amplitude (%) of <span class="html-italic">N<sub>O+</sub></span>. The red vertical lines in all panels mark the time of the EQ. In the first panel, the blue bars represent the <span class="html-italic">Dst</span> index, and the green curves correspond to the <span class="html-italic">Kp</span> index, while the red horizontal dashed lines represent the value of <span class="html-italic">Kp</span> = 3. In the second and fourth panels, the current variation trend of <span class="html-italic">Ne</span> and <span class="html-italic">N<sub>O+</sub></span> is depicted as blue curves, while the background trends (i.e., median values) are represented by black curves; the upper and lower boundaries are shown as pink curves. In the third and fifth panels, the blue lines represent the change amplitude (%) of <span class="html-italic">Ne</span> and <span class="html-italic">N<sub>O<b>+</b></sub></span>, and the red horizontal dashed lines represent the disturbance amplitude of 50% and −50% respectively.</p>
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<p>The difference maps of the magnetic field at the 70 Hz frequency from 11 January to 9 February 2023 around the Türkiye EQ influential area. The epicenters are marked with stars. Figures (<b>a</b>–<b>f</b>) correspond to the periods of 11–15 January, 16–20 January, 21–25 January, 26–30 January, 31 January–4 February, and 5–9 February, respectively. The black arrow in (<b>e</b>) indicates anomalies that may be related to earthquakes.</p>
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<p>The comparison maps of integrated energetic electron flux within the 0.1–0.3 MeV during the nightside, centered around Türkiye EQ. The stars denote the epicenters, while the arrows highlight the anomalies. (<b>a</b>–<b>f</b>) correspond to periods of 11–15 January, 16–20 January, 21–25 January, 26–30 January, 31 January–4 February, and 5–9 February, respectively. The red arrows indicate anomalies that may be related to earthquakes.</p>
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<p>The anomaly recognition rate to global shallow strong EQs (Ms ≥ 7 and depth ≤ 100 km) of each payload onboard CSES-01.</p>
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<p>The proportional distribution of synchronization anomalies from multiple payloads for each earthquake (EQ) case.</p>
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23 pages, 5624 KiB  
Article
Investigation on the Impact of the 2022 Luding M6.8 Earthquake on Regional Low-Frequency Time Code Signals in Northern China
by Fan Zhao, Ping Feng, Zhen Qi, Langlang Cheng, Xin Wang, Luxi Huang, Qiang Liu, Yingming Chen, Xiaoqian Ren and Yu Hua
Atmosphere 2024, 15(12), 1419; https://doi.org/10.3390/atmos15121419 - 26 Nov 2024
Viewed by 378
Abstract
Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky [...] Read more.
Low-Frequency Time Code time service technology, as an important means of ground-based radio time dissemination, can be divided into ground wave zone and sky wave zone according to different receiving and transmitting distances. Ground waves travel primarily along the Earth’s surface, while sky waves propagate over long distances by reflecting off the ionosphere. This paper utilizes the raw observation data received by the Low-Frequency Time Code dissemination monitoring stations before and after the 6.8 magnitude earthquake in Luding, Sichuan, China on 5 September 2022. A Low-Frequency Time Code time service monitoring system was built in Xi’an to continuously monitor the 68.5 kHz time signal broadcast by the BPC station. The data was then processed and analyzed through visualization. Simultaneously, we analyzed the signal fluctuation for multiple days before and after the earthquake to see the changes in the Low-Frequency Time Code signal during the earthquake. By combining seismic activity, solar activity, and geomagnetic data, this study aims to explore the causes and patterns of signal parameter variations. The results show that the field strength of the Low-Frequency Time Code signal fluctuated significantly within a short period during the earthquake. The value began to decrease about 60 min before the earthquake, dropping by approximately 8.9 dBμV/m, and gradually recovered 2 h after the earthquake. The phase also mutated by 1.36 μs at the time of the earthquake, and the time deviation fluctuated greatly compared to the 2 days before and after. Earthquake occurrences influence ionospheric variations, leading to changes in the sky wave propagation of Low-Frequency Time Code signals. Analysis of the influence of earthquakes on the propagation of Low-Frequency Time Code signals can provide references for research on Low-Frequency Time Code signal propagation models and earthquake prediction. Full article
(This article belongs to the Section Planetary Atmospheres)
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<p>Earthquake location on 5 September 2022 source: “<a href="https://www.mem.gov.cn/xw/yjglbgzdt/202209/t20220911_422190.shtml" target="_blank">https://www.mem.gov.cn/xw/yjglbgzdt/202209/t20220911_422190.shtml</a> (accessed on 20 September 2022)”.</p>
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<p>Principal diagram of the timing deviation calculation.</p>
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<p>Phase measurement diagram.</p>
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<p>Trend of DST and Kpindex over 5 days “<a href="https://omniweb.gsfc.nasa.gov/form/dx1.html" target="_blank">https://omniweb.gsfc.nasa.gov/form/dx1.html</a> (accessed on 3 May 2023)”.</p>
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<p>Location of BPC time service station, BPC receiving point (Test location), and earthquake location.</p>
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<p>Low-Frequency Time Code monitoring principal model diagram.</p>
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<p>Timing deviation test setup for the Low-Frequency Time Code monitoring receiver.</p>
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<p>The measurement results of field strength before and after the earthquake.</p>
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<p>The distribution map of field strength value.</p>
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<p>(<b>a</b>–<b>e</b>) Five charts for 3–7 September 2022, the measurement results of Low-Frequency Time Code timing deviation.</p>
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<p>Low-Frequency Time Code phase measurement results before and after the earthquake.</p>
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<p>Phase change diagram.</p>
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<p>Comparison between seismic day and non-geomagnetic day of the measurement results of field strength.</p>
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19 pages, 3890 KiB  
Article
Long-Baseline Real-Time Kinematic Positioning: Utilizing Kalman Filtering and Partial Ambiguity Resolution with Dual-Frequency Signals from BDS, GPS, and Galileo
by Deying Yu, Houpu Li, Zhiguo Wang, Shuguang Wu, Yi Liu, Kaizhong Ju and Chen Zhu
Aerospace 2024, 11(12), 970; https://doi.org/10.3390/aerospace11120970 - 26 Nov 2024
Viewed by 368
Abstract
This study addresses the challenges associated with single-system long-baseline real-time kinematic (RTK) navigation, including limited positioning accuracy, inconsistent signal reception, and significant residual atmospheric errors following double-difference corrections. This study explores the effectiveness of long-baseline RTK navigation using an integrated system of the [...] Read more.
This study addresses the challenges associated with single-system long-baseline real-time kinematic (RTK) navigation, including limited positioning accuracy, inconsistent signal reception, and significant residual atmospheric errors following double-difference corrections. This study explores the effectiveness of long-baseline RTK navigation using an integrated system of the BeiDou Navigation Satellite System (BDS), Global Positioning System (GPS), and Galileo Satellite Navigation System (Galileo). A long-baseline RTK approach that incorporates Kalman filtering and partial ambiguity resolution is applied. Initially, error models are used to correct ionospheric and tropospheric delays. The zenith tropospheric and inclined ionospheric delays and additional atmospheric error components are then regarded as unknown parameters. These parameters are estimated together with the position and ambiguity parameters via Kalman filtering. A two-step method based on a success rate threshold is employed to resolve partial ambiguity. Data from five long-baseline IGS monitoring stations and real-time measurements from a ship were employed for the dual-frequency RTK positioning experiments. The findings indicate that integrating additional GNSSs beyond the BDS considerably enhances both the navigation precision and the rate of ambiguity resolution. At the IGS stations, the integration of the BDS, GPS, and Galileo achieved navigation precisions of 2.0 cm in the North, 5.1 cm in the East, and 5.3 cm in the Up direction while maintaining a fixed resolution exceeding 94.34%. With a fixed resolution of Up to 99.93%, the integration of BDS and GPS provides horizontal and vertical precision within centimeters in maritime contexts. Therefore, the proposed approach achieves precise positioning capabilities for the rover while significantly increasing the rate of successful ambiguity resolution in long-range scenarios, thereby enhancing its practical use and exhibiting substantial application potential. Full article
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<p>Distribution of IGS sites map.</p>
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<p>Sailing trajectory of the test vessel.</p>
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<p>Flowchart of dual-frequency long-baseline RTK.</p>
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<p>Positioning root mean square (RMS) errors.</p>
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<p>Tropospheric errors.</p>
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<p>Ionospheric errors.</p>
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<p>Visual satellite count and PDOP values for the reference station NANO. C represents BDS, CG represents BDS/GPS, CE represents BDS/Galileo, and CGE represents BDS/GPS/Galileo.</p>
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<p>Positioning RMS errors and ambiguity fixing rates.</p>
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<p>RTK positioning errors for the 152 km baseline. The blue line represents the float solution, and the green line represents the fixed solution.</p>
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<p>Error sequence plot of epoch has not achieved single-system and dual-system shipborne data.</p>
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<p>Fixed ambiguity counts and ratios.</p>
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