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Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (1 May 2023) | Viewed by 26912

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Special Issue Editors

Dr. Xingliang Huo

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Guest Editor

State Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
Interests: ionospheric delay monitoring; modeling for the Global Navigation Satellite System (GNSS); the effects of ionosphere weather on GNSS navigation and positioning

Prof. Dr. Xiaohua Xu

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Guest Editor

School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China
Interests: GNSS radio occultation; atmospheric waves; climate change; ionosphere; sporadic E layers

Prof. Dr. Guanwen Huang

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Guest Editor

School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
Interests: GNSS orbit determination and positioning; GNSS deformation monitoring; GNSS space weather
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is well known that the Earth’s ionosphere has the greatest effect on GNSS signals. The knowledge about the effect of the ionosphere on GNSS applications depends on the measurements of key ionosphere-related parameters, the modeling of the ionospheric effects, and the understanding of the ionospheric weather. Important ionospheric parameters, such as the total electron content (TEC), the electron density, the maximum electron density, and its height, can be used to evaluate the conditions and to detect anomalies in the ionospheric weather. Therefore, the modeling and reconstruction of key ionospheric parameters is of great importance to ensure robust and reliable GNSS services.

This topic offers a platform on which to discuss the remote sensing of the Earth’s ionosphere with GNSS technology and the impacts of the ionosphere on high-accuracy GNSS applications.

In this context, the main aim of this Special Issue is to present the latest findings that describe new methods and algorithms for modeling and reconstructing the ionosphere-related parameters using ground and LEO-based GNSS data. Recent research on the impacts of the ionosphere on GNSS navigation and positioning applications are also welcome.

Dr. Xingliang Huo
Prof. Dr. Xiaohua Xu
Prof. Dr. Guanwen Huang
Guest Editors

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Keywords

Total electron content (TEC)
Ionospheric electron density (IED)
Global Navigation Satellite Systems (GNSS)
Space weather
Navigation and positioning

Published Papers (14 papers)

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22 pages, 8807 KiB

Open AccessArticle

A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction

by Jiaxuan Weng, Yiran Liu and Jian Wang

Remote Sens. 2023, 15(12), 2953; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15122953 - 06 Jun 2023

Cited by 1 | Viewed by 1213

Abstract

In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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18 pages, 2512 KiB

Open AccessArticle

An Extended Simultaneous Algebraic Reconstruction Technique for Imaging the Ionosphere Using GNSS Data and Its Preliminary Results

by Yuanliang Long, Xingliang Huo, Haojie Liu, Ying Li and Weihong Sun

Remote Sens. 2023, 15(11), 2939; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15112939 - 05 Jun 2023

Viewed by 1126

Abstract

To generate high-quality reconstructions of ionospheric electron density (IED), we propose an extended simultaneous algebraic reconstruction technique (ESART). The ESART method distributes the discrepancy between the actual GNSS TEC and the calculated TEC among the ray–voxels based on the contribution of voxels to GNSS TEC, rather than the ratio of the length of ray–voxel intersection to the sum of the lengths of all ray–voxel intersections, as is adopted by conventional methods. The feasibility of the ESART method for reconstructing the IED under different levels of geomagnetic activities is addressed. Additionally, a preliminary experiment is performed using the reconstructed IED profiles and comparing them with ionosonde measurements, which provide direct observations of electron density. The root mean square errors (RMSE) and absolute errors of the ESART method, the simultaneous algebraic reconstruction technique (SART) method, and the International Reference Ionosphere (IRI) 2016 model are calculated to evaluate the effectiveness of the proposed method. Compared to the conventional SART method of ionospheric tomography and the IRI-2016 model, the reconstructed IED profiles obtained using the ESART method are in better agreement with the electron density obtained from the ionosondes, especially for the peak electron densities (NmF2). In addition, a case study of an intense geomagnetic storm on 17–19 March 2015 shows that the spatial and temporal features of storm-related ionospheric disturbances can be more clearly depicted using the ESART method than with the SART method. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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11 pages, 5475 KiB

Open AccessCommunication

Analysis of a Low-Earth Orbit Satellite Downlink Considering Antenna Radiation Patterns and Space Environment in Interference Situations

by Eunjung Kang, Junmo Yang, YoungJu Park, JungHoon Kim, WookHyeon Shin, Yong Bae Park and Hosung Choo

Remote Sens. 2023, 15(7), 1748; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15071748 - 24 Mar 2023

Cited by 1 | Viewed by 2708

Abstract

This paper investigates a low-Earth orbit (LEO) satellite downlink for high-speed data communication in interference situations. A choke ring horn type antenna is used as the data transmitting antenna with an isoflux pattern in the LEO satellite, which has a beam coverage of ±51.6° and a bore-sight gain of 4.4 dBi at 8 GHz. The receiving antenna on the ground station is a parabolic type antenna with a diameter of 11.3 m, and it has a half-power beam width (HPBW) of 0.2° with a maximum gain of 59 dBi at 8 GHz. The jamming-to-signal ratio (J/S) is calculated assuming that the LEO satellite transmits signals to the ground station, and an elevation angle of the interference source varies from 0° to 90° at an altitude of 10 km. Applying antenna characteristics, such as HPBWs and side lobes, to the calculated space wave path loss makes it possible to predict the J/S results according to the location of the interference source and the satellite. The results show that it is necessary to consider the space environment to accurately analyze the LEO satellite downlink, especially at the low elevation angle of the satellite. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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18 pages, 9447 KiB

Open AccessArticle

Locating Earth Disturbances Using the SDR Earth Imager

by Radwan Sharif, Suleyman Gokhun Tanyer, Stephen Harrison, William Junor, Peter Driessen and Rodney Herring

Remote Sens. 2022, 14(24), 6393; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246393 - 18 Dec 2022

Cited by 1 | Viewed by 1616

Abstract

The Radio Wave Phase Imager uses monitoring and recording concepts, such as Software Defined Radio (SDR), to image Earth’s atmosphere. The Long Wavelength Array (LWA), New Mexico Observatory is considered a high-resolution camera that obtains phase information about Earth and space disturbances; therefore, it was employed to capture radio signals reflected from Earth’s F ionization layer. Phase information reveals and measures the properties of waves that exist in the ionization layer. These waves represent terrestrial and solar Earth disturbances, such as power losses from power generating and distribution stations. Two LWA locations were used to capture the ionization layer waves, including University of New Mexico’s Long Wavelength Array’s LWA-1 and LWA-SV. Two locations of the measurements showed wavevector directions of disturbances, whereas the intersection of wavevectors determined the source of the disturbance. The research described here focused on measuring the ionization layer wave’s phase shifts, frequencies, and wavevectors. This novel approach is a significant contribution to determine the source of any disturbance. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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20 pages, 9976 KiB

Open AccessArticle

Limb Sounders Tracking Tsunami-Induced Perturbations from the Stratosphere to the Ionosphere

by Xiangxiang Yan, Tao Yu and Chunliang Xia

Remote Sens. 2022, 14(21), 5543; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215543 - 03 Nov 2022

Viewed by 1153

Abstract

In this study, we employ three types of satellite data from two different limb sounders: the FORMOSAT-3/COSMIC (F3/C) radio occultation (RO) technique and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument to study the vertical coupling of the 16-09-2015 Chile tsunami-induced perturbations from the stratosphere to the ionosphere. All three types of datasets, including temperature profiles from 10 to 55 km and 16 to 107 km, and electron density profiles from 120 to 550 km, recognized perturbations of different scales at different heights after the Chile tsunami. The vertical scales identified by the wavelet analysis are from 1–2 km, 5–9 km, and 25–50 km in the stratosphere, mesosphere, and ionosphere, respectively. Meanwhile, as a comparison and validation of the reliability, we also revisited the 11-03-2011 Tohoku earthquake/tsunami-related perturbations from the stratosphere to the ionosphere using the same data. It is believed that the two tsunamis both disturbed the whole atmosphere space, and the scale of these signals gradually increases with the increase in altitude but decreases with time. In addition, the tsunami-related ionospheric gravity wavefronts are examined by the F3/C observations. Another interesting point is that the temperature perturbations recorded by the SABER from 70–100 km altitude are found to arrive earlier than the 2015 tsunami wavefront. The findings in this study suggest that the limb-sounding technique is a useful instrument for detecting the tsunami-coupling gravity wave and benefits the tsunami warning system. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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27 pages, 19590 KiB

Open AccessArticle

BDS and Galileo: Global Ionosphere Modeling and the Comparison to GPS and GLONASS

by Yafeng Wang, Hu Wang, Yamin Dang, Hongyang Ma, Changhui Xu, Qiang Yang, Yingying Ren and Shushan Fang

Remote Sens. 2022, 14(21), 5479; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215479 - 31 Oct 2022

Cited by 1 | Viewed by 1540

Abstract

The ionospheric delay is one of the important error sources in the Global Navigation Satellite System (GNSS) data processing. With the rapid construction and development of GNSS, the abundant satellite resources have brought new opportunities for ionospheric monitoring. To further investigate the performances and abilities of Galileo and BDS in ionosphere modeling, we study the ionosphere modeling based on the 15th order spherical harmonic function, and 364 stations around the world are selected for global ionospheric modeling of GPS, GLONASS, Galileo and BDS systems under ionospheric quiet and active conditions, respectively. The results show that the average biases of the ionospheric models built by GPS, GLONASS and Galileo are relatively small, which are within 2 Total Electron Content Unit (TECU) as compared to the Center for Orbit Determination in Europe (CODE) global ionospheric map (GIM), while the average biases of the models built by BDS are between 6 and 8 TECU during the ionospheric quiet and active days, respectively. In addition, in order to analyze the modeling performances before and after using BDS geostationary earth orbit (GEO) satellites, BDS is divided into two groups, in which one group contains medium earth orbit (MEO), inclined geosynchronous orbit (IGSO) and GEO satellites; and the other group contains only MEO and IGSO satellites. The results show that the influence of GEO satellites on ionospheric modeling is less than 1 TECU. Due to the distribution of the stations, the 0-value region in the ionospheric model is mainly distributed in the mid and high-latitude regions of the southern hemisphere. Since the ionospheric parameters are lumped with the Differential Code Bias (DCB), we also estimate the DCB parameters and analyze their performances. The DCB estimated in ionosphere modeling shows strong stability, with the average biases of GPS, GLONASS, Galileo and BDS under 0.25 ns, 0.25 ns, 0.2 ns and 0.42 ns, respectively. We also estimate other DCB types of the four GNSS systems. The results show that the DCB is stable and shows consistency with Chinese Academy of Sciences (CAS) DCB products. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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21 pages, 4499 KiB

Open AccessArticle

Spatial–Temporal Relationship Study between NWP PWV and Precipitation: A Case Study of ‘July 20’ Heavy Rainstorm in Zhengzhou

by Ying Xu, Xin Chen, Min Liu, Jin Wang, Fangzhao Zhang, Jianhui Cui and Hongzhan Zhou

Remote Sens. 2022, 14(15), 3636; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14153636 - 29 Jul 2022

Cited by 6 | Viewed by 1588

Abstract

In order to study and forecast extreme weather, a comprehensive and systematic analysis of the spatial and temporal relationship between Precipitable Water Vapor (PWV), predicted by Numerical Weather Predication (NWP) data, and precipitation, is necessary. The goal of this paper was to study the temporal and spatial relationship between PWV and precipitation during the so-called ‘July 20’ (18–21 July 2021) heavy rainstorm in Zhengzhou. Firstly, the PWV data provided by 120 radiosonde stations uniformly distributed throughout the world, and two IGS stations in China, in 2020, was used to evaluate the accuracy of PWV estimation by ERA5 and MERRA-2 data, and the factors affecting the accuracy of NWP PWV were explored. Secondly, ERA5 PWV and the precipitation data of six meteorological stations were used to qualitatively analyze the relationship between PWV and precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou. Finally, a quantitative study was conducted by an eigenvalue matching method. The main experimental results were as follows. Compared with MERRA-2 PWV, the accuracy of ERA5 PWV was slightly higher. Latitude, altitude and season were the influencing factors of the NWP PWV estimation accuracy. The change trend of ERA5 PWV was consistent with both 24 h cumulative precipitation and surface precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou. The average optimal matching degree and optimal matching time between NWP PWV and surface precipitation during the ‘July 20’ heavy rainstorm in Zhengzhou was 56.6% and 3.68 h, respectively. The maximum optimal matching degree was 80.3%. The spatial–temporal relationship between NWP PWV and surface precipitation was strong. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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16 pages, 4587 KiB

Open AccessArticle

Accuracy Evaluation and Analysis of GNSS Tropospheric Delay Inversion from Meteorological Reanalysis Data

by Guolin Liu, Guanwen Huang, Ying Xu, Liangyu Ta, Ce Jing, Yu Cao and Ziwei Wang

Remote Sens. 2022, 14(14), 3434; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143434 - 17 Jul 2022

Cited by 5 | Viewed by 1751

Abstract

Accurate estimation of tropospheric delay is significant for global navigation satellite system’s (GNSS) high-precision navigation and positioning. However, due to the random and contingent changes in weather conditions and water vapor factors, the classical tropospheric delay model cannot accurately reflect changes in tropospheric delay. In recent years, with the development of meteorological observation/detection and numerical weather prediction (NWP) technology, the accuracy and resolution of meteorological reanalysis data have been effectively improved, providing a new solution for the inversion and modeling of regional or global tropospheric delays. Here, we evaluate the consistency and accuracy of three different types of reanalysis data (i.e., ERA5, MERRA2, and CRA40) used to invert the zenith tropospheric delay (ZTD) from 436 international GNSS service (IGS) stations in 2020, based on the integral method. The results show that the ZTD inversion of the three types of reanalysis data was consistent with the IGS ZTD, even in heavy rain conditions. Furthermore, the average precision of the ZTD inversion of the ERA5 reanalysis data was higher, where the mean deviation (bias), mean absolute error (MAE), and root mean square (RMS) were –3.39, 9.69, and 12.55 mm, respectively. The ZTD average precisions of the MERRA2 and CRA40 inversions were comparable, showing slightly worse performance than the ERA5. In addition, we further analyzed the global distribution characteristics of the ZTD errors inverted from the reanalysis data. The results show that ZTD errors inverted from the reanalysis data were highly correlated with station latitude and climate type, and they were mainly concentrated in the tropical climate zone at low latitudes. Compared to dividing error areas by latitude, dividing error areas by climatic category could better reflect the global distribution of errors and would also provide a data reference for the establishment of tropospheric delay models considering climate type. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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22 pages, 8949 KiB

Open AccessArticle

An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network

by Jun Tang, Yinjian Li, Mingfei Ding, Heng Liu, Dengpan Yang and Xuequn Wu

Remote Sens. 2022, 14(10), 2433; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14102433 - 19 May 2022

Cited by 33 | Viewed by 3213

Abstract

Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R² of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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17 pages, 4326 KiB

Open AccessArticle

Local Persistent Ionospheric Positive Responses to the Geomagnetic Storm in August 2018 Using BDS-GEO Satellites over Low-Latitude Regions in Eastern Hemisphere

by Jun Tang, Xin Gao, Dengpan Yang, Zhengyu Zhong, Xingliang Huo and Xuequn Wu

Remote Sens. 2022, 14(9), 2272; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092272 - 08 May 2022

Cited by 5 | Viewed by 1800

Abstract

We present the ionospheric disturbance responses over low-latitude regions by using total electron content from Geostationary Earth Orbit (GEO) satellites of the BeiDou Navigation Satellite System (BDS), ionosonde data and Swarm satellite data, during the geomagnetic storm in August 2018. The results show that a prominent total electron content (TEC) enhancement over low-latitude regions is observed during the main phase of the storm. There is a persistent TEC increase lasting for about 1–2 days and a moderately positive disturbance response during the recovery phase on 27–28 August, which distinguishes from the general performance of ionospheric TEC in the previous storms. We also find that this phenomenon is a unique local-area disturbance of the ionosphere during the recovery phase of the storm. The enhanced

f_{o} F_{2}

and

h_{m} F_{2}

of the ionospheric F2 layer is observed by SANYA and LEARMONTH ionosonde stations during the recovery phase. The electron density from Swarm satellites shows a strong equatorial ionization anomaly (EIA) crest over the low-latitude area during the main phase of storm, which is simultaneous with the uplift of the ionospheric F2 layer from the SANYA ionosonde. Meanwhile, the thermosphere O/N₂ ratio shows a local increase on 27–28 August over low-latitude regions. From the above results, this study suggests that the uplift of F layer height and the enhanced O/N₂ ratio are possibly main factors causing the local-area positive disturbance responses during the recovery phase of the storm in August 2018. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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16 pages, 3696 KiB

Open AccessArticle

Ionospheric Nighttime Enhancements at Low Latitudes Challenge Performance of the Global Ionospheric Maps

by Yuyan Yang, Libo Liu, Xiukuan Zhao, Haiyong Xie, Yiding Chen, Huijun Le, Ruilong Zhang, M. Arslan Tariq and Wenbo Li

Remote Sens. 2022, 14(5), 1088; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051088 - 23 Feb 2022

Cited by 4 | Viewed by 1792

Abstract

In this study, two ionospheric nighttime enhancement (INE) events at low latitudes are selected to investigate their spatial features through the observations from Global Navigation Satellite System (GNSS) receivers and ionosondes. For the first time, we present the detailed spatial pictures of premidnight and postmidnight INEs under geomagnetically quiet conditions. The two INE events have the maximum extents of about 11° × 34° and 17° × 25° (longitude × latitude), respectively. Dramatic latitudinal and longitudinal features are revealed in the two INEs. We perform a comparison between the products of Global Ionospheric Maps (GIMs) and total electron content (TEC) measurement from GNSS receivers. However, GIMs fail to capture the TEC distribution during INEs owing to their limited spatial and temporal resolution. Considering the extent of INEs from the observations, the spherical harmonic (SH) expansion adopted by the GIM models needs to upgrade the degree and order to 36. The pixel-based methods developed from two GIM models are required to reduce their grid size for higher spatial resolution. The recommended time interval is shorter than 30 min. Among seven GIMs, CODG and JPLG maps generally have the best performance in reproducing the latitudinal structure of the ionosphere. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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13 pages, 2819 KiB

Open AccessArticle

A Correction Method of Height Variation Error Based on One SNR Arc Applied in GNSS–IR Sea-Level Retrieval

by Xiaolei Wang, Zijin Niu, Shu Chen and Xiufeng He

Remote Sens. 2022, 14(1), 11; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14010011 - 21 Dec 2021

Cited by 7 | Viewed by 2501

Abstract

Sea-level monitoring is important for the safety of coastal cities and analysis of ocean and climate. Sea levels can be estimated based using the global navigation satellite system–interferometry reflectometry (GNSS–IR). The frequency in a signal-to-noise ratio (SNR) arc has been found to be [...] Read more.

h

). The height variation of the reflecting surface causes an error, and this error is the most significant error in the GNSS–IR sea-level retrieval. The key to the correction of height variation error lies in the determination of the RH variation rate

\dot{h}

. The classical correction method determines

\dot{h}

based on tide analysis of a coarse RH series over a longer time period. Therefore,

\dot{h}

inherits errors in coarse RH series, which contains significant bias during a storm surge, and correcting this requires data accumulation. This study proposes a correction method of height variation error based on just one SNR arc based on wavelet analysis and least-square estimation. First, using wavelet analysis, instantaneous frequencies are extracted in one SNR arc; these frequencies are then converted to RH series. Second, using least-square estimation,

h

and

\dot{h}

are conjointly solved based on the RH series from wavelet analysis. Data of GNSS site HKQT located in Hong Kong, China, during a period of time that includes Typhoon Hato were used. The root-mean-square errors (RMSEs) of retrievals were 21.5 cm for L1, 9.5 cm for L2P, 9.3 cm for L2C, and 7.6 cm for L5 of GPS; 16.8 cm for L1C, 14.1 cm for L1P, 12.6 cm for L2C, and 10.7 cm for L2P of GLONASS; 15.7 cm for L1, 11.2 cm for L5, 12.2 cm for L7, and 9.6 cm for L8 of Galileo. Results showed this method can correct the height variation error based on just one SNR arc, can avoid the inheritance of errors, and can be used during periods of storm surge. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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15 pages, 4074 KiB

Open AccessTechnical Note

Rapid Tsunami Potential Assessment Using GNSS Ionospheric Disturbance: Implications from Three Megathrusts

by Jiafeng Li, Kejie Chen, Haishan Chai and Guoguang Wei

Remote Sens. 2022, 14(9), 2018; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092018 - 22 Apr 2022

Cited by 3 | Viewed by 1729

Abstract

The current tsunami early warning systems always issue alarms once large undersea earthquakes are detected, inevitably resulting in false warnings since there are no deterministic scaling relations between earthquake size and tsunami potential. In this paper, we assess tsunami potential by analyzing co-seismic ionospheric disturbances (CIDs). We examined CIDs of three megathrusts (the 2014 M_w 8.2 Iquique, the 2015 M_w 8.3 Illapel, and the recent 2021 M_w 8.2 Alaska events) as detected by Global Navigation Satellite System (GNSS) observations. We found that CIDs near the epicenter generated by the 2021 M_w 8.2 Alaska event were significantly weaker than those of the two Chilean events, despite having similar earthquake magnitudes. The propagation direction of CIDs from the M_w 8.2 Alaska earthquake further revealed ruptures toward the deeper seismogenic zone, implying less seafloor uplift and hazardous flooding. Our work sheds light on incorporating GNSS-based CIDs for more trustworthy tsunami warning systems. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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15 pages, 37504 KiB

Open AccessTechnical Note

The Feature of Ionospheric Mid-Latitude Trough during Geomagnetic Storms Derived from GPS Total Electron Content (TEC) Data

by Na Yang, Tao Yu, Huijun Le, Libo Liu, Yang-Yi Sun, Xiangxiang Yan, Jin Wang, Chunliang Xia, Xiaomin Zuo and Guangliang Huang

Remote Sens. 2022, 14(2), 369; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020369 - 13 Jan 2022

Cited by 1 | Viewed by 1651

Abstract

This study aims to investigate the features of the ionospheric mid-latitude trough over North America by using the MIT total electron content data obtained during three geomagnetic storms that occurred in August 2018, September 2017, and March 2015. The mid-latitude trough position sharply moves equatorward from the quiet-time subauroral latitude to mid-latitude with the decrease in SYM-H during geomagnetic storms. We find that the ionospheric behavior of TEC around the mid-latitude trough position displays three kinds of ionospheric storm effect: negative ionospheric storm effect, unchanged ionospheric behavior, and positive ionospheric storm effect. These ionospheric storm effects around the mid-latitude trough position are not always produced by the mid-latitude trough. The ionospheric storm effects produced by the mid-latitude trough are limited in the narrow mid-latitude trough regions, and are transmitted to other regions with the movement of the mid-latitude trough. Full article

(This article belongs to the Special Issue Monitoring and Reconstruction of Key Parameters for Ionospheric Weather Using Ground and LEO Based GNSS Data)

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