Ionospheric Maps Research Articles

Smartphone-Based Positioning Augmented by BDSBAS Ionospheric Corrections

BeiDou Satellite Based Augmentation System (BDSBAS) can notably improve the GNSS L1 positioning performance by transmitting the orbit, clock and ionosphere corrections. Aside from terrestrial geodetic receivers, the mass-market smartphones also benefit from the augmentation service. In this paper, the smartphone-based GPS-L1 standard point positioning (SPP) augmented by BDSBAS corrections is analyzed using Huawei-P40 smartphone. The quality of BDSBAS-B1C ionospheric information is evaluated by ionospheric observables retrieved from the selected 20 stations. Compared to the BDS-3 global ionosphere model (BDGIM) and the Global Ionosphere Maps (GIM) of the International GNSS Service (IGS), the accuracy of BDSBAS ionospheric correction is about 28% better than BDGIM and 11% better than the IGS-GIM. Compared to the GPS-L1 SPP, the accuracy of BDSBAS augmented smartphone positioning increases by 27% in the horizontal component and 41% in vertical component in the open-sky environment, and 50% improvement in both directions in the second experiment where the smartphone connects with an external geodetic GNSS antenna to reduce the negative effects of multipath errors.

Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning

<h3>Abstract</h3> High-precision ionospheric corrections are essential for precise positioning using low-cost single-frequency GNSS receivers. Although Real-Time Global Ionosphere Maps (RT-GIMs) are available from the International GNSS Service (IGS), their ionospheric predictions continue to rely on networks of globally-distributed GNSS stations and real-time data links. In this paper, we develop a regional real-time ionospheric prediction model based on a long short-term memory (LSTM) deep learning method. Because the GIMs from the IGS are used as prediction bases, the requirement for real-time GNSS data-links is eliminated. A comparison of the ionospheric predictions generated over 24 hours by the proposed method and the IGS GIM revealed a prediction accuracy root mean square error of 0.8 TECU. These results suggest that the proposed model may be suitable for use in real-time applications.

A Novel Approach for Establishing the Global Ionospheric Model With High Spatiotemporal Resolution

The global ionospheric model is the most effective way to study the structure and variation of the global ionosphere. However, the current global ionosphere maps (GIMs) have the defect of low spatiotemporal resolution and cannot reflect the short-term nonlinear changes and small-scale structures of the vertical total electron content (VTEC). This article proposes a new method for establishing a global ionospheric model with high spatiotemporal resolution. The spherical harmonic (SH) expansions are used to model the VTEC observations, and the Kalman filter is used to estimate the model’s coefficients with high accuracy. The method calculates SH coefficients every 5 min, increasing the spatial resolution to 7.2°. Precise determination methods for the state noise covariance matrix and the observation noise covariance matrix in the Kalman filter are also proposed. The model with a high spatiotemporal resolution was established using the observations of 300 global navigation satellite system (GNSS) tracking stations worldwide. The results show that the model with high spatiotemporal resolution can more finely reflect the nonlinear changes of VTEC in a short period and the small-scale structure of the ionosphere. The accuracy of the high-resolution model is validated using high-precision differential slant total electron content (dSTEC) observations from three sets of tracking stations and compared with the final product of three international GNSS service ionosphere associate analysis centers (IGS IAACs). The results show that the accuracy of the high spatiotemporal resolution model in this article is better than the products of IGS IAACs. The research in this article provides a new idea for establishing GIMs with higher spatiotemporal resolution and accuracy.

A New GNSS TEC Neural Network Prediction Algorithm With the Data Fusion of Physical Observation

This work models and predicts anomalous jumps in GNSS total electron content (TEC) caused by geomagnetic disturbance or solar radiation by introducing F10.7 and regional Dst indices, which are used to characterize solar radiation and geomagnetic disturbance status, respectively. We then introduced long-distance constraint stations to restrain ionospheric variations during high solar activity, which reduced the influence of unmodelled factors on prediction accuracy. In our experimental work, Global Ionosphere Map (GIM) TEC data from 11 stations located at different latitudes were used for comparison during maximum (2014) and minimum (2018) of solar cycle 24. Sample data for >27 days were used for learning, and then ionospheric changes for the whole year were predicted using the sliding window method. The results showed that, the standard deviation for the global ionospheric prediction products could be guaranteed to be within 2 TECU. The proximity station constraint method proposed in this paper and the Dst high-order polynomial correction method during geomagnetic disturbance control the prediction error within -4.4 TECU, while the change of adjacent days is -51.4 TECU in 2014. The new algorithm reduced the TEC jump effect in predictions, and the prediction of ionospheric products provided by this algorithm can meet the basic requirements of ionospheric delay correction in high-precision positioning.

A Consistent Regional Vertical Ionospheric Model and Application in PPP-RTK Under Sparse Networks

<h3>Abstract</h3> Ionospheric augmentation is one of the most important dependences of PPP-RTK. Because of the dispersive features of the ionosphere, the ionospheric information is usually coupled with satellite- and receiver-related biases. This will pose a hidden trouble of inconsistent ionospheric corrections if different numbers of reference stations are involved in calculation. In this paper, we aimed at introducing a consistent regional vertical ionospheric model (RVIM) by estimating receiver biases. We first presented the inconsistent ionospheric corrections under sparse networks. Then the RVIM is compared with the International GNSS Service (IGS) final global ionospheric map (GIM) product, and the average of differences between them is 1.13 TECU. Furthermore, the slant ionospheric corrections were employed as a reference to evaluate both RVIM and GIM. The mean RMS values are 1.48 and 2.23 TECU for the RVIM and GIM, respectively. Finally, we applied the RVIM into PPP-RTK. Results indicate that the PPP-RTK with RVIM constraints achieves improvements in horizontal errors, vertical errors, and convergence time by 43.45, 29.3, and 22.6% under the 68% confidence level, compared with the conventional PPP-AR.

Low latitude monthly total electron content composite correlations

Spatial correlations of total electron content (TEC) variability are compared among two SAMI3 model runs and Jet Propulsion Laboratory Global Ionospheric Maps (JPL/GIM). Individual monthly correlation maps are constructed with Equatorial reference points at 12 evenly spaced longitudes and 12 universal times. TEC composite correlations (TCCs) are then calculated by averaging the individual maps, shifted zonally to synchronize local time. The TCC structures are quantified using Gaussian fits in the zonal and meridional directions. A non-zero large-scale “base correlation” is found in all three datasets for 2014, a year with high solar activity. Higher base correlations generally occur in the SAMI3 runs than in JPL/GIM. The SAMI3 run driven with climatological neutral fields shows higher correlations than the run driven with neutrals from a Whole Atmosphere Community Climate Model with thermosphere–ionosphere eXtension (WACCM-X) simulation. Base correlation values strongly correlate with monthly F10.7 standard deviations. Empirical orthogonal function (EOF) analyses confirm that large-scale correlations are usually, although not always, related to solar forcing. Strong correlations between the Ap index and EOF modes are also observed, consistent with the geomagnetic forcing of the TEC field. The widths of the correlation structures are also examined, and these vary considerably with local time, month, and dataset. Off-Equator conjugate point correlations are also calculated from each dataset and variations with the month and local time are analyzed. Analysis of TCCs for 2010, a year with low solar activity, shows that base correlations as well as correlations of the first EOF mode with F10.7 are generally weaker than in 2014.

Global and Regional Consistency of VTECs Inversed by IPM and GNSS

The ionospheric photometer (IPM) onboard the Fengyun-3D satellite is the first optical remote sensing payload in China designed for space-based surveillance of the ionosphere and capable of inversing the Vertical Total Electron Content (VTEC) at night with high sensitivity. Using the VTECs inversed by IPM, along with other sources of ionospheric observations, such as the ground-based Global Satellite Navigation System (GNSS) VTECs, ionospheric modeling can be established based on multi-source space observation techniques and is expected to improve the modeling accuracy in marine areas. Before the ionospheric modeling using multi-source data, the consistency between different sources of VTECs should be analyzed. However, the consistency between the IPM-VTEC and GNSS-VTEC has not been adequately investigated. The authors employ the Global Ionospheric Map (GIM) products from the International GNSS Service to assess the global-scale consistency between them, and the analysis reveals a generally high global-scale consistency, yet the IPM-VTEC values in specific regions are too large due to the influence of auroras and ionospheric equatorial anomalies.

Storm-Time Relative Total Electron Content Modelling Using Machine Learning Techniques

Accurately predicting total electron content (TEC) during geomagnetic storms is still a challenging task for ionospheric models. In this work, a neural-network (NN)-based model is proposed which predicts relative TEC with respect to the preceding 27-day median TEC, during storm time for the European region (with longitudes 30°W–50°E and latitudes 32.5°N–70°N). The 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 are used as inputs and the output of the network is the relative TEC. The relative TEC can be converted to the actual TEC knowing the median TEC. The median TEC is calculated at each grid point over the European region considering data from the last 27 days before the storm using global ionosphere maps (GIMs) from international GNSS service (IGS) sources. A storm event is defined when the storm time disturbance index Dst drops below 50 nanotesla. The model was trained with storm-time relative TEC data from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. Unseen storm data from 33 storm events during 2015 and 2020 were used to test the model. The UQRG GIMs were used because of their high temporal resolution (15 min) compared to other products from different analysis centers. The NN-based model predictions show the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and show a mixture of both phases during equinoxes. The model’s performance was also compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet-time TEC model, both developed at the German Aerospace Agency (DLR). The storm model has a root mean squared error (RMSE) of 3.38 TEC units (TECU), which is an improvement by 1.87 TECU compared to the NTCM, where an RMSE of 5.25 TECU was found. This improvement corresponds to a performance increase by 35.6%. The storm-time model outperforms the quiet-time model by 1.34 TECU, which corresponds to a performance increase by 28.4% from 4.72 to 3.38 TECU. The quiet-time model was trained with Carrington averaged TEC and, therefore, is ideal to be used as an input instead of the GIM derived 27-day median. We found an improvement by 0.8 TECU which corresponds to a performance increase by 17% from 4.72 to 3.92 TECU for the storm-time model using the quiet-time-model predicted TEC as an input compared to solely using the quiet-time model.

Regional Ionospheric Maps with Quad-Constellation Raw Observations as Applied to Single-Frequency PPP

Ionospheric delay is one of the most problematic errors in single-frequency (SF) global navigation satellite system (GNSS) data processing. Global/regional ionospheric maps (GIM/RIM) are thus vitally important for positioning users. Given the coexistence of multi-GNSS, the integration of quad-constellation observations is essential for improving the distribution of ionospheric penetration points (IPPs) and increasing redundant observations compared with the existing GIM products from the IGS analysis center. In this paper, quad-constellation (GPS/GLONASS/Galileo/BDS) observations are applied to set up the RIM over Australia with uncombined precise point positioning (UC-PPP) and a low-order spherical harmonic function. The generated RIMs are then introduced to ionosphere-corrected (IC) and ionosphere-weighted (IW) single-frequency PPP (IC-SFPPP and IW-SFPPP) to verify their performance in terms of positioning accuracy and convergence time. Taking the CODE GIM as a reference, the results show that the mean root mean square (RMS) of VTEC differences is 0.867 TECUs, and the quad-constellation RIM (referred as ‘RIM4′) can improve the RMS of RIMs compared to single-constellation mode at the edge of regional experiment area. The application of the RIM4 in the BDS IC-SFPPP results in a 18.38% improvement (from 100.47 cm to 82.00 cm) of 3D positioning RMS compared to the CODE-GIMs, whereas 35.36% enhancement (from 115.92 cm to 74.62 cm) of 3D positioning RMS is achievable during an active ionospheric period. Moreover, if the criterion of the convergence time is defined as when positioning errors in the horizontal and vertical directions are less than 0.3 m and 0.6 m for 20 consecutive epochs, the IW-SFPPP can significantly speed up the convergence time compared to the uncombined SFPPP; that is, the convergence time is reduced by 52.7% (from 37 min to 17.5 min), 37.2% (from 72.5 min to 45.5 min), and 37.1% (from 62.0 min to 39.0 min) in the north, east and up direction, respectively, at the 68% confidence level.

Ionospheric Response to the Coronal Hole Activity of August 2020: A Global Multi‐Instrumental Overview

AbstractWe have studied the ionospheric response to a coronal hole event of August 2020 using the data from global ionospheric maps, ground magnetometers and parameters from the instruments onboard SWARM and thermosphere, ionosphere, mesosphere energetics and dynamics satellites. The role of different physical drivers, responsible for observed ionospheric disturbance, has been identified. On the storm day (2 August), a steady southward directed interplanetary magnetic field Bz caused the penetration of magnetospheric convection electric field and positive storm effect in daytime sectors. As a result of prompt penetration electric field (PPEF) on 2 August, a polar‐ward expansion of day‐side ionospheric plasma. On 3 August, the signatures of both disturbance dynamo electric field (DDEF) caused by disturbed thermospheric winds and PPEF have been observed. The westward PPEF and eastward disturbance DDEF on the night‐side caused a strong enhancement in ionospheric plasma parameters at the corresponding sectors. The effect of disturbed thermospheric winds and resultant electric field persisted till the end of 7 August. The large decrease in O/N2 ratio at northern mid‐latitudes which is a consequence of the seasonal impact resulted in the negative storm effects at corresponding latitudinal regions. This study has shown that although the storm of August 2020 was a minor one, the associated high speed solar wind streams emanating from a coronal hole resulted in drastic changes in ionospheric parameters including global electron content, in situ electron density and total electron content at the equatorial ionization anomaly and equatorial electric field.

The Variation Characteristics and Prediction Performance of TEC in the Geomagnetic Latitude and Local Time Coordinate

AbstractTotal electron content (TEC), as one of the most valuable ionosphere parameters, has many variation characteristics. The TEC has specific characteristics at a given time, suggesting that the global TEC morphology distribution is maintained for a period. We consider the global ionosphere map (GIM) to be the actual ionospheric electron density integration, predict the GIM by a previous period in a geomagnetic latitude and local time (LT) coordinate system, and compare the differences between these two GIMs. The objective of this research is to find the morphological properties of global TEC in the geomagnetic latitude and LT coordinate system, reveal ionospheric behavior and predict TEC using an uncomplicated method. The GIM data for a solar cycle (2009–2020) are used to find the error characteristics and prediction performance of TEC in this coordinate. The equatorial ionization anomaly is the dominant error region. This error shows diurnal and seasonal variations and has a strong correlation with solar activity. The prediction error of this method is smaller than that of the International Reference Ionosphere (IRI) model, especially in high solar activity years. The root mean square error of the 3‐hr prediction is 6.53 in 2014 and 2.02 TECU in 2019, and it is 28.5% smaller than that of IRI on average for 2009–2020.

Lithosphere-atmosphere-ionosphere coupling associated with four Yutian earthquakes in China from GPS TEC and electromagnetic observations onboard satellites

During 2008–2020, four strong earthquakes occurred in Yutian, Xinjiang Uygur Automous Region, northwest China, in particular, two M7 + and two M6 + earthquakes demonstrating the high tectonic activity of this region. We systematically use multiple electromagnetic data from satellites and ground, such as GIM TEC (Global Ionospheric Mapping Total Electron Content) published by JPL (Jet Propulsion Laboratory), and the ULF (Ultra Low Frequency) electromagnetic waves and plasma parameters onboard DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions), Swarm and CSES (China Seismo-Electromagnetic Satellite) satellites. The ionospheric perturbations were revealed frequently around the four case studies, but mostly within 10 days before, over the epicentral area, and sometimes over its conjugate region at southern hemisphere. The abnormal amplitude is quite larger in years with high solar activity than in those with low solar activity. We employ the SAMI2 model to simulate the variations from the effects of E × B under different plasma background in 2008 and 2014 to explain the great difference in different solar years. The similarity of the anomalies in this region demonstrates the higher electromagnetic and chemical emissions, implying that the electric field is possibly generated by the preparation of the seismic events in the epicentral area inducing the ionospheric disturbances above this area and its conjugate region through this coupling channel.

Quasi-4-dimension ionospheric modeling and its application in PPP

Ionospheric delay modeling is not only important for Global Navigation Satellite System (GNSS) based space weather study and monitoring, but also an efficient tool to speed up the convergence time of Precise Point Positioning (PPP). In this study, a novel model, denoted as Quasi-4-Dimension Ionospheric Modeling (Q4DIM) is proposed for wide-area high precision ionospheric delay correction. In Q4DIM, the Line Of Sight (LOS) ionospheric delays from a GNSS station network are divided into different clusters according to not only the location of latitude and longitude, but also satellite elevation and azimuth. Both Global Ionosphere Map (GIM) and Slant Ionospheric Delay (SID) models that are traditionally used for wide-area and regional ionospheric delay modeling, respectively, can be regarded as the special cases of Q4DIM by defining proper grids in latitude, longitude, elevation, and azimuth. Thus, Q4DIM presents a resilient model that is capable for both wide-area coverage and high precision. Four different sets of clusters are defined to illustrate the properties of Q4DIM based on 200 EUREF Permanent Network (EPN) stations. The results indicate that Q4DIM is compatible with the GIM products. Moreover, it is proved that by inducting the elevation and azimuth angle dependent residuals, the precision of the 2-dimensional GIM-like model, i.e., Q4DIM 2-Dimensional (Q4DIM-2D), is improved from around 1.5 Total Electron Content Units (TECU) to better than 0.5 TECU. In addition, treating Q4DIM as a 4-dimensional matrix in latitude, longitude, elevation, and azimuth, whose sparsity is less than 5%, can result in its feasibility in a bandwidth-sensitive applications, e.g., satellite-based Precising Point Positioning Real-Time Kinematic (PPP-RTK) service. Finally, the advantages of Q4DIM in PPP over the 2-dimensional models are demonstrated with the one month's data from 30 EPN stations in both high solar activity year 2014 and low solar activity year 2020.

Longitudinal Variations in Equatorial Ionospheric TEC from GPS, Global Ionosphere Map and International Reference Ionosphere-2016 during the Descending and Minimum Phases of Solar Cycle 24

Research on longitudinal discrepancies in local ionospheric variability, especially in equatorial and low-latitude regions, is a focal point of interest for the space weather modeling community. The ionosphere over these regions is influenced by complex electrodynamics, wind, and temperature dynamics that can seriously impact dynamic technological systems such as satellite tracking and positioning, satellite radio communication, and navigation control systems. Here, we researched the longitudinal variability in the ionospheric total electron content (TEC) by analyzing observed global positioning system (GPS)-derived TEC values along with those extracted from the most reliable global ionospheric maps (GIMs) and the International Reference Ionosphere (IRI-2016) model at selected stations in the vicinity of the magnetic equator along the American, African, and Asian longitude sectors. The period of study covered the descending (2016–2017) and deep solar minimum (2018–2019) years in the 24th solar cycle. Apart from the decreasing trend of the TEC from the descending to deep solar minimum period irrespective of season and longitude sector, the results showed a relatively higher magnitude of TEC in the African longitude than the other two longitude sectors. Despite evident overestimation and underestimations of TEC in both models, GIM predictions generally looked better in terms of observed variation patterns, especially in the African longitude. The study also highlights the seasonal and semiannual effects of longitudinal variations in TEC, manifesting in local time offsets and some peculiar anomalies, which seemed to be different from previously reported results, especially during the solar minimum years at the three longitude sectors. The insignificant effects of longitudinal variations on the equinoctial asymmetry are attributed to the diverse electron density distribution and ionospheric morphology at the three longitude sectors that will prompt further investigations in the future. The outcomes from this study may augment the past efforts of scientists to understand the seasonal effects of the longitudinal variations in TEC, thereby complementing the improvements of ionospheric representations in global ionosphere models and maps.

Investigation of the planar midlatitude ionospheric trend under different levels of solar activity

A better understanding of the ionosphere through accurate mathematical models is no doubt a crucial element. This study focuses on the challenging problem of building a model representing the complex structure of the midlatitude ionosphere. Previous studies have shown that a regional planar model is suitable in representing the total electron content (TEC) trend in the midlatitude ionosphere in both hemispheres. In this study, the planar trend model for 12 non-overlapping northern hemisphere regions in three groups of geographically near 4 regions is further investigated under different levels of solar activity; low, moderate and high. To that end, the coefficients of the model are estimated in the least squares sense using total electron content values from global ionospheric maps (GIMs) for the years 2009, 2012 and 2014. Subsequently, these coefficients are used to reconstruct estimated TEC maps which are then compared with actual GIM-TEC by investigating their difference in normalized L2 norm squared sense. The regional planar trend model provides a particularly successful representation in the years 2012 and 2014 for which the solar activity level is the dominant factor determining the TEC trend. Under low solar activity conditions of 2009, other factors such as ocean currents, temperature variations and meteorological phenomena are suspected to have a considerable effect in some regions depending on their geographic location and on seasonal trends in those regions. As an example, studies show that under the influence of the Pacific Decadal Oscillation (PDO) and Siberian High (SH), a significant cooling trend between 2004 and 2018 in autumn is observed in Eurasia, which, in conjunction with the low solar activity levels, may be related to the deviations from the actual GIM-TEC in 2009 in these regions. As solar radiation increases, however, such bottom-side forcings are masked in 2012 and 2014 and these deviations are no longer observed.

Ionospheric response to the 23–31 August 2018 geomagnetic storm in the Europe-African longitude sector using multi-instrument observations

This study presents ionospheric responses of the mid and low-latitude region in the Europe-African longitude sector (along 30°±10°E) to the intense geomagnetic storm of 23–31 August 2018 (SYM-Hmin = −207 nT) using the Global Ionospheric Map (GIM) and Global Positioning System (GPS) receivers data, the satellite data (SWARM, Defense Meteorological Satellite Program (DMSP), Global Ultraviolet Imager on board the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (GUVI/TIMED)), and Prompt Penetration Equatorial Electric Field model (PPEFM). The percentage deviation in total electron content (TEC) denoted by ΔTEC (%) was used to observe the ionospheric storm effects. The rate of change of TEC index (ROTI) derived from GPS-TEC and the rate of change of plasma density index (RODI) obtained from SWARM satellites were utilized to quantify the occurrence of ionospheric irregularities. Results obtained from GPS receivers and GIM data revealed a large increase in TEC (positive ionospheric storm effect) in the equatorial and low-latitude region of Africa, and a decrease in TEC (negative ionospheric storm effect) over the midlatitude region of Europe and Africa during the storm recovery phase. The decrease in [O]/[N2] ratio is the possible cause for the observed negative ionospheric storm effect. Hemispheric asymmetry were noticed over Europe-African longitude sector during the storm main and recovery phases. The occurrence of ionospheric irregularities over the low-latitude region of Africa in the premidnight and post-midnight was suppressed (ROTI<0.4 TECU/min). This could be related to the local time at which the minimum SYM-H occurred which corresponded to daytime over Europe-African longitude sector. This, on the other hand, may not support the development of conducive environment for the generation of ionospheric irregularities. However, significant fluctuation in the plasma density was noticed by the SWARM-C on 23, 25, 30, and 31 August 2018 over equatorial and low-latitude region of Africa during the post-midnight period.

Daily Dynamo Electric Fields Derived by Using Equatorial Ionization Anomaly Crests of the Total Electron Content

AbstractLatitudinal distances between the two equatorial ionization anomaly (EIA) crests in total electron content (TEC) of global ionosphere map (GIM) are used to derive the daily dynamo eastward electric field by comparing the eastward electric field calculated by ROCSAT‐1 vertical ion velocity in four seasons. The electric fields derived by the EIA crests of GIM TEC, ROCSAT‐1 ion velocity, and the incoherent scatter radar (ISR) observations at Jicamarca are generally in good agreement in longitudes and seasons. Meanwhile, the electric fields derived by the EIA crests agree well with those by ROCSAT‐1, FORMOSAT‐7/COSMIC‐2, and Jicamarca ISR on individual days. A long‐term study of the GIM‐TEC crest distance reveals obvious solar activity dependency of the daily dynamo electric field during 1999–2020. These agreements confirm that the EIA crest can be used to derive the daily eastward dynamo electric field and the associated vertical ion velocity which is responsible to the strength of equatorial plasma fountain and the latitudinal extend of the EIA. Moreover, the vertical ion velocity derived by the GIM‐TEC crest distance is further used to improve the SAMI2 model for space weather applications.

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

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.

The Seismo-Ionospheric Disturbances before the 9 June 2022 Maerkang Ms6.0 Earthquake Swarm

Based on the multi-data of the global ionospheric map (GIM), ionospheric total electron content (TEC) inversed from GPS observations, the critical frequency of the F2 layer (fOF2) from the ionosonde, electron density (Ne), electron temperature (Te), and He+ and O+ densities detected by the China Seismo-Electromagnetic Satellite (CSES), the temporal and spatial characteristics of ionospheric multi-parameter perturbations were analyzed around the Maerkang Ms6.0 earthquake swarm on 9 June 2022. The results showed that the seismo-ionospheric disturbances were observed during 2–4 June around the epicenter under quiet solar-geomagnetic conditions. All parameters we studied were characterized by synchronous changes and negative anomalies, with a better consistency between ionospheric ground-based and satellite observations. The negative ionospheric anomalies for all parameters appeared 5–7 days before the Maerkang Ms6.0 earthquake swarm can be considered as significant signals of upcoming main shock. The seismo-ionospheric coupling mechanism may be a combination of two coupling channels: an overlapped DC electric field and an acoustic gravity wave, as described by the lithosphere–atmosphere–ionosphere coupling (LAIC). In addition, in order to make the investigations still more convincing, we completed a statistical analysis for the ionospheric anomalies of earthquakes over Ms6.0 in the study area (20°~40° N, 92°~112° E) from 1 January 2019 to 1 July 2022. The nine seismic events reveal that most strong earthquakes are preceded by obvious synchronous anomalies from ground-based and satellite ionospheric observations. The anomalous disturbances generally appear 1–15 days before the earthquakes, and the continuity and reliability of ground-based ionospheric anomaly detection are relatively high. Based on the integrated ionospheric satellite–ground observations, a cross-validation analysis can effectively improve the confidence level of anomaly identification and reduce the frequency of false anomalies.

Analysis of Ionospheric Perturbations Possibly Related to Yangbi Ms6.4 and Maduo Ms7.4 Earthquakes on 21 May 2021 in China Using GPS TEC and GIM TEC Data

On 21 May 2021 (UT), Yangbi Ms6.4 and Maduo Ms7.4 earthquakes occurred in mainland China. This paper analyzed the ionospheric perturbations possibly related to the earthquake, based on global positioning system (GPS) total electron content (TEC) and global ionosphere map (GIM) TEC data. We identified GPS TEC anomalies by the sliding quartile, based on statistical analysis. After eliminating the days with high solar activity levels and strong geomagnetic disturbances, the time series analysis of GPS TEC data showed that there were significant TEC anomalies from 5 to 10 May. TEC anomalies were mainly positive anomalies. We obtained the spatial and temporal distributions of TEC anomalies using natural neighbor interpolation (NNI). The results showed that the TEC anomalies were distributed in the seismogenic zone and surrounded the epicenters of the Maduo and Yangbi earthquakes, indicating that they may be related to the earthquakes. From the GIM TEC difference map, we found the TEC enhancement in the seismogenic zone and its magnetic conjugate area of the Maduo and Yangbi earthquakes at 10:00–12:00 (UT) on the 5 and 6 May. We discussed our results according to the lithosphere-atmosphere-ionosphere coupling mechanism. Finally, based our results, we suggested that the Yangbi and Maduo earthquakes may affect the ionosphere through seismogenic electric field and thermal anomalies generated during the process of lithosphere-atmosphere-ionosphere coupling.