Ionospheric Maps Research Articles

Modeling TEC Maps Over China Using Particle Swarm Optimization Neural Networks and Long‐Term Ground‐Based GPS, COSMIC, and Fengyun Data

AbstractThis paper presents a new model for ionospheric total electron content (TEC) over China. The new model is developed using a hybrid method composed of the particle swarm optimization (PSO) and artificial neural network and long‐term observations from 257 ground‐based global navigation satellite systems (GNSS) stations and space‐borne GNSS radio occultation systems (COSMIC and Fengyun) during the 14‐year period of 2008–2021. The PSO algorithm is used to optimize the traditional back‐propagation neural network (BP‐NN) model by reducing the effects of the local minimum problem. The new model is validated using out‐of‐sample data, and its results are compared to the BP‐NN, IRI‐2016 model, and global ionospheric maps provided by the International GNSS Service. Results show that TEC predicted from the new model agrees better with the reference TEC than the BP‐NN and IRI‐2016 models. The improvements made by the new model over the BP‐NN and IRI‐2016 models in the equinox, summer, and winter seasons of the solar maximum year (2015) are 4%–20%/20%–36%, 9%–21%/26%–42%, and 6%–22%/21%–43%, respectively, and their corresponding results in the solar minimum year (2019) are 12%–24%/41%–59%, 9%–24%/28%–56%, and 10%–26%/53%–72%. Furthermore, the new model well captures the diurnal evolution, seasonal variation, and variations in the ionospheric TEC under different solar activity levels. It also well captures the mid‐latitude summer nighttime anomaly over China, and the diurnal anomaly is more pronounced in the solar minimum year (2019) than in the solar maximum year (2015) in terms of the nighttime‐to‐noontime ratio and the range of months it lasts in a year.

Single-band VLBI Absolute Astrometry

The ionospheric path delay impacts single-band, very long baseline interferometry (VLBI) group delays, which limits their applicability for absolute astrometry. I consider two important cases: when observations are made simultaneously in two bands, but delays in only one band are available for a subset of observations; and when observations are made in one-band design. I developed optimal procedures of data analysis for both cases using Global Navigation Satellite System (GNSS) ionosphere maps, provided a stochastic model that describes ionospheric errors, and evaluated their impact on source position estimates. I demonstrate that the stochastic model is accurate at a level of 15%. I found that using GNSS ionospheric maps as is introduces serious biases in estimates of declination and I developed a procedure that almost eliminates them. I found serendipitously that GNSS ionospheric maps have multiplicative errors and have to be scaled by 0.85 in order to mitigate the declination bias. A similar scale factor was found in comparison of the vertical total electron content from satellite altimetry against GNSS ionospheric maps. I favor interpretation of this scaling factor as a manifestation of the inadequacy of the thin-shell model of the ionosphere. I showed that we are able to model the ionospheric path delay to the extent that no noticeable systematic errors emerge and we are able to assess adequately the contribution of the ionosphere-driven random errors on source positions. This makes single-band absolute astrometry a viable option that can be used for source position determination.

An Ionospheric Total Electron Content Model with a Storm Option over Japan Based on a Multi-Layer Perceptron Neural Network

Ionospheric delay has a severe effect on reducing the accuracy of positioning and navigation of single-frequency receivers. Therefore, it is necessary to construct a precise regional ionospheric model for real-time Global Navigation Satellite System (GNSS) applications. The total electron contents (TECs) of 839 GNSS stations affiliated with the GPS Earth Observation Network were used to build a Japanese ionospheric model (JIM) based on a multi-layer perceptron neural network. During quiet space conditions, the correlation coefficient between the targets and the predictions of the JIM was about 0.98, and the root-mean square error (RMSE) of TEC residuals was ~1.5TECU, while under severe space events, the correlation coefficient increased to 0.99, and the corresponding RMSE dropped to 0.96 TECU. Moreover, the JIM model successfully reconstructed the two-dimensional (time vs latitude) TEC maps, and the TEC maps had evident hourly and seasonal variations. Most of TEC residuals accumulated between universal time 01–06 with an averaged magnitude of 1-2TECU. Furthermore, the JIM model had a perfect prediction performance under various kinds of complex space environments. In the quiet days, the prediction accuracy of the JIM was nearly equal to the global ionosphere map (GIM), and in some moments, the JIM was more competitive than the GIM. In the disturbed days, the RMSEs of TEC residuals were proportional to the solar wind speed and were inversely proportional to the geomagnetic Dst value. The maximum RMSE of the JIM was lower than 2TECU, while the corresponding RMSEs for the IRI and TIE-GCM exceeded 5TECU.

Seismo Ionospheric Anomalies around and over the Epicenters of Pakistan Earthquakes

Global Navigation Satellite System (GNSS)-based ionospheric anomalies are nowadays used to identify a possible earthquake (EQ) precursor and hence a new research topic in seismic studies. The current study also aims to provide an investigation of ionospheric anomalies associated to EQs. In order to study possible pre-and post-seismic perturbations during the preparation phase of large-magnitude EQs, statistical and machine learning algorithms are applied to Total Electron Content (TEC) from the Global Positioning System (GPS) and Global Ionosphere Maps (GIMs). We observed TEC perturbation from the Sukkur (27.8° N, 68.9° E) GNSS station near the epicenter of Mw 5.4 Mirpur EQ within 5–10 days before the main shock day by implementing machine learning and statistical analysis. However, no TEC anomaly occurred in GIM-TEC over the Mirpur EQ epicenter. Furthermore, machine learning and statistical techniques are also implemented on GIM TEC data before and after the Mw 7.7 Awaran, where TEC anomalies can be clearly seen within 5–10 days before the seismic day and the subsequent rise in TEC during the 2 days after the main shock. These variations are also evident in GIM maps over the Awaran EQ epicenter. The findings point towards a large emission of EQ energy before and after the main shock during quiet storm days, which aid in the development of lithosphere ionosphere coupling. However, the entire analysis can be expanded to more satellite and ground-based measurements in Pakistan and other countries to reveal the pattern of air ionization from the epicenter through the atmosphere to the ionosphere.

Single-station quad-constellation GNSS regional ionospheric modelling for navigation

Conventionally, regional ionospheric models (RIMs) are established using GNSS at multiple stations. However, this will severely rely on the availability of the local GNSS observation network. In this study, a single-station quad-constellation GNSS regional ionospheric modelling (SQG-RIM) approach is proposed by establishing the RIM with the GPS/GLONASS/BDS/Galileo observations at a single station. To facilitate its application, an algorithm is further proposed to divide the entire modelling region into the core area and the non-core area. Compared with the global ionospheric map (GIM), the SQG-RIM in the core area can increase the ionospheric error correction rate by about 9%. Further, the SQG-RIM is applied to single point positioning (SPP) using a differentiated weighting scheme for different signal-passing areas. Test results indicate that the three-dimensional quad-constellation SPP accuracies are improved by 57% and 52% after applying the SQG-RIM as compared to the Klobuchar and GIM models, respectively.

Near real-time global ionospheric total electron content modeling and nowcasting based on GNSS observations

For the purposes of routinely providing reliable and low-latency Global Ionosphere Maps (GIMs), a method of estimating hourly updated near real-time GIM with a time latency of about 1–2 h based on a 24-h data sliding window of Global Navigation Satellite System (GNSS) near real-time observations and real-time data streams was presented. On the basis of the implementation of near real-time GIM estimation, an hourly updated GIM nowcasting method was further proposed to improve the accurate of short-term total electron content (TEC) prediction. We estimated the Shanghai Astronomical Observatory near real-time GIM (SHUG) and nowcasting GIM (SHPG) in the solar relatively active year (2014) and quiet year (2021), and employed GIMs provided by the International GNSS Service, the Global Positioning System (GPS) differential slant TECs (dSTECs) extracted from global independent GNSS stations, and the vertical TECs (VTECs) inverted from satellite altimetry as the references to validate the estimated results. The GPS dSTECs evaluation results show that SHUG behaves fairly consistent with the rapid GIMs, with a discrepancy of less than 1 TEC unit (TECu) overall. The standard deviations (STDs) of SHUG with respect to Jason-2/-3 VTECs are no more than 10% over the majority of rapid GIMs due to the instability of observations. The performance of 1-h nowcasting SHPG is significantlybetter than the Center for Orbit Determination in Europe (CODE) 1-day predicted GIM (C1PG). GPS dSTEC validation results indicate that 1-h nowcasting SHPG is 1 to 2 TECu more reliable than C1PG in eventful ionospheric electron activity regions, and it outperforms the C1PG by 10% overall versus Jason-2/-3 VTECs. The hourly updated SHUG and SHPG have relatively high reliability and low time latency, and thus can provide excellent service for (near) real-time users and offer more accurate TEC background information than daily predicted GIM for real-time GIM estimation.

Machine learning based approach for modeling and forecasting of GPS–TEC during diverse solar phase periods

The ionosphere is a dispersive medium and it is the most important source of error affecting radio communications. As a result, precise improvements in ionospheric Total Electron Content (TEC) predictions and early warning alerts are critical for space-based radio communication and navigation systems services. In the present study, we examined the Global Positioning System (GPS)–derived TEC observations of Bangalore station (Geog. Lat. 13.02° N and Geog. Long. 77.57° E) during the 24th solar cycle, which extends the 11 years from 2009 to 2019. Based on Solar Radio Flux (sfu) and seasonal features, machine learning frameworks have been developed to forecast monthly/hourly GPS-TEC values. Motivated by the preliminary results, the problem of traditional approaches for predicting ionospheric GPS-TEC values and improvements using the machine learning approach is discussed. The Kernel extreme learning machine (KELM) model has characteristics of accuracy and good generalization performance compared with the traditional method of Holt-Winters (HW) and (Auto Regressive Moving Average (ARMA). The experimental results show that the statistical validations of the KELM model are superior to the other statistical models, with average Mean Absolute Error (MAE) and Root Mean Square Error values, which is of 0.94 and 1.305 TECU (KELM), 1.70 and 2.29 TECU (ARMA) and 2.91 and 3.86 TECU (HW) during different phases of the solar cycle. In addition, the proposed model is validated by comparison with global ionospheric models (International Reference Ionosphere (IRI) −2016 and Global Ionosphere Map (GIM). The results show that the proposed approach is superior to other alternatives using prediction accuracy. This paper outlines a machine learning framework for space weather predictions and recommends KELM classifier could be used for other query functions in Active Learning (AL) practice approaches and other datasets of the Global Navigation Satellite System (GNSS).

Performance and Consistency of Final Global Ionospheric Maps from Different IGS Analysis Centers

Ionospheric delay is one of the most problematic errors in satellite-based positioning data processing. The Global Ionospheric Map (GIM), which is publicly available daily in various analysis centers, is thus vitally important for positioning users. There are variations in the accuracy and consistency of GIMs issued by Ionosphere Associate Analysis Centers (IAACs) due to the differences in ionospheric modeling methods and selected tracking stations. In this study on the International GNSS Service’s (IGS) final GIM, the ionospheric total electron content (TEC) (from 243 global navigation satellite system (GNSS) monitoring stations around the world) and the ionospheric TEC (from the Jason-3 altimeter satellite) are selected as reference. By using these three references, we evaluate the performance and consistency of final GIM products from seven IGS IAACs, including the Chinese Academy of Sciences (CAS), the Center for Orbit Determination in Europe (CODE), Natural Resources Canada (EMR), the European Space Agency (ESA), the Jet Propulsion Laboratory (JPL), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU) in the mid-solar activity year (2022) and the low-solar activity year (2020). Firstly, the comparison with the IGS final GIM shows that the consistency of each GIMs is basically the same, with the mean value ranging from −0.3 TECu (total electron content unit) to 1.4 TECu. Secondly, the validation with Jason-3 altimeter satellite shows that the accuracy of several GIMs is almost the same, except for the JPL with the worst accuracy and an overall mean deviation (BIAS) between 2 and 6 TECu. Thirdly, the comparison with VTEC extracted from GNSS monitor stations shows that the CAS has the best accuracy in different latitude bands with a root mean square (RMS) of about 2.2–4.7 TECu. In addition, it is found that the accuracy in areas with more stations for ionospheric modelling is better than those with less stations in different latitude bands; meanwhile, the accuracy is closely related to the modeling methods of different GIMs.

Two‐Way Assessment of Ionospheric Maps Performance Over the Brazilian Region: Global Versus Regional Products

AbstractVertical total electron content (VTEC) has great importance in describing the ionosphere. VTEC values are commonly distributed in regular grids by means of so‐called global ionospheric maps (GIMs) and regional ionospheric maps (RIMs). Although considerable research has been conducted to develop regional and global models, there is no clear understanding of the benefits of using RIMs over GIMs. Aiming to contribute to this discussion, our investigation presents a comparison between seven global and regional ionospheric maps considering two approaches: (a) ionosonde data‐based assessment and (b) global navigation satellite systems (GNSS) positioning assessment. A challenging low latitude ionosphere scenario, the Brazilian region, was selected during a week with an active geomagnetic storm. The assessment results with ionosonde data have shown better performance of the RIM products named OTHR and OTRG. Among the global products, CODG and UQRG have shown the best performances. The worst results were obtained with the RIM named Instituto Nacional de Pesquisas Espaciais. The assessment with GNSS positioning led to larger and noisier errors close to the equatorial anomaly. Two of the analyzed RIMs presented expected large errors in stations at the edges of the coverage area. To overcome this issue, a hybrid product was proposed to extend the RIM covered region. The proposed hybrid product (OTRG) presented the best results in the GNSS positioning domain.

Some Key Issues on Pseudorange-Based Point Positioning with GPS, BDS-3, and Galileo Observations

Nowadays, BDS-3 and Galileo are still developing and have global service capabilities. This study aims to provide a comprehensive analysis of pseudorange-based/single point positioning (SPP) among GPS, BDS-3, and Galileo on a global scale. First, the positioning accuracy distribution of adding IGSO and GEO to the MEO of BDS-3 is analyzed. The results show that after adding IGSO and GEO, the accuracy of 3D in the Asia-Pacific region is significantly improved. Then, the positioning accuracy of the single-system and single-frequency SPP was validated and compared. The experimental results showed that the median RMS values for the GPS, Galileo, and BDS-3 are 1.10/1.10/1.30 m and 2.57/2.69/2.71 m in the horizontal and vertical components, respectively. For the horizontal component, the GPS and Galileo had better positioning accuracy in the middle- and high-latitude regions, while BDS-3 had better positioning accuracy in the Asia-Pacific region. For the vertical component, poorer positioning accuracy could be seen near the North Pole and the equator for all three systems. Meanwhile, in comparison with the single-system and single-frequency SPP, the contribution of adding pseudorange observations from the other satellite system and frequency band was analyzed fully. Overall, the positioning accuracy can be improved to varying degrees. Due to the observation of noise amplification, the positioning errors derived from dual-frequency SPP were much noisier than those from single-frequency SPP. Moreover, the positioning performance of single-frequency SPP with the ionosphere delay corrected with CODE final (COD), rapid (COR), 1-day predicted (C1P), and 2-day predicted (C2P) global ionospheric map (GIM) products was investigated. The results showed that SPP with COD had the best positioning accuracy, SPP with COR ranked second, while C1P and C2P were comparable and slightly worse than SPP with COR. SPP with GIM products demonstrated a better positioning accuracy than that of the single- and dual-frequency SPP. The stability and variability of the inter-system biases (ISBs) derived from the single-frequency and dual-frequency SPP were compared and analyzed, demonstrating that they were stable in a short time. The differences in ISBs among different receivers with single-frequency SPP are smaller than that of dual-frequency SPP.

Using DORIS data for validating real-time GNSS ionosphere maps

The ionospheric information retrieved from the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) system provides valuable and external data sources to examine the quality of existing Global Navigation Satellite System (GNSS) generated ionospheric models. The concept of DORIS differential Slant Total Electron Content (dSTEC) analysis is presented, to check the feasibility of DORIS data in the quality assessment of GNSS derived ionosphere models. Thanks to the large relative frequency ratio between the two frequencies of DORIS, the theoretical precision of DORIS dSTEC is at the level of 0.028 TECu, which is about 10 times better than that of GNSS derived dSTEC. Using 48 co-located DORIS beacon sites of the International DORIS service (IDS) and GNSS stations of the International GNSS Service (IGS), the comparison between DORIS and GNSS dSTEC assessments of Real-Time Global Ionospheric Maps (RT-GIMs) from different analysis centers is performed in this paper. The analysis is performed during the first 110 days of 2022. Based on the analysis results of more than 18,000,000 Jason-3 DORIS ionospheric observables, no systematical deviation is found between DORIS and RT-GIM derived dSTECs, with a mean bias of 0.14 TECu. Compared to DORIS dSTEC, the root-mean-square (RMS) of those RT-GIMs reaches 4.5–5.4 TECu at low-latitudes, which is 2.2–3.6 TECu in mid- and high-latitude regions. The latitudinal variation of RT-GIM errors is clearly observed in DORIS dSTEC analysis. In addition, the Pearson correlation coefficient between Jason-3 DORIS dSTEC RMS and GPS-plus-GLONASS dSTEC RMS is 0.81, and no significant dependence on GPS-only or GLONASS-only data is found. It is suggested to perform the dSTEC analysis with higher satellite elevation cutoff angle, e.g., 45°, to ensure a better consistency between DORIS and GNSS dSTEC assessments. Overall, DORIS dSTEC assessment provides an independent reference for the validation of GNSS based ionosphere maps.

Study of Ionosphere Irregularities over the Iberian Peninsula during Two Moderate Geomagnetic Storms Using GNSS and Ionosonde Observations

Studies on the irregularities of the ionosphere during disturbed geomagnetic conditions are fundamental to understanding the complex dynamics taking place in the upper atmosphere. In this work, different data sources are used to study the ionosphere effects of two moderate geomagnetic storms, 26–27 February 2014 and 17–18 September 2021, over the Iberian Peninsula. Data are obtained from digital ionosondes in Spain, Italy and Greece; the Global Navigation Satellite System (GNSS) derived Total Electron Content (TEC) and Rate Of TEC Index (ROTI) from several receiver stations in Spain, Portugal and Morocco; and the UPC Quarter-of-an-hour time resolution Rapid GIM (UQRG), vertical TEC global ionosphere maps (GIMs), produced at 15 min intervals by the Universitat Politecnica de Catalunya (UPC, Spain). This analysis showed that, during the two moderate storms, spread-F and high values of ROTI, indicating the presence of irregularities, are found in a very localized area (Southern Iberian Peninsula and northwest Africa) and local times (night-time). However, no irregularities are found eastwards and northwards of the location indicated. We propose some possible explanations for these observations for both the storms, one of them related to the position of the Equatorial Ionosphere Anomaly (EIA) and the other one attributed to the Perkins’ instabilities.

Comparison of different methods to account for the plasmaspheric electron content in GNSS-derived ionospheric altimeter corrections and their impact on sea level trend estimation

Global ionospheric maps based on GNSS measurements are nowadays often used to correct satellite altimeter measurements when the instruments have only one frequency or measure over coasts and inland waters. If these corrections do not account for the free electron fraction over the altimeter satellites, this leads to systematic deviations in the range measurements and thus in the estimated sea level. This study compares and assesses different approaches to reduce GNSS-based corrections for the plasmaspheric electron content. It is shown that using a simple scaling with a constant factor of 0.881 gives the best results for the Jason-1 mission, while correcting with the commonly used model ratios leads to higher sea level trend artefacts and larger noise levels, especially for periods of lower solar activity. Using this approach, the sea level trend error for both Jason-1 and Sentinel-6A can be reduced to below 0.1 mm/year, with standard deviations of the differences from the dual-frequency altimeter corrections of 6.74 mm. A simple machine learning approach (boosted regression tree) is also investigated and shows promising results. However, due to the higher processing capacity requirements and the larger deviations from long-term trend, further improvements are recommended before such an approach can be used in routine processing of altimeter corrections.Graphical

A THREE-DIMENSIONAL APPROACH TO IMPROVE THE INTERNATIONAL REFERENCE IONOSPHERE (IRI) MODEL

Abstract. A three-dimensional model based on time and location is introduced to enhance the accuracy of vertical total electron content (VTEC) global ionosphere maps (GIMs) of ionosphere empirical models. The chosen empirical model is the International Reference Ionosphere 2016 (IRI-2016) model and observations are the GIMs VTEC values from Center for Orbit Determination in Europe (CODE). For this purpose, IRI-2016 is considered as a background model and the VTECs from GIMs are considered as the correction term. The correction term is modelled using spherical harmonics expansion base functions with a degree equal to 15 in Earth-fixed coordinate system with a polynomial B-spline base function for time resolution. The improved VTEC maps are constructed on 1st January 2018 with a Kp index of 4. The root mean square error (RMSE) improvements range from 1.65 TECU to 2.81TECU about 70 percent to 85 percent and the bias improvements are from 0.28 TECU to 1.00 TECU with an average of 0.75 TECU, respectively.

Using Deep Learning to Map Ionospheric Total Electron Content over Brazil

The low-latitude ionosphere has an active behavior causing the total electron content (TEC) to vary spatially and temporally very dynamically. The solar activity and the geomagnetic field have a strong influence over the spatiotemporal distribution of TEC. These facts make it a challenge to attempt modeling the ionization response. Single frequency GNSS users are particularly vulnerable due to these ionospheric variations that cause degradation of positioning performance. Motivated by recent applications of machine learning, temporal series of TEC available in map formats were employed to build an independent TEC estimator model for low-latitude environments. A TEC dataset was applied along with geophysical indices of solar flux and magnetic activity to train a feedforward artificial neural network based on a multilayer perceptron (MLP) approach. The forecast for the next 24 h was made relying on TEC maps over the Brazilian region using data collected on the previous 5 days. The performance of this approach was evaluated and compared with real data. The accuracy of the model was evaluated taking into account seasonality, spatial coverage and dependence on solar flux and geomagnetic activity indices. The results of the analysis show that the developed model has a superior capacity describing the TEC behavior across Brazil, when compared to global ionosphere maps and the NeQuick G model. TEC predictions were applied in single point positioning. The achieved errors were 27% and 33% lower when compared to the results obtained using the NeQuick G and global ionosphere maps, respectively, showing success in estimating TEC with small recent datasets using MLP.

Assessment of Dynamic Mode Decomposition (DMD) Model for Ionospheric TEC Map Predictions

In this study, we assess the Dynamic Mode Decomposition (DMD) model applied with global ionospheric vertical Total Electron Content (vTEC) maps to construct 24-h global ionospheric vTEC map forecasts using the available International GNSS Service (IGS) 2-h cadence vTEC maps. In addition, we examine the impact of a EUV 121.6 nm time series data source with the DMD control (DMDc) framework, which shows an improvement in the vTEC Root Mean Square Error (RMSE) values compared with the IGS final solution vTEC maps. Both the DMD and DMDc predictions present close RMSE scores compared with the available CODE 1-day predicted ionospheric maps, both for quiet and disturbed solar activity. Finally, we evaluate the predicted global ionospheric vTEC maps with the East-North-Up (ENU) coordinate system errors metric, as an ionospheric correction source for L1 single-frequency GPS/GNSS Single Point Positioning (SPP) solutions. Based on these findings, we argue that the commonly adopted vTEC map comparison RMSE metric fails to correctly reflect an informative impact with L1 single-frequency positioning solutions using dual-frequency ionospheric corrections.

Mapping of the Ionospheric Total Electron Content over the East African Low–Latitude Region

Ionospheric behaviour is often monitored using total electron content (TEC) measurements from Global Navigation Satellite System (GNSS) receivers. However, the GNSS receivers are sparse over the East African low-latitude region, resulting in spatio-temporal gaps. In an attempt to address this challenge, the possibility of constructing East African ionospheric maps using Kriging method was investigated in this study. Prior to constructing the maps, the behaviour of the low-latitude ionosphere over this region was characterised using TEC data for 2014 and 2018 during quiet and disturbed conditions. The results show that the highest values of TEC exist between 10:00 UT and 13:00 UT and the minimum values exist at around 03:00 UT. The seasonal trend of TEC reveals that the highest values of TEC occur during March equinox and the lowest during June solstice. The post-sunset TEC often show enhancements between 18:00 UT and 21:00 UT. The enhancements were more pronounced in 2014 than in 2018, especially during the equinoctial months for stations at the crest of the equatorial ionisation anomaly region. These enhancements occurred about an hour earlier at MAL2 than at ADIS during March and September, 2014. The spatial variation of TEC over the East African region was analysed by fitting different semivariogram models. The results reveal that the best semivariogram that describes the spatial variation of TEC over the East African low-latitude region is the Gaussian model. Further analysis shows that the spatial variation of TEC is larger in the meridional direction than in the zonal direction. The Gaussian semivariogram model was then used to construct the TEC maps over the region. The TEC maps were validated using TEC derived from GNSS receivers. The results show very high positive correlation with correlation coefficients between 0.9360 and 0.9970. This suggests that the TEC maps generated by the Kriging interpolation method can be used as good estimates of the GPS-derived TEC at the unmeasured grid points over the region. Therefore, the maps can be used to fill the gaps that exist in the TEC data over the region.

Advanced Warning of Threatening Equatorial Plasma Bubbles to Support GBAS in Low Latitudes

In low-latitude regions such as Brazil, the single-frequency Ground-Based Augmentation System (GBAS) is not yet fully operational due to the influence of ionospheric variability. The most critical issue for GBAS is the occurrence of ionospheric gradients large enough to compromise the system's integrity. This can result in unsafe operation because a single GBAS ground station cannot identify all threatening gradients in real time. This paper develops and validates a strategy to detect and alert in advance the presence and effects of equatorial plasma bubbles (EPBs), which create the largest threat to GBAS operations in low latitudes. This alerting system uses surrounding stations in the vicinity of the GBAS facilities being supported to monitor the ionosphere state in real-time and send alerts to the served GBAS facility when threatening conditions are detected on one or more tracked satellites. Time-step measurements of ionospheric gradients and data availability at these surrounding stations are used to generate these alerts when needed. An architecture is proposed for such a system along with a complete validation of the method. Validation uses a reliable dataset from the peak of ionospheric Solar Cycle 24 containing post-processed station-pair gradients in time, amplitude scintillation indices, and ionospheric maps. All gradients from the dataset classified as threatening to GBAS operation were encompassed by the alerts issued by the real-time methodology within the adopted success criteria. Based on these results, an EPB alerting approach based on this method presented and validated in this paper can significantly enhance single-frequency GBAS integrity and availability in low latitudes.

A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps

Ionospheric scintillation disturbs radio frequency signals affecting navigation based on Global Navigation System (GNSS), especially in Brazil, due to the large magnetic declination. The generation of real-time scintillation maps is an important way to provide scintillation monitoring. This work considers amplitude scintillation, given by the S4 index. Some existing and some proposed approaches for generating these maps are presented and tested, being each one a combination of an interpolation method with some existing and/or proposed sets of pre-processing options. These approaches are named after the related interpolation method as GRIDDATA, Inverse Distance Weighting, Radial Basis Functions and Gaussian Process Regression. The making of scintillation maps requires the interpolation of Ionospheric Pierce Point (IPP) samples, given by the S4 values for each IPP of each satellite-station link considering the set of GNSS stations of the given area and time interval. Some intervals of time that presented strong scintillation over Brazil were selected and the corresponding sets of IPP samples were used to obtain sequences of maps using all possible combinations of interpolation and pre-processing options. Furthermore, a fifth, more recent, approach was also included in the comparison. The quality of the resulting maps was assessed, concluding that the Gaussian Process Regression approach, with a specific set of pre-processing options, allows to generate the most accurate scintillation maps. The proposed map generation approach is part of a broader proposal being implemented to provide real-time scintillation maps covering the Brazilian territory.

Aeronomic and Dynamic Correction of the Global Model GTEC for Disturbed Conditions

An aeronomic and dynamic correction of the GTEC median global model of the total electroncontent for disturbed conditions (Ap ≥ 15 nT) is proposed. The GTEC global median model is developedfor quiet conditions (Ap 15 nT) as a function of the geographic coordinates, universal time UT, day of theyear, and solar activity level (the solar radio emission flux F10.7). The model is based on a spherical harmonicanalysis of the GIM-TEC Global Ionospheric Maps (1996–2019) provided by the Jet PropulsionLaboratory (NASA). The proposed GDMTEC global dynamic model (Global Dynamic Model of TEC)consists of the GTEC median model updated with several dynamic and aeronomic corrections related toformation of the main ionospheric trough, position of the auroral ionization maximum and changes of thethermospheric temperature and composition. The advantage of the proposed corrections of the medianmodel is the independence of forecast of the data in real time from assimilation of the current observationaldata. Testing of the model for disturbed conditions shows an improvement of the forecast compared to theIRI-Plas ionospheric reference model