Global Ionospheric Maps Research Articles

Enhanced Forecasting of Global Ionospheric Vertical Total Electron Content Maps Using Deep Learning Methods

The ionospheric state holds significant implications for satellite navigation, radio communication, and space weather; however, precise forecasting of the ionosphere remains a formidable challenge. To improve the accuracy of traditional forecasting models, we developed an enhancement model based on the CODE and IRI forecasting methods, termed the Global Ionospheric Maps Forecast Enhancement Model (GIMs-FEM). The results indicated that by extracting the GIM features from existing forecasts and incorporating additional proxies for geomagnetic and solar activity, the GIMs-FEM provided stable and reliable forecasting outcomes. Compared to the original forecasting models, the overall model error was reduced by approximately 15–17% on the test dataset. Furthermore, we analyzed the model’s performance under different solar activity conditions and seasons. Additionally, the RMSE for the C1pg model ranged from 0.98 TECu in the solar minimum year (2019) to 6.91 TECu in the solar maximum year (2014), while the enhanced GIMs (C1pg) model ranged from 0.91 to 5.75 TECu, respectively. Under varying solar activity conditions, the RMSE of GIMs-FEM for C1pg (C2pg) ranged from 0.98 to 6.91 TECu (0.96 to 7.26 TECu). Seasonally, the GIMs-FEM model performed best in the summer, with the lowest RMSE of 1.9 TECu, and showed the highest error in the autumn, with an RMSE of 2.52 TECu.

Intensity of 27-day variations in solar emission and ionospheric electron content

The rotation of the Sun modulates solar emission with 27-day variations. Large-scale structures in the solar atmosphere, like sunspots and plages, rotate with the Sun and emit at extreme ultraviolet (EUV) and microwaves that produce the varying part of the emission. As EUV emission is the main driver of ionization in the Earth ionosphere, its variations are reflected in the electron content. In this paper, we analyze wide range of data from radio emission in 245-17000 MHz range and the entire EUV spectrum to total and global electron contents (TEC and GEC, respectively) in the ionosphere to find out where the 27-day variations are most prominent. We show that the relative contribution of 27-day variations to radio emission is maximal at 1000 MHz and decreases at 245 and 17000 MHz. The variations are pronounced throughout the EUV spectrum, their intensity vary at different spectral lines and, on average, is higher than in radio emission. The distribution of the variations in global ionospheric maps (GIM) shows an intensification of 27-day variations in TEC in the North America region, which can be related to the winter anomaly as it is also more significant in this region. Temporal variations of the 27-day component of emission are similar in shape for radio and EUV emission, as well as for GEC, and have a high correlation, up to 0.8. Cycle-averaged temporal variations imply that the 27-day component weakens significantly during solar minimum but can intensify during the remaining phases of the solar cycle. We also find that the flux at 1000 MHz, which originate from free-free emission in contrast to F10.7, can be used as a proxy index of solar activity because it has the highest intensity of 27-day variations and has been observed since 1956.

Graph-enabled spatio-temporal transformer for ionospheric prediction

The ionosphere, crucial for satellite navigation and radio communication, is highly sensitive to solar and geomagnetic activities. Traditional ionospheric prediction models often struggle to capture its complexities. This paper introduces a Graph-Enabled Spatio-temporal transformer (GEST) model, designed to predict the Total electron content (TEC) in the ionosphere with higher accuracy. The GEST model innovatively integrates six dynamic space weather parameters, including the Kp index, and refines grid distances and weights in the Global ionospheric maps (GIM). Leveraging the efficiency of the Transformer architecture in processing spatio-temporal data, the GEST model adapts to varying ionospheric conditions and provides superior precision. Our evaluations, based on data from the Center for orbit determination in Europe (CODE), demonstrate that the GEST model significantly outperforms traditional methods and other intelligent algorithms such as conv-LSTM. It shows marked improvements in Mean absolute error (MAE), Root mean square error (RMSE), Pearson correlation coefficient (CC), and Mean absolute percentage error (MAPE), particularly during periods of intense solar and geomagnetic activity. These advancements highlight the GEST model’s substantial contributions to the field of ionospheric prediction technology, offering a robust tool for enhancing the reliability of satellite-based systems.

Differences among the total electron content derived by radio occultation, global ionospheric maps and satellite altimetry

In recent years, significant progress has been in ionospheric modeling research through data ingestion and data assimilation from a variety of sources, including ground-based global navigation satellite systems, space-based radio occultation and satellite altimetry (SA). Given the diverse observing geometries, vertical data coverages and intermission biases among different measurements, it is imperative to evaluate their absolute accuracies and estimate systematic biases to determine reasonable weights and error covariances when constructing ionospheric models. This study specifically investigates the disparities among the vertical total electron content (VTEC) derived from SA data of the Jason and Sentinel missions, the integrated VTEC from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) and global ionospheric maps (GIMs). To mitigate the systematic bias resulting from differences in satellite altitudes, the vertical ranges of various VTECs are mapped to a standardized height. The results indicate that the intermission bias of SA-derived VTEC remains relatively stable, with Jason-1 serving as a benchmark for mapping other datasets. The mean bias between COSMIC and SA-derived VTEC is minimal, suggesting good agreement between these two space-based techniques. However, COSMIC and GIM VTEC exhibit remarkable seasonal discrepancies, influenced by the solar activity variations. Moreover, GIMs demonstrate noticeable hemispheric asymmetry and a degradation in accuracy ranging from 0.7 to 1.7 TECU in the ocean-dominant Southern Hemisphere. While space-based observations effectively illustrate phenomena such as the Weddell Sea anomaly and longitudinal ionospheric characteristics, GIMs tend to exhibit a more pronounced mid-latitude electron density enhancement structure.

An investigation into the influence of solar flares and geomagnetic storms on the F2 layer of the ionosphere in Western Europe during March 2024

On March 23 and 24, 2024, the M-class and X-class solar storms significantly intensified. Subsequently, from March 24 to 26, the monitoring station’s Kp index notably increased, at times exceeding 8, indicating that a severe geomagnetic storm was occurring. During this period, the IGS Global Ionosphere Maps (GIMs) displayed a substantial decrease in Total Electron Content (TEC) in Western Europe due to the impact of the intense geomagnetic storms. To investigate this occurrence, data from the Lowell GIRO Data Center were utilized. The analysis revealed that a concentrated solar flare eruption on March 23, 2024, triggered geomagnetic storm events on Earth, causing significant ionospheric anomalies in Western European countries. Specifically, compared to typical levels, parameters such as foF2, m3000F2, and TEC values experienced significant reductions in Western European countries. Moreover, the hmF2 data exhibited higher values at night and lower values during the day, deviating from the expected diurnal pattern. Analysis of the Rate of TEC Index (ROTI) indicated varying effects of geomagnetic storms across different latitudes in Western Europe. Through detailed graphical analysis, this study elucidates the alterations in the ionosphere under geomagnetic storm conditions in Western Europe, shedding light on the dynamic behavior of the ionosphere during a specific timeframe.

Contributions of Ionospheric Migrating Tides to Ionospheric Intra‐Annual Variations

AbstractThe Earth's ionosphere undergoes regular intra‐annual variations (IAVs) characterized by two peaks and troughs around the equinoxes and solstices. This phenomenon is crucial for analyzing the ionospheric response to geomagnetic storms. This study presents a comprehensive analysis of the IAVs contributed by diurnal and semidiurnal migrating tides (DW1 and SW2) using Global Ionospheric Maps (GIMs) data from 2017 to 2021. Through data stacking techniques, the seasonal variability and splitting phenomenon of DW1 and SW2 across different latitudes are examined. The findings indicate that the splitting of these tides can be attributed to their quasi‐periodic variations, predominantly composed of annual oscillation (AO) and semiannual oscillation (SAO). The combination of DW1, SW2, and their side‐band harmonics results in beats with annual and semiannual periodicities, enabling the restoration of the seasonal variations in DW1 and SW2. The ionospheric day‐to‐day variations were reconstructed by superimposing DW1 and SW2, and their IAVs were evaluated using the envelope method. Comparison with IAVs driven by Earth's orbital geometry reveals that tide‐driven IAVs are more significant, and both exhibit solar activity dependence. The results advance the understanding of ionospheric variability, emphasizing the critical role of tidal contributions.

Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms.

Assessment of selected ionospheric mapping functions using SF-PPP on different solar activities

In single-frequency precise point positioning (SF-PPP), the ionospheric delays provided by global ionosphere maps (GIMs) are in the vertical direction. Therefore, an ionospheric mapping function is applied to convert the vertical direction to the slant one. However, the performance of mapping functions (MF) applied in SF-PPP under different solar activities is unknown. Meanwhile, understanding their performance can help us better improve the accuracy of the ionospheric mapping function. For this purpose, three traditional ionospheric mapping functions, such as the standard single-layer model mapping function (SLM MF), the modified single-layer model mapping function (MSLM MF), and the Klobuchar MF, are evaluated. Additionally, the mapping function named SGG MF, which considers the effect of ionospheric gradients, is also assessed. The positioning results indicate that the SGG MF has an improvement of (50.3 %, 37.3 %), (31.7 %, 23.4 %), and (16.8 %, 13.3 %) compared with Klobuchar MF, SLM MF, and MSLM MF during the year (2014, 2021), respectively. The mean positioning errors of SLM MF, MSLM MF, and SGG MF are about (0.20 m, 0.05 m), (0.25 m, 0.10 m), and (0.30 m, 0.10 m) smaller than that of Klobuchar MF over high-/mid- latitude during the year (2014, 2021), while the values are (0.25 m, 0.20 m), (0.40 m, 0.35 m), and (0.55 m, 0.50 m) over low-latitude region. Furthermore, the correlation coefficients between positioning results and solar activities are (0.114, 0.354), (0.058, 0.324), (0.098, 0.295), and (0.235, 0.271) for Klobucahr MF, SLM MF, MSLM MF, and SGG MF during corresponding year.

GRIMS: global and regional ionosphere monitoring system

The ionosphere shows regular changes such as daily, 27 days, seasonal, semi-annual, annual, and 11 years. These changes can be modeled and their effects largely determined. However, in addition to regular changes, irregular changes occur in the ionosphere due to space weather conditions, natural disasters, and human-induced irregularities. GNSS is one of the instruments along with many others that can give a piece of information on the ionospheric state. Various indices/parameters are used to determine the effect of space weather conditions. The well-known ones are solar activity indices, geomagnetic storm indices, magnetic field components, proton density, and proton flux parameters. It is important to take all of these indices into consideration when investigating the source of the anomaly. Considering only some of them may lead to incorrect inferences about the source of possible anomalies. To carry out comprehensive research in this field, it is necessary to analyze a very large data set. This indicates the requirement for an automatic system. With the Global and Regional Ionosphere Monitoring System (GRIMS) designed within the scope of this study, the ionosphere can be monitored globally and regionally. The GRIMS is online at https://www.online-grims.com/. By using Global ionospheric maps and GNSS receiver data, global, regional, and station-specific anomalies can be detected regularly through methods such as HDI (Highest Density Interval) and ARIMA (Autoregressive Integrated Moving Average). GRIMS gathers space weather-related parameters from ionospheric data centers to help users interpret the situation, and it allows users to download the results and request data for specific days. The details of the experimental results and output products of the system designed during the geomagnetic active days of March 17, 18, 2015 are given in this paper. Moreover, geomagnetic active days that occurred between 2000 and 2023 are given in the GRIMS.

Investigation of the Contribution of Five Broadcast Ionospheric Models (GPSK, NTCMG, NEQG, BDGIM, and BDSK) and IRTG to GNSS Positioning During Different Solar Activities in Solar Cycle 25

AbstractAdditional ionospheric information is essential for mitigating errors in single‐frequency (SF) Global Navigation Satellite Systems (GNSS) positioning. The increasing number of low‐cost dual‐frequency (DF) receiver users faces limitations in tracking DF observables compared to traditional geodetic receivers. Consequently, ionospheric correction algorithms (ICAs) are also essential for low‐cost DF devices in hybrid‐frequency positioning. To evaluate the performance of commonly used ICAs during solar cycle 25, our study presents a global statistical investigation of the contribution of five broadcast ionospheric models (BIMs) and the International GNSS Service (IGS) combined real‐time global ionospheric maps (IRTG) to the positioning domain, covering both quiet and perturbed ionospheric conditions. The BIMs investigated include the GPS Klobuchar (GPSK), Galileo NequickG (NEQG), NTCM‐GlAzpar (NTCMG), BDS‐2 Klobuchar (BDSK), and BeiDou Global Ionospheric delay correction Model (BDGIM). Experimental results from standard point positioning indicate that IRTG demonstrates superior overall accuracy compared to all BIMs, with a mean 3D root mean squared (RMS) of 2.76 m during perturbed period. Specifically, GPSK, NTCMG, NEQG, BDGIM, and BDSK exhibit RMS values of 2.03, 1.67, 1.72, 1.62, and 2.36 m during quiet conditions, and 4.02, 3.17, 2.86, 3.14, and 4.44 m during perturbed conditions, respectively. Among the BIMs, NEQG demonstrates superior performance at middle and high latitudes but exhibits lower accuracy than NTCMG and BDGIM at low latitudes during daytime under quiet conditions. BDGIM performs slightly better than NTCMG at low latitudes but slightly worse at high latitudes. BDSK shows notable improvement for high‐ and mid‐latitude regions since 3 June 2020.

Precise point positioning (PPP) based on the machine learning-based ionospheric tomography

Undifferenced and Uncombined Precise Point Positioning (UPPP) currently stands as a prominent research area, where the integration of high-precision ionospheric products holds the potential to substantially enhance accuracy and convergence performance in UPPP. Presently, the majority of external ionospheric constraints for UPPP rely on Global Ionospheric Maps (GIM). However, the accuracy and resolution of GIM fall short, imposing significant limitations on the positioning performance of UPPP. Consequently, this paper introduces the application of Computerized Ionospheric Tomography based on Machine Learning (CIT-ML) to enhance UPPP performance (PPP-CIT-ML). In this approach, we convert the three-dimensional electron density of CIT-ML into vertical total electron content (VTEC), and then it is compiled into ionospheric grid files essential for UPPP. Simultaneously, the traditional ionospheric tomography methods based on the improved algebraic reconstruction technique (IART) is also employed for UPPP (PPP-CIT-IART), alongside the ionospheric grid files broadcasted by the Center for Orbit Determination in Europe (CODE) for UPPP (PPP-CODE), where PPP-CIT-IART and PPP-CODE are used as the reference methods to test the performance of PPP-CIT-ML. In Static and Kinematic UPPP, compared to PPP-CODE, PPP-CIT-IART demonstrated average improvements in positioning accuracy and convergence performance by over 10% and 7%, respectively. PPP-CIT-ML showed average improvements in positioning accuracy and convergence performance by over 26% and 28%, respectively. The extrapolated ionospheric electron density (IED) applied to UPPP (ECIT-ML-PPP) and compared with PPP-CODE displayed average improvements in positioning accuracy by over 21% and convergence performance by over 26%. Compared with PPP-CIT-IART, ECIT-ML-PPP displayed average improvements in positioning accuracy and convergence performance by over 6% and 21%, respectively. These findings highlight that ionospheric error correction information obtained through ionospheric tomography significantly enhances the positioning accuracy and convergence performance of UPPP. Moreover, the performance enhancement achieved through machine learning-based ionospheric tomography is more pronounced. This study provides preliminary validation for the feasibility of applying machine learning-based ionospheric tomography results to navigation positioning.

Analysis of Ionospheric Delay Correction Model Performance During Geomagnetic Storms

AbstractIonospheric delay, as one of the largest error sources in radio propagation, can only be corrected for this error using the ionospheric delay correction model for Global Navigation Satellite System (GNSS) single‐frequency users. In this paper, the 2021 geomagnetic storm event is selected, and based on the measured ionospheric data from the GNSS observatory, the perturbation of the ionosphere by the geomagnetic storm event is analyzed, and it is found that the response of the ionosphere to the geomagnetic storm has obvious differences in the response characteristics and response time in different latitude regions. The performance of the global ionospheric map (GIM), the empirical model, and the broadcast ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is analyzed and the change in the accuracy of each ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is investigated, using the measured electron content of the GNSS as a benchmark. The results show that there is good agreement between the GIM products and the measured electron content during the period of ionospheric calm and the period of ionospheric perturbation. It is worth noting that geomagnetic storms do not necessarily lead to a decrease in the accuracy of ionospheric delay‐correction models, and in some cases, the models that were originally under‐accurate show a tendency to improve their accuracy during the period of perturbation instead. Neither the broadcast ionospheric model nor the electron content of the empirical model output responds to geomagnetic storm‐induced ionospheric perturbations.

Study of the response of the upper atmosphere during the annular solar eclipse on October 14, 2023

A solar eclipse is a fascinating event that gives a unique opportunity to study the ionospheric characteristics in a controlled blockage of solar radiation. We present the upper atmosphere’s response, mostly F-layer, during the annular eclipse on October 14, 2023. This eclipse was mostly visible from the North and South American regions with a maximum obscuration of around 91%. We study the response of the ionospheric Total Electron Content (TEC) and the critical frequency of the F2 layer (f0F2) for three consecutive days (October 13 to 15, 2023) using ground and space-based techniques. We present the ionospheric response for a large area that covers the path of the solar eclipse of latitude and longitude range from 50°N to 10°S and 30°W to 150°W. For the ground-based observation, we use fifty-five International GNSS Service (IGS) stations and eleven Global Ionospheric Radio Observatory (GIRO) Digisonde stations that cover the annular and partial solar eclipse and are in the close vicinity of the annularity belt. We compute the vertical TEC (VTEC) and f0F2 from this database and check their temporal and spatial variations. It is found that the VTEC, as computed from the IGS stations, mostly shows decrements during the partial blockage of the sun by the lunar disc. The VTEC, as computed from the GIRO database, also shows a decreasing trend but with comparatively more oscillating in nature. The f0F2 shows a similar decreasing trend due to the solar eclipse. For space-based observation, we use the Swarm satellite data for the tracks that have gone through the annularity belt. Also, a spatiotemporal study is executed using the Global Ionospheric Map (GIM) database. For both cases, the VTEC shows a prominent depletion. We investigate the variation in the percentage change in VTEC as a function of obscuration and observe a positive correlation between them. We also compute the time difference between the maximum eclipse and the maximum TEC modulation to investigate the response time profiles of the ionosphere at different receiving stations. All the methods give satisfactory and consistent outcomes that corroborate the physical mechanism of ionospheric variabilities due to the effect of the solar eclipse. We check the geomagnetic condition to eliminate the scope of contamination and observe that their values are normal during the study.

Analysis of Ionospheric VTEC Retrieved from Multi-Instrument Observations

This study examines the Vertical Total Electron Content (VTEC) estimation performance of multi-instruments on a global scale during different ionospheric conditions. For this purpose, GNSS-based VTEC data from Global Ionosphere Maps (GIMs), COSMIC (F7/C2)—Feng–Yun 3C (FY3C) radio occultation (RO) VTEC, SWARM–VTEC, and JASON–VTEC were utilized. VTEC assessments were conducted on three distinct days: geomagnetic active (17 March 2015), solar active (22 December 2021), and quiet (11 December 2021). The VTEC values of COSMIC/FY3C RO, SWARM, and JASON were compared with data retrieved from GIMs. According to the results, COSMIC RO–VTEC is more consistent with GIM–VTEC on a quiet day (the mean of the differences is 4.38 TECU), while the mean of FY3C RO–GIM differences is 7.33 TECU on a geomagnetic active day. The range of VTEC differences between JASON and GIM is relatively smaller on a quiet day, and the mean of differences on active/quiet days is less than 6 TECU. Besides the daily comparison, long-term results (1 January–31 December 2015) were also analyzed by considering active and quiet periods. Results show that Root Mean Square Error (RMSE) values of COSMIC RO, FY3C RO, SWARM, and JASON are 5.02 TECU, 6.81 TECU, 16.25 TECU, and 5.53 TECU for the quiet period, and 5.21 TECU, 7.07 TECU, 17.48 TECU, and 5.90 TECU for the active period, respectively. The accuracy of each data source was affected by solar/geomagnetic activities. The deviation of SWARM–VTEC is relatively greater. The main reason for the significant differences in SWARM–GIM results is the atmospheric measurement range of SWARM satellites (460 km–20,200 km (SWARM A, C) and 520 km–20,200 km (SWARM B), which do not contain a significant part of the ionosphere in terms of VTEC estimation.

Comprehensive analysis of the equatorial ionization anomaly based on global ionospheric maps with a high spatiotemporal resolution

The Equatorial Ionization Anomaly (EIA), a manifestation of Ionospheric plasma irregularities, is closely linked to the equatorial fountain, influenced by solar activity and Earth’s geomagnetic field. While previous studies on EIA have predominantly focused on specific regional analyses using TEC data from GPS stations, revealing correlations between peaks, discrepancies with models, and temporal variations, there exists a gap in comprehensive global statistics, consideration of different solar activity years, and exploration of EIA characteristics across various local time scales. To address these, this study employs high spatiotemporal data from the Global Ionospheric Map (GIM) to comprehensively analyze EIA across different periods of the day during high solar activity in 2014 and low solar activity in 2020. The study delves into the subtle variations of EIA under different solar activity levels and seasonal conditions, focusing on changes in occurrence rate, intensity, and geomagnetic latitude positions concerning longitude, month, and local time. Our study reveals several findings: (1) EIA exhibited more frequent and intense trends during high solar activity years, with higher occurrence rates and intensity observed during the equinoxes; (2) EIA exhibits significant variations across different longitudes and local times, showing systematic patterns in its occurrence rates over time; (3) The crest-to-trough TEC Ratio (CTR) effectively characterizes nighttime EIA intensity, while the crest-to-trough TEC Difference (CTD) is suitable for representing daytime EIA intensity; (4) The temporal changes in EIA positions may be related to electric fields and wind patterns, while differences in longitudes are attributed to variations in the geomagnetic field.

An EOF-Based Global Plasmaspheric Electron Content Model and Its Potential Role in Vertical-Slant TEC Conversion

Topside total electron content (TEC) data measured by COSMIC/FORMAT-3 during 2008 and 2016 were used to analyze and model the global plasmaspheric electron content (PEC) above 800 km with the help of the empirical orthogonal function (EOF) analysis method, and the potential role of the proposed PEC model in helping Global Navigation Satellite System (GNSS) users derive accurate slant TEC (STEC) from existing high-precision vertical TEC (VTEC) products was validated. A uniform gridded PEC dataset was first obtained using the spherical harmonic regression method, and then, it was decomposed into EOF basis modes. The first four major EOF modes contributed more than 99% of the total variance. They captured the pronounced latitudinal gradient, longitudinal differences, hemispherical differences, diurnal and seasonal variations, and the solar activity dependency of global PEC. A second-layer EOF decomposition was conducted for the spatial pattern and amplitude coefficients of the first-layer EOF modes, and an empirical PEC model was constructed by fitting the second-layer basis functions related to latitude, longitude, local time, season, and solar flux. The PEC model was designed to be driven by whether solar proxy or parameters derived from the Klobuchar model meet the real-time requirements. The validation of the results demonstrated that the proposed PEC model could accurately simulate the major spatiotemporal patterns of global PEC, with a root-mean-square (RMS) error of 1.53 and 2.24 TECU, improvements of 40.70% and 51.74% compared with NeQuick2 model in 2009 and 2014, respectively. Finally, the proposed PEC model was applied to conduct a vertical-slant TEC conversion experiment with high-precision Global Ionospheric Maps (GIMs) and dual-frequency carrier phase observables of more than 400 globally distributed GNSS sites. The results of the differential STEC (dSTEC) analysis demonstrated the effectiveness of the proposed PEC model in aiding precise vertical-slant TEC conversion. It improved by 18.52% in dSTEC RMS on a global scale and performed better in 90.20% of the testing days compared with the commonly used single-layer mapping function.

Forecasting 24‐Hr Total Electron Content With Long Short‐Term Memory Neural Network

AbstractAn accurate prediction of the ionospheric state is important for correcting ionospheric propagation effects on Global Navigation Satellite Systems (GNSS) signals used in precise navigation and positioning applications. The main objective of the present work is to find a total electron content (TEC) model which gives a good estimate of ionospheric state not only during quiet but also during perturbed ionospheric conditions. For this, we implemented several long short‐term memory (LSTM)‐based models capable of predicting TEC up to 24 hr ahead. For the first time, we used the solar wind forcing parameters Wprot (a measure of the ionospheric disturbance during storm time) and Econv (measure of the solar wind parameters) as driver parameters. We found that using external drivers does not improve the accuracy of TEC predictions significantly. The final model is trained with data from the last two solar cycles using TEC from the rapid UQRG global ionosphere maps (GIMs). Data from the years 2015 and 2020 were excluded from the training data set and used for testing. The performance of the LSTM‐based TEC model is tested for near real‐time (RT) cases as well by using RT products (IRTG GIMs) as historical TEC inputs. We compared the performance of the LSTM‐based model to a quiet‐time feed forward neural network (FNN)‐based model and the Neustrelitz TEC model (NTCM). The results indicate that the LSTM‐based model proposed here is outperforming the FNN‐based model and NTCM in both cases, that is, using the UQRG or the IRTG GIMs as input for the historical TEC.

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model

In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5° longitude and 2.5° latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE’s 1-day forecast product c1pg, and comparable to CODE’s 2-day forecast c2pg. The model’s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

Analyzing the spatial variation characteristics of grid TEC using long-term GIM data

Global ionosphere maps (GIM), a grid model generated based on observations from global GNSS stations, is often used for ionospheric delay correction and total electron content (TEC) analysis, and is an important data for ionospheric modeling and code bias estimation, as well as related applications. Understanding the spatial variation characteristics of the grid TEC in GIM data can help the better use of GIM data. In this paper, the GIM data of CODE (Center for Orbit Determination in Europe) analysis center for a total of 15 years from 2008 to 2022 are used to analyze the variation of TEC of adjacent grid points and the difference of TEC of four grid points in each grid. The results show that the mean values of the grid TEC variations range from about 0.26–1.86 TECu in the longitude direction and 0.36–2.76 TECu in the latitude direction, and most of the differences in the TECs of neighboring grid points are within ± 2 TECu. In the low solar activity years, the STD of the TEC of the four grid points in most of the grids in the GIM data is less than 1.2 TECu, indicating that the TEC values of the four grid points in most of the grids in their GIMs are relatively close to each other and the differences are small, whereas the differences are larger in the high solar activity years. In other words, the TEC values of the areas covered by the GIM grids in the low solar activity years are close. The results of the study can provide some references and lessons for the interpolation and application of the TEC of the GIM grid, and the TEC modeling of the single-station area.