Years Of Solar Activity Research Articles

Impact of temporal resolution in global ionospheric models on satellite positioning during low and high solar activity years of solar cycle 24

Abstract The ionosphere, partially ionized by solar radiation, is rich in free electrons and ions, affecting satellite navigation signals by altering their speed and path. This interaction often leads to signal delays of 5–10 m, complicating accurate positioning in satellite-based systems. This paper investigates the influence of global ionospheric models (GIMs) with varying Temporal Resolutions (TR) on satellite positioning accuracy and convergence time under different solar activities, represented by the years 2009 (low solar activity) and 2014 (high solar activity). The study utilizes Global Positioning System (GPS) data from three GIMs: CODG, representing the Center for Orbit Determination in Europe (CODE) GNSS model with a 2-h TR; bcom, with a 1-h TR; and b5mg, with a 5-min TR. Analysis was conducted using the GNSS Analysis Software for Multi-constellation and Multi-frequency Precise Positioning across 46 international GNSS service stations under single and dual-frequency strategies. The results indicate that precise point positioning convergence time improved by approximately 18 % and 78 % using single and dual frequencies, depending on the GIM applied. Consequently, positioning accuracy after convergence improved by about 16 % and 27 % in the horizontal and up components for ionospheric-constrained single-frequency PPP models and by 68 % and 79 % in the horizontal and up components for dual-frequency PPP models. Furthermore, vertical total electron content analysis at the MARS station revealed significant variations correlating with solar activity, underscoring the importance of selecting appropriate GIMs for accurate GNSS positioning. Future studies, including multi-solar events, are recommended for comprehensive analysis.

A Lightweight Prediction Model for Global Ionospheric Total Electron Content Based on Attention-BiLSTM

The ionospheric total electron content (TEC) is a critical parameter for space weather and Global Navigation Satellite System (GNSS) applications. A Bidirectional Long Short-Term Memory neural network model with an added attention mechanism (BiLSTM-Attention) was developed in this study to predict 256 spherical harmonic coefficients (SHC). Input data for the forecasting model includes F10.7, Dst, and other feature parameters, along with a lightweight historical time series of SHC from the past day. The model output is the next day’s SHC. Then, we compare the results of SHC with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The correlation coefficient form model TEC with respect to the CODE TEC are 0.93 and 0.96 in 2018 and 2022, respectively, while the correlation coefficient of the 1-day CODE prediction model are 0.91 and 0.94. The results illustrate established model both in high and low solar activity years, and exhibiting enhanced robustness during geomagnetic storms. Furthermore, typical ionospheric structures such as Equatorial Ionization Anomaly (EIA) is well reproduced in the TEC prediction maps. Compared to C1PG, the proposed model offers a lighter computational load while maintaining competitive performance in global TEC prediction.

Magnetospheric response on impact of solar wind diamagnetic structures borne by eruptive prominence

We address the sequence of Sun-to-Earth phenomena, that enables to study the mechanism for geoefficiency of eruptive prominences propagating from the Sun inside coronal mass ejections (CMEs). An eruptive prominence ejected in the solar wind (SW) moves at the SW velocity Earthward like adiamagnetic structure of eruptive prominence (DSEP).The key feature of the latter is a largesharp plasma concentration jump N inside the DSEP at a simultaneous sharp drop in the interplanetary magnetic field (IMF) modulus B. It is the anti-correlation between the N and B profiles in DSEP, due to which its contact with the magnetosphere may lead not only to magnetosphere compression, but also to penetration of DSEP substance into the magnetosphere. The duration of the magnetospheric disturbance (in the form of dayside auroras), global increase in the current systems, charged particle flux enhancement in the radiation belts, and generation of the irregular Pi2-3 oscillations aredetermined by the DSEP size. We present statistical investigations into DSEPs observed in different years of solar activity and builta qualitative modelfor DSEP geoefficiency.

Evaluating solar-like behavior in turbulent alpha and Babcock-Leighton mechanisms using non-kinematic nonlinear flux-transport solar dynamos.

In this study, we simulate 30,000years of solar activity using turbulent-alpha (TA) and Babcock-Leighton (BL) mechanisms in a non-kinematic, nonlinear mean field flux-transport solar dynamo model. We evaluate their performances against observational data from proxies, like C, and direct solar observations. Both the TA and BL dynamos generate Schwabe-like variations, with the TA dynamo also generating periods that can be related to the QBOs and the Gleissberg cycle. The TA dynamo spends 13.3% (12.2%) of its time in a grand minimum (maximum) state, closely match the historical solar activity reconstructions from proxy records, while the BL dynamo underperforms. For the TA dynamo, the Schwabe cycle length variations during grand minima and the latitudinal and radial dependencies of the amplitude of variations both in Schwabe and QBO timescales align well with C data and solar observations, whereas the BL dynamo fails to reproduce these features.

Role of thermospheric winds on the generation of F-region irregularities over Indonesian sector during the solar minimum year 2020

Analysis of ionosonde observations over Chumpon (10.7°N, 99.4°E, 3.3° Mag. Lat.) and Kototabang (0.2° S, 100.3° E, 10° S Mag. Lat.) during the low solar activity year 2020 reveals that the occurrence of post-sunset spread-F maximises during winter (November-February), whereas that of post-midnight spread-F maximises during summer (May-August). The thermospheric winds observed by the Michelson Interferometer for Global High-Resolution Thermospheric Imaging (MIGHTI) on board the Ionospheric Connection Explorer (ICON) satellite over the Indonesian longitudes (95°E-115°E) are investigated to determine their impact on the generation of spread-F. The thermospheric winds at equatorial and low latitudes (10°N) are strongly eastward (> 50 m/s) around post-sunset hours (∼19:00 LST) in winter and equinox. The strong eastward wind is present only around midnight hours (after 22:00 LST) in summer. However, the present study reveals that the zonal wind does not contribute to the generation of plasma bubbles. Besides, the ICON-MIGHTI meridional wind at low latitude (10°N) is observed to be equatorward throughout the day during summer. Compared to the post-sunset hours, the intensity of trans-equatorial wind is stronger on the windward side (60–70 m/s) than on the leeward side (10–20 m/s) during midnight hours. The component of the equatorward meridional wind along the magnetic field lines can lift the F-layer to higher heights leading to a net decrease in the field line-integrated Pedersen conductivity thereby increasing the growth rate of the Rayleigh Taylor instability that can result in the generation of post-midnight spread F.

Modeling and Forecasting Ionospheric foF2 Variation Based on CNN-BiLSTM-TPA during Low- and High-Solar Activity Years

The transmission of high-frequency signals over long distances depends on the ionosphere’s reflective properties, with the selection of operating frequencies being closely tied to variations in the ionosphere. The accurate prediction of ionospheric critical frequency foF2 and other parameters in low latitudes is of great significance for understanding ionospheric changes in high-frequency communications. Currently, deep learning algorithms demonstrate significant advantages in capturing characteristics of the ionosphere. In this paper, a state-of-the-art hybrid neural network is utilized in conjunction with a temporal pattern attention mechanism for predicting variations in the foF2 parameter during high- and low-solar activity years. Convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), which is capable of extracting spatiotemporal features of ionospheric variations, are incorporated into a hybrid neural network. The foF2 data used for training and testing come from three observatories in Brisbane (27°53′S, 152°92′E), Darwin (12°45′S, 130°95′E) and Townsville (19°63′S, 146°85′E) in 2000, 2008, 2009 and 2014 (the peak or trough years of solar activity in solar cycles 23 and 24), using the advanced Australian Digital Ionospheric Sounder. The results show that the proposed model accurately captures the changes in ionospheric foF2 characteristics and outperforms International Reference Ionosphere 2020 (IRI-2020) and BiLSTM ionospheric prediction models.

On equatorial spread F occurrence: A multi-dimensional quantitative assessment

The present study investigates the role of gravity wave induced seed perturbations in the occurrence of Equatorial Spread F (ESF) under the influence of the post sunset background conditions modulated by prevailing electrodynamics and neutral wind. Ionospheric foF2 data sets over geomagnetic equatorial station Trivandrum (8.5°N, 77°E and magnetic dip 0.68°N-corresponding to the period of study) corresponding to vernal and autumnal equinoctial periods encompassing high, low and moderate solar activity years, are used for the study. Meridional wind data is obtained either from ESA’s sun-synchronous satellite GOCE (Gravity field and steady-state Ocean Circulation Explorer) or derived using ionosonde h’F (base height of ionosphere at 2.5 MHz) data from Trivandrum (TVM- 8.5°N, 77°E and magnetic dip 0.68°N) and Sriharikota (SHAR−13.7°N, 80.2°E and magnetic dip 6.9°N-for period of study). This particular study is carried out for geomagnetically quiet days of Vernal Equinox (VE) and Autumnal Equinox (AE) seasons, which are most favoured for ESF occurrence over Indian longitudes. Considering thermospheric wind, ion-neutral collisions, and electric field effects in association with gravity wave seed, threshold curve is generated, which clearly demarcates ESF and NSF (Non spread F) days. Previous studies have addressed ESF variability in electrodynamical domain alone (wherein the layer is above a threshold level). The present study, for the first time, succeeds in demarcating ESF and NSF days by incorporating effects of electric field, neutral wind, collisional RT instability term, and gravity wave seed perturbations simultaneously irrespective of threshold height.

Investigation of the global climatologic performance of ionospheric models utilizing in-situ Swarm satellite electron density measurements

The Swarm constellation is a triplet of satellites, flying, since their final configuration reached in April 2014, at altitudes of about 420 to 490 km (the lower pair) and about 500 to 530 km (the upper satellite). All the three satellites provide in-situ measurements of the plasma density in the topside ionosphere using Langmuir Probe sensors onboard the Electrical Field Instrument. The present study is a comprehensive investigation into the climatologic performance of three ionospheric models when compared to the Swarm satellite in-situ measurements. The models are the International Reference Ionosphere (IRI) model, a quick run ionospheric electron density model (NeQuick), and a 3-dimensional electron density model based on artificial neural network training of COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) satellites radio occultation measurements (3D-NN). The mean monthly quiet-time latitudinal profile of Swarm measurements was computed by binning the Swarm electron density measurements in 15-degree longitudes starting from longitude −180° in steps of 15° to 180°, and corresponding model predictions were obtained. The data used in the study covers the years 2014, 2016, 2019, and 2022, capturing various phases of the solar activity cycle. Results from the study show that modelled electron density predictions from all three climatologic models are fairly good representations of the Swarm satellite measurements, with some exceptions in which the models underestimate or overestimate the Swarm satellite values. The IRI model performed best at the northern hemisphere mid latitude, and it overestimated the Swarm measurements at altitudes of ∼ 450 km, especially at the southern hemisphere mid and high latitudes. The NeQuick performed best during the night times, and it overestimated the Swarm measurements, especially at the mid latitudes. The NeQuick was also observed to overestimate the Swarm measurements during the winter solstices at both hemispheres, which is June solstice in the southern hemisphere and December solstice in the northern hemisphere. Overall, the 3D-NN model most often performed better than the IRI model and the NeQuick, especially during the day times and during the high solar activity year (2014), but it underestimated the Swarm measurements, especially at the low and mid latitudes. For all categories explored in the study, the 3D-NN consistently performed better than the other two models. The NeQuick performed better than the IRI model at altitude of satellite B, while the IRI model performed slightly better than the NeQuick at altitude of satellites A and C. The NeQuick also performed better than the IRI model in the local time category, whereas the IRI model performed better than the NeQuick in categories of season, solar activity, and longitudinal sector.

Evaluation of GNSS-TEC Data-Driven IRI-2016 Model for Electron Density

The ionosphere is one of the important error sources that affect the communication of radio signals. The international reference ionosphere (IRI) model is a commonly used model to describe ionospheric parameters. The driving parameter IG12 of the IRI-2016 model was optimally updated based on GNSS-TEC data from 2015 and 2019. The electron density profiles and NmF2 calculated by the IRI-2016 model (upda-IRI-2016) driven by the updated IG12 value (IG-up) were evaluated for their accuracy using ionosonde observations and COSMIC inversion data. The experiments show that both the electron density profiles and NmF2 calculated by upda-IRI-2016 driven by IG-up show significant optimization effects, compared to the IRI-2016 model driven by IG12. For electron density, the precision improvement (PI) for both MAE and RMSE at the Beijing station exceed 31.2% in January 2015 and 16.0% in January 2019. While the PI of MAE and RMSE at the Wuhan station, which is located at a lower latitude, both exceed 32.5% in January 2015, both exceed 42.1% in January 2019, which is significantly higher than that of the Beijing station. In 2015, the PI of MAE and RMSE compared with COSMIC are both higher than 20%. For NmF2, the PI is greater for low solar activity years and low latitude stations, with the Wuhan station showing a PI of more than 11.7% in January 2019 compared to January 2015. The PI compared to COSMIC was higher than 17.2% in 2015.

Global ionospheric total electron content short-term forecast based on Light Gradient Boosting Machine, Extreme Gradient Boosting, and Gradient Boost Regression

Total Electron Content (TEC) forecasting using machine learning has been extensively preferred in characterizing the spatio-temporal variability of the ionosphere, to support space communication and navigation applications. Although, a variety of machine learning methods have been evolved, still, there is a greater persistence in the prediction of ionospheric TEC due to adverse space weather impacts and the complex behavior of ionosphere. And hence, more sophisticated short-term ionospheric TEC forecasting models should be developed for better prediction accuracy. The present study investigated and evaluated the performance of three models – Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boost Regression (GBR) algorithms using the data set for 25 years (1997–2021) GNSS Earth Observation Network System (GEONET) Global mean TEC time-series data. For better prediction accuracy, the data of geomagnetic storm indices Dst and Mg-II index were also utilized in training the three models. One year of data for both high (2015) and low (2020) solar activity were used to test the three models. The prediction plots are used to visualize the level of prediction error for the three algorithms. The statistical results illustrate that the LightGBM model performed better in predicting TEC with an RMSE value of 2.25 TECU (2015) and 0.52 TECU (2020) for high and low solar activity years respectively for global mean TEC data and with an RMSE of 2.34 TECU at Japan grid point location of (34.95°N and 134.05°E). In turn the XGBoost and GBR models provide an absolute TEC forecasting with an RMSE of 2.76 and 2.59 TECU (2015) and 0.60 and 0.55 TECU (2020) correspondingly for global data and as 2.39 and 2.93 TECU for grid point location.

MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction

AbstractThe high‐precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine‐grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper‐parameters of TEC prediction models. Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA‐Residual‐Attitude‐BiConvLSTM. This framework includes a TEC prediction model, Residual‐Attention‐BiConvLSTM, which can simultaneously consider both coarse‐grained and fine‐grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED‐ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models.

Моделирование влияния вариаций солнечной активности на глобальную атмосферную циркуляцию

In this study, we continue a series of works devoted to modeling and studying the sensitivity of global atmospheric dynamic processes to variations in solar emissions during the 11-year solar activity (SA) cycle. We focus on studying the response of meridional atmospheric circulation in the middle atmosphere to changes in thermosphere characteristics with changes in SA. For this purpose, numerical simulations of the general atmospheric circulation were carried out using the nonlinear numerical circulation model of the middle and upper atmosphere MUAM. The main mechanism of the influence of thermospheric disturbances on the underlying layers is assumed to be a change in the conditions of propagation and reflection of planetary waves (PWs) due to changes in SA. Changes in temperature, zonal and meridional wind are shown to localize along waveguides, demonstrating the significant role of PWs in transmitting thermospheric disturbances, caused by changes in SA, to the middle atmosphere. The magnitude of changes in meridional circulation can reach 10 % in the northern stratosphere between SA maxima and minima.

Modeling the impact of solar activity variations on global atmospheric circulation

In this study, we continue a series of works devoted to modeling and studying the sensitivity of global atmospheric dynamic processes to variations in solar emissions during the 11-year solar activity (SA) cycle. We focus on studying the response of meridional atmospheric circulation in the middle atmosphere to changes in thermosphere characteristics with changes in SA. For this purpose, numerical simulations of the general atmospheric circulation were carried out using the nonlinear numerical circulation model of the middle and upper atmosphere MUAM. The main mechanism of the influence of thermospheric disturbances on the underlying layers is assumed to be a change in the conditions of propagation and reflection of planetary waves (PWs) due to changes in SA. Changes in temperature, zonal and meridional wind are shown to localize along waveguides, demonstrating the significant role of PWs in transmitting thermospheric disturbances, caused by changes in SA, to the middle atmosphere. The magnitude of changes in meridional circulation can reach 10 % in the northern stratosphere between SA maxima and minima.

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.

Seasonal and Daily Features of the Characteristics of Medium-Scale Traveling Ionospheric Disturbances in Asian Russia in Years of Moderate Solar Activity

Seasonal and Daily Features of the Characteristics of Medium-Scale Traveling Ionospheric Disturbances in Asian Russia in Years of Moderate Solar Activity

Reduced‐Order Probabilistic Emulation of Physics‐Based Ring Current Models: Application to RAM‐SCB Particle Flux

AbstractIn this work, we address the computational challenge of large‐scale physics‐based simulation models for the ring current. Reduced computational cost allows for significantly faster than real‐time forecasting, enhancing our ability to predict and respond to dynamic changes in the ring current, valuable for space weather monitoring and mitigation efforts. Additionally, it can also be used for a comprehensive investigation of the system. Thus, we aim to create an emulator for the Ring current‐Atmosphere interactions Model with Self‐Consistent magnetic field (RAM‐SCB) particle flux that not only improves efficiency but also facilitates forecasting with reliable estimates of prediction uncertainties. The probabilistic emulator is built upon the methodology developed by Licata and Mehta (2023), https://doi.org/10.1029/2022sw003345. A novel discrete sampling is used to identify 30 simulation periods over 20 years of solar and geomagnetic activity. Focusing on a subset of particle flux, we use Principal Component Analysis for dimensionality reduction and Long Short‐Term Memory (LSTM) neural networks to perform dynamic modeling. Hyperparameter space was explored extensively resulting in about 5% median symmetric accuracy across all data sets for one‐step dynamic prediction. Using a hierarchical ensemble of LSTMs, we have developed a reduced‐order probabilistic emulator (ROPE) tailored for time‐series forecasting of particle flux in the ring current. This ROPE offers accurate predictions of omnidirectional flux at a single energy with no pitch angle information, providing robust predictions on the test set with an error score below 11% and calibration scores under 8% with bias under 2% providing a significant speed up as compared to the full RAM‐SCB run.

Exploring ionospheric plasma density trends in the Indian equatorial crest region under varying solar activity conditions

Long-term trends in the evolution of ionospheric plasma at Hyderabad (17.38∘N, 78.48∘E; 8.52∘N Magnetic Latitude), a near-equatorial anomaly (EIA) crest region of the Indian ionospheric sector, have been studied using 9 years of vertical Total Electron Content (TEC) data from 2004 to 2009 and 2011 to 2013 using global positioning satellites (GPS) measurements. The study examined the mean diurnal, monthly, seasonal, and yearly variations of TEC during geomagnetic quiet days in different seasons from 2004 to 2013. The findings reveal that the daytime TEC at the anomaly crest region exhibits semi-annual variations throughout the study period, while midnight TEC shows semi-annual variation only during the high solar activity years of 2011–2013. The winter anomaly was observed in 2004 and 2006. The study also assessed the performance of the International Reference Ionosphere (IRI) 2016 model in reproducing GPS TEC variability at the equatorial crest region. The diurnal and seasonal variation patterns in IRI-TEC show a good correlation with GPS TEC. However, the IRI 2016 model tends to overestimate TEC values during low solar activity conditions (2006–2009) but represents TEC variations reasonably well during high solar activity periods (2011–2013). Nevertheless, the IRI model fails to capture the wide plateau-like structure in the peak TEC, typically occurring between 1200–1600 IST at Hyderabad. Additionally, IRI-TEC consistently indicates very low TEC values during the early morning hours, whereas GPS-TEC measurements suggest a significant presence of plasma density. The study suggests a strong influence of the solar cycle on TEC variations at Hyderabad, evident from the positive correlation (R2= 0.71) with the F10.7 cm index. This characteristic is also well represented by the IRI 2016 model.

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.

Forecasting ionospheric TEC using least squares support vector machine and moth-flame optimization methods in China

The total electron content (TEC) of the ionosphere is an important parameter to describe the ionosphere, and it is a great significance to monitor and predict it accurately. In this paper, a hybrid ionospheric TEC prediction model based on the least squares support vector machine (LSSVM) and the Moth-Flame Optimization (MFO) algorithm is proposed. The parameters of the LSSVM model are optimized by the MFO algorithm. We use observation data of 15 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to extract ionospheric TEC from 2012 to 2019. The ionospheric TEC is forecasted using solar and geomagnetic activity indices in both the low solar activity year (2019) and the high solar activity year (2015). The results show that the prediction performance of the MFO_LSSVM model is significantly better than that of the IRI model, SVM model, and LSSVM model. Compared with the other three models, there are more stable prediction results in the low and high solar activity years. At the same time, the predicted value of the MFO_LSSVM model has a good correlation with the measured value, and it also has good prediction potential in areas with active geomagnetic activity. The comparison with the Long Short-Term Memory (LSTM) model shows that the MFO_LSSVM model has better performance than the single LSTM model. In conclusion, the MFO_LSSVM model can accurately predict ionospheric TEC in China, and has better accuracy than traditional long-term and short-term models.

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.