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The Hunga-Tonga volcanic eruption on 15 January 2022, produced significant atmospheric and ionospheric disturbances that may degrade global navigation satellite system (GNSS) and precise point positioning (PPP) accuracy. Using data from the GEONET GNSS network and Soratena barometric pressure sensors across Japan, we analyzed the eruption’s effects through the gradient ionospheric index (GIX) and the rate of TEC index (ROTI) to characterize the propagation and effects of these disturbances on ionospheric total electron content (TEC) gradients. Our analysis identified two separate ionospheric disturbance events. The first event, coinciding with the arrival of atmospheric Lamb waves, was characterized by wave-like pressure anomalies, differential TEC (dTEC) fluctuations, and modest horizontal gradients of vertical TEC (VTEC). In contrast, the second, more pronounced disturbance was driven by equatorial plasma bubbles (EPBs), which generated severe ionospheric irregularities and large TEC gradients. Further analysis revealed that these two disturbances had markedly different impacts on GNSS positioning accuracy. The Lamb wave–induced disturbance mainly caused moderate TEC fluctuations with limited effects on positioning accuracy, and mid-latitude stations maintained both average and 95th percentile positioning (ppp,P95) errors below 0.1 m throughout the event. In contrast, the EPB-driven disturbance had a substantial impact on low-latitude regions, where the average horizontal PPP error peaked at 0.5 m and the horizontal and vertical ppp,P95 errors exceeded 1 m. Our findings reveal two episodes of spatial-gradient enhancement and successfully estimate the propagation speed and direction of the Lamb waves, supporting the potential application of ionospheric gradient monitoring in forecasting GNSS performance degradation.

The purpose of this paper is to discuss the lithosphere–atmosphere–ionosphere coupling (LAIC) effects with the use of multiparameter precursor observations for two successive Japanese earthquakes (EQs) (with a magnitude of around 7) in February and March 2021, respectively, considering a seemingly significant difference in seismological and geological hypocenter conditions for those EQs. The second March EQ is very similar to the famous 2011 Tohoku EQ in the sense that those EQs took place at the seabed of the subducting plate, while the first February EQ happened within the subducting plate, not at the seabed. Multiparameter observation is a powerful tool for the study of the LAIC process, and we studied the following observables over a 3-month period (January to March): (i) ULF data (lithospheric radiation and ULF depression phenomenon); (ii) ULF/ELF atmospheric electromagnetic radiation; (iii) atmospheric gravity wave (AGW) activity in the stratosphere, extracted from satellite temperature data; (iv) subionospheric VLF/LF propagation data; and (v) GPS TECs (total electron contents). In contrast to our initial expectation of different responses of anomalies to the two EQs, we found no such conspicuous differences of electromagnetic anomalies between the two EQs, but showed quite similar anomaly responses for the two EQs. It is definite that atmospheric ULF/ELF radiation and ULF depression as lower ionospheric perturbation are most likely signatures of precursors to both EQs, and most importantly, all electromagnetic anomalies are concentrated in the period of about 1 week–9 days before the EQ to the EQ day. There seems to exist a chain of LAIC process (cause-and-effect relationship) for the first EQ, while all of the observed anomalies seem to occur nearly synchronously in time for the send EQ. Even though we tried to discuss possible LAIC channels, we cannot come to any definite conclusion about which coupling channel is plausible for each EQ.

Space weather describes varying conditions between the Sun and Earth that can degrade Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be precisely and timely corrected for accurate and reliable GNSS applications. That can be modeled with the Vertical Total Electron Content (VTEC) in the Earth’s ionosphere. This study investigates different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for low-, mid- and high-latitude ionospheric grid points along the same longitude. VTEC models are developed using learning algorithms of Decision Tree and ensemble learning of Random Forest, Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost). Furthermore, ensemble models are combined into a single meta-model Voting Regressor. Models were trained, optimized, and validated with the time series cross-validation technique. Moreover, the relative importance of input variables to the VTEC forecast is estimated. The results show that the developed models perform well in both quiet and storm conditions, where multi-tree ensemble learning outperforms the single Decision Tree. In particular, the meta-estimator Voting Regressor provides mostly the lowest RMSE and the highest correlation coefficients as it averages predictions from different well-performing models. Furthermore, expanding the input dataset with time derivatives, moving averages, and daily differences, as well as modifying data, such as differencing, enhances the learning of space weather features, especially over a longer forecast horizon.

A new observational phenomenon, named Simultaneous Global Ionospheric Density Disturbance (SGD), is identified in GNSS total electron content (TEC) data during periods of three typical geospace disturbances: a Coronal Mass Ejection‐driven severe disturbance event, a high‐speed stream event, and a minor disturbance day with a maximum Kp of 4. SGDs occur frequently on dayside and dawn sectors, with a ∼1% TEC increase. Notably, SGDs can occur under minor solar‐geomagnetic disturbances. SGDs are likely caused by penetration electric fields (PEFs) of solar‐geomagnetic origin, as they are associated with Bz southward, increased auroral AL/AU, and solar wind pressure enhancements. These findings offer new insights into the nature of PEFs and their ionospheric impact while confirming some key earlier results obtained through alternative methods. Importantly, the accessibility of extensive GNSS networks, with at least 6,000 globally distributed receivers for ionospheric research, means that rich PEF information can be acquired, offering researchers numerous opportunities to investigate geospace electrodynamics. Plain Language Summary Electric fields of solar wind and geomagnetic disturbance origin can penetrate into the low latitude upper atmosphere, influencing the ionospheric dynamics and electron density variations. This study employs a new method that utilizes global and continuous GNSS total electron content (TEC) observations to investigate the electric field effects. The analysis focuses on three geospace disturbance events of different intensities and solar‐terrestrial conditions. The study identifies a novel phenomenon named Simultaneous Global Ionospheric Density Disturbance (SGD), primarily occurring on the sunlit portion of the Earth's ionosphere and also near dawn hours with 1% or larger amplitudes of the background TEC, or a few tenths of a TEC unit (1016 m3). The remarkable global extent of ionospheric responses to minor solar‐geomagnetic conditions is noteworthy. The solar wind magnetic field directed southward is highly correlated with most SGDs, lasting for up to 30 min. The findings present an effective approach for continuously monitoring electric field penetration and ionospheric impacts, leading to an improved understanding of space weather and its technological implications. Key Points Simultaneous global ionospheric disturbances (SGDs) are often observed even during minor solar and geomagnetic disturbances SGDs occur predominately on dayside and are related to penetration electric fields (PEFs) of solar wind and geomagnetic disturbance origin Global GNSS networks offer a novel and effective technique for continuous PEF monitoring, providing rich data sets for further study

In this work, an investigation was conducted to study the influence of the geomagnetic storms on the Earth’s upper atmosphere (ionosphere layer). For this purpose, a strong geomagnetic storm with a Dst value of -147 nT that occurred on October 25, 2011 was selected as the case study for this investigation. Also, the ionospheric total electron content (TEC) parameter was adopted to examine the impact of the tested geomagnetic storm on the ionosphere layer. Four stations located in different mid-latitude regions in the northern and southern hemispheres were designated; the two selected northern mid-latitude stations are: Millstone Hill (42.6 N, 288.50 W) and Rome (41.90 N, 12.50 E) whereas the other two selected southern mid-latitude stations are: Port Stanley (-51.60 S, 302.10 W) and Grahamstown (-33.30 S, 26.50 E). The analysis was conducted by examining the behaviour and variations of the TEC parameter over a period of five days; these are the event day and two days preceding and succeeding the event’s day. Also, the relative deviation of the TEC parameter D(TEC)% for each station during the study period was calculated. The outcomes of the conducted investigation showed that there is a prominent influence of the tested storm on the behaviour of the TEC parameter over the Millstone Hill, Port Stanley, and Grahamstown stations by noting a decrease in the TEC parameter values during the day of the event, with the exception of the Rome station, which showed slight disturbances during daytime hours. In contrast, the calculated relative deviation of the TEC parameter showed a clear negative deviation in the TEC over all stations except Rome, which showed only a positive deviation in the parameter values.

In this paper, the prediction model for ionospheric total electron content (TEC) based on Long Short-Term Memory (LSTM) deep learning network and its performance are discussed. The input parameters of the model are previous values of daily TEC, solar radio flux at 10.7 cm parameter of 81 day moving average (F107_81‾), sunspot number (SSN), geomagnetic Kp index, and disturbance storm time (Dst) index, and the outputs are TEC values for the target day. TEC data from January 1, 2001 to December 31, 2016 were used in this study. The dataset almost covers most of the years of the last two solar cycles (23, 24), and it is separated as 81.3% for training, 6.2% for validation, and 12.5% for testing. At BJFS IGS station (39.61° N, 115.89° E), LSTM yielded good TEC estimates with an RMSE of 4.07 TECU in 2001, it was 33% and 48% lower than the RMSE observed in TEC prediction using BP and IRI-2016 models, respectively. In the year of low solar activity (2016), the RMSE predicted by LSTM was 1.78 TECU, it provided 30% and 54% lower RMSE for TEC prediction than for BP and IRI-2016 models. Under the condition of magnetic storm, the LSTM TEC predictions are more consistent with the corresponding IGS Global Ionospheric Maps (GIMs) TEC than TEC predictions by BP and IRI-2016 models. LSTM can better grasp the influence of different external conditions on TEC. Seventeen grid points along 120° E meridian in latitude range from 80° S to 80° N were selected to further study the performance of LSTM model in different latitude. Results show that the prediction accuracy of LSTM is better than that of BP at different latitudes, especially at low latitudes. The performances of the two models are highly correlated with latitude and solar activity, and are both better than that of IRI-2016.

We introduce a novel empirical model to forecast, 24 h in advance, the Total Electron Content (TEC) at global scale. The technique leverages on the Global Ionospheric Map (GIM), provided by the International GNSS Service (IGS), and applies a nonlinear autoregressive neural network with external input (NARX) to selected GIM grid points for the 24 h single-point TEC forecasting, taking into account the actual and forecasted geomagnetic conditions. To extend the forecasting at a global scale, the technique makes use of the NeQuick2 Model fed by an effective sunspot number R12 (R12eff), estimated by minimizing the root mean square error (RMSE) between NARX output and NeQuick2 applied at the same GIM grid points. The novel approach is able to reproduce the features of the ionosphere especially during disturbed periods. The performance of the forecasting approach is extensively tested under different geospatial conditions, against both TEC maps products by UPC ( Universitat Politècnica de Catalunya ) and independent TEC data from Jason-3 spacecraft. The testing results are very satisfactory in terms of RMSE, as it has been found to range between 3 and 5 TECu. RMSE depend on the latitude sectors, time of the day, geomagnetic conditions, and provide a statistical estimation of the accuracy of the 24-h forecasting technique even over the oceans. The validation of the forecasting during five geomagnetic storms reveals that the model performance is not deteriorated during disturbed periods. This 24-h empirical approach is currently implemented on the Ionosphere Prediction Service (IPS), a prototype platform to support different classes of GNSS users.

In this paper, we forecasted the ionospheric total electron content (TEC) over the region of China using the bidirectional convolutional gated recurrent unit (BiConvGRU) model. We first generated the China Regional Ionospheric Maps (CRIMs) using GNSS observations provide by the Crustal Movement Observation Network of China (CMONOC). We then used gridded TEC maps from 2015 to 2018 with a 1 h interval from the CRIMs as the dataset, including quiet periods and storm periods of ionospheric TEC. The BiConvGRU model was then utilized to forecast the ionospheric TEC across China for the year 2018. The forecasted TEC was compared with the TEC from the International Reference Ionosphere (IRI-2016), Convolutional Long Short-Term Memory (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM), and the 1-day Predicted Global Ionospheric Map (C1PG) provided by the Center for Orbit Determination in Europe (CODE). In addition, indices including Kp, ap, Dst and F10.7 were added to the training dataset to improve the forecasting accuracy of the model (-A indicates no indices, while -B indicates with indices). The results verified that the prediction accuracies of the models integrated with these indices were significantly improved, especially during geomagnetic storms. The BiConvGRU-B model presented a decrease of 41.5%, 22.3%, and 13.2% in the root mean square error (RMSE) compared to the IRI-2016, ConvGRU, and BiConvLSTM-B models during geomagnetic storm days. Furthermore, at a specific grid point, the BiConvGRU-B model showed a decrease of 42.6%, 49.1%, and 31.9% in RMSE during geomagnetic quiet days and 30.6%, 34.1%, and 15.1% during geomagnetic storm days compared to the IRI-2016, C1PG, and BiConvLSTM-B models, respectively. In the cumulative percentage analysis, the BiConvGRU-B model had a significantly higher percentage of mean absolute error (MAE) within the range of 0–1 TECU in all seasons compared to the BiConvLSTM-B model. Meanwhile, the BiConvGRU-B model outperformed the BiConvLSTM-B model with lower RMSE for each month of 2018.

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.

An accurate estimation of ionospheric variables such as the total electron content (TEC) is important for many space weather, communication, and satellite geodetic applications. Empirical and physics-based models are often used to determine TEC in these applications. However, it is known that these models cannot reproduce all ionospheric variability due to various reasons such as their simplified model structure, coarse sampling of their inputs, and dependencies to the calibration period. Bayesian-based data assimilation (DA) techniques are often used for improving these model’s performance, but their computational cost is considerably large. In this study, first, we review the available DA techniques for upper atmosphere data assimilation. Then, we will present an empirical decomposition-based data assimilation (DDA), based on the principal component analysis and the ensemble Kalman filter. DDA considerably reduces the computational complexity of previous DA implementations. Its performance is demonstrated by updating the empirical orthogonal functions of the empirical NeQuick and the physics-based TIEGCM models using the rapid global ionosphere map (GIM) TEC products as observation. The new models, respectively, called ‘DDA-NeQuick’ and ‘DDA-TIEGCM,’ are then used to predict TEC values for the next day. Comparisons of the TEC forecasts with the final GIM TEC products (that are available after 11 days) represent an average 42.46% and 31.89% root mean squared error (RMSE) reduction during our test period, September 2017.