Showing papers on "Total electron content published in 2022"

Electromagnetic conjugacy of ionospheric disturbances after the 2022 Hunga Tonga-Hunga Ha’apai volcanic eruption as seen in GNSS-TEC and SuperDARN Hokkaido pair of radars observations

Abstract: Abstract To elucidate the characteristics of electromagnetic conjugacy of traveling ionospheric disturbances just after the 15 January 2022 Hunga Tonga-Hunga Ha’apai volcanic eruption, we analyze Global Navigation Satellite System-total electron content data and ionospheric plasma velocity data obtained from the Super Dual Auroral Radar Network Hokkaido pair of radars. Further, we use thermal infrared grid data with high spatial resolution observed by the Himawari 8 satellite to identify lower atmospheric disturbances associated with surface air pressure waves propagating as a Lamb mode. After 07:30 UT on 15 January, two distinct traveling ionospheric disturbances propagating in the westward direction appeared in the Japanese sector with the same structure as those at magnetically conjugate points in the Southern Hemisphere. Corresponding to these traveling ionospheric disturbances with their large amplitude of 0.5 – 1.1 × 10 16 el/m 2 observed in the Southern Hemisphere, the plasma flow direction in the F region changed from southward to northward. At this time, the magnetically conjugate points in the Southern Hemisphere were located in the sunlit region at a height of 105 km. The amplitude and period of the plasma flow variation are ~ 100–110 m/s and ~ 36–38 min, respectively. From the plasma flow perturbation, a zonal electric field is estimated as ~ 2.8–3.1 mV/m. Further, there is a phase difference of ~ 10–12 min between the total electron content and plasma flow perturbations. This result suggests that the external electric field variation generates the traveling ionospheric disturbances observed in both Southern and Northern Hemispheres. The origin of the external electric field is an E-region dynamo driven by the neutral wind oscillation associated with atmospheric acoustic waves and gravity waves. Finally, the electric field propagates to the F region and magnetically conjugate ionosphere along magnetic field lines with the local Alfven speed, which is much faster than that of Lamb mode waves. From these observational facts, it can be concluded that the E-region dynamo electric field produced in the sunlit Southern Hemisphere is a main cause of the two distinct traveling ionospheric disturbances appearing over Japan before the arrival of the air pressure disturbances. Graphical Abstract

Pronounced Suppression and X‐Pattern Merging of Equatorial Ionization Anomalies After the 2022 Tonga Volcano Eruption

Abstract: Following the 2022 Tonga Volcano eruption, dramatic suppression and deformation of the equatorial ionization anomaly (EIA) crests occurred in the American sector ∼14,000 km away from the epicenter. The EIA crests variations and associated ionosphere-thermosphere disturbances were investigated using Global Navigation Satellite System total electron content data, Global-scale Observations of the Limb and Disk ultraviolet images, Ionospheric Connection Explorer wind data, and ionosonde observations. The main results are as follows: (a) Following the eastward passage of expected eruption-induced atmospheric disturbances, daytime EIA crests, especially the southern one, showed severe suppression of more than 10 TEC Unit and collapsed equatorward over 10° latitudes, forming a single band of enhanced density near the geomagnetic equator around 14–17 UT, (b) Evening EIA crests experienced a drastic deformation around 22 UT, forming a unique X-pattern in a limited longitudinal area between 20 and 40°W. (c) Thermospheric horizontal winds, especially the zonal winds, showed long-lasting quasi-periodic fluctuations between ±200 m/s for 7–8 hr after the passage of volcano-induced Lamb waves. The EIA suppression and X-pattern merging was consistent with a westward equatorial zonal dynamo electric field induced by the strong zonal wind oscillation with a westward reversal.

Significant Ionospheric Hole and Equatorial Plasma Bubbles After the 2022 Tonga Volcano Eruption

Abstract: This paper investigates the local and global ionospheric responses to the 2022 Tonga volcano eruption, using ground-based Global Navigation Satellite System (GNSS) total electron content (TEC), Swarm in-situ plasma density measurements, the Ionospheric Connection Explorer (ICON) Ion Velocity Meter (IVM) data, and ionosonde measurements. The main results are as follows: (1) A significant local ionospheric hole of more than 10 TECU depletion was observed near the epicenter ∼45 min after the eruption, comprising of several cascading TEC decreases and quasi-periodic oscillations. Such a deep local plasma hole was also observed by space-borne in-situ measurements, with an estimated horizontal radius of 10–15° and persisted for more than 10 hours in ICON-IVM ion density profiles until local sunrise. (2) Pronounced post-volcanic evening equatorial plasma bubbles (EPBs) were continuously observed across the wide Asia-Oceania area after the arrival of volcano-induced waves; these caused a Ne decrease of 2–3 orders of magnitude at Swarm/ICON altitude between 450-575 km, covered wide longitudinal ranges of more than 140°, and lasted around 12 hours. (3) Various acoustic-gravity wave modes due to volcano eruption were observed by accurate Beidou geostationary orbit (GEO) TEC, and the huge ionospheric hole was mainly caused by intense shock-acoustic impulses. TEC rate of change index revealed globally propagating ionospheric disturbances at a prevailing Lamb-wave mode of ∼315 m/s; the large-scale EPBs could be seeded by acoustic-gravity resonance and coupling to less-damped Lamb waves, under a favorable condition of volcano-induced enhancement of dusktime plasma upward E×B drift and postsunset rise of the equatorial ionospheric F-layer.

Prediction of Global Ionospheric TEC Based on Deep Learning

Abstract: The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS-TEC maps are established based on testing several different long short-term memory (LSTM) network (LSTM)-based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi-step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS-TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time-shift algorithm of IGS-TEC. The result suggests that the Multi-step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time.

An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network

Abstract: Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R2 of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions.

Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events

Abstract: There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

Abstract: 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.

Ionospheric–Thermospheric Responses in South America to the August 2018 Geomagnetic Storm Based on Multiple Observations

Abstract: The ionospheric storm time responses during August 2018 are investigated over South American region using multiple observables, for example, Global Navigation Satellite System (GNSS) derived vertical total electron content (VTEC) from International GNSS Service, magnetic field data, geomagnetic indices, global ionospheric maps, thermospheric mass density (TMD), and [O/N2] ratio measurement. Strong-ionospheric and upper-atmospheric disturbances affected the ionospheric variables with long duration during the storm recovery phase and following after. First, daytime VTEC (9:00–20:00 UT) presented variations of >15 TECU during days 25 to 30 of August 2018 in low and middle latitudes of South America, this after sudden storm commencement (SSC). Furthermore, nighttime (21:00–24:00 and 00:00–05:00 UT) VTEC presented low values (58 TECU) in the recovery phase. Second, the ionospheric values during the storm main phase and following after, at low- and mid-latitudes, caused the equatorial ionization anomaly to expand due to prompt penetration electric field. Furthermore, VTEC enhancements are likely to occur few hours after the SSC of 25 August 2018, while enhancements of TMD and [O/N2] ratio started to appear later on 26 and 27 of August 2018.

Swarm-TEC Satellite Measurements as a Potential Earthquake Precursor Together With Other Swarm and CSES Data: The Case of Mw7.6 2019 Papua New Guinea Seismic Event

Abstract: On May 14, 2019, a strong Mw = 7.6 shallow earthquake occurred in Papua New Guinea. This paper explores for the first time the analysis of total electron content (TEC) products measured for 6 months by GPS antenna onboard Swarm satellites, to detect possible seismo-ionospheric anomalies around the time and location of the above-mentioned earthquake. The night-time vertical total electron content (VTEC) time series measured using Swarm satellites Alpha and Charlie, inside the earthquake Dobrovolsky’s area show striking anomalies 31 and 35 days before the event. We successfully verified the possible presence of concomitant anomalous values of in situ electron density detected by the new Chinese satellite dedicated to search for electromagnetic earthquake precursors [China Seismo-Electromagnetic Satellite (CSES)-01]. On the other hand, the analysis of VTEC night time measured by Swarm Bravo shows gradual and abnormal increase of the VTEC parameter from about 23 days before the earthquake, which descends 3 days before the earthquake and reaches its lowest level around the earthquake day. We also analyzed the time series and tracks of other six in situ parameters measured by Swarm satellites, electron density from CSES, and also GPS-TEC measurements. As it is expected from the theory, the electron density anomalous variations acknowledge the Swarm VTEC anomalies, confirming that those anomalies are real and not an artifact of the analysis. The comparative analysis with measurements of other Swarm and CSES sensors emphasizes striking anomalies about 2.5 weeks before the event, with a clear pattern of the whole anomalies typical of a critical system as the earthquake process is for Earth. A confutation analysis outside the Dobrovolsky area and without significant seismicity shows no anomalies. Therefore based on our study, the VTEC products of Swarm satellites could be an appropriate precursor aside from the other measured plasma and magnetic parameters using Alpha, Bravo, and Charlie Swarm and CSES satellites that can be simultaneously analyzed to reduce the overall uncertainty.

Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements

Abstract: Modeling ionospheric variability throughout a proper total electron content (TEC) parameter estimation is a demanding, however, crucial, process for achieving better accuracy and rapid convergence in precise point positioning (PPP). In particular, the single-frequency PPP (SF-PPP) method lacks accuracy due to the difficulty of dealing adequately with the ionospheric error sources. In order to apply ionosphere corrections in techniques, such as SF-PPP, external information of global ionosphere maps (GIMs) is crucial. In this article, we propose a deep learning model to efficiently predict TEC values and to replace the GIM-derived data that inherently have a global character, with equal or better in accuracy regional ones. The proposed model is suitable for predicting the ionosphere delay at different locations of receiver stations. The model is tested during different periods of time, under different solar and geomagnetic conditions and for stations in various latitudes, providing robust estimations of the ionospheric activity at the regional level. Our proposed model is a hybrid model comprising of a 1-D convolutional layer used for the optimal feature extraction and stacked recurrent layers used for temporal time series modeling. Thus, the model achieves good performance in TEC modeling compared to other state-of-the-art methods.

An Approach for Predicting Global Ionospheric TEC Using Machine Learning

Abstract: Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE’S 1-Day (C1PG) and CODE’S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days.

Developing a Deep Learning-Based Detector of Magnetic, Ne, Te and TEC Anomalies from Swarm Satellites: The Case of Mw 7.1 2021 Japan Earthquake

Abstract: Since the appearance and evolution of earthquake ionospheric precursors are expected to show a nonlinear and complex behaviour, the use of nonlinear predictor models seems more appropriate. This paper proposes a new approach based on deep learning as a powerful tool for extracting the nonlinear patterns from a time series of ionospheric precursors. A Long Short-Term Memory (LSTM) network as a type of Recurrent Neural Network (RNN) was used to investigate 52 six-month time series, deduced from the three Swarm satellite (Alpha (A), Bravo (B) and Charlie (C)) measurements, including electron density (Ne), electron temperature (Te), magnetic scalar and vector (X, Y, Z) components, Slant and Vertical Total Electron Content (STEC and VTEC), for day and night periods around the time and location of a seismic event. This new approach was tested on a strong Mw = 7.1 earthquake in Japan on 13 February 2021, at 14:07:50 UTC by comparing the results with two implemented methods, i.e., Median and LSTM methods. Furthermore, clear anomalies are seen by a voting classification method 1, 6, 8, 13, 31 and 32 days before the earthquake. A comparison with atmospheric data investigation is further provided, supporting the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism as a suitable theory to explain the alteration of upper geolayers in the earthquake preparation phase. In other words, using multi-method and multi-precursor analysis applied to 52 time series and also to the orbit-by-orbit investigation, the observed anomalies on the previous day and up to 32 days before the event in normal solar and quiet geomagnetic conditions could be considered as a striking hint of the forthcoming Japan earthquake.

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

Abstract: The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short-term memory (bi-LSTM) method and observations from 257 ground-based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi-LSTM-based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI-2016, artificial neural network and LSTM-based models, correspondingly. The bi-LSTM-based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi-LSTM-based model. Moreover, the TEC maps resulting from the bi-LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

Resident Waves in the Ionosphere Before the M6.1 Dali and M7.3 Qinghai Earthquakes of 21–22 May 2021

Abstract: Geostationary BeiDou satellites monitor the total electron content (TEC) in the ionosphere over certain locations 24 hr per day without interruption and act as ionosphere-based seismometers. The system detected perturbations in TEC before both the M6.1 Dali and M7.3 Qinghai earthquakes that occurred during the night of 21–22 May 2021. The TEC perturbations reside mainly over an area within a distance of ∼700 km from the epicenters of the earthquakes. The standing waves revealed the persistence of a subsurface wave source before the occurrences of the earthquakes, which differs from the co-seismic ionospheric distributions propagating away from the epicenters. The resident waves in TEC and ground vibrations share a frequency of ∼0.004 Hz, which can be attributed to the resonant coupling between the lithosphere and ionosphere.

3‐D Regional Ionosphere Imaging and SED Reconstruction With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS)

Abstract: A new TEC-based ionospheric data assimilation system (TIDAS) over the continental US and adjacent area (20°–60°N, 60°–130°W, and 100–600 km) has been developed through assimilating heterogeneous ionospheric data, including dense ground-based Global Navigation Satellite System (GNSS) Total Electron Content (TEC) from 2,000+ receivers, Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation data, JASON satellite altimeter TEC, and Millstone Hill incoherent scatter radar measurements. A hybrid Ensemble-Variational scheme is utilized to reconstruct the regional 3-D electron density distribution: a more realistic and location-dependent background error covariance matrix is calculated from an ensemble of corrected NeQuick outputs, and a three-dimensional variational (3DVAR) method is adopted for measurement updates to obtain an optimal state estimation. The spatial-temporal resolution of the reanalyzed 3-D electron density product is as high as 1° × 1° in latitude and longitude, 20 km in altitude, and 5 min in universal time, which is sufficient to reproduce ionospheric fine structure and storm-time disturbances. The accuracy and reliability of data assimilation results are validated using ionosonde and other measurements. TIDAS reanalyzed electron density is able to successfully reconstruct the 3-D morphology and dynamic evolution of the storm-enhanced density (SED) plume observed during the St. Patrick's day geomagnetic storm on 17 March 2013 with high fidelity. Using TIDAS, we found that the 3-D SED plume manifests as a ridge-like high-density channel that predominantly occurred between 300 and 500 km during 19:00–21:00 UT for this event, with the F2 region peak height being raised by 40–60 km and peak density enhancement of 30%–50%.

Low-Latitude Ionospheric Responses and Coupling to the February 2014 Multiphase Geomagnetic Storm from GNSS, Magnetometers, and Space Weather Data

Abstract: The ionospheric response and the associated mechanisms to geomagnetic storms are very complex, particularly during the February 2014 multiphase geomagnetic storm. In this paper, the low-latitude ionosphere responses and their coupling mechanisms, during the February 2014 multiphase geomagnetic storm, are investigated from ground-based magnetometers and global navigation satellite system (GNSS), and space weather data. The residual disturbances between the total electron content (TEC) of the International GNSS Service (IGS) global ionospheric maps (GIMs) and empirical models are used to investigate the storm-time ionospheric responses. Three clear sudden storm commencements (SSCs) on 15, 20, and 23 February are detected, and one high speed solar wind (HSSW) event on 19 February is found with the absence of classical SSC features due to a prevalent magnetospheric convection. The IRI-2012 shows insufficient performance, with no distinction between the events and overestimating approximately 20 TEC units (TECU) with respect to the actual quiet-time TEC. Furthermore, the median average of the IGS GIMs TEC during February 2014 shows enhanced values in the southern hemisphere, whereas the IRI-2012 lacks this asymmetry. Three low-latitude profiles extracted from the IGS GIM data revealed up to 20 TECU enhancements in the differential TEC. From these profiles, longer-lasting TEC enhancements are observed at the dip equator profiles than in the profiles of the equatorial ionospheric anomaly (EIA) crests. Moreover, a gradual increase in the global electron content (GEC) shows approximately 1 GEC unit of differential intensification starting from the HSSW event, while the IGS GIM profiles lack this increasing gradient, probably located at higher latitudes. The prompt penetration electric field (PPEF) and equatorial electrojet (EEJ) indices estimated from magnetometer data show strong variability after all four events, except the EEJ’s Asian sector. The low-latitude ionosphere coupling is mainly driven by the variable PPEF, DDEF (disturbance dynamo electric fields), and Joule heating. The auroral electrojet causing eastward PPEF may control the EIA expansion in the Asian sector through the dynamo mechanism, which is also reflected in the solar-quiet current intensity variability.

A new climatological electron density model for supporting space weather services

Abstract: The ionosphere is the ionized part of the Earth’s atmosphere, ranging from about 60 km up to several Earth radii, whereas the upper part above about 1000 km height up to the plasmapause is usually called the plasmasphere. We present a new three-dimensional electron density model to support space weather services and mitigate propagation errors for trans-ionospheric signals. The model is developed by superposing the Neustrelitz Plasmasphere Model (NPSM) to an ionosphere model composed of separate F and E-layer distributions. It uses the Neustrelitz TEC model (NTCM), Neustrelitz Peak Density Model (NPDM), and the Neustrelitz Peak Height Model (NPHM) for the total electron content (TEC), peak ionization, and peak height information. These models describe the spatial and temporal variability of the key parameters as a function of local time, geographic/geomagnetic location, solar irradiation, and activity. The model is developed to calculate the electron concentration at any given location and time in the ionosphere for trans-ionospheric applications and named the Neustrelitz Electron Density Model (NEDM2020). A comprehensive validation study is conducted against electron density in-situ data from DMSP and Swarm, Van Allen Probes and ICON missions, and topside TEC data from COSMIC/FORMOSAT-3 mission, bottom side TEC data from TOPEX/Poseidon mission, and ground-based TEC data from International GNSS Service (IGS) covering both high and low solar activity conditions. Additionally, the model performance is compared with the 3D electron density model NeQuick2. Our investigation shows that the NEDM2020 performs better than the NeQuick2 compared with the in-situ data from Van Allen Probes and ICON satellites and TEC data from COSMIC and TOPEX/Poseidon missions. When compared with DMSP and IGS TEC data, both NEDM2020 and NeQuick2 perform very similarly.

Detection of Different Properties of Ionospheric Perturbations in the Vicinity of the Korean Peninsula After the Hunga‐Tonga Volcanic Eruption on 15 January 2022

Abstract: This study reports different properties of ionospheric perturbations detected to the west and south of the Korean Peninsula after the Hunga-Tonga volcanic eruption on 15 January 2022. Transient wave-like total electron content (TEC) modulations and intense irregular TEC perturbations are detected in the west and south of the Korean Peninsula, respectively, about 8 hr after the eruption. The TEC modulations in the west propagate away from the epicenter with a speed of 302 m/s. Their occurrence time, propagation direction and velocity, and alignment with the surface air pressure perturbations indicate the generation of the TEC modulations by Lamb waves generated by the eruption. The strong TEC perturbations and L band scintillations in the south are interpreted in terms of the poleward extension of equatorial plasma bubbles (EPBs). We demonstrate the association of the EPBs with the volcanic eruption using the EPB occurrence climatology derived from Swarm satellite data.

A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting

Abstract: In the Global Navigation Satellite System, ionospheric delay is a significant source of error. The magnitude of the ionosphere total electron content (TEC) directly impacts the magnitude of the ionospheric delay. Correcting the ionospheric delay and improving the accuracy of satellite navigation positioning can both benefit from the accurate modeling and forecasting of ionospheric TEC. The majority of current ionospheric TEC forecasting research only considers the temporal or spatial dimensions, ignoring the ionospheric TEC’s spatial and temporal autocorrelation. Therefore, we constructed a spatiotemporal network model with two modules: (i) global spatiotemporal characteristics extraction via forwarding spatiotemporal characteristics transfer and (ii) regional spatiotemporal characteristics correction via reverse spatiotemporal characteristics transfer. This model can realize the complementarity of TEC global spatiotemporal characteristics and regional spatiotemporal characteristics. It also ensures that the global spatiotemporal characteristics of the global ionospheric TEC are transferred to each other in both temporal and spatial domains at the same time. The spatiotemporal network model thus achieves a spatiotemporal prediction of global ionospheric TEC. The Huber loss function is also used to suppress the gross error and noise in the ionospheric TEC data to improve the forecasting accuracy of global ionospheric TEC. We compare the results of the spatiotemporal network model with the Center for Orbit Determination in Europe (CODE), the convolutional Long Short-Term Memory (convLSTM) model and the Predictive Recurrent Neural Network (PredRNN) model for one-day forecasts of global ionospheric TEC under different conditions of time and solar activity, respectively. With internal data validation, the average root mean square error (RMSE) of our proposed algorithm increased by 21.19, 15.75, and 9.67%, respectively, during the maximum solar activity period. During the minimum solar activity period, the RMSE improved by 38.69, 38.02, and 13.54%, respectively. This algorithm can effectively be applied to ionospheric delay error correction and can improve the accuracy of satellite navigation and positioning.

ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model

Abstract: In this paper, we proposed an innovative encoder-decoder structure with a convolution long short-term memory (ED-ConvLSTM) network to forecast global total electron content (TEC) based on the International GNSS Service (IGS) TEC maps from 2005 to 2018 with 1-hr time cadence. The ED-ConvLSTM model is used to forecast TEC maps 1–7 days in advance through iterations. To investigate the model's performance, we compared the model with International Reference Ionosphere (IRI2016) model in 2014 and 2018, and compared the model with 1-day Beijing University of Aeronautics and Astronautics (BUAA) model in 2018. The results show that our 7-day ED-ConvLSTM model (ED-ConvLSTM model that forecasts 7 days in advance) outperforms IRI2016 in 2014 and 2018, and our 5-day ED-ConvLSTM model (ED-ConvLSTM model that forecasts 5 days in advance) outperforms 1-day BUAA model. Furthermore, the root mean square error (RMSE) from the 1-day ED-ConvLSTM model with respect to the IGS TEC maps decreases by 51.5% and 43%, respectively, in 2014 and 2018 compared with that from IRI2016 model. The RMSE from the 1-day ED-ConvLSTM model is 20.3% lower than that from the 1-day BUAA model in 2018. In addition, our model has the highest RMSE in the Equatorial Ionospheric Anomaly (EIA) region, but can roughly predict the features and locations of EIA. However, the model fails to forecast localized TEC enhancement and the sudden ionospheric response to the geomagnetic storms. Overall, the model shows competitive performance in medium-term global TEC maps prediction during geomagnetic quiet periods.

Unexpected Regional Zonal Structures in Low Latitude Ionosphere Call for a High Longitudinal Resolution of the Global Ionospheric Maps

Abstract: This study reports unexpected strong longitudinal structures from Global Navigation Satellite System (GNSS) derived total electron content (TEC) observations in the low-latitude ionosphere over Asia. The observations during 2019–2020 show diverse patterns in the zonal difference of regional TEC, even under geomagnetically quiet conditions. The TEC in the northern hemisphere occasionally exhibits drastic zonal gradients. The intense regional gradients in TEC span a longitudinal extent of about 20°. The higher values may appear on the east or the west side. Strong zonal gradients may appear in all seasons, regardless of geomagnetically quiet or active conditions. The 15 December 2019 and 16 March 2020 cases depict an intense zonal differences cluster in the narrow latitudinal band of 16°N to 28°N, spanning a regional scale smaller than the normal longitudinal structures. In contrast, the Global Ionospheric Maps (GIMs) with a longitudinal resolution of 5° show a much flatter zonal picture. Such intense and regional-scale zonal structures in the low-latitude ionosphere call for a high zonal resolution of GIMs in terms of better geographically distributed observations. Notably, no counterpart regional structures are found at the conjugated points in the southern hemisphere during the two cases. Although the physical drivers are not certain, the appearance only in the northern hemisphere possibly excludes the dominant contribution to forming the regional structures from the equatorial electric field.

CAiTST: Conv-Attentional Image Time Sequence Transformer for Ionospheric TEC Maps Forecast

Abstract: In recent years, transformer has been widely used in natural language processing (NLP) and computer vision (CV). Comparatively, forecasting image time sequences using transformer has received less attention. In this paper, we propose the conv-attentional image time sequence transformer (CAiTST), a transformer-based image time sequences prediction model equipped with convolutional networks and an attentional mechanism. Specifically, we employ CAiTST to forecast the International GNSS Service (IGS) global total electron content (TEC) maps. The IGS TEC maps from 2005 to 2017 (except 2014) are divided into the training dataset (90% of total) and validation dataset (10% of total), and TEC maps in 2014 (high solar activity year) and 2018 (low solar activity year) are used to test the performance of CAiTST. The input of CAiTST is presented as one day’s 12 TEC maps (time resolution is 2 h), and the output is the next day’s 12 TEC maps. We compare the results of CAiTST with those of the 1-day Center for Orbit Determination in Europe (CODE) prediction model. The root mean square errors (RMSEs) from CAiTST with respect to the IGS TEC maps are 4.29 and 1.41 TECU in 2014 and 2018, respectively, while the RMSEs of the 1-day CODE prediction model are 4.71 and 1.57 TECU. The results illustrate CAiTST performs better than the 1-day CODE prediction model both in high and low solar activity years. The CAiTST model has less accuracy in the equatorial ionization anomaly (EIA) region but can roughly predict the features and locations of EIA. Additionally, due to the input only including past TEC maps, CAiTST performs poorly during magnetic storms. Our study shows that the transformer model and its unique attention mechanism are very suitable for images of a time sequence forecast, such as the prediction of ionospheric TEC map sequences.