Distribution Of Total Electron Content Research Articles

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach

AbstractThis study evaluates the performance of deep learning approach in the prediction of the ionospheric total electron content (TEC) during magnetically quiet periods. Two deep learning techniques, long short‐term memory (LSTM) and convolutional LSTM (ConvLSTM), are employed to predict TEC values 24 hr ahead in the vicinity of the Korean Peninsula (26.5°–40°N, 121°–134.5°E). The LSTM method predicts TEC at a single point based on time series of data at that point, whereas the ConvLSTM method simultaneously predicts TEC values at multiple points using spatiotemporal distribution of TEC. Both the LSTM and ConvLSTM models are trained using the complete regional TEC maps reconstructed by applying the Deep Convolutional Generative Adversarial Network–Poisson Blending (DCGAN‐PB) method to observed TEC data. The training period spans from 2002 to 2018, and the model performance is evaluated using 2019 data. Our results show that the ConvLSTM method outperforms the LSTM method, generating more reliable TEC maps with smaller root mean square errors when compared to the ground truth (DCGAN‐PB TEC maps). This outcome indicates that deep learning models can improve the prediction accuracy of TEC at a specific point by taking into account spatial information of TEC. We conclude that ConvLSTM is a reliable and efficient approach for the prompt ionospheric prediction.

A model of TEC perturbations possibly related to seismic activity

For application of observed anomalies of the total electron content (TEC) in the ionosphere for short-term earthquake prediction, it is necessary to understand the physical nature of such anomalies during the preparation of a strong seismic event. The physical mechanism of occurrence of such anomalies is considered in order to distinguish the TEC disturbance of a seismic origin and to clear the processes in the lithosphere resulted in an appearance of the TEC anomaly in the ionosphere. It is supposed that the observed TEC anomalies arise as a result of occurrence of additional electric field in the ionosphere demonstrated by numerous satellite data before the impending earthquakes. At the same time, an injection of charged aerosols into the atmosphere in the epicentral earthquake area was detected. As a result, an electromotive force arises in the surface layer of the atmosphere, which initiates a perturbation of the electric current in the global circuit and the electric field appearance in the ionosphere. We have shown that the TEC disturbance arises as a result of the heating of the ionosphere by electric current and the plasma drift in the electric field of this current. The spatial distribution of TEC arises as a result of the combined action of these two factors, and its nature depends on the relationship between them. Based on the developed model the numerical study of the spatial distribution of TEC in the ionosphere is possible for a given horizontal distribution of the concentration of charged aerosols in the atmosphere near the Earth’s surface.

Spatio-Temporal Validation of GNSS-Derived Global Ionosphere Maps Using 16 Years of Jason Satellites Observations

Existing ionospheric models perform very well in mapping the calm state of the ionosphere. However, the problem is accurately determining the total electron content (TEC) for disturbed days. Knowledge of the exact electron density is essential for single−frequency receivers, which cannot eliminate the ionospheric delay. This study aims to investigate temporal and spatial variability in the distribution of TEC based on differences between maps of individual Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS) and aligned altimetry−TEC from 2005–2021. Based on the temporal distribution, we have observed a significant effect of solar activity on the mean and standard deviation behavior of the differences between global ionospheric maps (GIMs) and Jason−derived TEC. We determined the biases for the entire calculation period, through which it can be concluded that the upcg-Jason and igsg-Jason differences have the lowest standard deviation (±1.81 TECU). In addition, the temporal analysis made it possible to detect annual, semi−annual, and 117-day oscillations occurring in the Jason−TEC data, as well as 121-day oscillations in the GIMs. It also allowed us to analyze the potential sources of these cyclicities, solar and geomagnetic activity, in the case of the annual and semi−annual periodicities. When considering spatial variations, we have observed that the most significant average differences are in the intertropical areas. In contrast, the smallest differences were recorded in the southern hemisphere, below the Tropic of Capricorn (23.5°S). However, the slightest variations were noted for the northern hemisphere above the Tropic of Cancer (23.5°N). Our research presented in this paper allows a better understanding of how different methods of GNSS TEC approximation affect the model’s accuracy.

The Ionospheric Response to the 2013 SSW Event Over the Asian‐Australian Sector

AbstractThis study investigates the response of the Asian‐Australian low‐latitude ionosphere to the 2013 Sudden Stratospheric Warming (SSW) event. The equatorial ionization anomaly structures were built from the latitudinal distribution of Total Electron Content (TEC) measured from 10 Global Positioning System receivers along 115°E in the Asian‐Australian sector. A pair of magnetometers and NASA Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics satellite airglow instrument were also employed to measure the equatorial electrojet strength (inferred E × B drift) and to unveil the changes in the neutral thermospheric O/N2 ratio during the SSW event, respectively. The relocation of the northern crests to higher latitudes during the SSW compared to the non‐SSW periods was mainly attributed to the combined effect of enhanced magnetometer‐inferred upward‐directed E × B drift and increasing solar flux. The local time shift in the thermospheric O/N2 ratio, a slight enhancement in the magnitude of the semi‐diurnal magnetometer‐inferred upward‐directed E × B drift, and changes in solar flux were primarily responsible for the hemispheric TEC enhancement during mid‐January compared to SSW onset. Additionally, the photo‐ionization effect due to increasing solar flux further enhances the TEC magnitude during the relaxing phase. On the other hand, the strong thermospheric equatorward wind played a crucial role in moving the plasma to lower latitudes during the zonal wind transition phases. A moderate geomagnetic storm of 17 January 2013 did not perturb the Asian‐Australian ionosphere. However, on 18 January 2013, the storm had a significant impact and greatly diminished the effect of the SSW.

A High-Precision and Lightweight Prediction Model for Global Total Electron Content

Precise prediction of the global spatial–temporal distribution of total electron content (TEC) is a challenge in space weather. Existing models are generally able to provide rather good prediction results at the cost of a large amount of computing resources. This limits the application of the method. A lightweight and highly accurate global TEC prediction model was developed in this study. Our model is capable of forecasting the global TEC map up to 12 h in advance with a step of one hour. The predicted results during geomagnetic quiet periods were consistent with measurements, with a maximum and average mean error (ME) of 1.5 TECU and −0.04 TECU under conditions of high solar activity, respectively. Our model also performed well during geomagnetic disturbed periods, with a maximum ME of 4.5 TECU and 2.5 TECU under conditions of high and low solar activities, respectively. Our model significantly reduces the training time (47%) and basic requirement of memory (60%) relative to the model of Liu et al. (2022) with no remarkable loss of model accuracy.

Ionospheric TEC variation over Svalbard archipelago, Norway and assessment of Bilinear interpolated GIM model

The Svalbard archipelago region is a group of islands in the Arctic Ocean, located between the North Pole and the northern coastal region of Norway at high latitudes. The current study focuses on the spatiotemporal distribution of total electron content (TEC) over the Svalbard archipelago region with the ground-based global positioning system (GPS) tracking stations during the years 2020–2022. The spatial ionospheric TEC characteristics from GPS observables and their correlation with the Bilinear interpolated global ionospheric map (GIM) and the latest edition of the international reference ionosphere (IRI- 2020) model are investigated. The results showed that both GPS-TEC and GIM-TEC have minimum ionospheric TEC during the winter season while they have maximum TEC occurrence in the summer and spring seasons with a higher difference of magnitude. We suspect such discrepancy could be due to expected uncertainties in the estimation of GIM-TEC interpolation over the region. The correlation coefficients between GPS-TEC versus GIM-TEC and GPS-TEC versus IRI-TEC showed that they are correlated with an almost equivalent correlation coefficient of approximately 0.76 which is reasonably acceptable but relatively lower than other middle latitude regions. This poor correlation happened because of the diurnal variation in the polar cap region during the summer season which is minimal. Finally, a Multitaper spectral analysis is employed on the frequency spectra of the time-varying periodograms of the four oscillation components. The outcome of the spectral analysis confirms that the first principal component (PC) of GPS-TEC is well correlated with the respective PC of GIM and IRI models. Although the higher order PCs of GIM models remain well-correlated with GPS-TEC, the subdued correlation between IRI model outputs and GPS-TEC suggests further improvements in the IRI model representation with respect to the high latitude characteristics.

Ionosonde and GPS total electron content observations during the 26 December 2019 annular solar eclipse over Indonesia

Abstract. We report the investigation of the ionospheric response to the passage of an annular solar eclipse over Southeast Asia on 26 December 2019 using multiple sets of observations. Two ionosondes (one at Kototabang and another at Pontianak) were used to measure dynamical changes in the ionospheric layer during the event. A network of ground-based GPS receiver stations in Indonesia was used to derive the distribution of total electron content (TEC) over the region. In addition, extreme ultraviolet (EUV) images of the Sun from the Atmospheric Imaging Assembly (AIA) instrument on board the Solar Dynamics Observatory (SDO) satellite were also analyzed to determine possible impacts of solar-active regions on the changes that occurred in the ionosphere during the eclipse. We found −1.62 and −1.90 MHz reductions (24.0 % and 27.5 % relative reduction) in foF2 during the solar eclipse over Kototabang and Pontianak, respectively. The respective TEC reductions over Kototabang and Pontianak during the eclipse were −4.34 and −5.45 TECU (24.9 % and 27.9 % relative reduction). Data from both ionosondes indicate a consistent 34–36 min delay between maximum eclipse and minimum foF2. The corresponding time delays for eclipse-related TEC reduction at these two locations were 40 and 16 min, respectively. The ionospheric F layer was found to descend with a speed of 9–19 m s−1 during the first half of the eclipse period. We also found an apparent rise in the ionospheric F-layer height near the end of the solar eclipse period, equivalent to a vertical drift velocity of 44–47 m s−1. The GPS TEC data mapping along a set of cross-sectional cuts indicates that the greatest TEC reduction actually occurred to the north of the solar-eclipse path, opposite of the direction from which the lunar shadow fell. As the central path of the solar eclipse was located just to the north of the southern equatorial ionization anomaly (EIA) crest, it is suspected that such a peculiar TEC reduction pattern was caused by plasma flow associated with the equatorial fountain effect. Net perturbations of TEC were also computed and analyzed, which revealed the presence of some wavelike fluctuations associated with the solar-eclipse event. Some of the observed TEC perturbation patterns that propagated with a velocity matching the lunar shadow may be explained in terms of nonuniform EUV illumination that arose as various active regions on the Sun went obstructed and unobstructed during the eclipse. The remaining wavelike features are likely to be traveling ionospheric disturbances (TIDs) generated by the passage of the solar eclipse on top of other diurnal factors.

Using Deep Learning to Map Ionospheric Total Electron Content over Brazil

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

Solar and interplanetary events that drove two CIR-related geomagnetic storms of 1 June 2013 and 7 October 2015, and their ionospheric responses at the American and African equatorial ionization Anomaly regions

This study investigates the sequence of solar and interplanetary events that drove the 1 June 2013 and October 2015 geomagnetic storms and how the American (68°–78oE) and African (32°–42oE) Equatorial Ionization Anomaly (EIA) regions responded to them. We constructed the EIA structures by using Total Electron Content (TEC) and ionospheric irregularities derived from Global Navigation Satellite System (GNSS) receivers along with the study locations. We also analyzed disturbed time ionospheric electric field and model data alongside the GNSS data. The 1 June 2013 geomagnetic storm was driven by a combination of a weak CME and HSSs from solar coronal holes, while the 7 October 2015 storm was solely driven by HSSs. Storm-time hemispherical asymmetry in ionospheric TEC and irregularities distributions was consistently observed. Storm with minimum SYM-H value at day-side locations caused enhancement in plasma ionization and pole-ward movement of EIA crests, while storm with minimum SYM-H value at night-side locations caused reduction in plasma ionization and equator-ward movement of EIA crests. The phase of responses of the ionosphere to geomagnetic storms depends on the local time of storm’s onset and local time of the storm’s main phase minimum which also determine the orientation of Prompt Penetration Electric Field (PPEF). At storm’s onset time in the low latitude regions, the main storm-induced electric field is PPEF. Daytime eastward PPEF intensified plasma fountain to increase the EIA crests locations, while nighttime westward PPEF reversed plasma fountain to cause equator-ward collapse of the EIA crests. However, around the storm’s recovery phase, under southward turning of IMF Bz, depending on their orientations, PPEF and Disturbed Dynamo Electric Field (DDEF) collectively influenced low latitude ionosphere. Eastward PPEF at the Pre-Reversal Enhancement (PRE) time enhanced irregularities generation, while westward DDEF at PRE time inhibited irregularities generation. The season of storm’s occurrence is also a factor that dictates ionospheric response to a storm, for instance, the 7 October storm (SYM-H −124 nT) influenced the ionosphere more than the 1 June storm (SYM-H −137 nT). Both storms had long recovery phase. On pre-storm days, we observed stronger and well-developed EIA crests over the American sector than over the African sector.

Multi‐Scale Density Structures in the Plasmaspheric Plume During a Geomagnetic Storm

AbstractEvolution of large‐scale and fine‐scale plasmaspheric plume density structures was examined using space‐ground coordinated observations of a plume during the 7–8 September 2015 storm. The large‐scale plasmaspheric plume density at Van Allen Probes A was roughly proportional to the total electron content (TEC) along the satellite footprint, indicating that TEC distribution represents the large‐scale plume density distribution in the magnetosphere. The plasmaspheric plume contained fine‐scale density structures and subauroral polarization streams (SAPS) velocity fluctuations. High‐resolution TEC data support the interpretation that the fine‐scale plume structures were blobs with ∼300 km size and ∼500–800 m/s in the ionosphere (∼3,000 km size and ∼5–8 km/s speed in the magnetosphere), emerging at the plume base and drifting to the plume. The short‐baseline Global Navigation Satellite System receivers detected smaller‐scale (∼10 km in the ionosphere, ∼100 km in the magnetosphere) TEC gradients and their sunward drift. Fine‐scale density structures were associated with enhanced phase scintillation index. Velocity fluctuations were found to be spatial structures of fine‐scale SAPS flows that drifted sunward with density irregularities down to ∼10 s of meter‐scale. Fine‐scale density structures followed a power law with a slope of ∼−5/3, and smaller‐scale density structures developed slower than the larger‐scale structures. We suggest that turbulent SAPS flows created fine‐scale density structures and their cascading to smaller scales. We also found that the plume fine‐scale density structures were associated with whistler‐mode intensity modulation, and localized electron precipitation in the plume. Structured precipitation in the plume may contribute to ionospheric heating, SAPS velocity reduction, and conductance enhancements.

Ionospheric Nighttime Enhancements at Low Latitudes Challenge Performance of the Global Ionospheric Maps

In this study, two ionospheric nighttime enhancement (INE) events at low latitudes are selected to investigate their spatial features through the observations from Global Navigation Satellite System (GNSS) receivers and ionosondes. For the first time, we present the detailed spatial pictures of premidnight and postmidnight INEs under geomagnetically quiet conditions. The two INE events have the maximum extents of about 11° × 34° and 17° × 25° (longitude × latitude), respectively. Dramatic latitudinal and longitudinal features are revealed in the two INEs. We perform a comparison between the products of Global Ionospheric Maps (GIMs) and total electron content (TEC) measurement from GNSS receivers. However, GIMs fail to capture the TEC distribution during INEs owing to their limited spatial and temporal resolution. Considering the extent of INEs from the observations, the spherical harmonic (SH) expansion adopted by the GIM models needs to upgrade the degree and order to 36. The pixel-based methods developed from two GIM models are required to reduce their grid size for higher spatial resolution. The recommended time interval is shorter than 30 min. Among seven GIMs, CODG and JPLG maps generally have the best performance in reproducing the latitudinal structure of the ionosphere.

Storm Time Effects on Latitudinal Distribution of Ionospheric TEC in the American and Asian‐Australian Sectors: August 25–26, 2018 Geomagnetic Storm

AbstractOn the dayside of August 25–26, 2018 (main phase, MP of the storm), we unveiled the storm time effects on the latitudinal distribution of ionospheric total electron content (TEC). We used 17 and 19 Global Positioning System receivers in American and Asian‐Australian sectors, respectively. Also, we employed a pair of magnetometers in each sector to unveil storm time effects on vertical E × B upward directed inferred drift velocity in the F region ionosphere. Also used is NASA Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite airglow instrument to investigate storm time changes in neutral composition, O/N2 ratio. In this investigation, we corrected the latitudinal offset found in the works of Younas et al. (2020, https://doi.org/10.1029/2020JA027981). Interestingly, we observed that a double‐humped increase (DHI) seen at a middle latitude station (MGUE, ∼22°S) after the MP on the dayside in American sector (Younas et al., 2020, https://doi.org/10.1029/2020JA027981) did straddle ∼23.58°N and ∼22°S. On August 25, 2018, storm commencement was evident in Sym‐H (∼−8 nT) around 18:00 UT. It later became intensified (∼−174 nT) on August 26 around 08:00 UT. During storm's MP (after the MP), fountain effect operation was significantly enhanced (inhibited) in Asian‐Australian (American) sector. Middle latitude TEC during MP got reduced in American sector (13:00 LT–15:40 LT) compared to those seen in Asian‐Australian sector (13:00 LT–15:40 LT). The northern equatorial peak (∼25 TECU) seen at IHYO (14:00 LT) after MP in the American sector is higher when compared with that (∼21 TECU) seen at PPPC (11:40 LT) during MP in Asian‐Australian sector.

Determination of possible responses of Radon-222, magnetic effects, and total electron content to earthquakes on the North Anatolian Fault Zone, Turkiye: an ARIMA and Monte Carlo Simulation

Around the world, earthquake forecasting studies have become very important nowadays due to the increase in number of fatal earthquakes annually. This paper proposes to achieve a possible relationship between soil radon gas concentration and atmospheric total electron content (TEC) during earthquakes taking into account magnetic effects on the North Anatolian Fault Zone (NAFZ) in Turkiye. The ARIMA and Monte Carlo simulation (MCS) are employed for determining radon gas concentrations by taking into account magnetic effects as an innovative approach. In the study area, relatively small and medium-scale earthquakes have taken place during the observation period. As a result of the investigations, the relationships between each of the parameters and earthquakes are determined; hence, a good relationship is obtained between Rn gas anomaly and micro-seismic activity. In the ionosphere, geomagnetic activity has a primary impact and long duration on TEC distribution, but due to micro-seismic events it has rather small influence. The proposed ARIMA and MCS simulations to detect changes in soil Rn gas concentrations have significant results for detecting micro-seismic activity anomalies.

Plasma depletions lasting into daytime during the recovery phase of a geomagnetic storm in May 2017: Analysis and simulation of GPS total electron content observations

This paper reports that plasma density depletions appearing at middle latitudes near sunrise survived until afternoon on 29 May 2017 during the recovery phase of a geomagnetic storm. By analyzing GPS data collected in Japan, we investigate temporal variations in the horizontal two-dimensional distribution of total electron content (TEC) during the geomagnetic storm. The SYM-H index reached −142 nT around 08 UT on 28 May 2017. TEC depletions extending up to approximately 38°N along the meridional direction appeared over Japan around 05 LT (LT = UT + 9 hours) on 29 May 2017, when TEC rapidly increased at sunrise due to the solar extreme ultraviolet (EUV) radiation. The TEC depletions appeared sequentially over Japan for approximately 8 hours in sunlit conditions. At 06 LT on 29 May, when the plasma depletions first appeared over Japan, the background TEC was enhanced to approximately 17 TECU, and then decreased to approximately 80% of the TEC typical of magnetically quiet conditions. We conclude that this temporal variation of background plasma density in the ionosphere was responsible for the persistence of these plasma depletions for so long in daytime. By using the Naval Research Laboratory: Sami2 is Another Model of the Ionosphere (SAMI2), we have evaluated how plasma production and ambipolar diffusion along the magnetic field may affect the rate of plasma depletion disappearance. Simulation shows that the plasma density increases at the time of plasma depletion appearance; subsequent decreases in the plasma density appear to be responsible for the long-lasting persistence of plasma depletions during daytime. The plasma density depletion in the top side ionosphere is not filled by the plasma generated by the solar EUV productions because plasma production occurs mainly at the bottom side of the ionosphere.

Analysis of Ionospheric TEC Anomalies during Super Typhoons Megi and Meranti

Sun, J.L.; Zhang, Z.Y.; Liu, X.; Zhang, C., Mou, Z.H., 2020. Analysis of ionospheric TEC anomalies during super typhoons Megi and Meranti. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 692-695. Coconut Creek (Florida), ISSN 0749-0208.In this paper, we detected and analyzed ionospheric anomalies during the 2016 super typhoons Megi and Meranti using the sliding interquartile range method applied to the ionospheric total electron content (TEC) grid data from the Global Ionospheric Model (GIM) provided by the Center for Orbit Determination in Europe (CODE). We found that ionospheric TEC anomalies occurred 2 days before the origination of super typhoon Megi. The anomalies occurred from 02:00 to 12:00 local time over a period of 10 h, with a maximum outlier of 12 TEC units over the typhoon center. Anomalies occurred in the ionosphere over both the area near the typhoon center and the corresponding equatorial magnetic conjugate region. The anomalies in both areas showed approximately the same trends. It is very likely that the disturbances from the ionospheric anomalies over the typhoon center were one of the precursors for the typhoon event. Similar ionospheric anomalies also occurred in the ionosphere 2 days before the formation and development of super typhoon Meranti. The global ionospheric TEC distribution was even more complex on that day. The existence of multiple peaks and valleys in the maximum outlier near the typhoon center was possibly associated with the position of the typhoon center and the GIM grid points or the wind speeds over the typhoon center.

Comparison of quiet-time ionospheric total electron content from the IRI-2016 model and from gridded and station-level GPS observations

Abstract. Earth's ionosphere is an important medium of radio wave propagation in modern times. However, the effective use of the ionosphere depends on the understanding of its spatiotemporal variability. Towards this end, a number of ground- and space-based monitoring facilities have been set up over the years. The information from these stations has also been complemented by model-based studies. However, assessment of the performance of ionospheric models in capturing observations needs to be conducted. In this work, the performance of the IRI-2016 model in simulating the total electron content (TEC) observed by a network of Global Positioning System (GPS) receivers is evaluated based on the RMSE, the bias, the mean absolute error (MAE) and skill score, the normalized mean bias factor (NMBF), the normalized mean absolute error factor (NMAEF), the correlation, and categorical metrics such as the quantile probability of detection (QPOD), the quantile categorical miss (QCM), and the quantile critical success index (QCSI). The IRI-2016 model simulations are evaluated against gridded International Global Navigation Satellite System (GNSS) Service (IGS) GPS-TEC and TEC observations at a network of GPS receiver stations during the solar minima in 2008 and solar maxima in 2013. The phases of modeled and simulated TEC time series agree strongly over most of the globe, as indicated by a high correlations during all solar activities with the exception of the polar regions. In addition, lower RMSE, MAE, and bias values are observed between the modeled and measured TEC values during the solar minima than during the solar maxima from both sets of observations. The model performance is also found to vary with season, longitude, solar zenith angle, and magnetic local time. These variations in the model skill arise from differences between seasons with respect to solar irradiance, the direction of neutral meridional winds, neutral composition, and the longitudinal dependence of tidally induced wave number four structures. Moreover, the variation in model performance as a function of solar zenith angle and magnetic local time might be linked to the accuracy of the ionospheric parameters used to characterize both the bottom- and topside ionospheres. However, when the NMBF and NMAEF are applied to the data sets from the two distinct solar activity periods, the difference in the skill of the model during the two periods decreases, suggesting that the traditional model evaluation metrics exaggerate the difference in model skill. Moreover, the performance of the model in capturing the highest ends of extreme values over the geomagnetic equator, midlatitudes, and high latitudes is poor, as noted from the decrease in the QPOD and QCSI as well as an increase in the QCM over most of the globe with an increase in the threshold percentile TEC values from 10 % to 90 % during both the solar minimum and the solar maximum periods. The performance of IRI-2016 in simulating observed low (as low as the 10th percentile) and high (higher than the 90th percentile) TEC correctly over equatorial ionization anomaly (EIA) crest regions is reasonably good given that IRI-2016 is a climatological model. However, it is worth noting that the performance of the IRI-2016 model is relatively poor in 2013 compared with 2008 at the highest ends of the TEC distribution. Therefore, this study reveals the strengths and weaknesses of the IRI-2016 model in simulating the observed TEC distribution correctly during all seasons and solar activities for the first time.

Ionospheric total electron content responses to HILDCAA intervals

Abstract. The High-Intensity Long-Duration and Continuous AE Activities (HILDCAA) intervals are capable of causing a global disturbance in the terrestrial ionosphere. However, the ionospheric storms' behavior due to these intervals is still not widely understood. In the current study, we seek to comprise the HILDCAA disturbance time effects in the total electron content (TEC) values with respect to the quiet days' pattern by analyzing local time and seasonal dependences, and the influences of the solar wind velocity on a sample of 10 intervals that occurred in the years 2015 and 2016. The main results showed that the hourly distribution of the disturbance TEC may vary substantially between one HILDCAA interval and another. An equinoctial anomaly was found since the equinoxes represent more ionospheric TEC responses than the solstices. Regarding the solar wind velocities, although HILDCAA intervals are associated with high-speed streams, this association does not present a direct relation to TEC disturbance magnitudes at low and equatorial latitudes.

Support Vector Machine for Regional Ionospheric Delay Modeling.

The distribution of total electron content (TEC) in the ionosphere is irregular and complex, and it is hard to model accurately. The polynomial (POLY) model is used extensively for regional ionosphere modeling in two-dimensional space. However, in the active period of the ionosphere, the POLY model is difficult to reflect the distribution and variation of TEC. Aiming at the limitation of the regional POLY model, this paper proposes a new ionosphere modeling method with combining the support vector machine (SVM) regression model and the POLY model. Firstly, the POLY model is established using observations of regional continuously operating reference stations (CORS). Then the SVM regression model is trained to compensate the model error of POLY, and the TEC SVM-P model is obtained by the combination of the POLY and the SVM. The fitting accuracies of the models are verified with the root mean square errors (RMSEs) and static single-frequency precise point positioning (PPP) experiments. The results show that the RMSE of the SVM-P is 0.980 TECU (TEC unit), which produces an improvement of 17.3% compared with the POLY model (1.185 TECU). Using SVM-P models, the positioning accuracies of single-frequency PPP are improved over 40% compared with those using POLY models. The SVM-P is also compared with the back-propagation neural network combined with POLY (BPNN-P), and its performance is also better than BPNN-P (1.070 TECU).

Anomaly distribution of ionospheric total electron content responses to some solar flares

Previous studies have shown that the ionospheric responses to a solar flare are significantly dependent on the solar zenith angle (SZA): the ionospheric responses are negatively related to the SZAs. The largest enhancement in electron density always occurs around the subsolar point. However, from 2001 to 2014, the global distribution of total electron content (TEC) responses showed no obvious relationship between the increases in TEC and the SZA during some solar flares. During these solar flares, the greatest enhancements in TEC did not appear around the subsolar point, but rather far away from the subsolar point. The distribution of TEC enhancements showed larger TEC enhancements along the same latitude. The distribution of anomalous ionospheric responses to the solar flares was not structured the same as traveling ionospheric disturbances. This anomaly distribution was also unrelated to the distribution of background neutral density. It could not be explained by changes in the photochemical process induced by the solar flares. Thus, the transport process could be one of the main reasons for the anomaly distribution of ionospheric responses to the solar flares. This anomaly distribution also suggests that not only the photochemical process but also the transport process could significantly affect the variation in ionospheric electron density during some solar flares.

Impact of magnetic storms on the global TEC distribution

Abstract. The study is focused on the analysis of total electron content (TEC) variations during six geomagnetic storms of different intensity: from Dstmin=-46 nT to Dstmin=-223 nT. The values of TEC deviations from its 27-day median value (δTEC) were calculated during the periods of the storms along three meridians: American, Euro-African and Asian-Australian. The following results were obtained. For the majority of the storms almost simultaneous occurrence of δTEC maximums was observed along all three meridians at the beginning of the storm. The transition from a weak storm to a superstorm (the increase of magnetic activity) almost does not affect the intensity of the δTEC maximum. The seasonal effect was most pronounced along the Asian-Australian meridian, less often along the Euro-African meridian and was not revealed along the American meridian. Sometimes the seasonal effect can penetrate to the opposite hemisphere. The character of average δTEC variations for the intense storms was confirmed by GOES satellite data. Though there are some common features of TEC variation revealed during each storm phase, in general no clear dependence of TEC responses on the storm phases was found: the effects were different during each storm at different locations. The behavior of the correlation coefficient (R) between δTEC values along the three meridians was analyzed for each storm. In general, R>0.5 between δTEC values averaged along each meridian. This result is new. The possible reasons for the exceptions (when R<0.5) were provided: the complexity of phenomena during the intense storms and discordance in local time of the geomagnetic storm beginning along different meridians. Notwithstanding the complex dependence of R on the intensity of magnetic disturbance, in general R decreased with the growth of storm intensity.