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L-band spaceborne polarimetric synthetic aperture radar (PolSAR) data has been well used for the high-resolution retrieval of the ionospheric Faraday rotation (FR). A vast majority of these retrievals were, however, conducted on PolSAR data of lands, with only a very few over oceans. For the global imaging of the Earth's ionosphere, the retrieval of oceanic ionosphere is indispensable. This paper aims to furnish a deep insight into the FR retrieval on oceans using the widely used Bickel-Bates estimator based on the two cross-circular polarizations ${Z_{12}}$ Z 12 and ${Z_{21}}$ Z 21 . The FR estimates (FRE) on oceans are evaluated in detail by comparing with those on lands on four ALOS PALSAR datasets. The achieved oceanic FRE are shown to possess comparable or even better quality than those on lands. It occurs in three of the four scenes that the selected ocean sub-area holds a smaller standard deviation (STD) of FRE (less than 0.7000°) than all the selected land sub-areas. Especially, the quality of FRE is shown to be closely linked to the dispersed degree of the magnitude of the conjugate product of ${Z_{12}}$ Z 12 and ${Z_{21}}$ Z 21 via the analysis of coefficient of variation (CV), according to which the unusual occurrence of larger magnitude level corresponding to larger STD of FRE can be explained. Nevertheless, compared with CV, the magnitude of the polarimetric coherence between ${Z_{12}}$ Z 12 and ${Z_{21}}$ Z 21 (i.e. $\left| \gamma \right|$ γ ) is identified as a better index to measure the quality of FRE. A smaller STD of FRE appears along with a larger $\left| \gamma \right|$ γ . Three of the four scenes show that the selected ocean sub-area bears a larger mean of $\left| \gamma \right|$ γ (greater than 0.9890) in comparison with all the selected land sub-areas. This work can promote the ionospheric research over oceans and to the correction of ionospheric effects in oceanic L-band spaceborne polarimetric radar data as well. [ABSTRACT FROM AUTHOR]

Accurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting. Plain Language Summary: The ionosphere is prone to complex fluctuations that are difficult to predict. To enhance the 1‐day global total electron content (TEC) prediction ability, we first augmented the limited International Global Navigation Satellite Service TEC data set by generating synthetic data sets. These synthetic data sets can better capture the intricate patterns in TEC fluctuations. Then, we built an auto‐correlation‐based transformer to model the temporal dependencies in TEC by presenting series‐wise connections between time steps. Experimental results demonstrate that our proposed model is highly effective in predicting global TEC compared with the Transformer model, prior C1PG model, and B1PG model. Key Points: Total electron content (TEC) Data augmentation: synthesize the TEC sample by feeding the selected original TEC map data set into a variational auto‐encode modelTraining Auto‐correlation‐based transformer and Transformer models using the imitation samples without any further action on fine‐tuningImproved the accuracy of the predictive auto‐correlation‐based transformer models through data augmentation [ABSTRACT FROM AUTHOR]

Differential code bias (DCB) is one of the main errors involved in ionospheric total electron content (TEC) retrieval using a global navigation satellite system (GNSS). It is typically assumed to be constant over time. However, this assumption is not always valid because receiver DCBs have long been known to exhibit apparent intraday variations. In this paper, a combined method is introduced to estimate the epoch-wise receiver DCB, which is divided into two parts: the receiver DCB at the initial epoch and its change with respect to the initial value. In the study, this method was proved feasible by subsequent experiments and was applied to analyze the possible reason for the intraday receiver DCB characteristics of 200 International GNSS Service (IGS) stations in 2014 (high solar activity) and 2017 (low solar activity). The results show that the proportion of intraday receiver DCB stability less than 1 ns increased from 72.5% in 2014 to 87% in 2017, mainly owing to the replacement of the receiver hardware in stations. Meanwhile, the intraday receiver DCB estimates in summer generally exhibited more instability than those in other seasons. Although more than 90% of the stations maintained an intraday receiver DCB stability within 2 ns, substantial variations with a peak-to-peak range of 5.78 ns were detected for certain stations, yielding an impact of almost 13.84 TECU on the TEC estimates. Moreover, the intraday variability of the receiver DCBs is related to the receiver environment temperature. Their correlation coefficient (greater than 0.5 in our analyzed case) increases with the temperature. By contrast, the receiver firmware version does not exert a great impact on the intraday variation characteristics of the receiver DCB in this case. [ABSTRACT FROM AUTHOR]

In this study, the variability of the ionospheric total electron content (TEC) in the South Atlantic Anomaly (SAA) in the solar maximum of 2002 and the solar minimum of 2008 were compared by using an empirical orthogonal function (EOF) analysis. The ionospheric TEC data were simulated using the National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The first three EOFs accounted for 94.8% and 93.86% of the variability in the data in 2002 and 2008, respectively. The results showed that the TEC variations of the first three EOFs were generally consistent in 2002 and 2008. The first mode showed the equatorial anomaly caused by plasma drift and the east–west asymmetry possibly caused by the change in geomagnetic declination and zonal wind; EOF2 exhibited the zonal variation influenced by the solar EUV radiation and the semiannual variation possibly controlled by the [O/N2], solar zenith angle, and atmospheric circulation. EOF3 suggested an equatorial anomaly and winter anomaly influenced by the [O/N2] variation. However, the values and amplitude variations in the TEC were significantly greater in the solar maximum than that in the solar minimum, and the spring–autumn asymmetry of the TEC was more obvious in the solar minimum. In addition, we used the EOF method to extract the annual variation characteristics of the time coefficients and carried out a correlation analysis. The results showed that the annual variation in the TEC in 2002 was mainly affected by the solar EUV radiation, which was strongly correlated with F10.7 (r = 0.7348). In contrast, the TEC was mainly influenced by the geomagnetic activity in 2008 and had a strong correlation with Dst (r = −0.7898). [ABSTRACT FROM AUTHOR]

The main aim of this research focuses on the seismo-geodetic data integration to identify the origin-time of seismic precursors through the ionospheric total electron content (TEC) parameter. The case study concerns the moderate-sized earthquake (Mw = 5.7) that occurred in Ain Témouchent-northwestern Algeria on December 22, 1999. The adopted approach is to reproduce the Global Positioning System (GPS)_TEC time-series signal in order to identify large-amplitude electron density perturbations in the ionized layer of the Earth’s atmosphere. Thus, we propose an analytical and interpretive method to identify the seismo-ionospheric precursor occurrence time of the polarized signal during the earthquake preparation phase. For this purpose, the use of geodetic data derived from signal processing of dual-frequency permanent ground-based GPS/Global Navigation Satellite Systems (GNSS) receivers allowed us to identify ionospheric disturbances preceding the triggering of the mainshock. Furthermore, modeling the time occurrence of aftershocks revealed a strong correlation exists between the seismo-ionospheric precursors and the main aftershocks. Space geodesy techniques integrated into seismic networks achieve valuable enhancement to reinforce the Earthquake and Tsunami Early Warning System. [ABSTRACT FROM AUTHOR]

Differential code bias (DCB) is one of the main errors involved in ionospheric total electron content (TEC) retrieval using a global navigation satellite system (GNSS). It is typically assumed to be constant over time. However, this assumption is not always valid because receiver DCBs have long been known to exhibit apparent intraday variations. In this paper, a combined method is introduced to estimate the epoch-wise receiver DCB, which is divided into two parts: the receiver DCB at the initial epoch and its change with respect to the initial value. In the study, this method was proved feasible by subsequent experiments and was applied to analyze the possible reason for the intraday receiver DCB characteristics of 200 International GNSS Service (IGS) stations in 2014 (high solar activity) and 2017 (low solar activity). The results show that the proportion of intraday receiver DCB stability less than 1 ns increased from 72.5% in 2014 to 87% in 2017, mainly owing to the replacement of the receiver hardware in stations. Meanwhile, the intraday receiver DCB estimates in summer generally exhibited more instability than those in other seasons. Although more than 90% of the stations maintained an intraday receiver DCB stability within 2 ns, substantial variations with a peak-to-peak range of 5.78 ns were detected for certain stations, yielding an impact of almost 13.84 TECU on the TEC estimates. Moreover, the intraday variability of the receiver DCBs is related to the receiver environment temperature. Their correlation coefficient (greater than 0.5 in our analyzed case) increases with the temperature. By contrast, the receiver firmware version does not exert a great impact on the intraday variation characteristics of the receiver DCB in this case.

Predicting where and when space weather events such as solar flares and X-rays bursts are likely to occur in a specific area of interest constitutes a significant challenge in space weather research. Space weather scientists are, therefore, gradually exploring multivariate data analysis techniques from the fields of data mining or machine learning in order to approximate future occurrences of space weather events from past distribution patterns. As solar flares emit extreme ultraviolet and X-ray radiation, which leads to ionization effect in different layers of the ionosphere, most recent works related to solar flare predictions using machine learning (ML) techniques, focused on X-ray time series predictions. Here, we suggest using support vector machine for classifying subdaily and diurnal total electron content (TEC) spatial changes prior to solar flare events, in order to assess the possibility of predicting B, C, M, and X-class solar flare events. This is done as opposed to predicting TEC time series using ML techniques. The predictions are estimated up to three days before each tested class events, along with different skill scores such as precision, recall, Heidke skill score (HSS), accuracy, and true skill statistics. The results indicate that the suggested approach has the ability to predict solar flare events of X and M-class 24 h prior to their occurrence with 91% and 76% HSS skill scores, respectively, which improves over most recent related works. However, for the small-size C and B-class flares, the suggested approach does not succeed in producing similar promising results.

In this study, the variability of the ionospheric total electron content (TEC) in the South Atlantic Anomaly (SAA) in the solar maximum of 2002 and the solar minimum of 2008 were compared by using an empirical orthogonal function (EOF) analysis. The ionospheric TEC data were simulated using the National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The first three EOFs accounted for 94.8% and 93.86% of the variability in the data in 2002 and 2008, respectively. The results showed that the TEC variations of the first three EOFs were generally consistent in 2002 and 2008. The first mode showed the equatorial anomaly caused by plasma drift and the east–west asymmetry possibly caused by the change in geomagnetic declination and zonal wind; EOF2 exhibited the zonal variation influenced by the solar EUV radiation and the semiannual variation possibly controlled by the [O/N2], solar zenith angle, and atmospheric circulation. EOF3 suggested an equatorial anomaly and winter anomaly influenced by the [O/N2] variation. However, the values and amplitude variations in the TEC were significantly greater in the solar maximum than that in the solar minimum, and the spring–autumn asymmetry of the TEC was more obvious in the solar minimum. In addition, we used the EOF method to extract the annual variation characteristics of the time coefficients and carried out a correlation analysis. The results showed that the annual variation in the TEC in 2002 was mainly affected by the solar EUV radiation, which was strongly correlated with F10.7 (r = 0.7348). In contrast, the TEC was mainly influenced by the geomagnetic activity in 2008 and had a strong correlation with Dst (r = −0.7898).

Ground-based GNSS (Global Navigation Satellite System) reference stations lack the capacity to provide data for ocean regions with sufficient spatial-temporal resolution, limiting the detailed study of the equatorial ionization anomaly (EIA). Thus, this study collected kinematic multi-GNSS data on the ionospheric Total Electron Content (TEC) during two research cruises across the equator in the Western Pacific Ocean in 2014 (31 October–8 November) and 2015 (29 March–6 April). The purpose of the study was to use sufficient spatial–temporal resolution data to conduct a detailed analysis of the diurnal variation of the equatorial ionization anomaly in different seasons. The two-year data collected were used to draw the following conclusions. During the test in 2014, the EIA phenomenon in the Northern and Southern Hemispheres was relatively obvious. The maximum values occurred at local time (LT) 15:00 (~136TECu) and LT22:00 (~107TECu) in the Northern Hemisphere and at LT14:00 (100TECu) and LT22:00 (80TECu) in the Southern Hemisphere. During the test in 2015, the EIA in the Southern Hemisphere reached its maximum level at LT14:00 (~115TECu) and LT20:00 (~85TECu). However, the EIA phenomenon in the Northern Hemisphere was weakened, and a maximum value occurred only at LT 15:00 (~85TECu). The intensity contrast was reversed. The EIA phenomenon manifests a strong hemisphere asymmetry in this region.

Due to high attitude and long synthetic aperture time of geosynchronous synthetic aperture radar (GEOSAR), working in the L band, ionosphere and troposphere severely impact the focusing performance of this radar. In this study, considering the movement characteristics of GEOSAR, the range error model caused by the atmospheric refractive index based on non ‘stop-and-go’ assumption is inferred. Then, the echo signals under the influence of ionosphere and troposphere are deduced, respectively. Finally, the influences of ionosphere and troposphere on GEOSAR focusing performance are analysed and simulated. The simulated results validate that troposphere and ionosphere are the important factors that affect the imaging quality. The tropospheric effects are relatively small. The influence of ionosphere on imaging performance along range and azimuth is much greater, often resulting in image drift and azimuth resolution decline. Change of ionospheric total electron content is more intense, they lower the imaging quality.

Inverse distance weighting (IDW) is a simple and practical interpolation method. Taking the global ionospheric map (GIM) product as a sample, the latitude and longitude anisotropy of the ionospheric total electron content (TEC) distribution is taken into account with an introduction of the latitude and longitude adjustment factor r. Six ionospheric distance calculation schemes are used for testing and results show that the correlation of the ionospheric TEC with the longitude direction is higher than the latitude direction and different ionospheric distance calculation schemes can effectively improve the IDW interpolation accuracy. Using the IDW interpolation based on an optimal scheme, a long-term interpolation accuracy is analyzed and results show that the precision of the optimal scheme interpolation is about 25% higher than the general IDW interpolation precision in the ionospheric activity area (20° north and south latitude) for GIM data before and after spring vernal and summer solstice, autumnal equinox and winter solstice in 12 consecutive years. When the ionospheric activity was active at 3-5 hours after 14 o'clock in the local time of 2014, the interpolation accuracy base on the optimal scheme was improved obviously with an interpolation error smaller than 4.0 TECU. [ABSTRACT FROM AUTHOR]

Due to high attitude and long synthetic aperture time of geosynchronous synthetic aperture radar (GEOSAR), working in the L band, ionosphere and troposphere severely impact the focusing performance of this radar. In this study, considering the movement characteristics of GEOSAR, the range error model caused by the atmospheric refractive index based on non 'stop-and-go' assumption is inferred. Then, the echo signals under the influence of ionosphere and troposphere are deduced, respectively. Finally, the influences of ionosphere and troposphere on GEOSAR focusing performance are analysed and simulated. The simulated results validate that troposphere and ionosphere are the important factors that affect the imaging quality. The tropospheric effects are relatively small. The influence of ionosphere on imaging performance along range and azimuth is much greater, often resulting in image drift and azimuth resolution decline. Change of ionospheric total electron content is more intense, they lower the imaging quality. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) is becoming important for monitoring the variations in the earth's ionosphere based on the total electron content (TEC) and ionospheric electron density (IED). The Crustal Movement Observation Network of China (CMONOC), which includes GNSS stations across mainland China, enables the continuous monitoring of the ionosphere over China as accurately as possible. A series of approaches for GNSS-based ionospheric remote sensing and software has been proposed and developed by the Institute of Geodesy and Geophysics (IGG) in Wuhan. Related achievements include the retrieval of ionospheric observables from raw GNSS data, differential code biases estimations in satellites and receivers, models of local and regional ionospheric TEC, and algorithms of ionospheric tomography. Based on these achievements, a software for processing GNSS data to determine the variations in ionospheric TEC and IED over China has been designed and developed by IGG. This software has also been installed at the CMONOC data centers belonging to the China Earthquake Administration and China Meteorological Administration. This paper briefly introduces the related research achievements and indicates potential directions of future work.

The latitudinal ionospheric response of the three strongest geomagnetic storms of 2015 of the current solar cycle 24 during 16-19 March 2015, 21-24 June 2015 and 19-22 December 2015 is investigated using the total electron content data derived from a latitudinal chain of Global Positioning System (GPS) receivers extending from 70°N to 70°S. The storm time perturbations of the ionosphere during main and recovery phase is presented by the GPS derived vertical total electron content (VTEC) data which is further supported by ionospheric F region critical frequency (foF2) and F region peak height (hmF2) data. We observed symmetrical hemispheric response of the ionosphere during the strongest 17th March (St. Patrick's Day) storm whereas asymmetrical hemispheric response of the ionosphere during 22nd June and 20th December storm over the Asian-Australian sector. The observations are explained by the combined transport of background inter-hemispheric seasonal wind and storm time disturbed meridional wind and by the global thermospheric compositional variation [O/N2] data. [ABSTRACT FROM AUTHOR]

Ionospheric delay is one of the major atmospheric effects on the performance of satellite-based radio navigation systems. It limits the accuracy and availability of Global Positioning System (GPS) measurements, related to critical societal and safety applications. The temporal and spatial gradients of ionospheric total electron content (TEC) are driven by several unknown priori geophysical conditions and solar-terrestrial phenomena. Thereby, the prediction of ionospheric delay is challenging especially over Indian sub-continent. Therefore, an appropriate short/long-term ionospheric delay forecasting model is necessary. Hence, the intent of this paper is to forecast ionospheric delays by considering day to day, monthly and seasonal ionospheric TEC variations. GPS-TEC data (January 2013–December 2013) is extracted from a multi frequency GPS receiver established at K L University, Vaddeswaram, Guntur station (geographic: 16.37°N, 80.37°E; geomagnetic: 7.44°N, 153.75°E), India. An evaluation, in terms of forecasting capabilities, of three ionospheric time delay models – an Auto Regressive Moving Average (ARMA) model, Auto Regressive Integrated Moving Average (ARIMA) model, and a Holt-Winter's model is presented. The performances of these models are evaluated through error measurement analysis during both geomagnetic quiet and disturbed days. It is found that, ARMA model is effectively forecasting the ionospheric delay with an accuracy of 82–94%, which is 10% more superior to ARIMA and Holt-Winter’s models. Moreover, the modeled VTEC derived from International Reference Ionosphere, IRI (IRI-2012) model and new global TEC model, Neustrelitz TEC Model (NTCM-GL) have compared with forecasted VTEC values of ARMA, ARIMA and Holt-Winter’s models during geomagnetic quiet days. The forecast results are indicating that ARMA model would be useful to set up an early warning system for ionospheric disturbances at low latitude regions. [ABSTRACT FROM AUTHOR]

The Ionospheric Total Electron Content (TEC) data measured simultaneously by analyzing dual-frequency signals of the Global Position System (GPS) in two northern Nigeria equatorial stations: Ahmadu Bello University Zaria (ABUZ) with geographic latitude (11.16° N) and Longitude(7.65° E) and Birnin Kebbi Polytechnic (BKFP) with geographic latitude(12.46° N) and Longitude(4.20°E) during the year 2012 (Rz =57.58) was used to study the diurnal, monthly and seasonal variation of equatorial ionosphere. The result shows that the mean TEC varies from a minimum at 0600 hrs LT to a peak value at about 1500 to 1700 hrs LT and then decreases. The seasonal variation in TEC maximizes during the equinox months followed by winter and is minimum during the summer months. [ABSTRACT FROM AUTHOR]

In this paper, the total electron content (TEC) data from eight global positioning system (GPS) stations of the EUREF network, provided by IONOLAB (Turkey), were analyzed using discrete Fourier analysis to investigate the TEC variations over the Mediterranean before and during the strong earthquake of 12th October 2013, which occurred west of Crete, Greece. In accordance with the results of similar analyses in the area, the main conclusions of this study are the following: (a) TEC oscillations in a broad range of frequencies occur randomly over an area of several hundred km from the earthquake and (b) high frequency oscillations ( f ⩾ 0.0003 Hz, periods T ⩽ 60 m) may point to the location of the earthquake with questionable accuracy. The fractal characteristics of the frequency distribution may point to the locus of the earthquake with higher accuracy. We conclude that the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism through acoustic or gravity waves could explain this phenomenology. [ABSTRACT FROM AUTHOR]

The effect of geomagnetic storms on low latitude ionosphere has been investigated with the help of Global Positioning System Total Electron Content (GPS-TEC) data. The investigation has been done with the aid of TEC data from the Indian equatorial region, Port Blair (PBR) and equatorial ionization anomaly region, Agartala (AGR). During the geomagnetic storms on 24th April and 15th July 2012, significant enhancement up to 150% and depression up to 72% in VTEC is observed in comparison to the normal day variation. The variations in VTEC observed from equatorial to EIA latitudes during the storm period have been explained with the help of electro-dynamic effects (prompt penetration electric field (PPEF) and disturbance dynamo electric field (DDEF)) as well as mechanical effects (storm-induced equatorward neutral wind effect and thermospheric composition changes). The current study points to the fact that the electro-dynamic effect of geomagnetic storms around EIA region is more effective than at the lower latitude region. Drastic difference has been observed over equatorial region (positive storm impact) and EIA region (negative storm impact) around same longitude sector, during storm period on 24th April. This drastic change as observed in GPS-TEC on 24th April has been further confirmed by using the O/N ratio data from GUVI (Global Ultraviolet Imager) as well as VTEC map constructed from IGS data. The results presented in the paper are important for the application of satellite-based communication and navigational system. [ABSTRACT FROM AUTHOR]

Ionospheric total electron content (TEC) data during the time period from 00:00 on 12 April to 23:00 on 18 April, 2012, which was 5 days before to 2 days after a deep earthquake at 07:13:50 on 17 April, 2012 UT (Mw=6.8) with a depth about 200km in Papua New Guinea, were examined by two-dimensional principal component analysis (2DPCA) to detect TEC anomaly related to this earthquake, because such TEC anomalies usually have shown up in earlier time periods (Liu et al. 2006). An earthquake- related TEC anomaly was highly localized around the epicenter during the time period from 07:15 to 07:30 on 17 April, where its duration time was at least 15 minutes. Radon gas release should be a possible reason to cause the anomalous TEC gradients or fluctuations. [ABSTRACT FROM AUTHOR]