Mapping high spatial resolution ionospheric total electron content by integrating Time Series InSAR with International Reference Ionosphere model

The international reference ionosphere (IRI) models have been widely used for correcting the ionospheric scintillations at different altitude levels. An evaluation on the performance of VTEC correction from IRI models (version 2007, 2012 and 2016) over Sukkur, Pakistan (27.71º N, 68.85º E) is presented in this work. Total Electron Content (TEC) from IRI models and GPS in 2019 over Sukkur region are compared. The main aim of this comparative analysis is to improve the VTEC in low latitude Sukkur, Pakistan. Moreover, this study will also help us to identify the credible IRI model for the correction of Global Positioning System (GPS) signal in low latitude region in future. The development of more accurate TEC finds useful applications in enhancing the extent to which ionospheric influences on radio signals are corrected. VTEC from GPS and IRI models are collected between May 1, 2019 and May 3, 2019. Additionally, Dst and Kp data are also compared in this work to estimate the geomagnetic storm variations. This study shows a good correlation of 0.83 between VTEC of GPS and IRI 2016. Furthermore, a correlation of 0.82 and 0.78 is also recorded for IRI 2012 and IRI 2007 respectively, with VTEC of GPS. The IRI TEC predictions and GPS-TEC measurements for the studied days reveal the potential of IRI model as a good candidate over Pakistan.

<p>The development of space-geodetic observation techniques has brought out a wide range of applications such as positioning and navigation, where the Global Navigation Satellite System (GNSS) is the main tools to provide surveying measurements in these applications. Though GNSS signals enable the calculation of receiver's position, some errors restrict their accuracy. Among these errors, the ionospheric delay is considered as an important error source in the Standard Point Positioning (SPP) applications. Empirical ionospheric models such as Klobuchar, International Reference Ionosphere (IRI), and NeQuick are often applied for computing the Total Electron Content (TEC) within ionosphere and its equivalent delays. However the simulation and forecasting skills of these models are limited due to the simplified model structures and model sensitivity to the calibration period. In this study, we present a novel sequential Calibration approach based on the Ensemble Kalman Filter (C-EnKF) to improve the performance of TEC estimations for SPP applications. To demonstrate the results, the IRI model is used as our basis and the TEC estimates from 56 IGS stations in Europe are applied as observation. The C-EnKF is applied to calibrate some selected model parameter so that IRI can be tuned over Europe. The numerical assessments are performed against the TEC estimates from dual frequency GNSS measurements and against the final IONEX products (that are available with 11 days delays). Based on the forecasting results (during September 2017), we show that the accuracy of TEC estimates from the C-EnKF is improved in the range of 3.7-64.87% compared to IRI. <strong>Keywords: </strong>Ionosphere, Sequential Calibration, Ensemble Kalman Filter (EnKF), IRI, Total Electron Content (TEC), Standard Point Positioning (SPP), GNSS</p>

A statistical comparison of IRI TEC prediction with GPS TEC measurement over Varanasi, India

The Global Positioning System (GPS) applications are highly vulnerable to the ionospheric space weather effects. Modelling and forecasting the ionospheric effects such as time delays for GPS signals are important for real time alerts of space weather effects on GPS services. In the present work, the performance of Neural Networks (NN) model is compared with International Reference Ionosphere (IRI) models. The ionospheric Total Electron Content (TEC) observations have been collected during 2015 year, descending phase of 24th solar cycle, over three Indian low latitude GPS stations namely, Bengaluru (Geographic coordinates: 13.02°N and 77.57°E), near to geomagnetic equator, Guntur (Geographic coordinates: 16.37°N and 80.37°E), which is at Equatorial Ionization Anomaly (EIA) and Lucknow (Geographic coordinates: 26.83°N and 80.92°E), which is beyond EIA region. The performance of NN model in predicting the ionospheric TEC values is compared with IRI (IRI-2012 and IRI-2016) models during test period, October–December 2015 over three Indian low latitude regions. It is observed that IRI models (IRI-2012 and IRI-2016) have shown more the temporal differences with GPS-VTEC during sunrise hours compared to sunset hours over three low latitude regions. The performance of IRI-2016 model has apparently better than IRI-2012 model. However, it is observed that IRI-2016 model has large discrepancies over Bengaluru and Guntur station due to high VTEC fluctuations at equatorial and low latitudes. The NN models are well predicted the measured diurnal mean VTEC variations with the less errors, ±5 TECU but the differences of IRI models are ±15 TECU over all the three stations. Later, GPS-data for 10 years, 2009–2018, is collected over Bengaluru station during 24th solar cycle. The performance of NN model is validated during 2016, 2017 and 2018 years over Bengaluru GPS station. The error measurements and experimental results reported that the measured GPS-VTEC values are well predicted by NN model compared to IRI-2016 model over equatorial and low latitude GPS stations.

The ionospheric error can significantly affect the synthetic aperture radar (SAR) signals, particularly in the case of L band and lower frequency SAR systems. The ionospheric distortions are mixed with terrain and ground deformation signals, lowering the precision of the interferometric measurements. Moreover, it is often difficult to detect the small-scale ionospheric structure due to its rapid changes and may have more influence on ionospheric phase compensation for InSAR measurements. In this paper, we present a Faraday rotation (FR) inversion method and corresponding procedure to compensate the ionospheric error for SAR interferograms and to detect the variations of small-scale ionospheric disturbances. This method retrieves the absolute total electron content (TEC) based on the FR estimation and corrects the ionospheric error for synthetic aperture radar interferometry (InSAR) measurements by transforming the differential TEC into the ionospheric phase. In two selected study cases, located in high latitude and equatorial regions where ionospheric disturbances occur frequently, we test the method using the Phased Array L-band Synthetic Aperture Radar (PALSAR) full-polarimetric SAR images. Our results show that the proposed procedure can effectively compensate the ionospheric phase. In order to validate the results, we present the results of ionospheric phase compensation based on the split-spectrum method as a comparison to the proposed method. To analyze the ability of our proposed method in detecting small-scale ionospheric disturbances, TEC derived from FR estimation are also compared with those derived from the global ionosphere maps (GIM). Our research provides a robust choice for the correction of ionospheric error in SAR interferograms. It also provides a powerful tool to measure small-scale ionospheric structure.

Mapping of the Ionospheric Total Electron Content over the East African Low–Latitude Region

Dual-frequency global navigation satellite systems (GNSS) observations provide most of the input data for development of global ionosphere map (GIM) of vertical total electron content (VTEC). The international GNSS service (IGS) develops different ionosphere products. The IGS tracking network stations are not homogeneously distributed around the world. The large gaps of this network in Middle East, e.g., Iran plateau, reduce the accuracy of the IGS GIMs over this region. Empirical ionosphere models, such as international reference ionosphere (IRI), also provide coarse forecasts of the VTEC values. This paper presents a new regional VTEC model based on the IRI 2007 and global positioning system (GPS) observations from Iranian Permanent GPS Network. The model consists of a given reference part from IRI model and an unknown correction term. Compactly supported base functions are more appropriate than spherical harmonics in regional ionosphere modeling. Therefore, an unknown correction term was expanded in terms of B-spline functions. The obtained results are validated through comparison with the observed VTEC derived from GPS observations.

Experimental analysis was performed using multiplicative algebraic reconstruction technique (MART) to map the ionosphere over Brazil. Code and phase observations from the global navigation satellite system (GNSS) together with the international reference ionosphere (IRI) enabled the estimation of ionospheric profiles and total electron content (TEC) over the entire region. Twenty-four days of data collected from existing ground-based GNSS receivers during the recent solar maximum period were used to analyze the performance of the MART algorithm. The results were compared with four ionosondes. It was demonstrated that MART estimated the electron density peak with the same degree of accuracy as the IRI model in regions with appropriate geometrical coverage by GNSS receivers for tomographic reconstruction. In addition, the slant TEC, as estimated with MART, presented lower root-mean-square error than the TEC calculated by ionospheric maps available from the International GNSS Service (IGS). Furthermore, the daily variations of the ionosphere were better represented with the algebraic techniques, compared to the IRI model and IGS maps, enabling a correlation of the elevation of the ionosphere at higher altitudes with the equatorial ionization anomaly intensification. The tomographic representations also enabled the detection of high vertical gradients at the same instants in which ionospheric irregularities were evident.

The present work analyzes results coming from global maps of ionospheric total electron content (TEC) obtained from observations and from different empirical models like the International Reference Ionosphere (IRI), the model family developed at Trieste and Graz (NeQuick, COSTprof, and NeUoG‐plas), and the GPS operational model (formulation by J. A. Klobuchar). Since they still appear in the context of assessment studies we have also included “old” models like the Bent model. The attention is focused on situations which occurred in the present period of high solar activity, pointing out features like the equatorial anomaly and polar regions, which are crucial test regions for ionospheric TEC models. Experimental estimates of slant TEC from International GPS Service (IGS) stations have been compared particularly with the predictions of the NeQuick and GPS models. A very simple picture of the ionosphere like the one given by the GPS operational model appears to be insufficient to reproduce the global complex behavior of the ionosphere, as it is needed for assessment studies or for modern operational real‐time corrections of transionospheric propagation errors. The IRI estimates of TEC still present serious problems, essentially owing to the topside under high solar activity conditions, and the model cannot be integrated to heights above 2000 km. With processing resources suitable for real‐time operation, it seems that the NeQuick model can give a more widely reliable picture of the TEC estimated from GPS measurements. Computing times for this model are considerably smaller than for more complex models like NeUoG‐plas.

Timely, reliable and comprehensive global observation information is essential for space weather research. However, limited observation technology hinders the consecutive global coverage of observation data. For the integrity and continuity of the global observation data, deep learning can obtain a global Ionospheric total electron content (TEC) map by fusing multi‐source TEC maps. Different from the previous methods, in the study, a deep learning hybrid model (RFGAN) based on Dual‐Discriminator Conditional Generative Adversarial Network (DDcGAN) and Free‐Form Image Inpainting with Gated Convolution (Deepfill v2) is proposed to fuse the Massachusetts Institute of Technology (MIT)—TEC, International Global Navigation Satellite System TEC (IGS‐TEC) and altimetry satellite TEC. Throughout the RFGAN structure, we use an autoencoder model with gated convolution to inpaint the missing parts of MIT‐TEC and altimetry satellite TEC. Meanwhile, DDcGAN fuses the inpainted MIT‐TEC (MIT'‐TEC) and IGS‐TEC to get a global TEC map with high accuracy. To a certain extent, we inpainted the ocean area of MIT‐TEC through RFGAN. At the same time, RFGAN keeps the consistency of RFGAN‐TEC and MIT‐TEC in the continent area. Our proposed deep learning hybrid model can be easily extended and widely applied to other fields of space science, especially in addressing observational data loss and multi‐source data fusion.

The accuracy of low frequency space-borne Interferometric Synthetic Aperture Radar (InSAR) is seriously affected by ionospheric path delay. Range Split-Spectrum (RSS) method is a widely-used method to remove ionospheric delay phase from interferogram. This method distinguishes ionospheric component from other non-dispersive components for its dispersive character. However, the estimated ionospheric delays differ a lot with different parameters (including multilook number and filter window). For general remote sensing users, setting above parameters is more subjective and arbitrary, which will finally affect the ionospheric delay correction effect. Based on International Reference Ionosphere (IRI) model, we present a method to determine RSS parameters. Different ionospheric delays estimated by RRS method with different parameters are compared and evaluated referring to IRI. Meanwhile, we find that there is a bias between the ionospheric delays estimated by RSS and IRI. Removing this bias may further improve the effect of ionospheric delay correction.

Performance of different TEC models to provide GPS ionospheric corrections

In this study, we make a global total electron content (TEC) forecasting using a novel deep learning method, which is based on conditional generative adversarial networks. For training, we use the International GNSS Service (IGS) TEC maps from 2003 to 2012 with 2‐h time cadence. Our model has two input images (IGS TEC map and 1‐day difference map between the present day and the previous day) and one output image (1‐day future map). The model is tested with two data sets: solar maximum period (2013–2014) and solar minimum period (2017–2018). Then, we compare the results of our model with those of 1‐day Center for Orbit Determination in Europe (CODE) prediction model. Our major results can be summarized as follows. First, we successfully apply our model to the forecast of global TEC maps. Second, our model well predicts daily TEC maps with 1 day in advance using only previous TEC maps. The averaged root mean square error, bias, and standard deviation between AI‐generated and IGS TEC maps are 2.74 TECU, −0.32 TECU, and 2.59 TECU, respectively. Third, our model generates some peak structures around equatorial regions. Fourth, our model shows better performance than 1‐day CODE prediction model during both solar maximum and minimum periods. Fifth, another model with additional input data Kp index gives a slight improvement of the results. Our study shows that our deep learning model based on an image translation method will be effective for forecasting of future images using previous data.

The global statistical median behavior of the electron temperature (Te) in the topside ionosphere was investigated through in-situ data collected by Langmuir Probes on-board the European Space Agency Swarm satellites constellation from the beginning of 2014 to the end of 2020. This is the first time that such an analysis, based on such a large time window, has been carried out globally, encompassing more than half a solar cycle, from the activity peak of 2014 to the minimum of 2020. The results show that Swarm data can help in understanding the main features of Te in the topside ionosphere in a way never achieved before. Te data measured by Swarm satellites were also compared to data modeled by the empirical climatological International Reference Ionosphere (IRI) model and data measured by Jicamarca (12.0°S, 76.8°W), Arecibo (18.2°N, 66.4°W), and Millstone Hill (42.6°N, 71.5°W) Incoherent Scatter Radars (ISRs). Moreover, the correction of Swarm Te data recently proposed by Lomidze was applied and evaluated. These analyses were performed for two main reasons: (1) to understand how the IRI model deviates from the measurements; and (2) to test the reliability of the Swarm dataset as a new possible dataset to be included in the underlying empirical dataset layer of the IRI model. The results show that the application of the Lomidze correction improved the agreement with ISR data above all at mid latitudes and during daytime, and it was effective in reducing the mismatch between Swarm and IRI Te values. This suggests that future developments of the IRI Te model should include the Swarm dataset with the Lomidze correction. However, the existence of a quasi-linear relation between measured and modeled Te values was well verified only below about 2200 K, while for higher values it was completely lost. This is an important result that IRI Te model developers should properly consider when using the Swarm dataset.

Research on longitudinal discrepancies in local ionospheric variability, especially in equatorial and low-latitude regions, is a focal point of interest for the space weather modeling community. The ionosphere over these regions is influenced by complex electrodynamics, wind, and temperature dynamics that can seriously impact dynamic technological systems such as satellite tracking and positioning, satellite radio communication, and navigation control systems. Here, we researched the longitudinal variability in the ionospheric total electron content (TEC) by analyzing observed global positioning system (GPS)-derived TEC values along with those extracted from the most reliable global ionospheric maps (GIMs) and the International Reference Ionosphere (IRI-2016) model at selected stations in the vicinity of the magnetic equator along the American, African, and Asian longitude sectors. The period of study covered the descending (2016–2017) and deep solar minimum (2018–2019) years in the 24th solar cycle. Apart from the decreasing trend of the TEC from the descending to deep solar minimum period irrespective of season and longitude sector, the results showed a relatively higher magnitude of TEC in the African longitude than the other two longitude sectors. Despite evident overestimation and underestimations of TEC in both models, GIM predictions generally looked better in terms of observed variation patterns, especially in the African longitude. The study also highlights the seasonal and semiannual effects of longitudinal variations in TEC, manifesting in local time offsets and some peculiar anomalies, which seemed to be different from previously reported results, especially during the solar minimum years at the three longitude sectors. The insignificant effects of longitudinal variations on the equinoctial asymmetry are attributed to the diverse electron density distribution and ionospheric morphology at the three longitude sectors that will prompt further investigations in the future. The outcomes from this study may augment the past efforts of scientists to understand the seasonal effects of the longitudinal variations in TEC, thereby complementing the improvements of ionospheric representations in global ionosphere models and maps.