Synthesizing Virtual and Real‑Height Ionograms using IONOLAB‑RAY

We have gathered total electron content (TEC) data from a range of mid‐latitudes and low latitudes and longitudes for a wide range of solar activity. This data was used to evaluate the performance of six publicly available ionospheric models as predictors of total electron content. TEC is important for correcting modern DoD space systems, which propagate radio waves from the earth to satellites, for time delay effects of the ionosphere. The TEC data were obtained from polarimeter receivers located in North America, the Pacific, and the East coast of Asia. The ionospheric models evaluated are (1) the International Reference Ionosphere, (2) the Bent model, (3) the Ionospheric Conductivity and Electron Density model, (4) the Penn State model, (5) the Fully Analytic Ionospheric Model, and (6) a hybrid model consisting of the Union Radio Scientifique Internationale 88 (URSI‐88) coefficients coupled with the Damen‐Hartranft profile model. We will present extensive comparisons between monthly median TEC and model TEC obtained by integrating electron density profiles produced by the six models. These comparisons demonstrate that although most of the models do very well at representing ƒ0F2, none of them do very well with TEC, probably because of inaccurate representation of the topside profile. We suggest that one approach to obtaining better representations of TEC is the use of ƒ0F2 from the CCIR or URSI‐88 coefficients coupled with a good climatological slab thickness model.

This study is a preliminary analysis of the accuracy of various ionosphere models to correct single frequency altimeter height measurements for ionospheric path delay. In particular, research focused on adjusting empirical and parameterized ionosphere models in the parameterized real-time ionospheric specification model (PRISM) 1.2 using total electron content (TEC) data from the Global Positioning System (GPS). The types of GPS data used to adjust PRISM included GPS line-of-sight (LOS) TEC data mapped to the vertical, and a grid of GPS derived TEC data in a Sun-fixed longitude frame. The adjusted PRISM TEC values, as well as predictions by IRI-90, a climatological model, were compared to TOPEX/Poseidon (T/P) TEC measurements from the dual-frequency altimeter for a number of TIP tracks. When adjusted with GPS LOS data, the PRISM empirical model predicted TEC over 24 1 h data sets for a given local time to within a global error of 8.60 TECU rms during a midnight centered ionosphere and 9.74 TECU rms during a noon centered ionosphere. Using GPS derived sun-fixed TEC data, the PRISM parameterized model predicted TEC within an error of 8.47 TECU rms centered at midnight and 12.83 TECU rms centered at noon. From these best results, it is clear that the proposed requirement of 3-4 TECU global rms for TOPEX/Poseidon Follow-On will be very difficult to meet, even with a substantial increase in the number of GPS ground stations, with any realizable combination of the aforementioned models or data assimilation schemes.

Global Navigation Satellite System (GNSS) is one of the valuable techniques used in researching ionospheric total electron content (TEC). GNSS observations above ground-based stations can be used to obtain high-precision ionospheric TEC with the so-called inverse technique. Subsequently, regional and/or global ionospheric TEC models could be established with some modeling techniques. Ionospheric TEC modeling with GNSS has become a great significance for improving the accuracy of GNSS navigation and positioning, as well as analyzing the ionospheric spatial structure, which is a great motivation to the development of ionospheric TEC modeling. There is no doubt that it is easier to get a satisfactory ionospheric TEC modeling result if the used stations are evenly distributed. However, stations are usually unevenly distributed because of some practical factors. For instance, there are few stations in ocean and Antarctic region. Due to lack of GNSS observations in ocean and Antarctic regions, ionosphere pierce points (IPPs) in these regions are also unevenly distributed or even blank. Consequently, the accuracy of ionospheric modeling is less satisfactory and some negative TEC values without physical meaning even occurred. In order to improve the accuracy of global ionospheric modeling, this work tries to solve this problem by using virtual TEC observations from empirical ionospheric models as constraints in global ionospheric TEC modeling. The spherical harmonic function was employed as the modeling technique, three empirical ionospheric models, Klobuchar, International Reference Ionosphere (IRI) and NeQuick, are used to calculate virtual TEC observations in four regions with no IPP, and GNSS observations above 279 global stations are used to calculate the ionospheric TEC values. Through experimental analysis, this work compares the accuracy improvement in global ionospheric modeling by using additional empirical constraints, and studies performance of the three used empirical ionospheric models in different IPP-blank regions. The results show that additional virtual TEC observations could effectively improve the accuracy of global ionospheric TEC modeling, especially for regions with very few IPPs. The contribution of TEC constraints from empirical models to global ionospheric modeling in different epochs is different. Taking the results in UT11 as an example, three empirical ionospheric models can improve the accuracy of global ionospheric modeling from 11.43 TECU to 3.28, 3.42 and 4.15 TECU, respectively. Generally, improvement performances of the three used empirical ionospheric models in mid-high latitude region and Antarctic are comparably, while Klobuchar model is relatively advantaged in mid-latitude region and IRI model outperforms the others in equator region.

We performed a comprehensive comparison between GPS global ionosphere map (GIM) and TOPEX/Jason (T‐J) total electron content (TEC) data for the periods of 1998–2009 in order to assess the performance of GIM over the global ocean where the GPS ground stations are very sparse. Using the GIM model constructed by the Center for Orbit Determination in Europe at the University of Bern, the GIM TEC values were obtained along the T‐J satellite orbit at specific locations and times of measurements and then binned into various geophysical conditions for direct comparison with the T‐J TEC. On the whole, the GIM model was able to reproduce the spatial and temporal variations of the global ionosphere as well as the seasonal variations. However, the GIM model was not accurate enough to represent the well‐known ionospheric structures such as the equatorial anomaly, the Weddell Sea Anomaly, and the longitudinal wave structure. Furthermore, a fundamental limitation of the model seems to be evident in the unexpected negative differences (i.e., GPS < T‐J) in the northern high‐latitude and the southern middle‐ and high‐latitude regions in comparison with the T‐J TECS. The positive relative differences (i.e., GIM > T‐J) at night represent the plasmaspheric contribution to GPS TEC, which is maximized, reaching up to 100% of the corresponding T‐J TEC values in the early morning sector. In particular, the relative differences decreased with increasing solar activity, and this may indicate that the plasmaspheric contribution to the maintenance of the nighttime ionosphere does not increase with solar activity, which is different from what we normally anticipate.

This study attempts to improve the estimation of ionospheric electron density profiles over Korea and adjacent areas by employing the classical Kalman filtering technique to assimilate Total Electron Content (TEC) data from various sources into the NeQuick model. Successive corrections method was applied to spread the effect of TEC data assimilation at a given location to others that lacked TEC observations. In order to reveal that the assimilation results emulate the complex ionospheric changes during geomagnetic storms, the selected study days included both quiet (Kp ≤ 3) and disturbed geomagnetic conditions in the year 2015. The results showed that assimilation of TEC data derived from ground-based Global Positioning System (GPS) receivers could improve the root mean squared error (RMSE) associated with the NeQuick model estimation of ionospheric parameters by ≥56%. The improvement of RMSE achieved by assimilating TEC data measured using ionosondes was ~50%. The assimilation of TEC observations made by the COSMIC radio occultation technique yielded results that depicted RMSE improvement of >10%. The assimilation of TEC data measured by GPS receiver onboard Low Earth Orbiting satellites yielded results that revealed deterioration of RMSE. This outcome might be due to either the fact that the receivers are on moving platforms, and these dynamics might not have been accounted for during TEC computation or the limitation of the assimilation process. Validation of our assimilation results with global ionosphere TEC data maps as processed at the Center for Orbit Determination in Europe (CODE) revealed that both depicted similar TEC changes, showing response to a geomagnetic storm.

Using total electron content (TEC) data from multiple ground‐based Global Navigation Satellite System (GNSS) receiver networks from 2010 to 2023, we reconstructed ionospheric TEC maps for East Asia. The maps cover longitudes from 70°E to 150°E and latitudes from 20°S to 60°N. The time resolution of reconstructed TEC map was 15 min. The region with the best latitude coverage of GNSS TEC observations is found between 100°E and 110°E. Consequently, we used the TEC latitudinal profile data at 105° longitude for empirical orthogonal function decomposition, which allowed us to derive a set of orthogonal basis functions for the TEC latitudinal profile. Using these basis functions, we fitted the TEC latitude profile for each meridian plane. By combining the results from the TEC latitudinal profiles across all meridian planes from 70°E to 150°E, we created the regional TEC maps. Comparing the observed TEC data with the CODE TEC maps reveals that the CODE maps contain consistent errors in the East Asian sector, primarily due to limited data availability in this region, particularly in China. Furthermore, comparisons with Jason's TEC data over the oceans demonstrated that our reconstructed TEC map could still accurately reproduce the latitude profile, even with some missing data at some latitudes. This reconstructed regional TEC maps provide us a crucial data product that can assist us in statistically analyzing finer structural features of the ionosphere in this region.

On 21 May 2021 (UT), Yangbi Ms6.4 and Maduo Ms7.4 earthquakes occurred in mainland China. This paper analyzed the ionospheric perturbations possibly related to the earthquake, based on global positioning system (GPS) total electron content (TEC) and global ionosphere map (GIM) TEC data. We identified GPS TEC anomalies by the sliding quartile, based on statistical analysis. After eliminating the days with high solar activity levels and strong geomagnetic disturbances, the time series analysis of GPS TEC data showed that there were significant TEC anomalies from 5 to 10 May. TEC anomalies were mainly positive anomalies. We obtained the spatial and temporal distributions of TEC anomalies using natural neighbor interpolation (NNI). The results showed that the TEC anomalies were distributed in the seismogenic zone and surrounded the epicenters of the Maduo and Yangbi earthquakes, indicating that they may be related to the earthquakes. From the GIM TEC difference map, we found the TEC enhancement in the seismogenic zone and its magnetic conjugate area of the Maduo and Yangbi earthquakes at 10:00–12:00 (UT) on the 5 and 6 May. We discussed our results according to the lithosphere-atmosphere-ionosphere coupling mechanism. Finally, based our results, we suggested that the Yangbi and Maduo earthquakes may affect the ionosphere through seismogenic electric field and thermal anomalies generated during the process of lithosphere-atmosphere-ionosphere coupling.

Three methods are described to obtain ionospheric electron densities from transionospheric, rocket‐beacon total electron content (TEC) data. First, when the line‐of‐sight from a ground receiver to the rocket beacon is tangent to the flight trajectory, the electron concentration can be obtained by differentiating the TEC with respect to the distance to the rocket. A similar method may be used to obtain the electron‐density profile if the layer is horizontally stratified. Second, TEC data obtained during chemical release experiments may be interpreted with the aid of physical models of the disturbed ionosphere to yield spatial maps of the modified regions. Third, computerized tomography (CT) can be used to analyze TEC data obtained along a chain of ground‐based receivers aligned along the plane of the rocket trajectory. CT analysis of TEC data is used to reconstruct a two‐dimensional image of a simulated equatorial plume. TEC data is computed for a linear chain of nine receivers with adjacent spacings of either 100 or 200 km. The simulation data are analyzed to provide an F region reconstruction on a grid with 15×15 km pixels. Ionospheric rocket tomography may also be applied to rocket‐assisted measurements of amplitude and phase scintillations and airglow intensities.

Ionospheric total electron content anomaly possibly associated with the April 4, 2010 Mw7.2 Baja California earthquake

Abstract. Long-term (1978–1990) total electron content (TEC) data have been analyzed to show the dependence of ambient ionization on EUV radiation from the Sun. TEC observations were made at Calcutta (22.58° N, 88.38° E geographic, dip: 32° N), situated virtually below the northern crest of the equatorial ionization anomaly. Day-to-day changes in TEC at different local times do not show any significant correlation with F10.7 solar flux. A good correlation is, however, observed between the F10.7 solar flux and the monthly mean TEC when both are considered on a long-term basis, i.e. either in the ascending (1986–1990) or in the descending (1979–1985) phase. In the early morning hours the correlation coefficient maximizes around the 08:00–10:00 h IST interval. The flux independent nature of diurnal TEC is evident around the noon time hours of only a few months in the descending phase for F10.7 values greater than 150 unit. Variation of TEC for the whole time period (1979–1990) also exhibits a prominent hysteresis effect. The remarkable feature of the hysteresis effect is its local time dependence, leading to a temporal flip-over. Solar flux-normalized TEC values show a clear seasonal dependence with asymmetrical variations in the two equinoxes. The amplitudes of the equinoctial peaks reveal a prominent local time dependence. A further normalization leads to a typical local time variation of TEC. Based on solar flux, seasonal and local time dependent features of TEC, an empirical formula has been developed to represent the TEC variation in the early morning hours. It yields a quantitative estimate of the solar flux dependent nature of the TEC variation. The formula has been validated using the available TEC data and data from the neural network.

This paper investigates the capabilities of Neural Network (NN) application in estimating the missing ionospheric total electron content (TEC) data via interpolation and extrapolation methods. TEC is an important parameter that describes the state of the ionosphere. A multilayer feed-forward network with a back propagation algorithm is applied to estimate the TEC over Parit Raja (Lat. 1°52′N, Long. 103°06′E) an equatorial latitude station in Malaysia. The solar and magnetic indices, seasonal variation as well as diurnal variation are used as the input spaces in the NN to estimate the missing GPS TEC. The studies period is based on short term data during the medium solar activity period from 2005 to 2006. Normalized RMSE, RMSE and relative correction are computed for both methods to evaluate the capability of NN to interpolate and extrapolate the missing data. The results show that NN is able to interpolate the missing TEC data within the input range better than extrapolating the missing data outside the input range. The NN2 model finds hard to extrapolate the missing TEC data especially during the night hours, where the GPS TEC values are underestimated. Overall the relative correction of NN1 model is above 90% while for NN2 is below 90% for all ranges of missing rate.

An accurate representation of the topside ionosphere and plasmasphere electron content is still one of the important issues for ionospheric models, particularly for those used in GNSS applications. In this work, a validation of NeQuick 2 empirical ionospheric model has been performed in terms of ionosphere topside and plasmasphere electron content in the altitude range ~800–20,200 km. For this purpose, total electron content (TEC) data derived from precise orbit determination (POD) antennas onboard COSMIC low earth orbit (LEO) satellites tracking GPS signals have been used. The data span the period from 2006 to 2018 and correspond to different heliogeophysical conditions. In order to remove unrealistic TEC values from the validation process, a specific filtering procedure has been applied to POD‐derived TEC data. Subsequently, the statistical analysis of the difference between the modeled and the corresponding experimentally derived TEC values has been performed on data obtained during geomagnetically quiet periods. The results are presented as a function of solar activity level, season, local time, and geomagnetic latitude plots. They show that NeQuick 2 model underestimates the ionosphere topside and plasmasphere electron content, particularly during the early morning hours. The conditions under which the discrepancy between the model and experimental data is the highest have been also identified and considered as indications for future model improvements.

A dual-frequency global positioning system (GPS) receiving set-up at Guwahati (26° 10′ N, 91° 45′ E), has been in operation for the last year and a half, providing total electron content (TEC) data as input for understanding pre-earthquake contributions to low-latitude atmospheric dynamics. The major China earthquake of 12 May 2008, with magnitude 8.0 and an epicentre at 31° 24′ N, 103° 58′ E is a rare event to facilitate extracting earthquake features on the TEC data, and hence low-latitude system perturbations. This paper begins with a brief discussion on the methods adopted in identifying TEC performance before an impending earthquake from ionospheric data, and presents results of analysis of the event of 12 May. TEC magnitudes recorded with latitude / longitude and elevation of satellites for every pass are linked with pre-earthquake TEC features and are used as inputs to identify epicentre position. The role of seismic-time refractive index variations is examined to explain the observed TEC characteristics.

GPS (Global Positioning System) satellites and a receiver located at Fairbanks, Alaska are used to detect auroral activity. A technique for using GPS total electron content (TEC) data to detect auroral‐E ionization (AEI) at all satellite line‐of‐sight elevations is presented. The location of AEI during auroral substorms is determined and is consistent with simultaneous magnetometer data. Maps of detected AEI events reveal the distribution of AEI in space and time. Additionally, a technique is presented for identifying the effects of the auroral oval E‐layer on the TEC data. Particle precipitation measured by the TIROS satellite is closely related to variations in the TEC data. The effects of the oval are consistently seen in the TEC data for a variety of magnetic conditions. The location of the equatorward edge of the oval is determined during auroral substorms and compares well with a model of the oval and with individual TIROS passes.

Comparison of standard TEC models with a Neural Network based TEC model using multistation GPS TEC around the northern crest of Equatorial Ionization Anomaly in the Indian longitude sector during the low and moderate solar activity levels of the 24th solar cycle