International Reference Ionosphere Model Research Articles

Modeling Total Electron Content and Critical Frequency at F1 and E Layer Boundary

This work investigates the variation of the total electron content TEC and the critical frequency fo in the boundary zone of the F1 and E layers at the low-latitude in the ionosphere. This study takes place at the Ouagadougou station (12.4°N and 358.5°E), in West Africa during the quiet geomagnetic activity of solar cycle 23. Ionosphere is the upper layer of the Earth's atmosphere ionized mainly by solar X- and UV-rays, extending from around 80km altitude up to 1000km [1] [2]. Ultraviolet light from the sun ionizes the atoms and molecules in the Earth's upper atmosphere [3]. For this study we use the 2016 version of International Reference Ionosphere (IRI) model. The quiet periods of maximum and minimum phase of solar cycle 23 are considered [4] [5]. From this study, it emerges that at the E and F1 layer boundary zone, TEC and fo increase during the day as solar irradiance increases and decrease as solar irradiance decreases.

Evaluating F2-region long-term trends using the International Reference Ionosphere (IRI) model: is this a feasible approximation for experimental trends?

Abstract. The International Reference Ionosphere (IRI) is a widely used empirical ionospheric model based on observations from a worldwide network of ionospheric stations. Therefore, it would be reasonable to expect it to capture long-term changes in key ionospheric parameters, such as foF2 and hmF2 linked to trend forcings like greenhouse gas increasing concentrations and the Earth's magnetic field secular variation. Despite the numerous reported trends in foF2 and hmF2 derived from experimental data and model results, there are inconsistencies that require continuous refinement of trend estimation methods and regular data updates. This ongoing effort is crucial to address the difficulties posed by the weak signal-to-noise ratio characteristic of ionospheric long-term trends. Furthermore, the experimental verification of these trends remains challenging, primarily due to time and spatial coverage limitations of measured data series. Achieving these needs for accurate detection of long-term trends requires extensive global coverage and high resolution of ionospheric measurements together with long enough periods spanning multiple solar cycles to properly filter out variations of shorter terms than the sought trend. Considering these challenges, IRI-modeled foF2 and hmF2 parameters offer a valuable alternative for assessing trends and obtaining a first approximation of a plausible global picture representative of experimental trends. This work presents these global trend patterns, considering the period 1960–2022 using the IRI-Plas 2020 version, which are consistent with other model predictions. While IRI explicitly takes into account the Earth's magnetic field variations, the increase in the concentrations of greenhouse gases appears indirectly through the Ionospheric Global index (IG) which is derived from ionospheric measurements. F2-region trends induced by the first mechanism should be important only around the magnetic equator at the longitudinal range with the strongest displacement, and it should be negligible out of this region. Conversely, trends induced by the greenhouse effect, which are the controversial ones, should be dominant away from the geomagnetic equator and should globally average to negative values in both cases, i.e., foF2 and hmF2. Effectively, these negative global means are verified by trends based on IRI-Plas, even though not for the correct reasons in the hmF2 case. In addition, a verification was performed for more localized foF2 trend values, considering data from nine mid-latitude stations, and a reasonable level of agreement was observed. It is concluded that the IRI model can be a valuable tool for obtaining preliminary approximations of the Earth's magnetic-field-induced long-term changes in foF2 and hmF2, as well as of experimental trends only in the foF2 case. The latter does not hold for hmF2, even if the trends obtained are close to the expected values.

Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism

AbstractThe critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short‐term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short‐term memory model based on attention mechanism (BiLSTM‐Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM‐Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM‐Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM‐Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.

Performance evaluation of IRI-Plas 2017 model with ionosonde data measurements of ionospheric parameters

This study presents an evaluation of the performance of the International Reference Ionosphere Model augmented with the Plasmasphere (IRI Plas 2017) from the Addis Ababa Ionosonde Station, Ethiopia, for measuring ionospheric parameters at geographic (9.00o N, 38.70o E) and geomagnetic (0.16o N, 110.44o E) location on selected days of 2014. During comparison hourly and daily variability of the ionospheric parameters of the electron density profile (Neh), maximum electron density (NmF2) and maximum height (hmF2) measurements are considered. When evaluating the IRI Plas model using ionosonde data, the percentage deviation and correlation coefficient (R) were used as measures of IRI Plas model performance. In general, the overall results of the study show that the IRI Plas 2017 model mostly overestimates at most altitudes and hours of electron density measurements. The IRI Plas model has an acceptable fit with the ionosonde electron density measurements at altitudes of 100 km–200 km, mostly during the hours between 03LT and 09 LT and 15 LT–21 LT, while the model biases in other altitudes and hours with overestimates or underestimates in the ionosonde electron density measurements. The IRI-Plas 2017 model has a good correlation after midnight and around midday hours, with about ±2% point deviation from the ionosonde electron density measurement. The model has a high percentage deviation value for electron density measurements, mostly between altitudes of 200 km and 450 km and during early nighttime and before midnight hours. IRI-Plas model measurements of NmF2 and hmF2 are mostly underestimated from the ionosonde data during the nighttime (21 LT–09 LT) and overestimated during the daytime (09 LT–21 LT). The NmF2 values measured with the model are more consistent with ionosonde values than hmF2 values.

A Comparison of a GNSS‐GIM and the IRI‐2020 Model Over China Under Different Ionospheric Conditions

AbstractThe ionosphere is a crucial factor affecting Global Navigation Satellite System positioning. The Global Ionosphere Map (GIM) or the International Reference Ionosphere (IRI) model can be used for regional ionospheric correction. Since southern China is located near the electron density equatorial anomaly, this study evaluates the performance of the Wuhan University GIM (WHU‐GIM) and the IRI‐2020 from 2008 to 2020 over the China region. The comparison indicates that the Total Electron Content (TEC) from IRI‐2020 is lower than that from WHU‐GIM overall, the discrepancy is more obvious in high solar conditions and low‐latitude regions. The differential Slant TEC (dSTEC) during a phase‐arc with about 0.1 TECU accuracy derived from Global Positioning System (GPS) observations is used for model validation, the results show that the accuracies of WHU‐GIM and IRI‐2020 are 3.14 and 4.57 TECU, respectively. The dSTEC error is larger at low latitudes and decreases with increasing latitude. GPS‐derived TEC is taken for reference to evaluate the model reliability. Results show that both models can reproduce the diurnal TEC variations, but IRI‐2020 is more influenced by geomagnetic activities. The TEC correction percentage for IRI‐2020 is about 60%–80% under different ionospheric conditions, while for WHU‐GIM is 80%–90%. The Single‐Frequency Precise Point Positioning is performed with the ionosphere delay corrected by the two models, respectively. The positioning errors show that using IRI‐2020 has a lower accuracy, and the TEC discrepancy of the IRI‐2020 can cause a large bias in the up direction, especially at low‐latitude regions.

Performance evaluation for vertical TEC predictions over the East Africa and South America: IRI-2016 and IRI-2020 versions

In this paper, we evaluate and report the International Reference Ionosphere (IRI) model's vertical Total Electron Content (TEC) regional profile. Diurnal, monthly, seasonal, and storm-time characteristics of IRI estimates over the equatorial region's ionosphere are validated. We compared the vertical TEC derived from IRI-2020 and its predecessor, IRI-2016, with the GPS-TEC measurements. Results show that IRI (both versions) agrees with observed TEC during solar cycle maximum periods. Exceptionally, over Turkwel station, IRI-2020 produced double peak profiles on the March equinox, June solstice, and September equinox and overestimated with larger discrepancies. Over the other three stations, both IRI versions reproduced the seasonal averaged observed TEC with only slight time lags and discrepancies. During geomagnetically perturbed times, IRI-2020 better indicated positively enhanced GPS-TEC than IRI-2016. However, during the March 17, 2015 storm, IRI-2020 underestimated the storm-enhanced TEC over Bahir Dar station at all phases of the storm. IRI-2016 shows good performances for negative storms where lower TEC measurements are recorded.

Validation of a neural network based model to predict foF2

This work presents the application of Neural Networks (NNs) techniques to develop an empirical model for foF2, the critical frequency of the F2 ionospheric layer. Prediction of foF2 parameter is important because of the paucity of data in some areas of the globe (due to uneven distributions of ground- based measuring equipment) and, also, because of the roles it plays in high frequency communications and navigation purposes. Hourly values of foF2 corresponding to the period 1985–2007 and obtained from 105 World-wide distributed ionospheric stations, have been considered in this work. A statistical analysis of the difference between NN model-derived and the corresponding experimental foF2 values was carried out for eleven (11) selected ionospheric stations during periods of very high (year 2000), relatively low (year 2006) and very low (year 2008) solar activity. In particular, the ionospheric behavior during the years 2000 and 2008 appeared to be very challenging to be captured for any empirical model. For comparison purposes, the same kind of statistical analysis was performed with foF2 values as obtained from the International Reference Ionosphere (IRI) model and from NeQuick 2 model. Results of the statistical comparison confirmed that the neural network modelling approach is suitable to describe in a climatological way the main characteristics of the ionosphere F2 layer peak frequency, also over the extended geographic region where the additional sensor stations were located. The results obtained also indicate that the NN predictions and NeQuick 2 algorithm driven by solar flux (F10.7) index have similar performance.

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.

Accuracy of hmF2 estimations, including IRI-2020 options and ionograms validated parameters, compared to ISR measurements at Millstone Hill

Several empirical formulations used over time to estimate the fundamental ionospheric parameter hmF2 have been compared in this study. These are the first formulation proposed by Shimazaki (1955) (SHI-1955) as a function of the propagation parameter M(3000)F2, the more accurate BSE-1979 formula proposed by Bilitza et al. (1979) and firstly adopted by the International Reference Ionosphere (IRI) model, and the newest Altadill-Magdaleno-Torta-Blanch (AMTB-2013) (Altadill et al., 2013) and SHU-2015 (Shubin, 2015) models, obtained with a different approach with no explicit dependence on any ionospheric parameter and added as alternative options in the IRI-2016. The evaluation of the accuracy of the available formulation is performed by comparing the modeled values of hmF2 with those simultaneously obtained with independent measurements from the Incoherent Scatter Radar (ISR) installed at the Millstone Hill ionospheric station. The database considered consists of 3626 measurements, thus allowing the evaluation of the results for different heliogeophysical conditions. SHI-1955 and BSE-1979 formulations are evaluated also using input data manually scaled from ionograms recorded at the same location, with the aim of evaluating their accuracy when updated with validated data rather than modeled ones. The SHU-2015 is confirmed the best option in any condition, while AMTB-2013 turns out to perform poorly during night, when SHI-1955 and BSE-1979 fed by validated data can be used for trend analyses due to the high correlation with ISR data. Despite this, BSE-1979 performs better with modeled parameters as input, in terms of RMSE and mean deviation from ISR data. The use of SHI-1955 with CCIR-modeled M(3000)F2 is discouraged under daytime conditions even for long trend analyses.

Finite element method study on VLF propagation in the fine Earth‐ionosphere waveguide

AbstractA fast and highly efficient frequency‐domain finite element method (FEM) is developed to simulate propagation characteristics of very low frequency (VLF) waves in the fine Earth‐ionosphere waveguide structure containing the transition period between day and night. The FEM is accelerated by embedding the GPU‐based parallel LU factorization method. We present an approach for determining the waveguide structure with the VLF transmitter and receiver locations. After validating the method, effects of the time‐ and location‐varying electric density on the amplitude and phase of the VLF wave are investigated. The electric density of the International Reference Ionosphere (IRI) is revised by coupling with the solar zenith angle. Numerical results simulated using the revised IRI model show excellent agreement with the measured data for the VLF wave from Novosibirsk to Qingdao. This work provides a promising way to research VLF electromagnetic properties of the Earth‐ionosphere waveguide and improve the prediction accuracy of VLF navigation and communication.

A Novel Technique for High-Precision Ionospheric VTEC Estimation and Prediction at the Equatorial Ionization Anomaly Region: A Case Study over Haikou Station

This paper introduces a novel technique that uses observation data from GNSS to estimate the ionospheric vertical total electron content (VTEC) using the Kriging–Kalman method. The technique provides a method to validate the accuracy of the Ionospheric VTEC analysis within the Equatorial Ionization anomaly region. The technique developed uses GNSS VTEC alongside solar parameters, such as solar radio flux (F10.7 cm), Disturbance Storm Time (Dst) and other data, and Long Short Term Memory (LSTM) Networks to predict the occurrence time of the ionospheric equatorial anomaly and ionospheric VTEC changes. The LSTM method was applied to GNSS data from Haikou Station. A comparison of this technique with the neural network (NN) model and International Reference Ionosphere model shows that the LSTM outperforms all of them at VTEC estimation and prediction. The results, which are based on the root mean square error (RMSE) between GNSS VTEC and GIM VTEC outside the equatorial anomaly region, was 1.42 TECU, and the results of GNSS VTEC and VTEC from Beidou geostationary orbit satellite, which lies inside the equatorial ionization anomaly region, was 1.92 TECU. The method developed can be used in VTEC prediction and estimation in real time space operations.

Seasonal Variation of the F2-Layer Critical Frequency and Comparison with the IRI-2016 Model during Two Extremes of Solar Activity Phase of Solar Cycle 22

Abstract: Introduction: The variation of the ionosphere is mostly studied using the critical frequency of the F2-layer (foF2) whose values can also be predicted by an ionospheric model. The widely used model for predicting ionospheric parameters is the International Reference Ionosphere (IRI). Aim: This paper aims to demonstrate how well the current International Reference Ionosphere (IRI-2016) model performs in predicting the critical frequency of the F2-layer (foF2) over two equatorial stations during two extremes of solar activity phase of solar cycle 22.. Methods: The hourly foF2 experimental data collected during the Maximum Phase of Solar Activity MPSA year (1989) and Minimum Phase of Solar Activity MnPSA year (1986) at Ougadougou (Geomagnetic Latitude 0.59 oN, Geomagnetic Longitude 71.46 oE) in the African longitudinal sector and Manila (Geomagnetic Latitude 3.4 oN, Geomagnetic Longitude 191.1 oE) in the Asia longitudinal sector as well as the predicted foF2 data by the IRI-2016 model were used in this study. Sunspot data from Zurich was utilized as a measure of solar activity phase. The foF2 data were grouped into four seasons before analysis began. Comparing the seasonal means of the experimental foF2 data and the IRI-2016 modeled foF2, it was possible to determine how closely the model matched the experimental data at the different seasons and longitudinal sectors Results: The results showed that the IRI-2016 model overestimate and underestimate the observed foF2 at different periods of the day during the equinox and solstice seasons. Observation showed that the highest positive and negative percentage deviations were observed mostly during the post-midnight hours. Observation also showed that Seasonal mean values of the IRI-2016 model of both options showed remarkable improvement at this two stations since their values have little difference from the observed foF2 values. Conclusion: The discrepancy (underestimation and overestimation) in the IRI-2016 model is found larger during MPSA year than during MnPSA year. The URSI option performs better than the CCIR option since its predicted values are much closer to the observed values. Both options of the model perform better in the Asian longitudinal sector than the African longitudinal sector.

A Prediction Method of Ionospheric hmF2 Based on Machine Learning

The ionospheric F2 layer is the essential layer in the propagation of high-frequency radio waves, and the peak electron density height of the ionospheric F2 layer (hmF2) is one of the important parameters. To improve the predicted accuracy of hmF2 for further improving the ability of HF skywave propagation prediction and communication frequency selection, we present an interpretable long-term prediction model of hmF2 using the statistical machine learning (SML) method. Taking Moscow station as an example, this method has been tested using the ionospheric observation data from August 2011 to October 2016. Only by inputting sunspot number, month, and universal time into the proposed model can the predicted value of hmF2 be obtained for the corresponding time. Finally, we compare the predicted results of the proposed model with those of the International Reference Ionospheric (IRI) model to verify its stability and reliability. The result shows that, compared with the IRI model, the predicted average statistical RMSE decreased by 5.20 km, and RRMSE decreased by 1.78%. This method is expected to provide ionospheric parameter prediction accuracy on a global scale.

Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers

The data assimilation algorithm is a common algorithm in space weather research. In this paper, the time-frequency information in the dispersion spectrum of lightning whistlers received by the ZH-1 satellite is used as the observed value, and the international reference ionospheric model serves as the background model to construct the calculation model of the propagation time of lightning whistlers in the ionosphere. Kalman filtering is adopted to assimilate the electron density distribution along the propagation path of lightning whistlers. The results show that the situation where the electron density of the background model deviates greatly from the true value is significantly improved through data assimilation. The electron density after assimilation is in good agreement with the true value, which effectively helps realize the process of using observed values to correct the background value. On this basis, the influence of the frequency difference on the assimilation inversion effect is studied, and the results show that the assimilation effect is worse when the frequency difference between frequency points is less than 1 kHz.

Modeling the Topside Ionosphere Effective Scale Height through In Situ Electron Density Observations by Low-Earth-Orbit Satellites

In this work, we aim to characterize the effective scale height at the ionosphere F2-layer peak (H0) by using in situ electron density (Ne) observations by Langmuir Probes (LPs) onboard the China Seismo-Electromagnetic Satellite (CSES—01). CSES—01 is a sun-synchronous satellite orbiting at an altitude of ~500 km, with descending and ascending nodes at ~14:00 local time (LT) and ~02:00 LT, respectively. Calibrated CSES—01 LPs Ne observations for the years 2019–2021 provide information in the topside ionosphere, whereas the International Reference Ionosphere model (IRI) provides Ne values at the F2-layer peak altitude for the same time and geographical coordinates as CSES—01. CSES—01 and IRI Ne datasets are used as anchor points to infer H0 by assuming a linear scale height in the topside representation given by the NeQuick model. COSMIC/FORMOSAT—3 (COSMIC—1) radio occultation (RO) data are used to constrain the vertical gradient of the effective scale height in the topside ionosphere in the linear approximation. With the CSES—01 dataset, we studied the global behavior of H0 for daytime (~14:00 LT) and nighttime (~02:00 LT) conditions, different seasons, and low solar activity. Results from CSES—01 observations are compared with those obtained through Swarm B satellite Ne-calibrated measurements and validated against those from COSMIC—1 RO for similar diurnal, seasonal, and solar activity conditions. H0 values modeled by using CSES—01 and Swarm B-calibrated observations during daytime both agree with corresponding values obtained directly from COSMIC—1 RO profiles. Differently, H0 modeling for nighttime conditions deserves further investigation because values obtained from both CSES—01 and Swarm B-calibrated observations show remarkable and spatially localized differences compared to those obtained through COSMIC—1. Most of the H0 mismodeling for nighttime conditions can probably to be attributed to a sub-optimal spatial representation of the F2-layer peak density made by the underlying IRI model. For comparison, H0 values obtained with non-calibrated CSES—01 and Swarm B Ne observations are also calculated and discussed. The methodology developed in this study for the topside effective scale height modeling turns out to be applicable not only to CSES—01 satellite data but to any in situ Ne observation by low-Earth-orbit satellites orbiting in the topside ionosphere.

A Comparative Study of VLF Transmitter Signal Measurements and Simulations during Two Solar Eclipse Events

To monitor the Very-Low-Frequency (VLF) environment, a VLF detection system has been installed in Suizhou, China, a location with the longitude almost identical to that of the NWC transmitter in Australia. In the years 2019 and 2020, two solar eclipses crossed the NWC–Suizhou path at different locations. Each solar eclipse event represents a naturally occurring controlled experiment, but these two events are unique in that similar levels of electron density variation occurred at different locations along the VLF propagation path. Therefore, we conducted a comparative study using the VLF measurements during these two eclipses. Previous studies mostly estimated a pair of the reflection height (h′) and sharpness parameter (β) using the Long Wavelength Propagation Capability code, whereas, in this study, we use the VLF amplitude and phase as constraints in order to find the electron density change that best explains the VLF measurements. The eclipse measurements could be best explained if the path-averaged β value was 0.56 and 0.62 km−1 for the 2019 and 2020 eclipse, respectively. The VLF reflection height increased from 71.5 to 73.3 km for the 2019 eclipse and from 71.1 to 72.8 km for the 2020 eclipse. The best-fit β values were consistent with the Faraday International Reference Ionosphere model and statistical studies, and the h′ change was also consistent with previous studies and theoretical calculations. Moreover, present results suggested that VLF signals collected by a single receiver were not sensitive to where the electron density change occurs along the propagation path but reflected the average path condition. Therefore, a network of VLF receivers is required in order to monitor in real time the spatial extent of the space weather events that disturb the lower ionosphere.

A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction

In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters.

A quantitative analysis of latitudinal variation of ionospheric total electron content and comparison with IRI-2020 over China

Many studies have investigated the spatial variations of the ionosphere, but the quantitative characteristics of the ionosphere are rarely reported. In this paper, we utilize the total electron content (TEC) data to evaluate the latitudinal gradient of the ionosphere within 10°-50° N over the China sector. It is found that the magnitudes of latitudinal gradient are significantly higher within 10°-40° N and 45°-50° N, respectively. The database of TEC from 1 November 2018 to 31 October 2022 is processed to figure out the local time, seasonal, and solar activity dependency of the latitudinal gradient. The results suggest that the gradient within 10°-40° N is higher in the daytime and during high solar activity period. They are more noticeable in the spring and autumn, and least visible in the summer. Conversely, the gradient within 45°-50° N strengthens in the nighttime and under lower solar activity, and has larger values in the summer months. Furthermore, the International Reference Ionosphere (IRI-2020) model is assessed in terms of the reproducibility of latitudinal gradient. The IRI-2020 basically represents the latitudinal gradient within 10°-40° N, whereas it overestimates the gradient in the low solar activity period and misses the gradient features near 45°–50° N.

Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)

Electron density (or electron concentration) is a critical metric for characterizing the ionosphere’s mobility. Shortwave technologies, remote sensing systems, and satellite communications—all rely on precise estimations of electron density in the ionosphere. Using electron density profiles from FORMOSAT-3/COSMIC (Constellation Observation System for Meteorology, Ionosphere, and Climate) from 2006 to 2013, a four-dimensional physical grid model of ionospheric electron density was created in this study. The model, known as EDG-DNN, utilizes a DNN (deep neural network), and its output is the electron density displayed as a physical grid. The preprocessed electron density data are used to construct training, validation, and test sets. The International Reference Ionosphere model (IRI) was chosen as the reference model for the validation procedure since it predicts electron density well. This work used the IRI-2016 version. IRI-2016 produced more precise results of electron density when time and location parameters were input. This study compares the electron density provided by IRI-2016 to the EDG-DNN to assess the merits of the latter. The final results reveal that EDG-DNN has low-error and strong stability, can represent the global distribution structure of electron density, has some distinctive features of ionospheric electron density distribution, and predicts electron density well during quiet periods.

Improvement of international reference ionospheric model total electron content maps: a case study using artificial neural network in Egypt

Abstract An essential ionosphere parameter that can be applied for ionosphere corrections in radio systems is the ionosphere’s total electron content (TEC). TEC is a crucial parameter for ionospheric correction in the Global Navigation Satellite Systems (GNSS) of positioning, navigation, and radio science. This study uses the artificial neural network (ANN) application to improve the International Reference Ionospheric Model (IRI-2016) TEC maps across Egypt. The study period is based on the data that were accessible between 2013 and 2020. The ANN model input parameters are (year, day, hour, latitude, and longitude). The ANN1 and ANN2 estimate TEC values of the enhanced IRI-2020 and IRI-2016 according to the Center for Orbit Determination in Europe (CODE), respectively. ANN3 and ANN4 estimate TEC values of the enhanced IRI-2020 and IRI-2016 regarding IGS stations data analyzed by GNSS Analysis software for the multi-constellation and multi-frequency Precise Positioning (GAMP) model, respectively. The ANN model’s validations were based on the root mean square error (RMSE), correlation coefficient (CC), and T-test. According to the results, the suggested ANN can accurately predict the TEC over Egypt. In comparison to the IRI model, the TEC maps that the ANN models produced are significantly more in accordance with the related CODE and GAMP TEC maps. These results demonstrate that the developed approach can enhance IRI 2016 and IRI-2020s ability to estimate global TEC maps. For the ANN1 model, the mean CC and RMSE are 0.92, and 5.15 TECU for all the global data sets compared by CODE. On the other hand, the CC and RMSE between IRI-2020 and CODE are 0.847 and 7.67 TECU. For the ANN2, the mean CC and RMSE are 0.87, 5.59 TECU compared by CODE, respectively. Although the CC and RMSE between IRI-2016 and CODE are 0.820 and 9.052 TECU respectively. For the ANN3, the CC and RMSE are 0.830 and 4.87 TECU compared with GAMP for all global data, respectively. On the other hand, the CC and RMSE between IRI-2020 and GAMP are 0.644 and 10.41, respectively. For the ANN4 the CC and RMSE are 0.82, and 5.95 TECU compared with GAMP, respectively. Although the CC and RMSE between IRI-2016 and GAMP are 0.665 and 12.347 TECU respectively.