Ionospheric Model Research Articles

New Expression of the Field‐Line Integrated Rayleigh‐Taylor Instability Growth Rate

AbstractAn expression of Rayleigh‐Taylor (R‐T) instability growth rate based on the field‐line integrated theory is newly established. This expression can be directly utilized in ionosphere models with magnetic flux tube structure based on Modified Apex Coordinates. The R‐T instability growth rates are calculated using the thermospheric and ionospheric conditions based on the coupled Whole Atmosphere Model and Ionosphere Plasmasphere Electrodynamic model (WAM‐IPE). The parameters used in this calculation include the field‐line integrated conductivities and currents, which consider the Quasi‐Dipole Coordinates and the modifications to the equations of electrodynamics. Detailed description of the new formulas and comprehensive analyses of diurnal, longitudinal, and seasonal variations of the R‐T instability growth rate are carried out. The dependencies of growth rates on pre‐reversal enhancement (PRE) vertical drifts and solar activity are also examined. The results show that pronounced R‐T growth rates are captured between 18 and 22 local time (LT) when strong PRE occurs in the equatorial ionosphere. The simulated R‐T growth rate increases with increasing solar activity levels and demonstrates strong correlations with the angle between the sunset terminator and the geomagnetic field line. These results are consistent with plasma irregularity occurrence rates shown in various satellite observations, suggesting that the newly developed R‐T growth rate calculation can effectively capture the probability of irregularities by considering the changes along magnetic flux‐tubes in the ionosphere. Since the WAM‐IPE is running in operation at National Oceanic and Atmospheric Administration Space Weather Prediction Center, the new calculations can be potentially implemented in the near future to provide forecasted information of the R‐T growth rate.

Generation of Top‐Boundary Conditions for 3D Ionospheric Models Constrained by Auroral Imagery and Plasma Flow Data

AbstractData products relating to auroral arc systems are often sparse and distributed while ionospheric simulations generally require spatially continuous maps as boundary conditions at the topside ionosphere. Fortunately, all‐sky auroral imagery can provide information to fill in the gaps. This paper describes three methods for creating electrostatic plasma convection maps from multi‐spectral imagery combined with plasma flow data tracks from heterogeneous sources. These methods are tailored to discrete arc structures with coherent morphologies. The first method, “reconstruction,” builds the electric potential map (from which the flow field is derived) out of numerous arc‐like ridges that are then optimized against the plasma flow data. This method is designed for data from localized swarms of spacecraft distributed in both latitude and longitude. The second method, “replication,” uses a 1D across‐arc flow data track and replicates these data along a determined primary and secondary arc boundary while simultaneously scaling and rotating to keep the flow direction parallel to the arc and the flow shear localized at the arc boundaries. The third, “weighted replication,” performs a replication on two data tracks and calculates a weighted average between them, where the weighting is based on data track proximity. This paper shows the use of these boundary conditions in driving and assessing 3D auroral ionospheric, multi‐fluid simulations.

ELF emission in the topside ionosphere from the ZEVS transmitter detected by CSES satellite

The extremely-low-frequency (ELF) response in the upper ionosphere to ground large-scale transmitter ZEVS on the Kola Peninsula has been detected by low-Earth orbiting satellite CSES (∼500 km altitude). When the satellite was in the vicinity of the ZEVS transmitter above the White Sea (horizontal distance ∼400–900 km), the electric and magnetic sensors detected a narrowband 82 Hz emission with amplitudes E≃1-3μV/m and B≃0.5-1.0 pT. We modeled the ELF wave field spatial structure in the upper ionosphere excited by an oscillating 82 Hz linear current with the 60 km length suspended above a high-resistive ground. Realistic altitudinal profiles of the plasma parameters during events under study have been reconstructed with the use of the IRI ionospheric model. The modeled amplitudes of electromagnetic response of the upper ionosphere are in reasonable agreement with the 82-Hz emission power recorded by the CSES satellite for a typical transmitter current intensity >100 A.

Statistical analysis of low latitude spread F at the American, Atlantic, and Pacific sectors using digisonde observations

Statistical analysis of low latitude spread F is presented for three different longitudinal sectors from Jicamarca (12°S, 76.8°W, −2.5° declination angle) from 2001 to 2016, Ascension Island (7.9°S, 14.4°W, −15.09° declination angle) from 2000 to 2014, Kwajalein (8.71°N, 167.7°E, 7.5° declination angle) from 2004 to 2012. Digisonde data from these stations have been processed and analyzed to study statistical variations of equatorial spread F, a diagnostic of irregular plasma structure in the ionosphere. A new automated method of spread F detection using pattern recognition and edge detection for low latitude regions is used to determine solar and seasonal variation over these three sites. An algorithm has been developed to detect the foF2 and hpF2 parameters and this has been validated by comparisons with manually scaled data as well as with SAMI2 and International Reference Ionosphere models showing good correlation. While significant variation is not observed over the solar cycle, the different longitudes and declination angles contribute to the variations over the seasonal cycle.

Ionospheric contributions to the excess power in high-redshift 21-cm power-spectrum observations with LOFAR

ABSTRACT The turbulent ionosphere causes phase shifts to incoming radio waves on a broad range of temporal and spatial scales. When an interferometer is not sufficiently calibrated for the direction-dependent ionospheric effects, the time-varying phase shifts can cause the signal to decorrelate. The ionosphere’s influence over various spatiotemporal scales introduces a baseline-dependent effect on the interferometric array. We study the impact of baseline-dependent decorrelation on high-redshift observations with the Low Frequency Array (LOFAR). Data sets with a range of ionospheric corruptions are simulated using a thin-screen ionosphere model, and calibrated using the state-of-the-art LOFAR epoch of reionization pipeline. For the first time ever, we show the ionospheric impact on various stages of the calibration process including an analysis of the transfer of gain errors from longer to shorter baselines using realistic end-to-end simulations. We find that direction-dependent calibration for source subtraction leaves excess power of up to two orders of magnitude above the thermal noise at the largest spectral scales in the cylindrically averaged autopower spectrum under normal ionospheric conditions. However, we demonstrate that this excess power can be removed through Gaussian process regression, leaving no excess power above the 10 per cent level for a $5~$ km diffractive scale. We conclude that ionospheric errors, in the absence of interactions with other aggravating effects, do not constitute a dominant component in the excess power observed in LOFAR epoch of reionization observations of the North Celestial Pole. Future work should therefore focus on less spectrally smooth effects, such as beam modelling errors.

Modeling Ion Transport in the Upper Ionosphere of Mars: Exploring the Effect of Crustal Magnetic Fields

AbstractStatistically ion and electron densities are enhanced above strong crustal magnetic field regions according to measurements made by the Mars Atmosphere and Volatile Evolution (MAVEN) spacecraft. Plasma created by ionization of neutrals in the lower ionosphere (where chemistry dominates) flows upward and becomes trapped on closed magnetic field loops. Enhanced ion density in the ionosphere (particularly O2+) is associated with enhanced photochemical escape of atomic oxygen. This paper presents a quasi‐1D multi‐fluid time‐dependent model of the Martian ionosphere for nine ion species. Ionospheric temperatures are adopted but ion densities and velocities (along the field lines) are determined using a numerical solution of the continuity and momentum equations. Diurnal effects are explored by varying photoionization rates. Three crustal field cases are considered: a low altitude closed, a high altitude closed, and a high altitude open field line. Additionally, a case with no crustal field is modeled to provide a comparison between regions with and without crustal fields in the upper Martian ionosphere. Model results show higher ion and electron densities in the crustal field cases than in the purely induced field case. Additionally, we find that densities are generally higher on the closed field lines than on the open field lines, and ion velocities are generally up the field lines, away from the Martian surface. We also find that velocities are larger on the open field line case. We compare modeled density results to MAVEN data and find general agreement. Implications for atmospheric escape, particularly photochemical escape of O, are also discussed.

Short-wave communication link prediction algorithm based on deep learning

In view of the shortcomings that the accuracy of the existing ionospheric models is not enough to reflect the local real-time changes, this paper uses the time series prediction tool of deep learning to obtain the model of a specific communication link, and combines it with the ionospheric theoretical calculation model to improve the accuracy of communication link prediction. The main work is to analyze and compare the international broadcasting data and the model calculation data, summarize and sort out the characteristics of the link data, and then on this basis, look at the change of the difference between the two, and carry out link prediction, which achieves the goal of improving the calculation effect of the link model, which is of great significance to the short-wave frequency management.

A Prediction Model of Ionospheric Total Electron Content Based on Grid-Optimized Support Vector Regression

Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters C and g by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters.

Detecting outliers in local ionospheric model for GNSS precise positioning

Global Navigation Satellite System fast precise positioning can be achieved with accurate ionospheric corrections computed from an adequate number of GNSS stations in a local region. In low-latitude regions, the presence of electron density gradients over short distances can lead to outliers in the map of ionospheric corrections and decrease its accuracy. In this study, we explored outlier detection in ionospheric correction mapping through statistical residuals during a four-month test in 2021. Our findings indicate that the residuals of the local ionospheric model conform to the Laplace distribution. To determine outliers, we use an empirical rule for the Laplace distribution, setting thresholds at μ ± 3b, μ ± 3.5b, and μ ± 5.8b for data retention rates of 95%, 97%, and 99.7%, respectively. Here, μ represents the location parameter, which corresponds to the median of data, and b is the scale parameter, calculated as the medium absolute deviation. We found that while removing outliers can improve model accuracy, it may result in unavailable prediction due to a lack of data in a spare network. For example, applying a μ ± 3.5b threshold for outlier removal led to approximately 2.5% of recording time having no ionospheric corrections map in low-latitude regions, however, the local model has the potential to improve its mean accuracy by up to 50% for both low and mid-latitudes. Therefore, choosing the appropriate percentile threshold depends on the network configuration and the desired accuracy. Removing erroneous satellite data to improve ionospheric accuracy brings positive impacts on precise positioning.

Intermediate E-F layer dynamics study in the Brazilian low-latitude sector: observational data and simulations

This study investigates the downward motion of Intermediate E-F Layers (ILs) in the Brazilian low latitude sector through observation and modeling. Ionosonde data from São José dos Campos (SJC) and Palmas (PAL) were analyzed to investigate the seasonal variation of the IL parameters, including the virtual height (h'IL) and the top frequency (ftIL). The ILs primarily originated from F layer detachment followed by downward motion, peaking before 11 LT and disappearing well before sunset. Daily height variability ranged between 130 and 190 km, with peak frequencies around 4–5 MHz. Using meteor radar data as input, the Ionospheric E-region Model (MIRE) simulated diurnal and semidiurnal tides to analyze neutral wind effects on ILs descent. Model simulations for SJC (October 2008) and PAL (April and June 2009) revealed distinct wind oscillations influencing IL dynamics at heights below 140 km. In SJC, meridional wind shears controlled IL descent, with possible zonal wind interactions weakening ILs. Conversely, in PAL during April 2009, both zonal and meridional winds contributed to IL formation and altitude descent. However, discrepancies between observed and modeled descent rates suggest the need for considering additional atmospheric wave interactions in future modeling studies. June 2009 over PAL presented unique IL behavior, exhibiting a lower observed decay rate and daily height oscillations potentially linked to local modulations. Meanwhile, MIRE indicated that meridional wind shearing predominantly controlled IL descent in the morning, with zonal wind becoming relevant post-midday. These findings enhance our understanding of IL dynamics and their atmospheric drivers.

Analysis of Ionospheric Delay Correction Model Performance During Geomagnetic Storms

AbstractIonospheric delay, as one of the largest error sources in radio propagation, can only be corrected for this error using the ionospheric delay correction model for Global Navigation Satellite System (GNSS) single‐frequency users. In this paper, the 2021 geomagnetic storm event is selected, and based on the measured ionospheric data from the GNSS observatory, the perturbation of the ionosphere by the geomagnetic storm event is analyzed, and it is found that the response of the ionosphere to the geomagnetic storm has obvious differences in the response characteristics and response time in different latitude regions. The performance of the global ionospheric map (GIM), the empirical model, and the broadcast ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is analyzed and the change in the accuracy of each ionospheric model during the geomagnetic storm‐induced ionospheric perturbation is investigated, using the measured electron content of the GNSS as a benchmark. The results show that there is good agreement between the GIM products and the measured electron content during the period of ionospheric calm and the period of ionospheric perturbation. It is worth noting that geomagnetic storms do not necessarily lead to a decrease in the accuracy of ionospheric delay‐correction models, and in some cases, the models that were originally under‐accurate show a tendency to improve their accuracy during the period of perturbation instead. Neither the broadcast ionospheric model nor the electron content of the empirical model output responds to geomagnetic storm‐induced ionospheric perturbations.

Testing the ionospheric model delay and uncertainty estimates with an uncombined navigation filter

In the last decade, new algorithmic positioning techniques have been developed for Global Navigation Satellite Systems (GNSS). These have brought a new focus on high accuracy applications which do not combine multiple frequencies to remove ionospheric errors (i.e. PPP-RTK, Fast-PPP). Not only do these algorithms focus on improvements in the position domain but also in acquiring the positioning solution as fast as possible. In this work, capabilities of different global ionospheric models are assessed, analyzing both the Ionospheric delay accuracy and the associated model uncertainty. Accurate model uncertainties are crucial for reducing the convergence time in uncombined filters, and to guarantee unbiased convergence in the first place. The assessment is done using an uncombined navigation filter with different ionospheric models: GPS ICA, IGS vTEC (vertical Total Electron Content) maps (IGSG, CODG and UQRG), two realizations of the ESA-UGI (Voxel and Multi-Layer), the Madrigal TEC, and the Spire Global vTEC maps. To quantify the model uncertainties without the use of a reference ionospheric model, global maps of an uncertainty inflation factor are computed to show the inflation required to produce optimal filter convergence. These maps demonstrate that some models are too optimistic in the reporting of their own uncertainty estimates, requiring an uncertainty factor up to 10 times the quoted value.

The Spatial Variation of Large‐ and Meso‐Scale Plasma Flow Vorticity Statistics in the High‐Latitude Ionosphere and Implications for Ionospheric Plasma Flow Models

AbstractThe ability to understand and model ionospheric plasma flow on all spatial scales has important implications for operational space weather models. This study exploits a recently developed method to statistically separate large‐scale and meso‐scale contributions to probability density functions (PDFs) of ionospheric flow vorticity measured by the Super Dual Auroral Radar Network (SuperDARN). The SuperDARN vorticity data are first sub‐divided depending on the Interplanetary Magnetic Field (IMF) direction, and the separation method is applied to PDFs of vorticity compiled in spatial regions of size 1° of geomagnetic latitude by 1 hr of magnetic local time, covering much of the high‐latitude ionosphere in the northern hemisphere. The resulting PDFs are fit by model functions using maximum likelihood estimation (MLE) and the spatial variations of the MLE estimators for both the large‐scale and meso‐scale components are presented. The spatial variations of the large‐scale vorticity estimators are ordered by the average ionospheric convection flow, which is highly dependent on the IMF direction. The spatial variations of the meso‐scale vorticity estimators appear independent of the senses of vorticity and IMF direction, but have a different character in the polar cap, the cusp, the auroral region, and the sub‐auroral region. The paper concludes by discussing the sources of the vorticity components in the different regions, and the consequences for the fidelity of ionospheric plasma flow models.

Validation of the IRI-2020 model for the topside-plasmasphere using GNSS TEC measurements

The 2020 update of the International Reference Ionosphere (IRI) model introduces a new topside option, COR2, for the formulation of its electron density profile, as well as two plasmaspheric extension options based on the GCPM model of Gallagher et al. (2000) and the IMAGE/RPI model of Ozhogin et al. (2012). We validate the COR2-Gallagher and COR2-Ozhogin topside-plasmasphere options of IRI-2020 using GNSS VTEC measurements and compare them to the NeQuick model used as the default option in IRI-2016. VTEC data from a total of 21 GNSS ground-based receivers are compared to predictions of the IRI model, ranging from low (< 30°) to middle (30°-60°) magnetic latitudes, during high (year 2014; F10.7∼140 sfu) and low (year 2019; F10.7∼70 sfu) solar activity. Peak plasma frequency (foF2) data from co-located digisondes are ingested into the model to ensure the relevance of the comparison. The three topside-plasmasphere options perform equally well at mid-latitudes (RMSE∼3 TECU). At low latitudes, the NeQuick option (RMSE∼9 TECU) performs better than both the COR2-Gallagher and COR2-Ozhogin options (RMSE > 10 TECU). For all options, the modeled VTEC is consistently overestimated during the autumn/winter daytime at mid-latitudes regardless of solar activity, and throughout the day at low latitudes during low solar activity.

Investigation of the Contribution of Five Broadcast Ionospheric Models (GPSK, NTCMG, NEQG, BDGIM, and BDSK) and IRTG to GNSS Positioning During Different Solar Activities in Solar Cycle 25

AbstractAdditional ionospheric information is essential for mitigating errors in single‐frequency (SF) Global Navigation Satellite Systems (GNSS) positioning. The increasing number of low‐cost dual‐frequency (DF) receiver users faces limitations in tracking DF observables compared to traditional geodetic receivers. Consequently, ionospheric correction algorithms (ICAs) are also essential for low‐cost DF devices in hybrid‐frequency positioning. To evaluate the performance of commonly used ICAs during solar cycle 25, our study presents a global statistical investigation of the contribution of five broadcast ionospheric models (BIMs) and the International GNSS Service (IGS) combined real‐time global ionospheric maps (IRTG) to the positioning domain, covering both quiet and perturbed ionospheric conditions. The BIMs investigated include the GPS Klobuchar (GPSK), Galileo NequickG (NEQG), NTCM‐GlAzpar (NTCMG), BDS‐2 Klobuchar (BDSK), and BeiDou Global Ionospheric delay correction Model (BDGIM). Experimental results from standard point positioning indicate that IRTG demonstrates superior overall accuracy compared to all BIMs, with a mean 3D root mean squared (RMS) of 2.76 m during perturbed period. Specifically, GPSK, NTCMG, NEQG, BDGIM, and BDSK exhibit RMS values of 2.03, 1.67, 1.72, 1.62, and 2.36 m during quiet conditions, and 4.02, 3.17, 2.86, 3.14, and 4.44 m during perturbed conditions, respectively. Among the BIMs, NEQG demonstrates superior performance at middle and high latitudes but exhibits lower accuracy than NTCMG and BDGIM at low latitudes during daytime under quiet conditions. BDGIM performs slightly better than NTCMG at low latitudes but slightly worse at high latitudes. BDSK shows notable improvement for high‐ and mid‐latitude regions since 3 June 2020.

Study of computational stability of the high-latitude ionosphere model

The high latitude ionospheric plasma is a difficult medium to describe due to the dependence of its parameters on heliogeophysical conditions. Its large-scale structure is influenced by processes such as magnetospheric convection, plasmaspheric flows of particles and heat, as well as the precipitation of energetic particles in the region of the auroral oval. These processes are non-stationary and their characteristics change significantly during periods of enhanced geomagnetic activity. Therefore, the modelling of the high-latitude ionosphere is associated with the development of a model that has a computationally stable numerical solution at a sufficiently high spatio-temporal resolution. For this purpose, in this work, we have carried out a study of the computational stability of the mathematical model of the high-latitude ionosphere (Eulerian approach) when different integration steps in time and space are specified. It is shown that the ionospheric model retains computational stability at all selected steps, and the results of numerical calculations are qualitatively consistence and describe the main large-scale structural formations of the high-latitude ionosphere. The results show that the developed model can be used in the study of non-stationary processes occurring in the ionospheric plasma, as well as in the study of the ionosphere during magnetic storms and substorms.

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

Total electron content (TEC) is a key parameter for characterizing the ionosphere. Conventional TEC measurement methods have low spatial resolution, hindering accurate representation of ionospheric spatial characteristics. Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) have shown their potential for high-spatial-resolution TEC estimation. However, SAR-based absolute TEC estimation typically relies on full-polarimetric SAR images, while the InSAR-based method can only extract differential TEC. This study proposes a novel TEC mapping method integrating Time Series InSAR (TS-InSAR) with the International Reference Ionospheric (IRI) model and does not leverage full-polarimetric SAR data. The Advanced Land Observing Satellite-1 (ALOS-1) SAR images covering the Chile and Greenland regions were collected to test the proposed method. The TECs derived from the IRI model and International GNSS Service (IGS) and the electron density observed by the Incoherent Scattering Radar (ISR) site of SONDRESTROM were acquired to evaluate the performance and validate the accuracy of the proposed method, respectively. The results show that the proposed method can achieve TEC mapping with resolutions of tens of meters, much higher than the tens or hundreds of kilometers of the IRI and IGS TECs. The spatial features of our method-derived TECs show overall good consistency with those of the IRI and IGS TECs, but the described spatial information is more detailed. Also, compared to the original IRI model, the improvement rates of root-mean-square errors (RMSEs) between the estimated electron density and ISR observations exceeded 21.67% after the update using the TEC obtained from the proposed method. These experiments demonstrate the proposed method’s feasibility and reliability for ionospheric TEC mapping.

VALIDATION OF THE NEQUICK AND IRI MODELS BASED ON DATA FROM IONOSONDES

&amp;lt;p class=&amp;quot;ABSTRACT&amp;quot;&amp;gt;This study compares the NeQuick and IRI ionospheric models using data from ionosondes as reference. Validation based on data obtained by direct measurement using ionosondes helps to refine empirical models and improves their reliability. In this way, it is possible to identify the differences and limitations of the model and contribute to its improvement. As the NeQuick and IRI models provide tools for the study of ionospheric variability and forecasts of the ionospheric TEC coefficient, their versions are constantly being improved and need to be validated accordingly. Therefore, this research is based on the comparison of the TEC coefficient for the validation of two different ionosphere models based on data from three different regions of Europe.&amp;lt;/p&amp;gt;

On the importance of middle-atmosphere observations on ionospheric dynamics using WACCM-X and SAMI3

Abstract. Recent advances in atmospheric observations and modeling have enabled the investigation of thermosphere–ionosphere interactions as a whole-atmosphere problem. This study examines how dynamical variability in the middle atmosphere (MA) affects intra-day changes in the thermosphere and ionosphere. Specifically, this study investigates ionosphere–thermosphere interactions during different time periods of January 2013 using the Specified Dynamics Whole Atmosphere Community Climate Model, eXtended version (SD-WACCM-X), coupled to the Naval Research Laboratory (NRL) ionosphere of the Sami3 is Another Model of the Ionosphere (SAMI3) model. To represent the weather of the day, the coupled thermosphere–ionosphere system is nudged below 90 km toward the atmospheric specifications provided by the Navy Global Environmental Model for High-Altitude (NAVGEM-HA). Hindcast simulations during January 2013 are carried out with the full dataset of observations normally assimilated by NAVGEM-HA and with a degraded dataset where observations above 40 km are not assimilated. Ionospheric regions with statistically significant changes are identified using key ionospheric properties, including the electron density, peak electron density, and height of the peak electron density. Ionospheric changes show a spatial structure that illustrates the impact of two different types of coupling between the thermosphere and the ionosphere: variability induced by wind-dynamo coupling through electric conductivity and ion-neutral interactions in the upper thermosphere. The two simulations presented in this study show that changing the state of the MA affects ionosphere–thermosphere coupling through changes in the behavior and amplitude of non-migrating tides, resulting in improved key ionospheric specifications.

Effects of local time on the variations of the total electron contents at an American and Asian longitudes and their comparison with IRI-2016, IRI-Plas2017 and NeQuick-2 models during solar cycle 24

Ionospheric modelling is one of the major tools to study the behavior of the ionosphere. Ionospheric models have been useful in predicting the true state of the ionosphere particularly in regions where Global Positioning System (GPS) are not readily available. This research paper aims to study the longitudinal variations and the effects of local time on the total electron content (TEC) recorded in two different sectors (Asia and America) during the ascending, maximum and descending phases of solar cycle 24 (2011–2017) and also to compare its values to IRI-2016, IRI-Plas2017 and NeQuick-2 models in order to evaluate their performances. An hourly interval profile computed on seasonal basis were used to study the behaviors of TEC diurnally and seasonally. A monthly interval error profile plotted on annual basis was also used to investigate the deviations of the models from the GPS values. Our results showed that the peak values of TEC in the Asian and American sectors were recorded around the dawn,06:00UT (13:00LT) and dusk, 18:00UT (15:00LT) respectively. We also affirmed from our results that seasonal/winter anomalies were recorded in all the phases of the solar cycle in both sectors. Equinoctial Asymmetry was also observed to be predominant during different phases of the solar cycle in both sectors except during ascending and descending phases in the Asian and American sectors respectively. Out of the 168 months of data collated for this study, only 162 months of data were available. The IRI-2016, IRI-Plas2017 and NeQuick-2 models have 11.7%, 23.5% and 64.8% better performance in all the months under consideration. Therefore, the NeQuick-2 model had the best performance in both the Asian and American sectors. Finally, from the results of our statistical analysis, Mean Absolute Error (MAE) has ∼3 TECU lower than the Root Mean Square Error (RMSE) values in both sectors and in all the solar cycle phase. Hence, MAE can evaluate the performance of ionospheric models better than RMSE.