Comparison of observed ionospheric vertical TEC over the sea in Indian region with IRI-2016 model

The vertical ionospheric TEC values obtained from GAGAN grid based ionospheric delay correction values over the sea in the Indian equatorial region have been compared with the corresponding values derived from the International Reference Ionosphere model, IRI-2016. The objective of this work is to study the deviation of the vertical TEC derived from the IRI model from ground truths over the sea for different conditions. This will serve the basic intention of assessing the candidature of the IRI model as an alternative ionospheric correction model in navigation receivers in terms of accuracy. We have chosen different solar activity periods, seasons, geomagnetic conditions, locations etc. for our comparison and analysis. The TEC values by the IRI-2016 were compared with the actual measured values for the given conditions and errors were obtained. The measured vertical TEC values at the ionospheric grid points were derived from the GAGAN broadcast ionospheric delay data and used as reference. The IRI model with standard internal functions was used in estimating the TEC at the same ionospheric grid points. The errors in the model derived values are statistically analysed. Broadly, the results show that, for the Indian sector over the sea, the IRI model performs better on quiet days in off equatorial regions, particularly in the northern region. The overall performance degrades for other conditions with the model generally underestimating the true TEC values and most severely in the equatorial region. The performance is worst in this region for the disturbed days of the equinoctial period. The comparison study is also done with the TEC data measured directly by dual frequency GPS receivers. The results were found to be in general agreement with those obtained by comparing the model with GAGAN broadcast data as reference. This study will be useful in considering the IRI-2016 model for real time estimates of TEC as an alternative to the current parametric model in a satellite navigation receiver in absence of other options.     

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

The highest Total Electron Content (TEC) values in the world normally occur at Equatorial Ionization Anomaly (EIA) region resulting in largest ionospheric range delay values observed for any potential Space Based Augmentation System (SBAS). Reliable forecasting of TEC is crucial for satellite based navigation systems. The day to day variability of the location of the anomaly peak and its intensity is very large. This imposes severe limitations on the applicability of commonly used ionospheric models to the low latitude regions. It is necessary to generate a mathematical ionospheric forecasting and mapping model for TEC based on physical ionospheric influencing parameters. A model, IRPE-TEC, has been developed based on real time low latitude total electron content data using GPS measurements from a number of stations situated around the northern crest of the EIA during 2007 through 2011 to predict the vertical TEC values during the low and moderate solar activity levels of the 24th solar cycle. This model is compared with standard ionospheric models like International Reference Ionosphere (IRI) and Parameterized Ionospheric Model (PIM) to establish its applicability in the equatorial region for accurate predictions.     

Comparison of observed TEC values with IRI-2007 TEC and IRI-2007 TEC with optional foF2 measurements predictions at an equatorial region,Chumphon, Thailand

In this research, as part of working towards improving the IRI over equatorial region, the total electron content (TEC) derived from GPS measurements and IRI-2007 TEC predictions at Chumphon station (10.72°N, 99.37°E), Thailand, during 2004–2006 is analyzed. The seasonal variation of the IRI-2007 TEC predictions is compared with the TEC from the IRI-2007 TEC model with the option of the actual F2 plasma frequency (foF2) measurements as well as the TEC from the GPS and International GNSS service (IGS). The Chumphon station is located at the equatorial region and the low latitude of 3.22°N. For a declining phase of the solar cycle (2004–2006), the study shows that the IRI-2007 TEC underestimates the IRI-2007 TEC with the foF2 observation at the nighttime by about 5 TECU. The maximum differences are about 15 TECU during daytime and 5 TECU during nighttime. The overestimation is more evident at daytime than at nighttime. When compared in terms of the root-mean square error (RMSE), we find that the highest RMSE between GPS TEC and IRI 2007 TEC is 14.840 TECU at 1230 LT in 2004 and the lowest average between them is 1.318 TECU at 0630 LT in 2006. The noon bite-out phenomena are clearly seen in the IRI-2007 TEC with and without optional foF2 measurements, but not on the GPS TEC and IGS TEC. The IRI TEC with optional foF2 measurements gives the lowest RMSE values between IRI TEC predicted and TEC measurement. However, the TEC measurements (GPS TEC and IGS TEC) are more correct to use at Chumphon station.     

Application of neural networks to South African GPS TEC modelling

The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa.     

A study on Nighttime Winter Anomaly (NWA) and other related Mid-latitude Summer Nighttime Anomaly (MSNA) in the light of International Reference Ionosphere (IRI) – Model

The ionospheric Nighttime Winter Anomaly (NWA) is a feature observed in the Northern Hemisphere at the American and in the Southern Hemisphere at the Asian longitude sector under low solar activity conditions. Jakowski et al. (2015) analyzed ground-based GPS derived TEC and peak electron density data from radio occultation measurements on Formosat-3/COSMIC satellites and confirmed the persistence of the phenomenon. Further, they assumed that Mid-latitude Summer Nighttime Anomaly (MSNA) and related special anomalies such as the Weddell Sea Anomaly (WSA) and the Okhotsk Sea Anomaly (OSA) are closely related to the NWA via enhanced wind-induced uplifting of the ionosphere. The aim of this paper is to study the factors causing these anomalies and also to investigate if these anomalies are re-produced by IRI. The results show that IRI model does include the NWA effect, though at a different longitude and could be improved for better predictions. The IRI-2016 model does show WSA in TEC but not in NmF2. Further, the IRI-2016 model could clearly predict the OSA both in NmF2 and TEC.     

Comparison of GPS derived TEC with the TEC predicted by IRI 2012 model in the southern Equatorial Ionization Anomaly crest within the Eastern Africa region

We have compared the TEC obtained from the IRI-2012 model with the GPS derived TEC data recorded within southern crest of the EIA in the Eastern Africa region using the monthly means of the 5 international quiet days for equinoxes and solstices months for the period of 2012 – 2013. GPS-derived TEC data have been obtained from the Africa array and IGS network of ground based dual-frequency GPS receivers from four stations (Kigali (1.95°S, 30.09°E; Geom. Lat. 11.63°S), Malindi (2.99°S, 40.19°E; Geom. Lat. 12.42°S), Mbarara (0.60°S, 30.74°E; Geom. Lat. 10.22°S) and Nairobi (1.22°S, 36.89°E; Geom. Lat. 10.69°S)) located within the EIA crest in this region. All the three options for topside Ne of IRI-2012 model and ABT-2009 for bottomside thickness have been used to compute the IRI TEC. Also URSI coefficients were considered in this study. These results are compared with the TEC estimated from GPS measurements. Correlation Coefficients between the two sets of data, the Root-Mean Square Errors (RMSE) of the IRI-TEC from the GPS-TEC, and the percentage RMSE of the IRI-TEC from the GPS-TEC have been computed. Our general results show that IRI-2012 model with all three options overestimates the GPS-TEC for all seasons and at all stations, and IRI-2001 overestimates GPS-TEC more compared with other options. IRI-Neq and IRI-01-corr are closely matching in most of the time. The observation also shows that, GPS TEC are underestimated by TEC from IRI model during noon hours, especially during equinoctial months. Further, GPS-TEC values and IRI-TEC values using all the three topside Ne options show very good correlation (above 0.8). On the other hand, the TEC using IRI-Neq and IRI-01- corr had smaller deviations from the GPS-TEC compared to the IRI-2001.     

A 12 year comparison of MIDAS and IRI 2007 ionospheric Total Electron Content

Data assimilation in conventional meteorological applications uses measurements in conjunction with a physical model. In the case of the ionised region of the upper atmosphere, the ionosphere, assimilation techniques are much less mature. The empirical model known as the International Reference Ionosphere (IRI) could be used to augment data-sparse regions in an ionospheric now-cast and forecast system. In doing so, it is important that it does not introduce systematic biases to the result. Here, the IRI model is compared to ionospheric observations from the Global Positioning System satellites over Europe and North America. Global Positioning System data are processed into hour-to-hour monthly averages of vertical Total Electron Content using a tomographic technique. A period of twelve years, from January 1998 to December 2009, is analysed in order to capture variations over the whole solar cycle. The study shows that the IRI model underestimates Total Electron Content in the daytime at solar maximum by up to 37% compared to the monthly average of GPS tomographic images, with the greatest differences occurring at the equinox. IRI shows good agreement at other times. Errors in TEC are likely due to peak height and density inaccuracies. IRI is therefore a suitable model for specification of monthly averages of Total Electron Content and can be used to initialise a data assimilation process at times away from solar maximum. It may be necessary to correct for systematic deviations from IRI at solar maximum, and to incorporate error estimation into a data assimilation scheme.     

Global validation of IRI TEC for high and medium solar activity conditions

The IRI model offers a choice of options for the computation of the electron density profile and electron content (TEC). Recently new options for the topside electron density profile have been developed, which have a strong impact on TEC. Therefore it is important to test massively the IRI and the new options with experimental data. A large number of permanent stations record dual frequency GPS data from which it is possible to obtain TEC values. Thirty-one worldwide distributed stations have been selected to investigate the capabilities of the IRI to reproduce experimental TEC. Data for years 2000 (high solar activity) and 2004 (medium solar activity) have been analyzed computing modeled values with the IRI-2001 and the IRI-2007-NeQuick topside options. It is found that IRI-2007-NeQuick option generally improves the estimate of the slant TEC, especially in the case of high latitudes stations during high solar activity.     

Assessment of improvement of the IRI model over Ethiopia for the modeling of the variability of TEC during the period 2013–2016

This paper discusses the monthly and seasonal variation of the total electron content (TEC) and the improvement of performance of the IRI model in estimating TEC over Ethiopia during the solar maximum (2013–2016) phase employing as reference the GPS derived TEC data inferred from four GPS receivers installed in different regions of Ethiopia; Assosa (geog 10.05°N, 34.55°E, Geom. 7.01°N), Ambo (8.97°N, 37.86°E, Geom. 5.42°N), Nazret (8.57°N, 39.29°E, Geom. 4.81°N) and Arba Minch (6.06°N, 37.56°E, Geom. 2.62°N). The results reveal that, in the years 2013–2016, the highest peak GPS-derived diurnal VTEC is observed in the March equinox in 2015 over Arba Minch station. Moreover, both the arithmetic mean GPS-derived and modelled VTEC values, generally, show maximum and minimum values in the equinoctial and June solstice months, respectively in 2014–2015. However, in 2013, the minimum and maximum arithmetic mean GPS-derived values are observed in the March equinox and December solstice, respectively. The results also show that, even though overestimation of the modelled VTEC has been observed on most of the hours, all versions of the model are generally good to estimate both the monthly and seasonal diurnal hourly VTEC values, especially in the early morning hours (00:00–03:00?UT or 03:00–06:00?LT). However, it has also been shown that the IRI 2007 and IRI 2012 versions generally perform best in matching the diurnal GPS derived TEC values as compared to that of the IRI 2016 version. In addition, the IRI 2012 version with IRI2001 option for the topside electron density shows the highest overestimation of the VTEC as compared to the other options. None of the versions of the IRI model are proved to be able to capture the effects of geomagnetic storms.     

NeQuick 2 and IRI Plas VTEC predictions for low latitude and South American sector

Using vertical total electron content (VTEC) measurements obtained from GPS satellite signals the capability of the NeQuick 2 and IRI Plas models to predict VTEC over the low latitude and South American sector is analyzed. In the present work both models were used to calculate VTEC up to the height of GPS satellites. Also, comparisons between the performance of IRI Plas and IRI 2007 have been done. The data correspond to June solstice and September equinox 1999 (high solar activity) and they were obtained at nine stations. The considered latitude range extends from 18.4°N to ?64.7°N and the longitude ranges from 281.3°E to 295.9°E in the South American sector. The greatest discrepancies among model predictions and the measured VTEC are obtained at low latitudes stations placed in the equatorial anomaly region. Underestimations as strong as 40?TECU [1?TECU?=?10¹⁶?m^?2] can be observed at BOGT station for September equinox, when NeQuick2 model is used. The obtained results also show that: (a) for June solstice, in general the performance of IRI Plas for low latitude stations is better than that of NeQuick2 and, vice versa, for highest latitudes the performance of NeQuick2 is better than that of IRI Plas. For the stations TUCU and SANT both models have good performance; (b) for September equinox the performances of the models do not follow a clearly defined pattern as in the other season. However, it can be seen that for the region placed between the Northern peak and the valley of the equatorial anomaly, in general, the performance of IRI Plas is better than that of NeQuick2 for hours of maximum ionization. From TUCU to the South, the best TEC predictions are given by NeQuick2.The source of the observed deviations of the models has been explored in terms of CCIR foF2 determination in the available ionosonde stations in the region. Discrepancies can be also related to an unrealistic shape of the vertical electron density profile and or an erroneous prediction of the plasmaspheric contribution to the vertical total electron content. Moreover, the results of this study could be suggesting that in the case of NeQuick, the underestimation trend could be due to the lack of a proper plasmaspheric model in its topside representation. In contrast, the plasmaspheric model included in IRI, leads to clear overestimations of GPS derived TEC.     

Using GPS-TEC data to calibrate VTEC computed with the IRI model over Nigeria

This paper presents the development of a Total Electron Content (TEC) map for the Nigerian ionosphere. In this work, TEC measurements obtained from the AFRL-SCINDA GPS (Air Force Research Laboratory-Scintillation Network Decision Aid, Global Positioning System) equipment installed at Nsukka (6.87°N, 7.38°E) are used to adapt the International Reference Ionosphere (IRI) model for the Nigerian Ionosphere. The map is being developed as a computer program (implemented in the MATLAB programming language) that shows spatial and temporal representations of TEC for the Nigerian ionosphere. The method is aimed at showing how the IRI model can be used to estimate VTEC over wide areas by incorporating GPS measurements. This method is validated by using GPS VTEC data collected from a station in Ilorin (8.50°N, 4.55°E).     

Ionospheric path delay models for spaceborne GPS receivers flying in formation with large baselines

GPS relative navigation filters could benefit notably from an accurate modeling of the ionospheric delays, especially over large baselines (>100 km) where double difference delays can be higher than several carrier wavelengths. This paper analyzes the capability of ionospheric path delay models proposed for spaceborne GPS receivers in predicting both zero-difference and double difference ionospheric delays. We specifically refer to relatively simple ionospheric models, which are suitable for real-time filtering schemes. Specifically, two ionospheric delay models are evaluated, one assuming an isotropic electron density and the other considering the effect on the electron density of the Sun aspect angle. The prediction capability of these models is investigated by comparing predicted ionospheric delays with measured ones on real flight data from the Gravity Recovery and Climate Experiment mission, in which two satellites fly separated of more than 200 km. Results demonstrate that both models exhibit a correlation in the excess of 80% between predicted and measured double-difference ionospheric delays. Despite its higher simplicity, the isotropic model performs better than the model including the Sun effect, being able to predict double differenced delays with accuracy smaller than the carrier wavelength in most cases. The model is thus fit for supporting integer ambiguity fixing in real-time filters for relative navigation over large baselines. Concerning zero-difference ionospheric delays, results demonstrate that delays predicted by the isotropic model are highly correlated (around 90%) with those estimated using GPS measurements. However, the difference between predicted and measured delays has a root mean square error in the excess of 30 cm. Thus, the zero-difference ionospheric delays model is not likely to be an alternative to methods exploiting carrier-phase observables for cancelling out the ionosphere contribution in single-frequency absolute navigation filters.     

Seasonal variation of total electron content at crest of equatorial anomaly station during low solar activity conditions

The variability of total electron content (TEC) over the crest of equatorial anomaly station Bhopal has been studied during the low solar activity period (2005–2006) using global positioning system (GPS) data. Diurnal variation of TEC is studied for different seasons. Interesting features like the winter anomaly, semiannual anomaly and noon bite out in TEC have been reported. GPS derived TEC is then compared with International Reference Ionosphere (IRI) 2001 model and the difference between predictions and observation is being studied. Using the variability index we have also studied the TEC variability for different seasons and also during quiet and disturbed conditions. A higher variability is observed on quiet days as compared to disturbed days during daytime and nighttime hours.     

Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area.     

Simulation study on slant-to-vertical deviation in two dimensional TEC mapping over the ionosphere equatorial anomaly

With the rapid increase of GPS/GNSS receivers being deployed and operated in China, real-time GPS data from nearly a thousand sites are available at the National Center for Space Weather, China Meteorology Administration. However, it is challenging to generate a high-quality regional total electron content (TEC) map with the traditional two-dimensional (2-D) retrieval scheme because a large horizontal gradient has been reported over east–south Asia due to the northern equatorial ionization anomaly. We developed an Ionosphere Data Assimilation Analysis System (IDAAS), which is described in this study, using an International Reference Ionosphere (IRI) model as the background and applying a Kalman filter for updated observations. The IDAAS can reconstruct a three-dimensional ionosphere with the GPS slant TEC. The inverse slant TEC correlates well with observations both for GPS sites involved in the reconstruction and sites that are not involved. Based on the IDAAS, simulations were performed to investigate the deviation relative to the slant-to-vertical conversion (STV). The results indicate that the relative deviation induced by slant-to-vertical conversion may be significant in certain instances, and the deviation varies from 0% to 40% when the elevation decreases from 90° to 15°, while the relative IDAAS deviation is much smaller and varies from −5% to 15% without an elevation dependence. Compared with ‘true TEC’ map derived from the model, there is large difference in STV TEC map but no obvious discrepancy in IDAAS map. Generally, the IDAAS TEC map is much closer to the “true TEC” than is STV TEC map is.     

基于BDS/GPS和经验模型的区域电离层建模

基于北斗卫星导航系统（BDS）和全球定位系统（GPS）实测电离层穿刺点（IPP）数据，结合国际参考电离层（IRI）经验模型历史数据，提出一种对区域二维电离层总电子含量（TEC）进行高精度建模的方法.针对缺乏穿刺点的区域内短时间电离层建模时精度较低且各时段穿刺点空间分布不同的问题，该方法使用IRI模型在建模区域内均匀添加虚拟穿刺点数据，并根据与实测穿刺点的距离，使用构造的权重计算公式赋予其动态权重值，通过加权最小二乘法进行球谐模型参数解算.与欧洲定轨中心（CODE）发布的全球电离层图（GIM）进行数据比对发现，相对于只使用BDS/GPS实测穿刺点数据的建模方法，利用本文建模方法计算获得的垂直总电子含量（VTEC）值对缺乏实测穿刺点的区域精度有明显的提升.      

TEC derived from some GPS stations in Nigeria and comparison with the IRI and NeQuick models

Total electron content (TEC) measured simultaneously using Global Positioning System (GPS) ionospheric monitors installed at some locations in Nigeria during the year 2011 (Rz = 55.7) was used to study the diurnal, seasonal, and annual TEC variations. The TEC exhibits daytime maximum, seasonal variation and semiannual variations. Measured TEC were compared with those predicted by the improved versions of the International Reference Ionosphere (IRI) and NeQuick models. The models followed the diurnal and seasonal variation patterns of the observed values of TEC. However, IRI model produced better estimates of TEC than NeQuick at all locations.     

Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations

A statistical evaluation of storm-time total electron content (TEC) modelling techniques over various latitudes of the African sector and surrounding areas is presented. The source of observational TEC data used in this study is the Global Navigation Satellite Systems (GNSS), specifically the Global Positioning Systems (GPS) receiver networks. For each selected receiver station, three different storm-time models based on empirical orthogonal functions (EOF) analysis, non-linear regression analysis (NLRA) and Artificial neural networks (ANN), were implemented. Storm-time GPS TEC data used for both development and validation of the models was selected based on the storm criterion of $\mathit{Dst}?-50$ nT or $K_p?4$ to take into account both coronal mass ejections (CMEs) and co-rotating interaction regions (CIRs) driven storms, respectively. To make an independent test of the models, storm periods considered for validation were excluded from datasets used during the implementation of the models and results are compared with observations, monthly median values, and International Reference Ionosphere (IRI-2016) predictions. Considering GPS TEC as reference, a statistical analysis performed over six storm periods reserved for validation revealed that ANN model is about 10%, 26%, and 58% more accurate than EOF, NLRA, and IRI models, respectively. It was further found that, EOF model performs 15%, and 44% better than NLRA, and IRI models, respectively, while NLRA is 25% better than IRI. On the other hand, results are also discussed referring to the background ionosphere represented by monthly median TEC (MM TEC) and statistics are provided. Moreover, strengths and weaknesses of each model are highlighted.     

Evaluation of the improvement of IRI-2016 over IRI-2012 at the India low-latitude region during the ascending phase of cycle 24

This paper investigated the performance of the latest International Reference Ionosphere model (IRI-2016) over that of IRI-2012 in predicting total electron content (TEC) at three different stations in the Indian region. The data used were Global Positioning System (GPS) data collected during the ascending phase of solar cycle 24 over three low-latitude stations in India namely; Bangalore (13.02°N Geographic latitude, 77.57°E Geographic longitude), Hyderabad (17.25°N Geographic latitude, 78.30°E Geographic longitude) and Surat (21.16°N Geographic latitude, 72.78°E Geographic longitude). Monthly, the seasonal and annual variability of GPS-TEC have been compared with those derived from International Reference Ionosphere IRI-2016 and IRI-2012 with two different options of topside electron density: NeQuick and IRI01-corr. It is observed that both versions of IRI (i.e., IRI-2012 and IRI-2016) predict the GPS-TEC with some deviations, the latest version of IRI (IRI-2016) predicted the TEC similar to those predicted by IRI-2012 for all the seasons at all stations except for morning hours (0500 LT to 1000?LT). This shows that the effect of the updated version is seen only during morning hours and also that there is no change in TEC values by IRI-2016 from those predicted by IRI-2012 for the rest of the time of the day in the Indian low latitude region. The semiannual variations in the daytime maximum values of TEC are clearly observed from both GPS and model-derived TEC values with two peaks around March-April and September-October months of each year. Further, the Correlation of TEC derived by IRI-2016 and IRI-2012 with EUV and F10.7 shows similar results. This shows that the solar input to the IRI-2016 is similar to IRI 2012. There is no significant difference observed in TEC, bottom-side thickness (B0) and shape (B1) parameter predictions by both the versions of the IRI model. However, a clear improvement is visible in hmF2 and NmF2 predictions by IRI-2016 to that by IRI-2012. The SHU-2015 option of the IRI-2016 gives a better prediction of NmF2 for all the months at low latitude station Ahmedabad compare to AMTB 2013.     

TEC variability near northern EIA crest and comparison with IRI model

Monthly median values of hourly total electron content (TEC) is obtained with GPS at a station near northern anomaly crest, Rajkot (geog. 22.29°N, 70.74°E; geomag. 14.21°N, 144.9°E) to study the variability of low latitude ionospheric behavior during low solar activity period (April 2005 to March 2006). The TEC exhibit characteristic features like day-to-day variability, semiannual anomaly and noon bite out. The observed TEC is compared with latest International Reference Ionosphere (IRI) – 2007 model using options of topside electron density, NeQuick, IRI01-corr and IRI-2001 by using both URSI and CCIR coefficients. A good agreement of observed and predicted TEC is found during the daytime with underestimation at other times. The predicted TEC by NeQuick and IRI01-corr is closer to the observed TEC during the daytime whereas during nighttime and morning hours, IRI-2001 shows lesser discrepancy in all seasons by both URSI and CCIR coefficients.