Total Electron Content Prediction Research Articles

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.

Spatio‐temporal interpolation of total electron content using a GPS network

Constant monitoring and prediction of Space Weather events require investigation of the variability of total electron content (TEC), which is an observable feature of ionosphere using dual‐frequency GPS receivers. Due to various physical and/or technical obstructions, the recordings of GPS receivers may be disrupted resulting in data loss in TEC estimates. Data recovery is very important for both filling in the data gaps for constant monitoring of ionosphere and also for spatial and/or temporal prediction of TEC. Spatial prediction can be obtained using the neighboring stations in a network of a dense grid. Temporal prediction recovers data using previous days of the GPS station in a less dense grid. In this study, two novel and robust spatio‐temporal interpolation algorithms are introduced to recover TEC through optimization by using least squares fit to available data. The two algorithms are applied to a regional GPS network, and for a typical station, the number of days with full data increased from 68% to 85%.

Comparison of GPS TEC variations with IRI-2007 TEC prediction at equatorial latitudes during a low solar activity (2009–2011) phase over the Kenyan region

This work presents an analysis of the Total Electron Content (TEC) derived from the International GNSS Service (IGS) receivers at Malindi (mal2: 2.9oS, 40.1oE, dip −26.813o), Kasarani (rcmn: 36.89oE, 1.2oS, dip −23.970o), Eldoret (moiu: 35.3oE, 0.3oN, dip −21.037o) and GPS-SCINDA (36.8oE, 1.3oS, dip −24.117o) receiver located in Nairobi for the period 2009–2011. The diurnal, monthly and seasonal variations of the GPS derived TEC (GPS-TEC) and effects of space weather on TEC are compared with TEC from the 2007 International Reference Ionosphere model (IRI-TEC) using the NeQuick option for the topside electron density. The diurnal peaks in GPS-TEC is maximum during equinoctial months (March, April, October) and in December and minimum in June solstice months (May, June, July). The variability in GPS-TEC is minimal in all seasons between 0:00 and 04:00 UT and maximum near noon between 10:00 and 14:00 UT. Significant variability in TEC at post sunset hours after 16:00 UT (19:00 LT) has been noted in all the seasons except in June solstice. The TEC variability of the post sunset hours is associated with the occurrence of the ionization anomaly crest which enhances nighttime TEC over this region. A comparison between the GPS-TEC and IRI-TEC indicates that both the model and observation depicts a similar trend in the monthly and seasonal variations. However seasonal averages show that IRI-TEC values are higher than the GPS-TEC. The IRI-TEC also depicts a double peak in diurnal values unlike the GPS-TEC. This overestimation which is primarily during daytime hours could be due to the model overestimation of the equatorial anomaly effect on levels of ionospheric ionization over the low latitude regions. The IRI-TEC also does not show any response to geomagnetic activity, despite the STORM option being selected in the model; the IRI model generally remains smooth and underestimates TEC during a storm. The GPS-TEC variability indicated by standard deviation seasonal averages has been presented as a basis for extending the IRI-model to accommodate TEC-variability.

TEC prediction with neural network for equatorial latitude station in Thailand

This paper describes the neural network (NN) application for the prediction of the total electron content (TEC) over Chumphon, an equatorial latitude station in Thailand. The studied period is based on the available data during the low-solar-activity period from 2005 to 2009. The single hidden layer feed-forward network with a back propagation algorithm is applied in this work. The input space of the NN includes the day number, hour number and sunspot number. An analysis was made by comparing the TEC from the neural network prediction (NN TEC), the TEC from an observation (GPS TEC) and the TEC from the IRI-2007 model (IRI-2007 TEC). To obtain the optimum NN for the TEC prediction, the root-mean-square error (RMSE) is taken into account. In order to measure the effectiveness of the NN, the normalized RMSE of the NN TEC computed from the difference between the NN TEC and the GPS TEC is investigated. The RMSE, and normalized RMSE, comparisons for both the NN model and the IRI-2007 model are described. Even with the constraint of a limited amount of available data, the results show that the proposed NN can predict the GPS TEC quite well over the equatorial latitude station.

On the IRI 2007 performance as a TEC predictor for the South American sector

In order to check the performance of International Reference Ionosphere 2007 model (IRI 2007) to predict total electron content (TEC) values, we compare IRI 2007 TEC predictions against TEC measurements obtained using GPS satellites signals. The measured TEC values were obtained at nine stations in the South American sector, ranging from 18.4 to −64.7 in geographic latitude. The data correspond to June solstice and September equinox,1999, a high solar activity year. Moreover comparisons with IRI 2001 performance, studied for the same period, have been done.The results show: (i) Cases with good agreement between modeled and experimental values for minimum ionization hours. (ii) Strong disagreements in some cases for maximum ionization hours. (iii) In general, the predicted values result firstly underestimated and then overestimated as we move from low to high latitude. (iv) For middle and high latitudes, IRI 2007 calculations are better than those obtained from IRI 2001; on the contrary, at low latitudes and for daytime hours, IRI 2001 shows a better accuracy than IRI 2007.

Modeling and Predicting Total Electron Content by Semi-Parametric Autoregressive (AR) Model

Based on dynamic data system(DDS) modeling methodology, after transformed a seasonal time series for total electron content(TEC) of the ionosphere into a stationary time series by differencing technique, stationary TEC values are modeled by the autoregressive(AR) model. In order to correct model’s systematic errors, authors proposed that AR model is improved by non-parameters introduced to AR model and the ionospheric TEC is predicted using the improved AR model which is called semi-parametric AR model. Preliminary results show that the semi-parametric AR model has a good performance than one of the AR model for short-term TEC prediction while, for relatively long-term TEC prediction, the performance of the semi-parametric AR model is no less than one of AR model.

Comparison of GPS TEC measurements with IRI-2007 TEC prediction over the Kenyan region during the descending phase of solar cycle 23

This paper presents an analysis of the Total Electron Content (TEC) derived from the International GNSS Service receiver (formerly IGS) at Malindi (2.9°S,40.1°E), Kenya for the periods 2004–2006 during the declining phase of solar cycle 23. The diurnal, monthly and seasonal variations of the TEC are compared with TEC from the latest International Reference Ionosphere model (IRI-2007). The GPS–TEC exhibits features such as an equatorial noon time dip, semi-annual variations, Equatorial Ionization Anomaly and day-to-day variability. The lowest GPS–TEC values are observed near the June solstice and September equinox whereas largest values are observed near the March equinox and December solstice. The mean GPS–TEC values show a minimum at 03:00 UT and a peak value at about 10:00 UT. These results are compared with the TEC derived from IRI-2007 using the NeQuick option for the topside electron density (IRI–TEC). Seasonal mean hourly averages show that IRI-2007 model TEC values are too high for all the seasons. The high prediction primarily occur during daytime hours till around midnight hours local time for all the seasons, with the highest percentage deviation in TEC of more 90% seen in September equinox and lowest percentage deviation in TEC of less than 20% seen in March equinox. Unlike the GPS–TEC, the IRI–TEC does not respond to geomagnetic storms and does overestimate TEC during the recovery phase of the storm. While the modeled and observed data do correlate so well, we note that IRI-2007 model is strongly overestimating the equatorial ion fountain effect during the descending phase of solar cycle, and this could be the reason for the very high TEC estimations.

Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks

[1] In this paper, the potential extrapolation capabilities and limitations of artificial neural networks (ANNs) are investigated. This is primarily done by generating total electron content (TEC) predictions using the regional southern Africa total electron content prediction (SATECP) model based on the Global Positioning System (GPS) data and ANNs with the aid of multiple inputs intended to enable the software to learn and correlate the relationship between their variations and the target parameter, TEC. TEC values are predicted over regions that were not covered in the model's development, although it is difficult to validate their accuracy in some cases. The SATECP model is also used to forecast hourly TEC variability 1 year ahead in order to assess the forecasting capability of ANNs in generalizing TEC patterns. The developed SATECP model has also been independently validated by ionosonde data and TEC values derived from the adapted University of New Brunswick Ionospheric Mapping Technique (UNB-IMT) over southern Africa. From the comparison of prediction results with actual GPS data, it is observed that ANNs extrapolate relatively well during quiet periods while the accuracy is low during geomagnetically disturbed conditions. However, ANNs correctly identify both positive and negative storm effects observed in GPS TEC data analyzed within the input space.

Comparison of GPS TEC measurements with IRI TEC prediction at the equatorial latitude station, Chumphon, Thailand

We have analyzed the total electron content (TEC) derived from dual-frequency GPS receivers (GPS TEC) at the Chumpon station, Thailand, during the period 2004–2006. The diurnal, monthly, and seasonal variation in the measured TEC is compared with the TEC derived from the IRI-2007 model as well as the TEC obtained from the International GNSS service (IGS). To date, TEC data at equatorial latitudes are limited. The Chumphon station (10.72°N, 99.37°E) is located at the equatorial latitude and the dip latitude of 3°N. The TEC from the IRI-2007 model is based on the actual F2 plasma frequency (foF2) measurement. The results of our study show that the TEC derived from the IRI-2007 model agrees with the GPS TEC data mostly in the morning hours, but that it generally underestimates the GPS TEC. The maximum differences are about 15 TECU during the daytime and 5 TECU during the nighttime. The underestimation is more evident at daytime than at nighttime. The noon-bite out phenomena are clearly seen for the IRI-2007 TEC, but not on the IGS TEC and GPS TEC. The general underestimation of the IRI-2007 model can be explained from the exclusion of the plasmasphere, whereas the large difference during noon bite-outs is caused by the difference in the slab thickness in the ionosphere between the IRI-2007 model and the actual measurement. When compared with the TEC from the IGS model, the TEC measurements at Chumpon appear to be quite similar.

Spherical cap harmonic model for mapping and predicting regional TEC

An approach to modeling the regional ionospheric total electron content (TEC) based on spherical cap harmonic analysis is presented. This approach not only provides a better regional TEC mapping accuracy, but also the capability for ionospheric model prediction based on spectrum analysis and least squares collocation. Unlike conventional approaches, which predict the immediate TEC with models using current observations, the spherical cap harmonic approach utilizes models using past observations to predict a model which will provide future TEC values. A significant advantage in comparison with conventional approaches is that the spherical cap harmonic approach can be used to predict the long-term TEC with reasonable accuracy. This study processes a set of GPS data with an observation time span of 1 year from two GPS networks in China. The TEC mapping accuracy of the spherical cap harmonic model is compared with the polynomial model and the global ionosphere model from IGS. The results show that the spherical cap harmonic model has a better TEC mapping accuracy with smoother residual distributions in both temporal and spatial domains. The TEC prediction with the spherical cap harmonic model has been investigated for both short- and long-term intervals. For the short-term interval, the prediction accuracies for the latencies of 1-day, 2-days, and 3-days are 2.5 TECU, 3.5 TECU, and 4.5 TECU, respectively. For the long-term interval, the prediction accuracy is 4.5 TECU for a 2-month latency.

IRI 2001/90 TEC predictions over a low latitude station

Total electron content (TEC) over Tucumán (26.9°S, 294.6°W) measured with Faraday technique during the high solar activity year 1982, is used to check IRI 2001 TEC predictions at the southern crest of the equatorial anomaly region. Comparisons with IRI 90 are also made. The results show that in general IRI overestimates TEC values around the daily minimum and underestimates it the remaining hours. Better predictions are obtained using ground ionosonde measurements as input coefficients in the IRI model. The results suggest that for hours of maximum TEC values the electron density profile is broader than that assumed by the model. The main reason for the disagreement would be the IRI shape of the electron density profile. In a previous work using TEC measurements over Tucumán, obtained from GPS satellite signals during the high solar activity year 1999, a better agreement between IRI predictions and measurements has been observed. That better agreement was produced by the fact that 1999 GPS TEC measurements are 50% lower than those obtained by Faraday rotation technique during 1982. An equator ward movement of the southern peak of the EA plus the minor ionization level in 1999 could produce this reduction in TEC values. Moreover, it can be seen that in most of cases IRI TEC values around daily minimum show an hour displacement with respect to the experimental data.

Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks

In this paper, first results from a national Global Positioning System (GPS) based total electron content (TEC) prediction model over South Africa are presented. Data for 10 GPS receiver stations distributed through out the country were used to train a feed forward neural network (NN) over an interval of at most five years. In the NN training, validating and testing processes, five factors which are well known to influence TEC variability namely diurnal variation, seasonal variation, magnetic activity, solar activity and the geographic position of the GPS receivers were included in the NN model. The database consisted of 1-min data and therefore the NN model developed can be used to forecast TEC values 1 min in advance. Results from the NN national model (NM) were compared with hourly TEC values generated by the earlier developed NN single station models (SSMs) at Sutherland (32.38°S, 20.81°E) and Springbok (29.67°S, 17.88°E), to predict TEC variations over the Cape Town (33.95°S, 18.47°E) and Upington (28.41°S, 21.26°E) stations, respectively, during equinoxes and solstices. This revealed that, on average, the NM led to an improvement in TEC prediction accuracy compared to the SSMs for the considered testing periods.

Altitudinal variation of midlatitude localized TEC enhancement from ground‐ and space‐based measurements

We present terrestrial and space‐based dual‐frequency observations of a region of enhanced total electron content (TEC) over the southeastern United States at local nighttime during the geomagnetic storm of 29–31 October 2003. The apparently localized, large‐amplitude, and nearly Earth‐fixed midlatitude ionosphere disturbance contained about 10 m higher delay at Global Positioning System (GPS) L1 frequency than the nighttime background ionosphere TEC. Using the dual‐frequency altimeter on board the Jason satellite, we show evidence that nearly all of the electron content was below its orbital altitude of 1300 km at 0000 local time on 31 October 2003. Dual frequency GPS measurements from the receiver on board the SAC‐C satellite indicate that some portion of the electron content existed above the 700 km orbit altitude of SAC‐C. We develop a horizontally piecewise constant regional model of the enhancement. We compare the model prediction of TEC with the SAC‐C satellite GPS data to constrain the altitude of this enhanced TEC region. Our model indicates that the peak density of the anomalous region is at slightly higher altitude and greater in amplitude than that of the background. The TEC enhancement provides a concrete case study of an extreme scenario that both space‐based and ground‐based GPS augmentation systems must take into account in order to offer high‐accuracy, high‐integrity corrections to GPS for safety‐of‐life applications.

The Direct and Indirect Methods of Ionospheric TEC Predictions and Their Comparison

The direct and indirect methods for predicting the ionospheric total electron content (TEC) by using the ARIMA ( p, d, q) model in the theory of time-series analysis are proposed. The direct method predicts directly the time series composed of the TECs at the every grid point. The indirect method is: at first, to fit the ionospheric TECs with the spherical harmonics and obtain the time series of the fitting coefficients; then, to predict forward the fitting coefficients with the ARIMA ( p, d, q) model in the theory of time-series analysis; and finally, to calculate the TEC for the specific time and grid point. Using the ionospheric data of the International GPS Service (IGS) in the period from 1 January 2004 to 31 January 2005, the testing and comparisons on the proposed methods are performed, the results indicate that within 12 days, the results of the two methods are basically consistent, and that for the predictions longer than 12 days, the accuracy of the indirect method is higher than the direct method. In the duration of 20 days, relative to the total ionospheric grid points, the percentage of the grid points with a prediction error less than 3 TECUs is about 80%; as the duration becomes longer, compared with the indirect method, the decline of this percentage is not apparent for the direct method. Obviously, the direct method suits the prediction of regional TECs, and the indirect method suits the prediction of global TECs.

A Study of Prediction Models for Ionosphere

AbstractThe radio wave used in communications at distance and navigation by satellite is affected by ionosphere. Therefore study on the total electron content (TEC) in the ionosphere is very important. There are some ionospheric prediction models, but the accuracy of prediction by them is 50%~60% only. A new ionosphere model to predict total electron content is proposed. The total electron content given by IGS at each node of grid is fitted by spherical‐harmonic expansion. The fitted coefficients in different time form the time series, these time series are predicted by ARMA (p, q) model so that the prediction of total electron content in ionosphere is implemented. The TEC data of IGS in 2004 and 2005 are analyzed by our method in order to predict the TEC in geographic frame. The relative predicting accuracy for 5 days obtained by our method is about 90%.

Assessment of ionosphere tomographic modeling performance using GPS data during the October 2003 geomagnetic storm event

Precise ionospheric modeling is important for single‐frequency Global Positioning System (GPS) users to achieve optimal positioning accuracy because the ionospheric signal delay is now the largest error source for positioning and navigation with GPS. The ionospheric modeling during ionospheric storms is particularly critical since the signal delay may be higher than normal and may differ significantly from the broadcast ionosphere model (currently employed by single‐frequency users). In this study, a tomographic technique is used to model the ionosphere over North America using data collected from a network of dual‐frequency GPS receivers. In support of real‐time applications of the ionosphere model, short‐term (5‐min) forecasts of ionospheric total electron content (TEC) are also performed. To validate the accuracy of the forecast ionospheric TEC, a comparison of the TEC predictions with the observed TEC data (which are inferred from dual‐frequency GPS observations) is carried out. Analyses are conducted using GPS data recorded during a 2003 geomagnetic storm event (29–31 October). Results indicate that under less disturbed conditions, an average accuracy of 5 ∼ 6.5 total electron content units (TECU, 1 TECU = 1016 el m−2) can be obtained for the vertical TEC prediction and that 80% of slant TEC can be recovered by the model predictions. During extreme ionospheric storm periods (Kp = 9), the vertical TEC forecasting accuracy has a degradation of 2 ∼ 3 TECU from the 3‐day mean value, and the relative error is several percent to 10% larger than the 3‐day average level.

Ionospheric TEC predictions over a local area GPS reference network

Single layer ionosphere models are frequently used for ionospheric modeling and estimation using GPS measurements from a network of GPS reference stations. However, the accuracies of single layer models are inherently constrained by the assumption that the ionospheric electrons are concentrated in a thin shell located at an altitude of about 350 km above Earth’s surface. This assumption is only an approximation to the physical truth because the electrons are distributed in the entire ionosphere region approximately from 50 to 1,000 km. To provide instantaneous ionospheric corrections for the real-time GPS positioning applications, the ionospheric corrections need to be predicted in advance to eliminate the latency caused by the correction computation. This paper will investigate ionospheric total electron content (TEC) predictions using a multiple-layer tomographic method for ionospheric modeling over a local area GPS reference network. The data analysis focuses on the accuracy evaluation of short-term (5 min in this study) TEC predictions. The results have indicated that the obtainable TEC prediction accuracy is at a level of about 2.8 TECU in the zenith direction and 95% of the total electron content can be recovered using the proposed tomography-based ionosphere model.

A global mapping technique for GPS‐derived ionospheric total electron content measurements

A worldwide network of receivers tracking the transmissions of Global Positioning System (GPS) satellites represents a new source of ionospheric data that is globally distributed and continuously available. We describe a technique for retrieving the global distribution of vertical total electron content (TEC) from GPS‐based measurements. The approach is based on interpolating TEC within triangular tiles that tessellate the ionosphere modeled as a thin spherical shell. The high spatial resolution of pixel‐based methods, where widely separated regions can be retrieved independently of each other, is combined with the efficient retrieval of gradients characteristic of polynomial fitting. TEC predictions from climatological models are incorporated as simulated data to bridge significant gaps between measurements. Time sequences of global TEC maps are formed by incrementally updating the most recent retrieval with the newest data as it becomes available. This Kalman filtering approach smooths the maps in time, and provides time‐resolved covariance information, useful for mapping the formal error of each global TEC retrieval. Preliminary comparisons with independent vertical TEC data, available from the TOPEX dual‐frequency altimeter, suggest that the maps can accurately reproduce spatial and temporal ionospheric variations over latitudes ranging from equatorial to about ±65°.

IRI-95 TEC predictions for the South American peak of the equatorial anomaly

Faraday electron content measurements obtained during a high solar activity year at Tucumán station in Argentina, located near the southern peak of the equatorial anomaly in the American sector, are used to check the validity of IRI-95 in predicting the total electron content (TEC) at this region. In general, the model overestimates TEC around the daily minimum and underestimates it the rest of the day. The results suggest that the main reason for the disagreement is the shape of the IRI electron density profile and not the peak density.

Ionospheric total electron content and slab thickness determined in Australia

Ionospheric total electron content (TEC) and slab thickness (τ) have been determined for southern Australia from July 1991 to June 1995 using Global Positioning System (GPS) satellite reception and ionograms (at 5‐min intervals) recorded at Salisbury, South Australia, and other Australian ionograms of the Ionospheric Prediction Service Radio and Space Services, Australian Department of Administrative Services. Seasonal, diurnal, and latitudinal variations in TEC and slab thickness are investigated. The removal of possible error sources in GPS measurements such as satellite and receiver biases is considered. Preliminary procedures are outlined in which protonospheric electron content is separated from GPS TEC measurements (up to 20,000 km height) by subtracting Navy Navigation Satellite System (NNSS) measurements (up to 1000 km height). Although slab thickness is substantially constant, there is a trend for increased values at times of reduced solar influence, such as when approaching sunspot minimum. A preliminary report is given of an extension of the current work at Salisbury by using Australian Surveying and Land Information Group GPS receivers at other Australian locations. A comparison is made between the experimental data and values derived from ionospheric models. The two models considered are the international reference ionosphere model (IRI90) and the parameterized ionospheric model ((PIM) version 1.4, February 1996). The GPS TEC measurements near solar minimum were consistently of the order of 5–10 TEC units greater than the model TEC predictions. This difference is attributed to the inclusion of protonospheric TEC in the GPS measurements but not in the model predictions.