Total Electron Content Model Research Articles

Variation of the TEC at a dip equatorial station, Trivandrum and a mid latitude station, Hanle during the descending phase of the solar cycle 24 (2014–2016)

Morphological variations in the Total Electron Content (TEC) of the ionosphere at a dip equatorial station, Trivandrum (8.47°N, 76.91° E) and a mid latitude station, Hanle (32.78° N, 78.95° E) during the period 2014–2016 is studied using the GPS TEC measurements from the InSWIM (Indian Network for Space Weather Impact Monitoring) network of stations. The variation of the TEC at Hanle, which is located in the same longitude sector as that of Trivandrum, is presented here for the first time. At Hanle, the TEC peaks at local noontime while its peak appears with a delay (1600 LT) at Trivandrum. The minima in the TEC at both stations appear during the early morning hours. Nighttime enhancements in the TEC are seen at Trivandrum during the equinoctial months while at Hanle they appear in the summer months. The TEC at both the stations exhibit semi-annual anomaly and equinoctial asymmetry. However, the winter anomaly is seen only at Trivandrum. The TEC at both Trivandrum and Hanle is seen to display strong solar activity dependence with the average TEC decreasing as solar activity decreases. We surmise that the observed diurnal/seasonal/annual variations in the TEC at the dip equatorial station, Trivandrum is controlled by the processes induced by electrodynamics while the TEC variations at the mid latitude station, Hanle is mostly affected by the neutral dynamical processes. The morphological comparison between the TEC at Trivandrum derived from the IRI 2012 model and GPS TEC measurements shows only a qualitative agreement. The IRI 2012 model is found to overly underestimate the daytime as well as nighttime TEC at the dip equatorial station, Trivandrum while it is well in agreement with the TEC measurements at the mid latitude station, Hanle.

The performance of the IRI-Plas model as compared with Alouette II and GIM-TEC data over the midlatitude station Alma-Ata

Climatic ionospheric models present an important medium for both investigation of physical structure and correction of detrimental effects of ionospheric variability on space based communication, navigation and positioning systems. International Reference Ionosphere Extended to Plasmasphere (IRI-Plas) is one of the empirical models that can provide ionospheric layer parameters and electron density profiles up to GPS satellite orbital height of 20,200 km. IRI-Plas can input not only F2 layer critical parameters, foF2 and hmF2, but also the Total Electron Content (TEC) values. In this study, Ne(h)-profiles and TEC values obtained from the IRI-Plas model are compared with topside Alouette II satellite profiles and Global Ionospheric Map (GIM) TEC values, respectively. The satellite Ne(h) profiles are averaged over a midlatitude region for various seasons of 1966 and 1969. It is observed that IRI-Plas is in good agreement with Alouette profiles between 300 km and 500 km. After 500 km up to 2000 km, the profiles seem to differ where IRI-Plas Ne(h) usually overestimates the Alouette profile. When IRI-Plas TEC is compared with GIM-TEC, it is observed that model TEC generally overestimates the GIM-TEC values during daytime hours. The lowest ΔTEC values (10–20%) are observed in March and September, and the highest values are observed in the months of January (around 30%) and July (up to 67%). During nighttime, the IRI-Plas model results mainly underestimate the observational ones, up to 30% in equinoctial months, and with ±10% discrepancies in January and July. Since the IRI-Plas model can scale foF2 and hmF2 values using an external TEC input by equating instantaneous and median slab thickness (TEC/NmF2 ratio), the correlation between GIM-TEC and ionosonde NmF2 values measured at the midlatitude station Alma-Ata located at [43.25°N, 76.92°E] is obtained for various seasons during 1999, 2000, 2008, 2009 and 2012. The study showed a positive correlation for all seasons and levels of solar activity. The correlation coefficients between the data sets were very high (greater than or equal to 0.79) for hourly data and (greater than or equal to 0.91) for monthly-medians at a significance level of 5%. Thus, for the practical applications, the correlation results can be used to update slab thickness model of IRI-Plas which will lead to better scaling of foF2 and hmF2 values and plasmaspheric Ne(h)-profile with external instantaneous TEC input.

Ionospheric Effects of the Sudden Stratospheric Warming in 2009: Results of Simulation with the First Version of the EAGLE Model

In this paper, we discuss perturbations in neutral temperature, total electron content (TEC), and critical frequency of the maximum of the F2 layer (foF2) during the sudden stratospheric warming in January 2009. The calculations were performed using the first version of the EAGLE (Entire Atmosphere Global Model), which is a combination of the models of the low–middle atmosphere (HAMMONIA) and the upper atmosphere (GSM TIP). The EAGLE reproduces observed stratospheric warming and related mesospheric cooling in the northern polar cap in January 2009. At thermospheric altitudes, the neutral temperature perturbations have a quasi-wave character with a wavelength of ∼40 km in the vertical direction. Our results indicate that the HAMMONIA model should be used in the EAGLE instead of the GSM TIP model for the neutral temperature calculations in the altitude region from 80 to 120 km. It is shown that the obtained model foF2 and TEC perturbations are mainly related to seasonal variations. The most-pronounced perturbations in the ionospheric electron density due to stratospheric warming are formed near the equator and are basically negative. Our analysis of the neutral temperature and electron density perturbations made it possible to conclude that the dependence of ionospheric parameters on seasonal changes in solar zenith angle is stronger than for the thermosphere parameters.

Modeling the Ionosphere with GPS and Rotation Measure Observations

AbstractThe ionosphere contributes time‐varying Faraday rotation (FR) to radio signals passing through it. Correction for the effect of the ionosphere is important for deriving magnetic field information from FR observations of polarized cosmic radio sources, as well as providing valuable diagnostics of the structure of the ionosphere. In this paper, we evaluate the accuracy of models commonly used to correct for its effects using new observations of pulsars at low frequencies, which provide total rotation measures (RMs) at better precision than previously available. We evaluate models of the ionosphere derived from modern digital ionosondes that provide electron density information as a function of height, as well as GPS‐derived total electron content (TEC) measurements. We combine these density models with reference global magnetic field models to derive ionospheric RM contributions. We find that the models disagree substantially with each other and seek corrections that may explain the differences in RM prediction. Additionally, we compare these models to global TEC models and find that local high‐cadence TEC measurements are superior to global models for ionospheric RM correction.

Empirical forecast of quiet time ionospheric Total Electron Content maps over Europe

An accurate forecast of the atmospheric Total Electron Content (TEC) is helpful to investigate space weather influences on the ionosphere and technical applications like satellite-receiver radio links. The purpose of this work is to compare four empirical methods for a 24-h forecast of vertical TEC maps over Europe under geomagnetically quiet conditions.TEC map data are obtained from the Space Weather Application Center Ionosphere (SWACI) and the Universitat Politècnica de Catalunya (UPC). The time-series methods Standard Persistence Model (SPM), a 27 day median model (MediMod) and a Fourier Series Expansion are compared to maps for the entire year of 2015. As a representative of the climatological coefficient models the forecast performance of the Global Neustrelitz TEC model (NTCM-GL) is also investigated. Time periods of magnetic storms, which are identified with the Dst index, are excluded from the validation.By calculating the TEC values with the most recent maps, the time-series methods perform slightly better than the coefficient model NTCM-GL. The benefit of NTCM-GL is its independence on observational TEC data. Amongst the time-series methods mentioned, MediMod delivers the best overall performance regarding accuracy and data gap handling. Quiet-time SWACI maps can be forecasted accurately and in real-time by the MediMod time-series approach.

TEC Regional Modeling and prediction using ANN method and single frequency receiver over IRAN

In order to study in the field of the dynamics and continuous variations in the ionosphere, the ionospheric measurement tools such as ionosondes, incoherent scatter radars, satellites, and Global Positioning System (GPS) networks should be used. Total Electron Content (TEC) is a key parameter in the investigation and identification of ionosphere layer. From observations of dual frequency GPS receivers, the ionospheric TEC can be extracted. Global Ionospheric Maps (GIM) are auxiliary Maps for a study on ionosphere layer in around the world. It is necessary to produce the regional TEC map for precise studying of the ionospheric TEC. Bernese software is used to extract TEC by dual frequency GPS receivers. Regional modeling of ionospheric TEC by Artificial Neural Network (ANN) is a significant domain for prediction TEC at both single and double frequency GPS receivers. Five locations in Iran during the period of 2006-2010 were identified and used in the development of an input space and ANN architecture for the TEC modeling. The input space included the day number (seasonal variation), hour (diurnal variation), sunspot number (a measure of the solar activity) and magnetic index (a measure of magnetic activity). Based on the results, the ANN have capability and flexibility to model and to predict TEC. TEC predicted by ANN A (NN TEC) and TEC obtained from the IRI2007 version of the International Reference Ionosphere (IRI TEC) are compared during equinoxes and solstices. Results show that ANN predicts GPS TEC more accurately than the IRI over Iran. The IRI-2007 model is not a suitable method to produce TEC over IRAN.

A single-station empirical TEC model based on long-time recorded GPS data for estimating ionospheric delay

Globally distributed GPS (global positioning system) stations have been continuously running for nearly 20 years, thereby accumulating numerous observations. These long-time recorded GPS data can be used to calculate continuous total electron content (TEC) values at single stations and provide an effective modeling dataset to establish single-station empirical TEC models. In this paper, a new empirical TEC model called SSM-T1 for single stations is proposed on the basis of GPS data calculated by IONOLAB-TEC application from 2004 to 2015. The SSM-T1 model consists of three parts: diurnal, seasonal, and solar dependency variations, with 18 coefficients fitted by the nonlinear least-squares method. The SSM-T1 model is tested at four stations: Paris (opmt), France; Bangalore (iisc), India; Ceduna (cedu), Australia; and O’Higgins (ohi3) over the Antarctic Peninsula. The RMS values of the model residuals at these four stations are 3.22, 4.46, 3.28, and 3.83 TECU. Assessment results show that the SSM-T1 model is in good agreement with the observed GPS-TEC data and exhibits good prediction ability at the Paris, Bangalore, and Ceduna stations. However, at the O’Higgins station, the SSM-T1 model seriously deviates from the observed GPS-TEC data and cannot effectively describe the variation of mid-latitude summer night anomaly.

Unbiased total electron content (UTEC), their fluctuations, and correlation with seismic activity over Japan

Earthquakes are among the most dangerous events that occur on earth and many scientists have been investigating the underlying processes that take place before earthquakes occur. These investigations are fueling efforts towards developing both single and multiple parameter earthquake forecasting methods based on earthquake precursors. One potential earthquake precursor parameter that has received significant attention within the last few years is the ionospheric total electron content (TEC). Despite its growing popularity as an earthquake precursor, TEC has been under great scrutiny because of the underlying biases associated with the process of acquiring and processing TEC data. Future work in the field will need to demonstrate our ability to acquire TEC data with the least amount of biases possible thereby preserving the integrity of the data. This paper describes a process for removing biases using raw TEC data from the standard Rinex files obtained from any global positioning satellites system. The process is based on developing an unbiased TEC (UTEC) data and model that can be more adaptable to serving as a precursor signal for earthquake forecasting. The model was used during the days and hours leading to the earthquake off the coast of Tohoku, Japan on March 11, 2011 with interesting results. The model takes advantage of the large amount of data available from the GPS Earth Observation Network of Japan to display near real-time UTEC data as the earthquake approaches and for a period of time after the earthquake occurred.

Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region

This paper proposes a linear time series model to represent the climatology of the ionosphere and to investigate the characteristics of hourly averaged total electron content (TEC). The GPS–TEC observation data at the Bengaluru international global navigation satellite system (GNSS) service (IGS) station (geographic $$13.02^{\circ }\hbox {N}$$ , $$77.57^{\circ }\hbox {E}$$ ; geomagnetic latitude $$4.4^{\circ }\hbox {N}$$ ) have been utilized for processing the TEC data during an extended period (2009–2016) in the $$24{\mathrm{th}}$$ solar cycle. Solar flux F10.7p index, geomagnetic Ap index, and periodic oscillation factors have been considered to construct a linear TEC model. It is evident from the results that solar activity effect on TEC is high. It reaches the maximum value ( $$\sim $$ 40 TECU) during the high solar activity (HSA) year (2014) and minimum value ( $$\sim $$ 15 TECU) during the low solar activity (LSA) year (2009). The larger magnitudes of semiannual variations are observed during the HSA periods. The geomagnetic effect on TEC is relatively low, with the highest being $$\sim $$ 4 TECU (March 2015). The magnitude of periodic variations can be seen more significantly during HSA periods (2013–2015) and less during LSA periods (2009–2011). The correlation coefficient of 0.89 between the observations and model-based estimations has been found. The RMSE between the observed TEC and model TEC values is 4.0 TECU (linear model) and 4.21 TECU (IRI2016 Model). Further, the linear TEC model has been validated at different latitudes over the northern low-latitude region. The solar component (F10.7p index) value decreases with an increase in latitude. The magnitudes of the periodic component become less significant with the increase in latitude. The influence of geomagnetic component becomes less significant at Lucknow GNSS station $$(26.76^{\circ }\hbox {N}, 80.88^{\circ }\hbox {E})$$ when compared to other GNSS stations. The hourly averaged TEC values have been considered and ionospheric features are well recovered with linear TEC model.

Empirical model of TEC response to geomagnetic and solar forcing over Balkan Peninsula

An empirical total electron content (TEC) model response to external forcing over Balkan Peninsula (35°N-50°N; 15°E-30°E) is built by using the Center for Orbit Determination of Europe (CODE) TEC data for full 17 years, January 1999 - December 2015. The external forcing includes geomagnetic activity described by the Kp-index and solar activity described by the solar radio flux F10.7. The model describes the most probable spatial distribution and temporal variability of the externally forced TEC anomalies assuming that they depend mainly on latitude, Kp-index, F10.7 and LT. The anomalies are expressed by the relative deviation of the TEC from its 15-day mean, rTEC, as the mean value is calculated from the 15 preceding days. The approach for building this regional model is similar to that of the global TEC model reported by Mukhtarov et al. (2013a) however it includes two important improvements related to short-term variability of the solar activity and amended geomagnetic forcing by using a “modified” Kp index. The quality assessment of the new constructing model procedure in terms of modeling error calculated for the period of 1999–2015 indicates significant improvement in accordance with the global TEC model (Mukhtarov et al., 2013a). The short-term prediction capabilities of the model based on the error calculations for 2016 are improved as well. In order to demonstrate how the model is able to reproduce the rTEC response to external forcing three geomagnetic storms, accompanied also with short-term solar activity variations, which occur at different seasons and solar activity conditions are presented.

Comparing models of total electron content in the ionosphere for GLONASS

Comparative analysis of quality has been performed for the models of total electron content which could be applied in single-frequency navigation receivers of GPS/GLONASS. The analysis is based on the evaluation of total electron content modeling errors versus its experimentally measured values represented by global ionosphere maps (GIM). The GEMTEC model jointly developed by Irkutsk State University (IGU) and Irkutsk Branch of Moscow State Technical University of Civil Aviation (MGTU GA) has proven to show the least averaged errors.

Modeling and analysis of GPS-TEC low latitude climatology during the 24th solar cycle using empirical orthogonal functions

The Total Electron Content (TEC) is an essential component describing the temporal and spatial characteristics of the ionosphere. In this paper, an empirical orthogonal function (EOF) model is constructed by using ground based Global Navigational Satellite System (GNSS) TEC observation data at the Bangalore International GNSS Service (IGS) station (geographic – 13.02° N, 77.57° E; geomagnetic latitude 4.4° N) during an extended period (2009–2016) in the 24th solar cycle. EOF model can be decomposed into base functions and its corresponding coefficients. These decomposed modes well represented the influence of solar and geomagnetic activity towards TEC. The first three EOFs modes constitute about 98% of the total variance of the observed data sets. The Fourier Series Analysis (FSA) is carried out to characterize the solar-cycle, annual and semi-annual dependences by modulating the first three EOF coefficients with solar (F10.7) and geomagnetic (Ap and Dst) indices. The TEC model is validated during daytime and nighttime conditions as well as under different solar activity and geomagnetic conditions. A positive correlation (0.85) of averaged daily GPS-TEC with averaged daily F10.7 strongly supports those time-varying characteristics of the ionosphere features depends on the solar activity. Further, the validity and reliability of EOF model is verified by comparing with the GPS-TEC data, and standard global ionospheric models (International Reference Ionosphere, IRI2016 and Standard Plasmasphere-Ionosphere Model, SPIM). The performances of the standard ionosphericmodelsare marked to be relatively better during High Solar Activity (HSA) periods as compared to the Low Solar Activity (LSA) periods.

TEC modelling via neural network using observations from the first GLONASS R&D data network in Brazil and the RBMC

This work presents a result on the use of neural networks (NNs) model to estimate Total Electron Content (TEC) behavior based on Global Navigation Satellite Systems (GNSS) measurements in the Brazilian equatorial and low latitude sectors. The main goal of the proposed NN is to estimate GPS (Global Positioning System) TEC values at locations without a GNSS receiver that may be used, for instance, as background models in regional TEC mapping procedures. The proposed approach is useful especially for single frequency users that rely on corrections of ionospheric range errors by TEC models. The data used was collected on the first GLONASS (Globalnaya Navigatsionnaya Sputnikovaya Sistema) network for research and development (GLONASS R&D network), recently inaugurated in Brazil, and also on the Brazilian Network for Continuous Monitoring of the GNSS Systems (RBMC), with a temporal interval of 15s or 30s and a spatial resolution of about 300 km over an area corresponding to a longitudinal extension of 650 km. The input parameters for the NN used in this work are the latitude, longitude, day of the year (doy), time of the day, the global geomagnetic storm index (Kp-index), and the solar radio flux at 10.7 cm, and the output the vertical TEC (vTECe). The vTEC used for training the NN is calculated with the GPS-TEC Analysis Application, version 2.9.3. Future work considers applying the vTEC calculated with the ICTP method in the training process which allows the use of both GPS and GLONASS TEC. Information on the new GLONASS R&D network, future research possibilities and collaborations are also provided.

Response of data‐driven artificial neural network‐based TEC models to neutral wind for different locations, seasons, and solar activity levels from the Indian longitude sector

AbstractThe perturbations imposed on transionospheric signals by the ionosphere are a major concern for navigation. The dynamic nature of the ionosphere in the low‐latitude equatorial region and the Indian longitude sector has some specific characteristics such as sharp temporal and latitudinal variation of total electron content (TEC). TEC in the Indian longitude sector also undergoes seasonal variations. The large magnitude and sharp variation of TEC cause large and variable range errors for satellite‐based navigation system such as Global Positioning System (GPS) throughout the day. For accurate navigation using satellite‐based augmentation systems, proper prediction of TEC under certain geophysical conditions is necessary in the equatorial region. It has been reported in the literature that prediction accuracy of TEC has been improved using measured data‐driven artificial neural network (ANN)‐based vertical TEC (VTEC) models, compared to standard ionospheric models. A set of observations carried out in the Indian longitude sector have been reported in this paper in order to find the amount of improvement in performance accuracy of an ANN‐based VTEC model after incorporation of neutral wind as model input. The variations of this improvement in prediction accuracy with respect to latitude, longitude, season, and solar activity have also been reported in this paper.

Performance evaluation of linear time-series ionospheric Total Electron Content model over low latitude Indian GPS stations

Precise modeling of the ionospheric Total Electron Content (TEC) is a critical aspect of Positioning, Navigation, and Timing (PNT) services intended for the Global Navigation Satellite Systems (GNSS) applications as well as Earth Observation System (EOS), satellite communication, and space weather forecasting applications. In this paper, linear time series modeling has been carried out on ionospheric TEC at two different locations at Koneru Lakshmaiah University (KLU), Guntur (geographic 16.44°N, 80.62°E; geomagnetic 7.55°N) and Bangalore (geographic 12.97°N, 77.59°E; geomagnetic 4.53°N) at the northern low-latitude region, for the year 2013 in the 24th solar cycle. The impact of the solar and geomagnetic activity on periodic oscillations of TEC has been investigated. Results confirm that the correlation coefficient of the estimated TEC from the linear model TEC and the observed GPS-TEC is around 93%. Solar activity is the key component that influences ionospheric daily averaged TEC while periodic component reveals the seasonal dependency of TEC. Furthermore, it is observed that the influence of geomagnetic activity component on TEC is different at both the latitudes. The accuracy of the model has been assessed by comparing the International Reference Ionosphere (IRI) 2012 model TEC and TEC measurements. Moreover, the absence of winter anomaly is remarkable, as determined by the Root Mean Square Error (RMSE) between the linear model TEC and GPS-TEC. On the contrary, the IRI2012 model TEC evidently failed to predict the absence of winter anomaly in the Equatorial Ionization Anomaly(EIA) crest region. The outcome of this work will be useful for improving the ionospheric now-casting models under various geophysical conditions.

GPS differential code biases considering the second-order ionospheric term

Differential code biases (DCBs) of the global positioning system (GPS) are generally estimated together with total electron content (TEC) along the signal transmission path through the ionosphere. However, better accuracy can be obtained theoretically by also considering the second-order ionospheric term, which is related to the magnetic field of the earth and TEC along the signal path. Using data from the international GNSS service tracking network stations, we built a regional TEC model by considering the second-order ionospheric term and estimated the DCBs of both GPS satellites and receivers. The geomagnetic field was calculated by IGRF-12 model. The results show that the second-order ionospheric term causes a 10−3 ns level effect to the estimated DCBs of GPS satellites and receivers and a 10−2 TECU (TEC unit) level change of modeled TEC. As higher accuracy is achieved, more input data such as magnetic field data of the earth are also needed. When the second-order ionospheric term is considered, the processing gets more complicated and needs more time. However, the effect of the second-order ionospheric term on DCB estimate also shows an obvious diurnal variation and showing the same monotonicity over time as local VTEC.

Features of TEC behaviour over the low-latitude North-American region during the period of medium solar activity

The study is focused on Total Electron Content (TEC) behavior in low-latitude North-American region within the twelve-year period from 2005 to 2016. The obtained systematic (regular) TEC variations (diurnal, seasonal patterns) conform to the known tendencies described by climatological TEC models. TEC variations during disturbances have a number of distinctive features. The majority of positive TEC enhancements occur already with Dst <−30nT. The intensity of TEC change does not depend on Dst-index minimum value. Almost all cases of day-time TEC disturbances are accompanied by night-time disturbances with the possibility of relative night-time deviations being larger that is confirmed by data of low-orbit satellites CHAMP and DMSP. The simultaneity of changes of critical frequencies of the ionosphere with changes of TEC during disturbances was observed at Ramey (Puerto Rico) and Eglin (U.S.A.) stations when ionosonde data was available. This proves that TEC reflects the character of ionospheric disturbances. Spatial non-synchronism of TEC change that was sometimes observed can be explained by local character of the area covered with positive TEC disturbance. It was shown the potential provided by local GNSS networks for the detailed analysis of TEC variations over the considered region and estimation of the ionosphere state in near-real time.

Short‐term forecasting of ionospheric total electron content over a low‐latitude global navigation satellite system station

The prediction and forecasting of ionospheric delay at equatorial and low-latitude regions is an essential contribution for improving the global positioning system services. In this study, hybrid auto-regressive integrated moving average (ARIMA) models are implemented based on wavelet transform (WT) and empirical mode decomposition (EMD) for 1 h ahead forecast of ionospheric total electron content (TEC). The performance of ARIMA and hybrid models, WT and EMD in combination with ARIMA (WARIMA and EARIMA) is evaluated during various seasons and March geomagnetic storm conditions in 2013 and 2015. The proposed models are validated with empirical global TEC models and results show that the EARIMA has less error measurements compared with ARIMA and WARIMA models. The EARIMA ionospheric forecasting model can be useful for developing an early warning ionospheric space weather system over low latitudes.

Short-term estimation of GNSS TEC using a neural network model in Brazil

This work presents a novel Neural Network (NN) model to estimate Total Electron Content (TEC) from Global Navigation Satellite Systems (GNSS) measurements in three distinct sectors in Brazil. The purpose of this work is to start the investigations on the development of a regional model that can be used to determine the vertical TEC over Brazil, aiming future applications on a near real-time frame estimations and short-term forecasting. The NN is used to estimate the GNSS TEC values at void locations, where no dual-frequency GNSS receiver that may be used as a source of data to GNSS TEC estimation is available. This approach is particularly useful for GNSS single-frequency users that rely on corrections of ionospheric range errors by TEC models. GNSS data from the first GLONASS network for research and development (GLONASS R&D network) installed in Latin America, and from the Brazilian Network for Continuous Monitoring of the GNSS (RMBC) were used on TEC calibration. The input parameters of the NN model are based on features known to influence TEC values, such as geographic location of the GNSS receiver, magnetic activity, seasonal and diurnal variations, and solar activity. Data from two ten-days periods (from DoY 154 to 163 and from 282 to 291) are used to train the network. Three distinct analyses have been carried out in order to assess time-varying and spatial performance of the model. At the spatial performance analysis, for each region, a set of stations is chosen to provide training data to the NN, and after the training procedure, the NN is used to estimate vTEC behavior for the test station which data were not presented to the NN in training process. An analysis is done by comparing, for each testing station, the estimated NN vTEC delivered by the NN and reference calibrated vTEC. Also, as a second analysis, the network ability to forecast one day after the time interval (DoY 292) based on information of the second period of investigation is also assessed in order to verify the feasibility on using low amount of data for short-term forecasting. In a third analysis, the spatial performance of the NN model is assessed and compared against CODE Global Ionospheric Maps during the geomagnetic storm registered on 13th and 14th October 2016. The results obtained from the three described analyses indicate that even using a ten-days period of data to train the network, the proposed NN model provides good spatial performance and presents to be a promising tool for short-term forecasting. The results obtained in the analysis presented a root mean squared error less than 7.9 TECU in all scenarios under investigation.

Regional TEC model under quiet geomagnetic conditions and low-to-moderate solar activity based on CODE GIMs

Global empirical total electron content (TEC) models based on TEC maps effectively describe the average behavior of the ionosphere. However, the accuracy of these global models for a certain region may not be ideal. Due to the number and distribution of the International GNSS Service (IGS) stations, the accuracy of TEC maps is geographically different. The modeling database derived from the global TEC maps with different accuracy is likely one of the main reasons that limits the accuracy of the new models. Moreover, many anomalies in the ionosphere are geographic or geomagnetic dependent, and as such the accuracy of global models can deteriorate if these anomalies are not fully incorporated into the modeling approach. For regional models built in small areas, these influences on modeling are immensely weakened. Thus, the regional TEC models may better reflect the temporal and spatial variations of TEC. In our previous work (Feng et al., 2016), a regional TEC model TECM-NEC is proposed for northeast China. However, this model is only directed against the typical region of Mid-latitude Summer Nighttime Anomaly (MSNA) occurrence, which is meaningless in other regions without MSNA. Following the technique of TECM-NEC model, this study proposes another regional empirical TEC model for other regions in mid-latitudes. Taking a small area BeiJing–TianJin–Tangshan (JJT) region (37.5°–42.5° N, 115°–120° E) in China as an example, a regional empirical TEC model (TECM–JJT) is proposed using the TEC grid data from January 1, 1999 to June 30, 2015 provided by the Center for Orbit Determination in Europe (CODE) under quiet geomagnetic conditions. The TECM–JJT model fits the input CODE TEC data with a bias of 0.11TECU and a root mean square error of 3.26TECU. Result shows that the regional model TECM–JJT is consistent with CODE TEC data and GPS–TEC data.