Ionospheric Total Electron Content Research Articles

Parametric Dependence of Topside Ionospheric Empirical Scale Height and Electron Density Profiles in NeQuick2 Model Over the Equatorial and Low Latitudes and Its Consequences on the Estimation of TEC

AbstractConstructing realistic vertical electron density profiles is a crucial step in accurately estimating the ionospheric total electron content (TEC) in empirical models. Inadequate representation of electron density profiles leads to under‐ or over‐estimation of TEC in model outputs. One of the widely used ionospheric empirical model, the NeQuick2 exhibits large uncertainties over equatorial and low latitudes, particularly in the topside ionosphere. The NeQuick2 model employs semi Epstein type of polynomials to characterize the topside ionospheric structure, wherein, the scale height (H) is the key parameter. NeQuick2 model estimates H value using empirical formulations, which contain three major parameters namely, Ho—the scale height at the F‐layer peak, g—height gradient in H, and r—which controls the increase in H at higher altitudes. In this study, a systematic analysis has been carried out with particular focus on the equatorial and low latitudes to explore the variability of topside scale height and electron density profiles on the above three empirical parameters using COSMIC observations over Trivandrum and Ahmedabad, in India. Model uncertainties in estimating Ho and their impacts on the estimation of TEC are investigated. Data assimilation analysis has been carried out to obtain a quantitative understanding on the effect of deviations in topside scale height on the uncertainties in accurate estimation of TEC over equatorial and low latitudes. This study reveals the impact of scale height uncertainties on errors in modeled TEC and demonstrates the need for improvement in empirical formulations for improving accuracies in ionospheric modeling over equatorial and low latitude sectors.

Responses of Total Electron Content to Solar Flares over Low and Mid-Latitude Regions during Sun Halo Day

The responses of total electron content (TEC) to solar flares over low- and mid-latitude regions during the sun halo days were investigated. The research is based on GPS data obtained from Bangalore (13.02117°N, 77.57038°E) on May 24, 2021; Cape Town (-33.918861°N, 18.423300°E) on October 26, 2020; and North Dakota (46.55756°N, -96.472300°E) on December 27, 2021, during the solar halo days. The results of this study demonstrate that the values of TEC increased at Bangalore and Cape Town stations during solar halo days as compared to other days. However, over the North Dakota station, TEC during the sun halo day was greater than that on the days before and after the halo day from around 19:00 UT to 23:59 UT hours. During the sun halo over Bangalore and Cape Town stations, positive relative changes in TEC prevail, suggesting that the action of the interplanetary electric field, the prompt penetration electric field, and the disturbance dynamo electric field lead to higher TEC values. However, during the sun halo over North Dakota station, negative relative changes in TEC prevail, which might be related to the consequence that low solar activity with no earth’s field disturbances (a positive Dst) leads to lower ionospheric TEC values. Stations that have greater relative changes in TEC have shown greater power spectrum and global wave spectrum energy. Cape Town has a greater relative change in TEC, a greater power spectrum, and a larger global wave spectrum than Bangalore and North Dakota stations. The values of TEC over three stations are different due to the latitudinal and longitudinal differences in addition to the universal time effects. Finally, solar flares have a major influence on ionosphere electrodynamics, and the upward drift velocity of ionospheres at the low latitude station is more strongly influenced by solar flares due to the effects of the ExB drifts, which induce TEC disturbances during the halo days over the suggested stations.

Ionospheric TEC variation over Svalbard archipelago, Norway and assessment of Bilinear interpolated GIM model

The Svalbard archipelago region is a group of islands in the Arctic Ocean, located between the North Pole and the northern coastal region of Norway at high latitudes. The current study focuses on the spatiotemporal distribution of total electron content (TEC) over the Svalbard archipelago region with the ground-based global positioning system (GPS) tracking stations during the years 2020–2022. The spatial ionospheric TEC characteristics from GPS observables and their correlation with the Bilinear interpolated global ionospheric map (GIM) and the latest edition of the international reference ionosphere (IRI- 2020) model are investigated. The results showed that both GPS-TEC and GIM-TEC have minimum ionospheric TEC during the winter season while they have maximum TEC occurrence in the summer and spring seasons with a higher difference of magnitude. We suspect such discrepancy could be due to expected uncertainties in the estimation of GIM-TEC interpolation over the region. The correlation coefficients between GPS-TEC versus GIM-TEC and GPS-TEC versus IRI-TEC showed that they are correlated with an almost equivalent correlation coefficient of approximately 0.76 which is reasonably acceptable but relatively lower than other middle latitude regions. This poor correlation happened because of the diurnal variation in the polar cap region during the summer season which is minimal. Finally, a Multitaper spectral analysis is employed on the frequency spectra of the time-varying periodograms of the four oscillation components. The outcome of the spectral analysis confirms that the first principal component (PC) of GPS-TEC is well correlated with the respective PC of GIM and IRI models. Although the higher order PCs of GIM models remain well-correlated with GPS-TEC, the subdued correlation between IRI model outputs and GPS-TEC suggests further improvements in the IRI model representation with respect to the high latitude characteristics.

Global Variations of the Total Electron Content in the Equatorial Ionosphere during the Annular Solar Eclipse of June 21, 2020

Global Variations of the Total Electron Content in the Equatorial Ionosphere during the Annular Solar Eclipse of June 21, 2020

Comparison of Simulated and Measured Power of the Earth-Space Link for Satellite-Based AIS Signals.

This research aims to analyze the impact of the Earth-Space link on the Automatic Identification System (AIS) signals of ships. To achieve this, we established a simulation system that measures the receiving power of AIS signals via satellite platforms. We validated the system by utilizing observation data from Tiantuo-5. Through this simulation, we quantitatively analyzed the effects of ionospheric TEC (Total Electron Content) and space loss on the received power. During the processing of observation data, we construct a geometric propagation model utilizing the measured positions of both the satellite and the ship. We then calculate the antenna gain and remove any system errors. Additionally, we eliminate the deviation of elevation and azimuth angles caused by satellite motion. This allows us to determine the actual power of different ships reaching the receiving platform. Upon comparing the measured power data with the simulated power, it was noted that both exhibited an increasing trend as the elevation angle increased. This led to an RMSE (Root Mean Square Error) result of approximately one, indicating the accuracy of the simulation system. These findings hold significant implications for analyzing interference factors in satellite-ground links.

Assimilating GNSS TEC with an LETKF over Yunnan, China

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation.

Low latitude ionospheric TEC response to the solar flares during the peak of solar cycle 24

We describe the newly discovered response of differential vertical total electron content (DVTEC) in the low-equatorial latitude ionosphere to the solar flares during the peak of solar cycle 24 i.e., the year 2014. GPS TEC data for the existing investigation has been acquired from the international GNSS services network station in Bangalore, India (geomagnetic latitude 4.58οN). For the year 2014, we inspected five X-class solar flares, forty-nine M-class solar flares, and three hundred ninety-six C-class solar flares in the current study. To ascertain the relationship between ΔDVTEC and the ΔX-ray and ΔEUV fluxes, the two main ionizing radiation fluxes during flare events are used. Additionally, to identify electron density change in the ionosphere due to solar flares, we used two well-known methodologies, the baseline method and the mean method. The baseline method provides a better correlation between ΔDVTEC and ΔX-ray for X-, M-, and C-class flares. For M- and C- class flares, both methods do not show any meaningful correlation. The Solar disc location effect shows an enhanced (0.573) correlation between ΔDVTEC and ΔX-ray*cos (CMD) for the X-class flares, whereas for M- and C-class flares, no significant change was observed, which indicates feeble or no CMD effect for lower class solar flares while it is opposite for X-class flares. Here we suggest that the high solar activity, and location of the observation station to the EIA trough region might be the possible reason for the observed results.

Prospects for meteotsunami detection in earth’s atmosphere using GNSS observations

We study, for the first time, the physical coupling and detectability of meteotsunamis in the earth’s atmosphere. We study the June 13, 2013 event off the US East Coast using Global Navigation Satellite System (GNSS) radio occultation (RO) measurements, Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures, and ground-based GNSS ionospheric total electron content (TEC) observations. Hypothesizing that meteotsunamis also generate gravity waves (GWs), similar to tsunamigenic earthquakes, we use linear GW theory to trace their dynamic coupling in the atmosphere by comparing theory with observations. We find that RO data exhibit distinct stratospheric GW activity at near-field that is captured by SABER data in the mesosphere with increased vertical wavelength. Ground-based GNSS-TEC data also detect a far-field ionospheric response 9 h later, as expected by GW theory. We conclude that RO measurements could increase understanding of meteotsunamis and how they couple with the earth’s atmosphere, augmenting ground-based GNSS TEC observations.

Regional ionospheric TEC modeling during geomagnetic storm in August 2021- data fusion using multi-instrument observations

Regional ionospheric Total Electron Content (TEC) models are most significant to investigate the variability of ionosphere during all-weather conditions. Since the ground-based TEC measurements are not available in oceanic and polar regions, space-based observations are incorporated to increase the positional accuracy and reliability of Global Ionospheric TEC Maps (GIMs). Therefore, data fusion techniques are necessary to enhance the prediction capability of regional ionospheric models by considering multi-source data. In view of this, an attempt is made to investigate Equatorial Ionization Anomaly (EIA) structures in low-latitudinal Indian region using multi-instrument data. Data observations from background model CODEGIM, available International GNSS Service (IGS) stations in India, GNSS receivers at KLEF and Tripura stations, FORMOSAT/COSMIC radio occultation and SWARM during geomagnetic storm period August 26–28, 2021 are used for the study. Spherical Harmonics Function (SHF) of order 4 is employed along with Weighted Least Squares analysis (WLS), where separate weight constraint is computed for each data source. TEC maps of spatial resolution 5° x 5° and temporal resolution of 1 h are generated to illustrate the dynamic behavior of ionosphere. The standard deviation for 3 days is obtained as 0.42, 0.61 and 1.43 TECU respectively. TEC depletions and enhancements are observed on August 27 and 28, with mean values of 12.74 and 15.27 TECU respectively, demonstrating negative and positive storm effects.

Influence of Stratosphere Polar Vortex Variability on the Mesosphere, Thermosphere, and Ionosphere

AbstractThe Whole Atmosphere Community Climate Model with thermosphere‐ionosphere eXtension (WACCM‐X) is used to investigate the influence of stratosphere polar vortex variability on the mesosphere, thermosphere, and ionosphere during Northern Hemisphere winter. Based on 40 simulated Northern Hemisphere winters, the mesosphere and lower thermosphere (MLT) residual circulation is found to depend on whether the stratosphere polar vortex is strong or weak. In particular, during weak stratosphere polar vortex time periods, the MLT circulation anomalies are characterized by clockwise and anti‐clockwise flow in the Northern and Southern Hemispheres, respectively. Opposite, though weaker, anomalies are found to occur during time periods when the stratosphere polar vortex is strong. The MLT circulation anomalies influence the composition of the lower thermosphere, leading to ±5% changes in the thermosphere column integrated atomic oxygen to molecular nitrogen ratio (ΣO/N2). Large differences between strong and weak stratosphere polar vortex events are also found to occur in the semidiurnal migrating tide (SW2) in the MLT, which leads to ±15%–20% differences in the SW2 component of the ionosphere total electron content (TEC) at low latitudes. The WACCM‐X simulation results indicate that variability in the stratosphere polar vortex can explain ∼30% and ∼18% of the quiet time variability in thermosphere ΣO/N2 and the SW2 component of TEC during Northern Hemisphere winter, respectively.

Response of the Ionospheric Total Electron Content During St. Patrick's Day Geomagnetic Storm in 2015 over the Philippine-Taiwan Region

The ionospheric total electron content (TEC) response during the St. Patrick's Day geomagnetic storm of 2015 (max Kp = 8) is an interest of study in the field of space weather since it is the strongest geomagnetic storm of Solar Cycle 24. The cause of this storm is a coronal mass ejection (CME) from the Sun on 15 Mar 2015 recorded by SOHO/LASCO and arrived at ~ 05:00 UT 17 Mar 2015. The CME speed is estimated at ~ 668 km/s. At the initial phase of the storm, the disturbance storm time (Dst) index rose from 15 nanoteslas nT at 03:21–56 nT at 05:31 UT. The main phase of the storm caused the Dst index to drop to the minimum value of –223 nT from 05:31–22:33 UT on 17 Mar 2015. In the Philippine-Taiwan sector, TEC during the main phase was enhanced by ~ 25 TECU on 10 GNSS receiver stations and was depleted afterward with a maximum depletion of ~ 33 TECU on PBAS. The recovery phase showed a TEC depletion on all stations for the whole day. The minimum dTEC (differential TEC) is observed at PBAS (Basco, Batanes, Philippines), and the maximum percent depletion is observed at TWTF (Taoyuan, Taiwan) during the early hours of photoionization. Results from this study verified that GNSS TEC measurements along Asian sectors dropped significantly during St. Patrick's geomagnetic storm.

Evaluation of the performance of F-layer peak height models used in IRI-2016 over the Indian equatorial and low latitudes

The performance of different height models (BSE-1979, AMTB-2013, and SHU-2015) in the International Reference Ionosphere (IRI-2016) is evaluated over the Indian equatorial and low latitude sectors. The impact of deviations in the modelled hmF2 on the estimation of electron density profiles and the Total Electron Content (TEC) is examined using the three different hmF2 methods in IRI-2016. For this study digisonde observations over Trivandrum (8.5 °N, 76.9 °E) and Ahmedabad (23.0 °N, 72.5 °E) along with GPS observations over Colombo (6.8 °N, 79.8 °E) and Ahmedabad during a high solar activity year 2014 are considered. A comparison of electron density profiles between digisonde and IRI-2016 at both the equatorial station, Trivandrum, and the low latitude station, Ahmedabad, revealed significant deviations with higher deviations in the post sunset hours at the equator. The three height methods have shown significant differences in their predictions of hmF2 at both locations. At the equator, the SHU-2015 method shows better estimates closer to the measurement values, than the other two methods except at Pre-Reversal Enhancement (PRE) time, during which, the AMTB-2013 method shows better performance. At the low latitude station, the newly added methods (SHU-2015 and AMTB-2013) do not indicate any improvements in estimating the hmF2. The availability of three different height methods in IRI-2016 is exploited to understand the modifications if any, in the ionospheric height structure and TEC for different hmF2 methods and the corresponding results are presented. As the variations in hmF2 are used to derive vertical drift on many occasions in the absence of incoherent scatter radar, this study is significant in assessing the relative efficacy of the three height models and hence useful to understand various ionospheric processes over the low/equatorial latitudes.

ThingSpeak an IOT Application and Analytics System for GNSS with MATLAB Analysis

The ionosphere is an essential component of the Global Navigation Satellite System (GNSS) that can affect the accuracy and dependability of GNSS location. MATLAB provides comprehensive tools for the analysis of ionospheric data and its impact on GNSS positioning. To examine the ionospheric data utilized in GNSS positioning, we advise utilizing MATLAB in this application. The analysis is based on a determination of the Total Electron Content (TEC) of the ionosphere in order to account for the ionospheric delay in the GNSS signals. The system consists of a GNSS receiver module connected to a microcontroller with Wi-Fi capabilities, which transmits data to Matlab. The data is processed in Matlab before being used there. Then, in order to account for the ionospheric delay in the GNSS signals, the TEC of the ionosphere is calculated using Matlab. The recommended system provides a scalable and flexible environment for GNSS-based ionospheric data analysis. The system is practical for a range of applications, including surveying, geodesy, and navigation, and it may be readily adapted to fulfill specific customer requirements.For Matlab analysis, the approach has a number of advantages. In order to measure the TEC of the ionosphere precisely, it first provides cutting-edge signal processing technologies. Additionally, it makes it possible to display ionospheric data in a number of different ways, such as maps, graphs, and time-series plots. This can be applied to the data to identify patterns and trends. The proposed MATLAB-based system offers a scalable, flexible, and cutting-edge platform for the estimation of the ionosphere's TEC and the correction of the ionospheric delay in the GNSS signals.

Predicting global ionospheric TEC maps using Gaussian process regression

The integration of machine learning technology into ionospheric prediction has emerged as a burgeoning field of research. However, several challenges still exist, such as the disregard of delays in real-world data acquisition, variations in accuracy due to different input data and methods. In this study, we present an ionospheric Gaussian process regression (GPR) model with multiple input parameters to forecast global ionospheric total electron content (TEC). We assess two solutions based on GPR model that leverage different ionospheric data, namely the vertical TEC (VTEC) and spherical harmonic coefficients (SHC) prediction solutions. Our findings demonstrate that both GPR models exhibit strong accuracy and stability. Specifically, for 1-day-ahead-predicted global ionospheric maps (GIMs) in 2015 year and 2019 year, the root mean square error (RMSE) of the SHC prediction solution is 4.343 TECU and 1.702 TECU, respectively, while the VTEC prediction solution has RMSE values of 4.321 TECU and 1.673 TECU. Moreover, in high and low solar activity, over 29% and 68% of the absolute residuals are within 1.0 TECU, respectively. Furthermore, we compare the GPR model with conventional methods (such as Adaptive Autoregressive Model (AAR)) and observe that the RMSE of the GPR model is lower than that of the AAR model for GIMs predicted under different solar and geomagnetic activities, with a difference ranging from 0.2 to 0.4 TECU. In addition, the GPR model can provide prediction intervals for the predicted values. These intervals are typically represented using the mean and standard deviation, where the mean represents the predicted value and the standard deviation represents the uncertainty associated with the prediction. Prediction intervals can assist users in understanding the uncertainty of the model's predictions.

LSTM based forecasting of the next day’s values of ionospheric total electron content (TEC) as an earthquake precursor signal

LSTM based forecasting of the next day’s values of ionospheric total electron content (TEC) as an earthquake precursor signal

Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model.

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

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

TECX-TCN: Prediction of ionospheric total electron content at different latitudes in China based on XGBoost algorithm and temporal convolution network

The correction of ionospheric delay error through the estimation of ionospheric total electron content (TEC) parameters is very important for the accuracy and rapid convergence of precise point positioning (PPP), especially for single frequency PPP position and double frequency undifferenced and uncombined PPP models. This paper studies the prediction method of ionospheric TEC by analyzing various influencing factors. Firstly, considering the influence of latitude, the TEC values of different observation stations in China at middle and low latitudes are used for research. Secondly, due to the influence of solar storms, the years with and without solar storms are selected for comparative study. At the same time, the moving average value of TEC and the lag value of each solar and geomagnetic index are also added. Because the influence of each feature on ionospheric TEC is not clear, the importance score of each feature is obtained according to the EXtreme Gradient Boosting (XGBoost) algorithm to establish a reasonable feature combination. Then, the temporal convolution network (TCN) is used to train according to data of the collected solar cycle and predict data for the next year. Through experiments, the prediction model with feature selection can obtain higher prediction accuracy, the error reaches ∼2TECU at middle latitude region and ∼3 TECU at low latitude region. Finally, compared with other algorithms, our ionospheric TEC prediction algorithm based on XGBoost and TCN (TECX-TCN) significantly has better performance. When the solar activity is low, its RMSE can reach ∼2.5TECU, the correlation coefficient can reach ∼0.9, and even in the low latitude area with high solar activity, its RMSE can reach ∼5TECU, the correlation coefficient can reach ∼0.85.

An improved Kloubuchar ionospheric correction model for single frequency GNSS receivers

Abstract The enhancement of positional accuracy of single frequency ionospheric correction models is an urgent need for low-cost and smartphone GNSS users. The available single frequency ionospheric correction models such as Klobuchar, NeQuick-G, NTCM, and Klob-BDS are providing ionospheric corrections for multi GNSS systems such as GPS, Galileo, BDS, and NAVIC systems. Otherwise, Global Ionospheric Map (GIM) Total Electron Content (TEC) corrections are also available to the GNSS users. In this letter, an improved Klobuchar ionospheric model is implemented. The slant TEC of dual Frequency GPS TEC observations is considered a reference. The Klobuchar model slant TEC observations are improved by taking the grid-based residual TEC corrections with the Adjusted Spherical Harmonic Function model. The Single frequency users can improve the ionospheric delay estimation using Klobuchar model and grid-based TEC residual corrections. The improved ionospheric correction model is tested under the biggest geomagnetic storm conditions during 24th Solar cycle that occurred in March 2015 over India. The proposed hybrid slant ionospheric TEC algorithms are evaluated with individual single frequency ionospheric models (Klobuchar, and GPS TEC) under adverse space weather conditions.

Validation of Ionospheric Modeled TEC in the Equatorial Ionosphere During the 2013 March and 2021 November Geomagnetic Storms

AbstractThis paper presents the validation of modeled total electron content (TEC) from 14 ionospheric models, including empirical, physics‐based, and data assimilation (DA) models, hosted by the NASA/NSF Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center, and NASA Jet Propulsion Laboratory (JPL). This study aims to assess the current progress and capability of the CCMC‐hosted ionospheric models in capturing the storm time ionosphere during the low and moderate solar flux years. We focus on the low‐latitude ionosphere (i.e., ±40° in magnetic latitude) and compare the modeled TEC with the Madrigal TEC during the 2013 March and 2021 November storms. Multiple metrics are used to quantitatively assess the models' accuracy, precision, association, bias, and capability in capturing the TEC changes in response to the storms. The skill score based on the metric scores is further proposed to evaluate the overall performance of ionospheric models against the reference model (International Reference Ionosphere 2016; IRI‐2016). The results indicate that the DA model GLObal Total Electron Content and JPL Global Ionospheric Map models show good performance in modeling the TEC and reasonably reflect the storm time TEC changes spatially and temporally. The empirical models IRI‐2016 and 2020 show relatively good performance compared with the physics‐based models regarding the model‐data comparison; however, it is difficult to characterize the TEC changes caused by storms. The physics‐based models can simulate the storm effect in spatial and temporal TEC variations better than the empirical model. The performance of ionospheric models in capturing the storm time TEC anomaly is presented and discussed.