Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN

Automated daily process for global ionospheric total electron content maps and satellite ocean altimeter ionospheric calibration based on Global Positioning System data

In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5° longitude and 2.5° latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE’s 1-day forecast product c1pg, and comparable to CODE’s 2-day forecast c2pg. The model’s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.

Obtaining ionospheric Total Electron Content (TEC) with high spatial and temporal resolution is crucial for satellite navigation and space weather monitoring. Global Ionospheric Maps (GIMs) are key sources of TEC data. However, the temporal resolution of GIMs from ionosphere associate analysis centers does not satisfy the requirements for space weather monitoring. To address this issue, the Massachusetts Institute of Technology (MIT) Haystack Observatory has provided high‐time‐resolution TEC maps since 1998, using data from ∼5,000 Global Navigation Satellite System (GNSS) tracking stations. However, our research reveals a significant systematic bias in MIT's TEC maps. Unlike the biases between GIMs, it exhibits an unexpected pattern of variation and is also present in comparisons with radio occultation TEC. Further analysis indicates that the systematic bias in MIT TEC decreases with increasing geomagnetic latitude and is correlated with solar activity. In low‐latitude regions, the bias can reach up to 30 TECU, while in high‐latitude regions, it can drop to nearly 0 TECU. During diurnal and seasonal variations, the systematic bias in MIT TEC follows TEC patterns but appears unaffected by small‐scale ionospheric structures. Finally, we analyze the factors potentially contributing to the systematic bias and find that Differential Code Biases (DCBs) provided by MIT exhibits errors of a magnitude comparable to the TEC bias, with a strong correlation between the two. Through further investigation, we confirm that the systematic bias in MIT TEC primarily stems from its DCB estimation strategy and choice of mapping function, and this bias persists both before and after MIT updated its DCB estimation approach.

In this study, we make a global total electron content (TEC) forecasting using a novel deep learning method, which is based on conditional generative adversarial networks. For training, we use the International GNSS Service (IGS) TEC maps from 2003 to 2012 with 2‐h time cadence. Our model has two input images (IGS TEC map and 1‐day difference map between the present day and the previous day) and one output image (1‐day future map). The model is tested with two data sets: solar maximum period (2013–2014) and solar minimum period (2017–2018). Then, we compare the results of our model with those of 1‐day Center for Orbit Determination in Europe (CODE) prediction model. Our major results can be summarized as follows. First, we successfully apply our model to the forecast of global TEC maps. Second, our model well predicts daily TEC maps with 1 day in advance using only previous TEC maps. The averaged root mean square error, bias, and standard deviation between AI‐generated and IGS TEC maps are 2.74 TECU, −0.32 TECU, and 2.59 TECU, respectively. Third, our model generates some peak structures around equatorial regions. Fourth, our model shows better performance than 1‐day CODE prediction model during both solar maximum and minimum periods. Fifth, another model with additional input data Kp index gives a slight improvement of the results. Our study shows that our deep learning model based on an image translation method will be effective for forecasting of future images using previous data.

Global ionospheric total electron content (TEC) is generally derived with ground-based Global Navigation Satellite System (GNSS) observations based on mathematical models in a solar-geomagnetic reference frame. However, ground-based observations are not well-distributed. There is a lack of observations over sparsely populated areas and vast oceans, where the accuracy of TEC derivation is reduced. Additionally, the modified dip (modip) latitude is more suitable than geomagnetic latitude for the ionosphere. This paper investigates the improvement of global TEC with multi-source data and modip latitude, and a simulation with International Reference Ionosphere (IRI) model is developed. Compared with using ground-based observations in geomagnetic latitude, the mean improvement was about 10.88% after the addition of space-based observations and the adoption of modip latitude. Nevertheless, the data from JASON-2 satellite altimetry and COSMIC occultation are sparsely-sampled, which makes the IRI TEC a reasonable estimation for the areas without observation. By using multi-source data from ground-based, satellite-based and IRI-produced observations, global TEC was derived in both geomagnetic and modip latitudes for 12 days of four seasons in 2014 under geomagnetic quiet conditions. The average root-mean-square error (RMSE) of the fitting was reduced by 7.02% in modip latitude. The improvement was largest in March and smallest in June.

This paper first applies a prediction model based on self‐attention memory ConvLSTM (SAM‐ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in Europe (CODE) as the training data set covering the period from 1999 to 2022. Besides that, we put several space environment data as additional multivariate‐features into the framework of the prediction model to enhance its forecasting ability. In order to confirm the efficiency of the proposed model, the other two prediction models based on convolutional long short‐term memory (LSTM) are used for comparison. The three models are trained and evaluated on the same data set. Results show that the proposed SAM‐ConvLSTM prediction model performs more accurately than the other two models, and more stably under space weather events. In order to assess the generalization capabilities of the proposed model amidst severe space weather occurrences, we selected the period of 22–25 April 2023, characterized by a potent geomagnetic storm, for experimental validation. Subsequently, we employed the 1‐day predicted global TEC products from the Center for Operational Products and Services (COPG) and the SAM‐ConvLSTM model to evaluate their respective forecasting prowess. The results show that the SAM‐ConvLSTM prediction model achieves lower prediction error. In one word, the ionospheric TEC prediction model proposed in this paper can establish the ionosphere TEC of spatio‐temporal data association for a long time, and realize high precision of prediction performance.

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

Mapping of the Ionospheric Total Electron Content over the East African Low–Latitude Region

Research on total electron content (TEC) empirical models is one of the important topics in the field of space weather services. Global TEC empirical models based on Global Ionospheric Maps (GIMs) TEC data released by the International GNSS Service (IGS) have developed rapidly in recent years. However, the accuracy of such global empirical models has a crucial restriction arising from the non-uniform accuracy of IGS TEC data in the global scope. Specifically, IGS TEC data accuracy is higher on land and lower over the ocean due to the lack of stations in the latter. Using uneven precision GIMs TEC data as a whole for model fitting is unreasonable. Aiming at the limitation of global ionospheric TEC modelling, this paper proposes a new global ionospheric TEC empirical model named the TECM-GRID model. The model consists of 5183 sections, corresponding to 5183 grid points (longitude 5°, latitude 2.5°) of GIM. Two kinds of single point empirical TEC models, SSM-T1 and SSM-T2, are used for TECM-GRID. According to the locations of grid points, the SSM-T2 model is selected as the sub-model in the Mid-Latitude Summer Night Anomaly (MSNA) region, and SSM-T1 is selected as the sub-model in other regions. The fitting ability of the TECM-GRID model for modelling data was tested in accordance with root mean square (RMS) and relative RMS values. Then, the TECM-GRID model was validated and compared with the NTCM-GL model and Center for Orbit Determination in Europe (CODE) GIMs at time points other than modelling time. Results show that TECM-GRID can effectively describe the Equatorial Ionization Anomaly (EIA) and the MSNA phenomena of the ionosphere, which puts it in good agreement with CODE GIMs and means that it has better prediction ability than the NTCM-GL model.

Using the spherical harmonic (SH) function model and the dual frequency GPS data of 139 International GPS Service (IGS) stations for July 15 of 2000, the global ionospheric total electron content (TEC) is calculated and the basic method is investigated. Here, preliminary results are reported and the problems and difficulties to be solved for using GPS data to determine the global ionospheric TEC are discussed.

The ability to predict the ionosphere activity is of interest for several applications such as satellite telecommunications or Global Navigation Satellite Systems (GNSS). A few studies have proposed models able to predict Total Electron Content (TEC) values of the ionosphere locally over measuring stations, but not worldwide for most of them. We propose a method using Deep Neural Networks (DNN) to predict a sequence of global TEC maps consecutive to an input sequence of past TEC maps, by combining Convolutional Neural Networks (CNNs) with convolutional Long Short-Term Memory (LSTM) networks. The numerical experiments show that the approach provides significant improvement over methods implemented for benchmarking and is competitive with state-of-the-art methods while providing global TEC predictions. The proposed architecture can be adapted to any sequence-to-sequence prediction problem.

We employed zonal wind data from Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics Doppler Interferometer, equatorial electrojet (EEJ) measurements from Jicamarca (12°S, 77°W), and global ionospheric total electron content (TEC) maps to investigate the effects of ultra‐fast Kelvin waves (UFKW) with zonal wavenumbers 2 and 3 propagating eastward (E2, E3) in the equatorial mesosphere on both ionospheric TEC and EEJ signatures, as well as their differences in ionospheric response characteristics compared to E1 waves. Periodic components in zonal wind, EEJ, and TEC are quantified through the least squares spectral fitting. Our findings reveal three distinct categories of UFKW events: Type 1 exhibits both TEC and EEJ responses, Type 2 shows TEC response without EEJ signature, and Type 3 displays neither TEC nor EEJ response. The finding reveals distinct seasonal dependencies in TEC responses: E2 and E3 waves exhibit significant seasonality, whereas E1 waves show negligible seasonal variation. Furthermore, E1 waves demonstrate higher ionospheric response occurrence rates compared to E2 and E3 waves. For E1 waves, shorter periods, larger amplitudes, and longer vertical wavelengths correlate strongly with enhanced ionospheric responsiveness. Conversely, amplitude exerts minimal influence on ionospheric responses for E2 and E3 waves. For E2 and E3 waves temporally coincident with E1 waves, E2 and E3 waves may elevate Type 2 event occurrence among concurrent E1 waves, while E1 waves tend to suppress ionospheric response capability in coincident E2 and E3 waves, increasing Type 3 event prevalence.

This work evaluates the performance of two standard models for specifying Earth's ionospheric parameters, the International Reference Ionosphere (IRI) and the NeQuick model.The performance of these models has been validated for Antarctica (ANT) (High latitude; Geographic: 70.46 S, 11.13 E; Geomagnetic: 62.45 S), Trivandrum (TRV) (Equatorial, Geographic: 8.524 N, 76.936 E; Geomagnetic:0.3N) and Delhi (DEL) (Low latitude, Geographic: 28.637 N, 77.174 E; Geomagnetic:22.4N).For this, the Total Electron Content (TEC) values estimated from these models for the locations are compared with the TEC values derived from the Global Ionospheric Scintillation and TEC Monitoring (GISTM) Systems present at the respective locations for the year 2008 which was at the descending phase of Solar Cycle 23.The results show that the equatorial TEC exhibits semi-annual, annual, and seasonal variations with maximum values appearing during equinoctial months and minimum during solstitial months.The low latitude TEC also exhibits similar variations but at times delayed.Generally, the TEC values produced by the IRI and NeQuick 2 models agree with the observed TEC for TRV and DEL but the models significantly underestimate the TEC at ANT at all times.For Diurnal variation, both the models show a good correlation during the noon time for the equatorial and low latitude stations but not for the high latitude station.For post-sunset and post-midnight periods, quite a disagreement is seen between the models and observed TEC for all three stations.So, it can be said that both IRI and NeQuick 2 models can predict TEC with good correlation in most cases for the Equatorial and Low Latitude stations but are incapable for high latitude regions.

Investigation of the variability of total electron content (TEC) is one of the most important parameters of the observation and monitoring of space weather, which is the main cause of signal disturbance in space-based communication, positioning, and navigation systems. TEC is defined as the total number of electrons on a ray path. The Global Positioning System (GPS) provides a cost-effective solution for the estimation of TEC. Due to various physical and operational disturbances, TEC may have temporal and spatial domain gaps. Global ionospheric maps (GIMs) provide worldwide TEC with 1- to 2-h temporal resolution and $2.5^{\circ}\, \times 5^{\circ}$ spatial resolution in latitude and longitude, respectively. The GIM-TEC with the highest possible accuracy can be obtained 10 days after the recording of the signals. Therefore, a high-resolution and accurate interpolation of TEC is necessary to image and monitor the regional distribution of TEC in near-real time. In this study, a novel spatiotemporal interpolation algorithm with automatic gridding is developed for 2-D TEC imaging by data fusion of GPS-TEC and GIM-TEC. The algorithm automatically implements optimum spatial resolution and desired temporal resolution with universal kriging with linear trend for midlatitude regions and ordinary kriging for other regions. The theoretical semivariogram function is estimated from GPS network data using a Matern family, whose parameters are determined with a particle swarm optimization algorithm. The developed algorithm is applied to the Turkish National Permanent GPS Network (TNPGN-Active), a dense midlatitude GPS network. For the first time in the literature, high spatial resolution TEC maps are obtained between May 2009 and May 2012 with a 2.5-min temporal update period. These TEC maps will be used to investigate the spatiotemporal variability of the ionosphere over the diurnal and annual trend structure, including seasonal anomalies and geomagnetic and seismic disturbances over ionosphere.

In Global Navigation Satellite Systems (GNSS) using L-band frequencies, the ionosphere causes signal delays that correspond with link related range errors of up to 100 m. In a first order approximation the range error is proportional to the total electron content (TEC) of the ionosphere. Whereas this first order range error can be corrected in dual-frequency measurements by a linear combination of carrier phase- or code-ranges of both frequencies, single-frequency users need additional information to mitigate the ionospheric error. This information can be provided by TEC maps deduced from corresponding GNSS measurements or by ionospheric models. In this paper we discuss and compare different ionospheric correction methods for single-frequency users. The focus is on the comparison of the positioning quality using dual-frequency measurements, the Klobuchar model, the NeQuick model, the IGS TEC maps, the Neustrelitz TEC Model (NTCM-GL) and the reconstructed NTCM-GL TEC maps both provided via the ionosphere data service SWACI (http://swaciweb.dlr.de) in near real-time. For that purpose, data from different locations covering several days in 2011 and 2012 are investigated, including periods of quiet and disturbed ionospheric conditions. In applying the NTCM-GL based corrections instead of the Klobuchar model, positioning accuracy improvements up to several meters have been found for the European region in dependence on the ionospheric conditions. Further in mid- and low-latitudes the NTCM-GL model provides results comparable to NeQuick during the considered time periods. Moreover, in regions with a dense GNSS ground station network the reconstructed NTCM-GL TEC maps are partly at the same level as the final IGS TEC maps.