A New Global Total Electron Content Empirical Model

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.

Aside from the ionospheric total electron content (TEC) information, root-mean-square (RMS) maps are also provided as the standard deviations of the corresponding TEC errors in global ionospheric maps (GIMs). As the RMS maps are commonly used as the accuracy indicator of GIMs to optimize the stochastic model of precise point positioning algorithms, it is of crucial importance to investigate the reliability of RMS maps involved in GIMs of different Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS), i.e., the integrity of GIMs. We indirectly analyzed the reliability of RMS maps by comparing the actual error of the differential STEC (dSTEC) with the RMS of the dSTEC derived from the RMS maps. With this method, the integrity of seven rapid IGS GIMs (UQRG, CORG, JPRG, WHRG, EHRG, EMRG, and IGRG) and six final GIMs (UPCG, CODG, JPLG, WHUG, ESAG and IGSG) was examined under the maximum and minimum solar activity conditions as well as the geomagnetic storm period. The results reveal that the reliability of the RMS maps is significantly different for the GIMs from different IAACs. Among these GIMs, the values in the RMS maps of UQRG are large, which can be used as ionospheric protection level, while the RMS values in EHRG and ESAG are significantly lower than the realistic RMS. The rapid and final GIMs from CODE, JPL and WHU provide quite reasonable RMS maps. The bounding performance of RMS maps can be influenced by the location of the stations, while the influence of solar activity and the geomagnetic storm is not obvious.

Transionospheric navigation and radar systems operating at single frequencies of less than 10 GHz are impacted by ionospheric refraction. Since the ionospheric delay is proportional to the total electron content (TEC) of the ionosphere along the ray path, a user friendly TEC model is helpful in various applications. Thus, a global TEC model (NTCM-GL) has recently been developed in DLR. The basic approach of the model is presented and the performance is discussed in comparison with GNSS single frequency correction models such as the Klobuchar or GPS model and the NeQuick model. The empirical approach describes functions on season, local time, geographic/geomagnetic location and solar irradiance conditions. The solar activity is controlled by the solar radio flux index F10.7. The approach needs only 12 coefficients for describing the main ionospheric features in good quality. The model approach is based on global TEC data provided by the Center for Orbit Determination in Europe (CODE) at the University of Berne over more than half a solar cycle (1998-2007). The model fits to these input data with a negative bias of 0.3 TECU (1 TECU=1x1016m-2) and a RMS deviation of 7.5 TECU. It has been found that the NeQuick and NTCM-GL model show a similar performance which is by a factor of nearly 2 better than the performance of the Klobuchar model at the European sector. Since the coefficients cover a full solar cycle, no updating is required. Hence, NTCM-GL may easily be used as a correction model for single frequency GNSS applications.

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.

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

The magnetosphere and ionosphere have a crucial interaction during severe geomagnetic storms. The Global Navigation Satellite System (GNSS) provides high-quality ionospheric observations with high temporal and spatial resolution, is generally preferred to investigate the ionospheric variation. In this study, the storm-time ionospheric response of the June 2015 severe storm (Dstmin = −208 nT) was investigated by the Total Electron Content (TEC) data of the Global Ionosphere Maps (GIMs), COSMIC radio occultation (RO) and a chain of International GNSS Service (IGS) stations. GIMs were used to show the global TEC variation during the storm. COSMIC RO data were also utilized to investigate the TEC variation and electron density profiles in an anomaly area with a limited time interval. GNSS TEC data of the 16 IGS stations were utilized to investigate the ionospheric variability on different latitudes of hemispheres. The results showed that intense positive phases formed in high latitudes of the Southern Hemisphere (SH) and the western longitudes of the Northern Hemisphere (NH), especially over the North Atlantic Ocean until the main phase of the storm. Besides, negative phases were observed in high latitudes and eastern longitudes of the NH. During the recovery phase of the storm, positive phases were seen in the low latitudes of the SH and the ionospheric conditions calmed down in the entire SH after the depletion of these phases. Negative phases were also observed almost wholly covered in the NH, which denser in the European-African sector. As a particular result, the electron density profiles of COSMIC RO showed that the ionospheric phases of the June 2015 storm not only related to TEC values but also the altitude of maximum electron density.

Validation of CAS’s final global ionospheric maps during different geomagnetic activities from 2015 to 2017

Global ionospheric mapping (GIM) is a new and emerging technique for determining global ionospheric TEC (total electron content) based on measurements from a worldwide network of Global Positioning System (GPS) receivers. In this study, GIM accuracy in specifying TEC is investigated by comparison with direct ionospheric measurements from the TOPEX altimeter. A climatological model (Bent model) is also used to compare with the TOPEX altimeter data. We find that the GIM technique has much better agreement with TOPEX in TEC measurements, compared with the predictions of the climatological model. The difference between GIM and TOPEX in TEC measurements is very small (less than 1.5 TEC units (TECU)) within a 1500‐km range from a reference GPS station. The RMS gradually increases with increasing distance from the station, while the Bent model shows a constant large RMS, unrelated to any station location. Within a 1000‐km distance of a GPS site (elevation angle > 25°), GIM has a good correlation (R > 0.93) to TOPEX with respect to TEC measurements. The slope of the linear fitting line to the data set from two TOPEX cycles is 44.5° (near the ideal 45°). In the northern hemispheric regions, ionospheric specification by GIM appears to be accurate to within 3‐10 TECU up to 2000+ km away from nearest GPS station (corresponding to ∼1° elevation angle cutoff). Beyond 2000 km, GIM accuracy, on average, is reduced to the Bent model levels. In the equatorial region, the Bent model predictions are systematically lower (∼5.0 TECU) than TOPEX values and often show a saturation at large TEC values. During ionospheric disturbed periods, GIM sometimes shows differences from TOPEX values due to transient variations of the ionosphere. Such problems may be improved by the continuous addition of new GPS stations in data‐sparse regions. Thus, over a GPS station's measurement realm (up to 2000 km in radius), GIM can produce generally accurate TEC values. Through a spatial and temporal extrapolation of GPS‐derived TEC measurements, the GIM technique provides a powerful tool for monitoring global ionospheric features in near real time.

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.

Abstract. Turkey is a country located in the middle latitude zone, where tectonic activity is intensive. Recently, an earthquake of magnitude 6.5 Mw occurred offshore in the Aegean Sea on 24 May 2014 at 09:25 UTC, which lasted about 40 s. The earthquake was also felt in Greece, Romania, and Bulgaria in addition to Turkey. In recent years, ionospheric anomaly detection studies have been carried out because of seismicity with total electron content (TEC) computed from the global navigation satellite system's (GNSS) signal delays and several interesting findings have been published. In this study, both TEC and positional variations have been examined separately following a moderate size earthquake in the Aegean Sea. The correlation of the aforementioned ionospheric variation with the positional variation has also been investigated. For this purpose, a total of 15 stations was used, including four continuously operating reference stations in Turkey (CORS-TR) and stations in the seismic zone (AYVL, CANA, IPSA, and YENC), as well as international GNSS service (IGS) and European reference frame permanent network (EPN) stations. The ionospheric and positional variations of the AYVL, CANA, IPSA, and YENC stations were examined using Bernese v5.0 software. When the precise point positioning TEC (PPP-TEC) values were examined, it was observed that the TEC values were approximately 4 TECU (total electron content unit) above the upper-limit TEC value at four stations located in Turkey, 3 days before the earthquake at 08:00 and 10:00 UTC. At the same stations, on the day before the earthquake at 06:00, 08:00, and 10:00 UTC, the TEC values were approximately 5 TECU below the lower-limit TEC value. The global ionosphere model TEC (GIM-TEC) values published by the Centre for Orbit Determination in Europe (CODE) were also examined. Three days before the earthquake, at all stations, it was observed that the TEC values in the time period between 08:00 and 10:00 UTC were approximately 2 TECU above the upper-limit TEC value; 1 day before the earthquake at 06:00, 08:00, and 10:00 UTC, the TEC values were approximately 4 TECU below the lower-limit TEC value. Again, by using the same 15 stations, positional variation investigation for before and after the earthquake was undertaken for the AYVL, CANA, IPSA, and YENC stations. As a result of the conducted analysis, positional displacements were seen before and after the earthquake at the CANA station, which is the nearest station to the earthquake centre. Before and after the earthquake, positional displacements were observed as 10 and 3 cm respectively.

In this study, we conduct a correlation analysis between the daily occurrence times of the increase and decrease anomalies in the global total electron content (TEC) in the ionosphere, and the daily earthquake energy release within 110–130°E longitude over the following three latitude regions: A: 13°S–0.5°S (22.3°S–10°S geomagnetic), B: 0.5°S–19.5°N (10°S–10°N geomagnetic), and C: 19.5°N–32.1°N (10°N–22.5°N geomagnetic). The TEC data from global ionosphere maps (GIMs) during earthquake events of M ≥ 2.5 that occurred in 2015–2018 are used in this study. The time series of daily seismic wave energy releases within the three regions and the daily occurrence times of the TEC anomalies in each GIM grid are computed. By time-shifting the time series, the correlations are calculated and compared globally, and the temporal characteristics are also examined. The disturbance storm time (Dst) index, planetary geomagnetic index Kp, and daily observed 10.7 cm solar flux (F10.7) are used to remove data associated with space weather variations. Although the seismo-ionospheric precursor is not confirmed by the statistical investigations, the greater occurrence times of TEC decrease anomalies are observed in the southeast in Region A, and the conjugate point 13 days prior to a M6.9 earthquake in Region A, which occurred on 5 August 2018, in accordance with the statistical results. Therefore, it is required to apply more parameters to understand the causes of the ionospheric TEC variations and investigate whether ionospheric variations are caused by earthquakes.

This paper studies the hemispheric asymmetry of the total electron content (TEC) by means of the Global Ionosphere Map (GIM), which has been routinely publishing every 2 hours by the Center for Orbit Determination in Europe (CODE) during 1999-2019.  The study period including the solar cycles 23 and 24 allows us globally uniformly examine the ionospheric asymmetry phenomenon of the GIM TEC in the different solar activities.  At each time point, summations of the GIM TEC at low-, mid-, and high-latitude in the northern and southern hemispheres are computed.  Results reveal the maximum summations of the GIM TEC occurring in April or November and the minimum summations of the GIM TEC occurring in July, which indicates the extreme TEC values in each year are not at the equinox or solstice.  We compare the sums of the two hemispheres in various local times, months, and solar activities.  By computing the annual magnitude of the asymmetry phenomenon, it is found that the asymmetric phenomenon is more prominent during the low solar activity, although its fluctuation is proportional to the F10.7 index.  We further study the asymmetry phenomenon in various latitudes and find the latitudes with prominent signatures.

Two major earthquakes separated by ∼5 minutes occurred in the same fault in Peru at depths of 606.2 and 620.6 km on November 24, 2015. By using Global Ionospheric Maps (GIMs) from the Center for Orbit Determination in Europe (CODE) and a broadly used statistical method, differential Vertical Total Electron Content (VTEC) maps were derived. Two positive ionospheric anomalies were clearly identified in the differential VTEC maps 2 and 1 day prior to the day of the earthquakes. These anomalies were located inside the earthquakes’ preparation regions defined by the Dobrovolsky equation. On the other hand, due to the low-latitude nature of the seismic events, the Equatorial Ionization Anomaly (EIA) shape was also analyzed. A third positive disturbance was revealed between November 20 and 21, 2015. For the aforementioned anomaly and the one on November 22 (2 days before the earthquakes), an enhancement of the VTEC was observe through the considerable modification of the EIA shape into a well-defined double-crest with a trough. By looking into the Dst and Kp indices, the geomagnetic conditions starting on November 20 until the 24 were very quiet; thus, it is considered that the three detected anomalies are precursors to the earthquake doublet. Moreover, it is suggested that the mechanism at work that produced the positive disturbances is the air ionization through the release of radon from the Earth’s crust.

Choi et al. (2019) suggested that ionospheric total electron content (TEC) and receiver differential code bias (rDCB) stability have a strong correlation during a period of two years from 2014 to 2016. This article is a response to Zhong et al. (2020), who pointed out that the long-term variations of the GPS DCBs are mainly attributed to the satellite replacement rather than the ionospheric variability. In this issue, we investigated the center for orbit determination in Europe (CODE) Global Ionosphere Maps (GIM) products from 2000 to 2020. In this study, changes in TEC and receiver DCB (rDCB) root mean squares (RMS) at Bogota (BOGT) station still have a clear correlation. In addition, there was a moderate correlation between satellite DCB RMS and rDCB RMS. As a result, we suggest that rDCB can be affected simultaneously by GPS sDCB as well as ionospheric activity.

Abstract. A three-dimensional model based on time and location is introduced to enhance the accuracy of vertical total electron content (VTEC) global ionosphere maps (GIMs) of ionosphere empirical models. The chosen empirical model is the International Reference Ionosphere 2016 (IRI-2016) model and observations are the GIMs VTEC values from Center for Orbit Determination in Europe (CODE). For this purpose, IRI-2016 is considered as a background model and the VTECs from GIMs are considered as the correction term. The correction term is modelled using spherical harmonics expansion base functions with a degree equal to 15 in Earth-fixed coordinate system with a polynomial B-spline base function for time resolution. The improved VTEC maps are constructed on 1st January 2018 with a Kp index of 4. The root mean square error (RMSE) improvements range from 1.65 TECU to 2.81TECU about 70 percent to 85 percent and the bias improvements are from 0.28 TECU to 1.00 TECU with an average of 0.75 TECU, respectively.