Center For Orbit Determination In Europe Research Articles

Different data-driven prediction of global ionospheric TEC using deep learning methods

Ionospheric Total Electron Content (TEC) is a crucial parameter for monitoring the ionosphere and space weather disasters. Its accurate prediction is vital for precise applications of Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS). This study proposes a novel method for ionospheric TEC prediction that considers multiple TEC-related factors. We present the random forest method and the Autoformer deep learning with multilayer perceptron (Autoformer-MLP) to predict the global TEC by incorporating the geomagnetic and solar activity parameters. Two schemes with different ionospheric data, i.e., spherical harmonic coefficients (SHC) and vertical TEC (VTEC), are performed by Autoformer-MLP. Ionospheric products from 2009 to 2019 (Cycle 24), obtained from the Center for Orbit Determination in Europe (CODE), are collected to test and evaluate the proposed method. Experimental results demonstrate that the root mean square errors (RMSEs) of predicted global ionospheric maps (GIMs) using the SHC scheme are 3.79 and 1.39 TECU in 2015 and 2019, respectively, and those are 3.55 and 1.28 TECU for the VTEC scheme. Moreover, the RMSEs of the prediction results are 0.5 and 0.2 TECU lower than that of CODE's 1-day predicted global ionospheric map (GIM) product (C1PG) during high and low solar activity years, respectively. These analyses indicate that the proposed random forest and Autoformer-MLP deep learning methods exhibit high accuracy and stability for data-driven prediction of global ionospheric TEC in various scenarios.

Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model

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.

Analyzing the spatial variation characteristics of grid TEC using long-term GIM data

Global ionosphere maps (GIM), a grid model generated based on observations from global GNSS stations, is often used for ionospheric delay correction and total electron content (TEC) analysis, and is an important data for ionospheric modeling and code bias estimation, as well as related applications. Understanding the spatial variation characteristics of the grid TEC in GIM data can help the better use of GIM data. In this paper, the GIM data of CODE (Center for Orbit Determination in Europe) analysis center for a total of 15 years from 2008 to 2022 are used to analyze the variation of TEC of adjacent grid points and the difference of TEC of four grid points in each grid. The results show that the mean values of the grid TEC variations range from about 0.26–1.86 TECu in the longitude direction and 0.36–2.76 TECu in the latitude direction, and most of the differences in the TECs of neighboring grid points are within ± 2 TECu. In the low solar activity years, the STD of the TEC of the four grid points in most of the grids in the GIM data is less than 1.2 TECu, indicating that the TEC values of the four grid points in most of the grids in their GIMs are relatively close to each other and the differences are small, whereas the differences are larger in the high solar activity years. In other words, the TEC values of the areas covered by the GIM grids in the low solar activity years are close. The results of the study can provide some references and lessons for the interpolation and application of the TEC of the GIM grid, and the TEC modeling of the single-station area.

Analysis of ionospheric anomalies before the Fukushima Mw 7.3 earthquake of March 16, 2022

Abstract In order to study the coupling relationship between earthquakes and ionospheric disturbances, TEC data during the Mw 7.3 earthquake that occurred near Fukushima, Japan on March 16, 2022, were processed using global ionospheric data provided by the Center for Orbit Determination in Europe (CODE). In this paper, the sliding quartile interval method is used to eliminate solar activity in a 27-day window, including sunspot number (SSN), F10.7 cm radio flux (F10.7), total solar irradiation (TSI), solar wind velocity (Vsw) and geomagnetic activity. The impact of disturbance storm time index (DST) and global geomagnetic activity index (KP) on TEC anomaly disturbance can obtain more accurate TEC anomaly information. The results indicate that when the solar and geomagnetic activity cycles are inconsistent with the TEC anomalies on fifth day before the earthquake, the TEC anomalies above the epicenter are significantly greater than those observed in other regions, and the corresponding magnetic conjugate region is accompanied by anomalies, which is the characteristic of TEC anomalies caused by earthquakes. This means that the detected TEC anomalies can be used as a potential ionospheric precursors, indicating that the Fukushima earthquake is imminent.

Characteristic analysis of the differences between total electron content (TEC) values in global ionosphere map (GIM) grids

Abstract. Using total electron content (TEC) from a global ionosphere map (GIM) for ionospheric delay correction is a common method of eliminating ionospheric errors in satellite navigation and positioning. On this basis, the TEC of a puncture point can be obtained by GIM grid TEC interpolation. However, in terms of grid, only few studies have analyzed the TEC value size characteristics of its four grid points, that is, the TEC difference characteristics among them. In view of this, by utilizing the GIM data from high solar-activity years (2014) and low solar-activity years (2021) provided by CODE (Center for Orbit Determination in Europe), this paper proposes the grid TEC difference as a way of analyzing TEC variation characteristics within the grid, which is conducive to exploring and analyzing the variation characteristics of the ionosphere TEC in the single-station area. The value is larger in high solar-activity years and generally small in low solar-activity years, and the value of high-latitude areas is always smaller than that of low-latitude areas. Specifically, in high solar-activity years, most of the GIM grid TEC internal differences are within 4 TECu (1 TECu = 1016 electrons m−2) in high-latitude and midlatitude regions, while only 78.17 % are in low-latitude regions. In low solar-activity years, the TEC difference values within a GIM grid are mostly less than 2 TECu, and most of them in the high and middle latitudes are within 1 TECu. The main finding of this analysis is that the grid TEC differences are small for most GIM grids, especially in the midlatitudes to high latitudes of low solar years. This means that relevant extraction methods and processes can be simplified when TEC within these GIM grids is needed.

Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model

AbstractThis 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.

Constructing a Regional Ionospheric TEC Model in China with Empirical Orthogonal Function and Dense GNSS Observation

Using Global Navigation Satellite Systems (GNSS) observation data for developing a high-precision ionospheric Total Electron Content (TEC) model is one of the essential subjects in ionospheric physics research and the application of satellite navigation correction. In this study, we integrate the Empirical Orthogonal Function (EOF) method with the TEC data provided by the Center for Orbit Determination in Europe (CODE), and observed by the dense GNSS receivers operated by the Crustal Movement Observation Network of China (CMONOC) to construct a regional ionospheric TEC model over China. The EOF analysis of CODE TEC in China from 1998 to 2010 shows that the first-order EOF component accounts for 90.3813% of the total variation of the ionospheric TEC in China. Meanwhile, the average value of CODE TEC is consistent with the spatial and temporal distribution characteristics of the first-order EOF base function, which mainly reflects the latitude and diurnal variations of TEC in China. The first-order coefficient after EOF decomposition shows an obvious 11-year period and semi-annual variations. The maximum amplitude of semi-annual variation mainly appears in March and October, which is closely associated with the variation in geographical longitude, the semi-annual change of the low-latitude electric field, and the ionospheric fountain effect. The second-order coefficient has an evident annual variation, the minimum amplitude mainly occurs in March, August, and September, and the amplitude values in the high solar activity years are more significant than those in the low solar activity years. The third-order coefficient mainly shows the characteristics of annual variation, and the fourth-order coefficient shows the noticeable semi-annual and annual variations. The third and fourth-order coefficients are both modulated by the solar activity index F10.7. The ionospheric TEC model in China, driven by CMONOC real-time GNSS observation data, can better reflect the latitude, local time and seasonal variation characteristics of ionospheric TEC over China. In particular, it can clearly show the spring and autumn asymmetry of ionospheric TEC in the low latitudes. The root mean square error of the absolute error between the model and the actual observation is mainly distributed around 2.45 TECU (1 TECU = 1016 electrons/m2). The values of the TEC model constructed in this study are closer to the actual observed values than those of the CODE TEC in China.

MLP-based user-applicable method for SBAS GIVD calibration: A comparative study based on CODE final GIM products

Satellite-Based Augmentation Systems (SBASs) are designed to enhance the performance of Global Navigation Satellite Systems (GNSSs) for civil aviation users. As SBAS Geostationary Earth Orbit (GEO) satellite signals are received in a wide area, far more non-aeronautical GNSS users utilize SBAS to improve navigation performance. Since ionospheric delay is the principal error source of GNSS and a leading correction aspect of SBAS, the GEO-broadcast ionospheric data quality of six SBASs over the past three years is assessed in this article. Results show that European Geostationary Navigation Overlay Service (EGNOS), System for Differential Correction and Monitoring (SDCM), and Wide-Area Augmentation System (WAAS) have the highest Grid Ionospheric Vertical Delay (GIVD) accuracy judged by the residual Standard Deviation (STD), then followed by BeiDou SBAS (BDSBAS) and Multi-functional Satellite Augmentation System (MSAS), while GPS-Aided GEO Augmented Navigation (GAGAN) is the worst. The 6 m Grid Ionospheric Vertical Error (GIVE) availability is over 90 percent of the time for most Ionospheric Grid Points (IGPs) of all tested SBASs. However, GAGAN and WAAS have a significant availability decline at 3 m GIVE. In addition, the integrity limits of MSAS and WAAS are fairly conservative. In contrast, the confidence bounds of BDSBAS and SDCM are excessively radical that fail to meet the integrity requirement. To improve the ionospheric data quality received from SBAS, a Multi-Layer Perceptron (MLP)-based user-applicable GIVD calibration method is proposed in the article. A well-trained MLP learned temporal and spatial correlations of the IGP delays and extracted transfer functions between broadcast and reference GIVDs to eliminate error. The SBAS processing errors in Differential Code Bias (DCB) compensation, GIVD estimation modeling, and GEO message quantization are rectified by the MLP trained by abundant historical data with Center for Orbit Determination in Europe (CODE) final Global Ionospheric Maps (GIMs) as reference. Test results on real-time data show that over 85 percent IGPs have STD decline after calibration. The STD declination rates of six SBASs except for SDCM are over 15 percent, and the Mean Absolute Error (MAE) and Root Mean Square Error (RMS) calibration effects are dramatic for all systems. Finally, the average GIVEs are reduced by more than 50 percent for EGNOS, GAGAN, MSAS, and WAAS with a 99.9 percent integrity level maintained. Despite no promotion on availability for BDSBAS and SDCM, the proposed method compensates for the integrity issues of the systems.

Ionospheric Weather at Two Starlink Launches during Two-Phase Geomagnetic Storms.

The launch of a series of Starlink internet satellites on 3 February 2022 (S-36), and 7 July 2022 (S-49), coincided with the development of two-phase geomagnetic storms. The first launch S-36 took place in the middle of the moderate two-phase space weather storm, which induced significant technological consequences. After liftoff on 3 February at 18:13 UT, all Starlink satellites reached an initial altitude of 350 km in perigee and had to reach an altitude of ~550 km after the maneuver. However, 38 of 49 launched spacecrafts did not reach the planned altitude, left orbit due to increased drag and reentered the atmosphere on 8 February. A geomagnetic storm on 3-4 February 2022 has increased the density of the neutral atmosphere up to 50%, increasing drag of the satellites and dooming most of them. The second launch of S-49 at 13:11 UT on 7 July 2022 was successful at the peak of the two-phase geomagnetic storm. The global ionospheric maps of the total electron content (GIM-TEC) have been used to produce the ionospheric weather GIM-W index maps and Global Electron Content (GEC). We observed a GEC increment from 10 to 24% for the storm peak after the Starlink launch at both storms, accompanying the neutral density increase identified earlier. GIM-TEC maps are available with a lag (delay) of 1-2 days (real-time GIMs have a lag less than 15 min), so the GIMs forecast is required by the time of the launch. Comparisons of different GIMs forecast techniques are provided including the Center for Orbit Determination in Europe (CODE), Beijing (BADG and CASG) and IZMIRAN (JPRG) 1- and 2-day forecasts, and the Universitat Politecnica de Catalunya (UPC-ionSAT) forecast for 6, 12, 18, 24 and 48 h in advance. We present the results of the analysis of evolution of the ionospheric parameters during both events. The poor correspondence between observed and predicted GIM-TEC and GEC confirms an urgent need for the industry-science awareness of now-casting/forecasting/accessibility of GIM-TECs during the space weather events.

BDS-3/BDS-2 FCB estimation considering different influencing factors and precise point positioning with ambiguity resolution

Since the full system deployment of BDS-3 in July 2020, it has provided stable and reliable positioning, navigation and timing (PNT) services for users around the world. It is an urgent task to explore the potential performance of precise point positioning (PPP) based on the full constellation of BDS-2/BDS-3. This research presented the global satellite visibility of BDS-2/BDS-3, analyzed the stability and accuracy of BDS-2 and BDS-3 rubidium clocks and hydrogen clocks, jointly estimated the BDS-2 and BDS-3 fractional cycle biases (FCB) and evaluated the PPP performance with ambiguity resolution (AR). Due to the fact that International GNSS Service (IGS) precise products have a great impact on narrow-lane (NL) FCB estimation, the FCB obtained by using the precise products from CODE (Center for Orbit Determination in Europe) and GBM (Deutsches GeoForschungsZentrum) in two different analysis centers and the FCB obtained by using GBM precise products with different calculation strategies are analyzed. In addition, the experimental data from Multi-GNSS Experiment (MGEX), EUREF (EPN) and Germany (GPN) observation networks are used to evaluate the influence of orbital errors on BDS FCB estimation. For wide-lane (WL) FCB, MGEX WL FCB has the highest accuracy, followed by EPN and GPN. On the contrary, for NL FCB, MGEX NL FCB has the worst accuracy, followed by EPN, and GPN is the best. Eventually, numerous experiment results showed that compared with the static float solution, the PPP fixed solutions in East (E), North (N) and Up (U) directions are improved by 32.9%, 27.7%, 13.8% for BDS-3 and 10.0%, 7.5%, 3.8% for BDS-2/BDS-3. Compared with the kinematic float solution, the PPP fixed solutions in E, N and U directions are improved by 27.9%, 23.4%, 16.6% for BDS-3 and 15.9%, 8.9%, 5.3% for BDS-2/BDS-3.

Performance of short-terms prediction methods of vertical total electron content using nonlinear autoregressive neuronal network and stochastic autoregressive model

In this contribution the performance of global short-term predictions methods of vertical total electron content (vTEC) is analyzed during high solar activity. Two kind of predicted global vTEC maps value every 1 h, one-day-ahead, are used. They are C1PG, produced by the Center for Orbit Determination in Europe (CODE), based on the extrapolation of Spherical Harmonic coefficient using Least-squares collocation and the M1PG, proposed in this work, based on multi-step Nonlinear Autoregressive Neural Network (NAR-NN).Global vTEC maps from CODE (CODG) along the year 2015, are used as reference data. The results are obtained for quiet and disturbed conditions, based on geomagnetic and ionospheric planetary indexes.The performance of the forecasting approach is extensively tested under different geospatial conditions. The testing results are very similar in terms of RMSE, as it has been found to range between 1.7 and 7 TECu. RMSE depend on the latitude sectors and geomagnetic conditions, in terms of Mean Forecast Error (MFE) the C1PG shows a clear systematic behavior being negative at Southern Hemisphere and positive at Northern Hemisphere. According to the Mean Absolute Percentage Error (MAPE) values, the relative behavior of vTEC prediction is better for M1PG than for C1PG specially during quiet days and at mid-high latitudes. In general, both models are less accuracy in the equatorial ionization anomaly region and the Southern Hemisphere.Other important contribution of this manuscript is the definition of a planetary ionospheric disturbance index, Wd, based on W-index. This parameter is useful to more complete definition in quiet and disturbed days selection, and it is shown that the dependence of the RMSE according to the latitudinal bands, it is strongly related with the respective value of Wd.

Assessing the influence of differential code bias and satellite geometry on GNSS ambiguity resolution through MANS-PPP software package

Abstract Ambiguity resolution (AR) is essential for quick and accurate Global Navigation Satellite System GNSS location and navigation. In addition to location parameters, there are various additional GNSS characteristics that are relevant for a wide range of applications such as instrumental calibrations, atmospheric sounding, and time transfer. We offer differential code bias and satellite geometry for the GNSS estimable parameters using MANS-PPP software backage. In this research, we used the MANS-PPP software package to execute the processing method and generate the PPP GNSS solution. We demonstrated how differential code bias and satellite geometry can effectively enhance initial time and positioning error for multi-GNSS satellites. PPP Processing observation data in static mode was used by the different DCB files the Chinese Academy of Sciences (CAS), the German Aerospace Centre (DLR), and the Centre for Orbit Determination in Europe (CODE), for the 12 stations from IGS, and we analyzed the impact of errors from the satellite geometry. The results illustration that the correction of DCB significantly improves the PPP ambiguity resolution success rate and quality, which have higher DCB values. The satellite geometry also has a substantial influence on the PPP ambiguity resolution, with a better geometry leading to a higher success rate and quality. Furthermore, the use of multiple GNSS constellations and the optimization of the satellite selection and weighting algorithms can further improve the PPP ambiguity resolution and the resulting positioning accuracy.

Comprehensive Analysis of the Global Zenith Tropospheric Delay Real-Time Correction Model Based GPT3

To obtain a higher accuracy for the real-time Zenith Tropospheric Delay (ZTD), a refined tropospheric delay correction model was constructed by combining the tropospheric delay correction model based on meteorological parameters and the GPT3 model. The meteorological parameters provided by the Global Geodetic Observing System (GGOS) Atmosphere and the zenith tropospheric delay data provided by Centre for Orbit Determination in Europe (CODE) were used as references, and the accuracy and spatial–temporal characteristics of the proposed model were compared and studied. The results show the following: (1) Compared with the UNB3m, GPT and GPT2w models, the accuracy and stability of the GPT3 model were significantly improved, especially the estimation accuracy of temperature, the deviation (Bias) of the estimated temperature was reduced by 90.60%, 32.44% and 0.30%, and the root mean square error (RMS) was reduced by 42.40%, 11.02% and 0.11%, respectively. (2) At different latitudes, the GPT3 + Saastamoinen, GPT3 + Hopfield and UNB3m models had great differences in accuracy and applicability. In the middle and high latitudes, the Biases of the GPT3 + Saastamoinen model and the GPT3 + Hopfield model were within 0.60 cm, and the RMS values were within 4 cm; the Bias of the UNB3m model was within 2 cm, and the RMS was within 5 cm; in low latitudes, the accuracy and stability of the GPT3 + Saastamoinen model were better than those of the GPT3 + Hopfield and UNB3m models; compared with the GPT3 + Hopfield model, the Bias was reduced by 22.56%, and the RMS was reduced by 5.67%. At different heights, the RMS values of the GPT3 + Saastamoinen model and GPT3 + Hopfield model were better than that of the UNB3m model. When the height was less than 500 m, the Biases of the GPT3 + Saastamoinen, GPT3 + Hopfield and UNB3m models were 3.46 cm, 3.59 cm and 4.54 cm, respectively. At more than 500 m, the Biases of the three models were within 4 cm. In different seasons, the Bias of the ZTD estimated by the UNB3m model had obvious global seasonal variation. The GPT3 + Saastamoinen model and the GPT3 + Hopfield model were more stable, and the values were within 5 cm. The research results can provide a useful reference for the ZTD correction accuracy and applicability of GNSS navigation and positioning at different latitudes, at different heights and in different seasons.

Reference clock impact on GNSS clock outliers

Abstract With the advent of the Global Navigation Satellite System (GNSS), the need for precise and highly accurate orbit and clock products becomes crucial in processing GNSS data. Clocks in GNSS observations form the basis of positioning. Their high quality and stability enable high accuracy and the reliability of the obtained results. The clock modelling algorithms are continuously improved; thus, the accuracy of the clock products is evolving. At present, 8 Analysis Centers (ACs) contribute to the International GNSS Service final clock products. These products are based on GNSS observations on a network of reference stations, where for a given day one of the reference station clocks is the reference clock. In this paper, the authors determined the impact of the reference clock on the quality of clock product, especially outliers, for the first time. For this purpose, the multi-GNSS final clock products provided by the Center for Orbit Determination in Europe (CODE) for the period 2014–2021 (1773–2190 GPS week, 2921 days) were analysed. Analysis shows that by applying the Median Absolute Deviation (MAD) algorithm for outlier detection, the Passive Hydrogen Maser (PHM) clock installed on board the GALILEO satellites have the lowest level of noise, whereas the Block IIR GPS satellite launched in 1999 appears to have the highest levels of noise. Furthermore, the GNSS station OHIE3, when used as a reference clock, generates an increase in the level of noise, especially noticeable on the G09 and E03 satellites.

A Double-Adaptive Adjustment Algorithm for Ionospheric Tomography

A double-adaptive adjustment algorithm (DAAA) is proposed to reconstruct three-dimensional ionospheric electron density (IED) distribution. In the DAAA method, the relaxation factor of the multiplicative algebraic reconstruction technique (MART) is first adaptively adjusted by introducing adaptive MART (AMART). To avoid the voxels without any rays traversing them becoming dependent on the initial IED values, smoothing constraints are generally imposed on the adaptive multiplicative algebraic reconstruction technique (AMART). In general, the elements of the smoothing matrices are invariant in the iterative process. They affect the accuracy and efficiency of the IED inversion. To overcome the above limitation, the adaptive adjustments of the constrained matrix elements are subsequently carried out. Both numerical simulation and actual global navigation satellite system (GNSS) experimental results validate that the accuracy and efficiency of ionospheric tomography have been improved by the DAAA method. Finally, the new algorithm is applied to reconstruct the three-dimensional structure of the ionosphere during different geomagnetic activities. The comparisons show that the vertical profiles of the DAAA method are in agreement with those recorded from the ionosonde, and the inverted vertical total electron content (VTEC) of the DAAA method also agrees with the ionospheric products of center for orbit determination in Europe (CODE) during geomagnetic quiet and geomagnetic storms. The comparisons confirm the reliability and superiority of the DAAA method.

Improvement of international reference ionospheric model total electron content maps: a case study using artificial neural network in Egypt

Abstract An essential ionosphere parameter that can be applied for ionosphere corrections in radio systems is the ionosphere’s total electron content (TEC). TEC is a crucial parameter for ionospheric correction in the Global Navigation Satellite Systems (GNSS) of positioning, navigation, and radio science. This study uses the artificial neural network (ANN) application to improve the International Reference Ionospheric Model (IRI-2016) TEC maps across Egypt. The study period is based on the data that were accessible between 2013 and 2020. The ANN model input parameters are (year, day, hour, latitude, and longitude). The ANN1 and ANN2 estimate TEC values of the enhanced IRI-2020 and IRI-2016 according to the Center for Orbit Determination in Europe (CODE), respectively. ANN3 and ANN4 estimate TEC values of the enhanced IRI-2020 and IRI-2016 regarding IGS stations data analyzed by GNSS Analysis software for the multi-constellation and multi-frequency Precise Positioning (GAMP) model, respectively. The ANN model’s validations were based on the root mean square error (RMSE), correlation coefficient (CC), and T-test. According to the results, the suggested ANN can accurately predict the TEC over Egypt. In comparison to the IRI model, the TEC maps that the ANN models produced are significantly more in accordance with the related CODE and GAMP TEC maps. These results demonstrate that the developed approach can enhance IRI 2016 and IRI-2020s ability to estimate global TEC maps. For the ANN1 model, the mean CC and RMSE are 0.92, and 5.15 TECU for all the global data sets compared by CODE. On the other hand, the CC and RMSE between IRI-2020 and CODE are 0.847 and 7.67 TECU. For the ANN2, the mean CC and RMSE are 0.87, 5.59 TECU compared by CODE, respectively. Although the CC and RMSE between IRI-2016 and CODE are 0.820 and 9.052 TECU respectively. For the ANN3, the CC and RMSE are 0.830 and 4.87 TECU compared with GAMP for all global data, respectively. On the other hand, the CC and RMSE between IRI-2020 and GAMP are 0.644 and 10.41, respectively. For the ANN4 the CC and RMSE are 0.82, and 5.95 TECU compared with GAMP, respectively. Although the CC and RMSE between IRI-2016 and GAMP are 0.665 and 12.347 TECU respectively.

Near real-time global ionospheric total electron content modeling and nowcasting based on GNSS observations

For the purposes of routinely providing reliable and low-latency Global Ionosphere Maps (GIMs), a method of estimating hourly updated near real-time GIM with a time latency of about 1–2 h based on a 24-h data sliding window of Global Navigation Satellite System (GNSS) near real-time observations and real-time data streams was presented. On the basis of the implementation of near real-time GIM estimation, an hourly updated GIM nowcasting method was further proposed to improve the accurate of short-term total electron content (TEC) prediction. We estimated the Shanghai Astronomical Observatory near real-time GIM (SHUG) and nowcasting GIM (SHPG) in the solar relatively active year (2014) and quiet year (2021), and employed GIMs provided by the International GNSS Service, the Global Positioning System (GPS) differential slant TECs (dSTECs) extracted from global independent GNSS stations, and the vertical TECs (VTECs) inverted from satellite altimetry as the references to validate the estimated results. The GPS dSTECs evaluation results show that SHUG behaves fairly consistent with the rapid GIMs, with a discrepancy of less than 1 TEC unit (TECu) overall. The standard deviations (STDs) of SHUG with respect to Jason-2/-3 VTECs are no more than 10% over the majority of rapid GIMs due to the instability of observations. The performance of 1-h nowcasting SHPG is significantlybetter than the Center for Orbit Determination in Europe (CODE) 1-day predicted GIM (C1PG). GPS dSTEC validation results indicate that 1-h nowcasting SHPG is 1 to 2 TECu more reliable than C1PG in eventful ionospheric electron activity regions, and it outperforms the C1PG by 10% overall versus Jason-2/-3 VTECs. The hourly updated SHUG and SHPG have relatively high reliability and low time latency, and thus can provide excellent service for (near) real-time users and offer more accurate TEC background information than daily predicted GIM for real-time GIM estimation.

Performance and Consistency of Final Global Ionospheric Maps from Different IGS Analysis Centers

Ionospheric delay is one of the most problematic errors in satellite-based positioning data processing. The Global Ionospheric Map (GIM), which is publicly available daily in various analysis centers, is thus vitally important for positioning users. There are variations in the accuracy and consistency of GIMs issued by Ionosphere Associate Analysis Centers (IAACs) due to the differences in ionospheric modeling methods and selected tracking stations. In this study on the International GNSS Service’s (IGS) final GIM, the ionospheric total electron content (TEC) (from 243 global navigation satellite system (GNSS) monitoring stations around the world) and the ionospheric TEC (from the Jason-3 altimeter satellite) are selected as reference. By using these three references, we evaluate the performance and consistency of final GIM products from seven IGS IAACs, including the Chinese Academy of Sciences (CAS), the Center for Orbit Determination in Europe (CODE), Natural Resources Canada (EMR), the European Space Agency (ESA), the Jet Propulsion Laboratory (JPL), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU) in the mid-solar activity year (2022) and the low-solar activity year (2020). Firstly, the comparison with the IGS final GIM shows that the consistency of each GIMs is basically the same, with the mean value ranging from −0.3 TECu (total electron content unit) to 1.4 TECu. Secondly, the validation with Jason-3 altimeter satellite shows that the accuracy of several GIMs is almost the same, except for the JPL with the worst accuracy and an overall mean deviation (BIAS) between 2 and 6 TECu. Thirdly, the comparison with VTEC extracted from GNSS monitor stations shows that the CAS has the best accuracy in different latitude bands with a root mean square (RMS) of about 2.2–4.7 TECu. In addition, it is found that the accuracy in areas with more stations for ionospheric modelling is better than those with less stations in different latitude bands; meanwhile, the accuracy is closely related to the modeling methods of different GIMs.

A THREE-DIMENSIONAL APPROACH TO IMPROVE THE INTERNATIONAL REFERENCE IONOSPHERE (IRI) MODEL

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.

Ionospheric anomalies probably related to the Mw 7.1 northern Mid-Atlantic Ridge earthquake

Discussions related to ionospheric anomalies ought to be carefully governed because of their dynamics. The anomalies in the ionosphere that happen before-after an earthquake play a crucial rule in estimations of earthquakes. The anomalies can be detected and discussed with the help of total electron content (TEC). This essay examines anomalies before and after the Mw 7.1 northern Mid-Atlantic Ridge (52.649° N-31.902° W) earthquake that occurred on February 13, 2015. This examination is performed with the help of the TEC (TECU) map. The paper employs the Global Navigation Satellite System-based TEC map. The TEC map obtained from the Center for Orbit Determination in Europe (CODE) is interpolated with the northern Mid-Atlantic at locations 52.250° N-52.500° N and 30.000° W-35.000° W. In the paper, the TEC map frequency-domain amplitudes are resolved by short-term Fourier transformation spectral analysis. To distinguish the anomaly reason from each other, the effect of solar activity with the F10.7 (sfu) index and the effect of geomagnetic storm with Bz (nT), v (km/s), P (nPa), E (mV/m), Kp (nT), and Dst (nT) parameters (index) is analyzed. The lower boundary and the upper boundary of interpolated CODE TEC map are specified with the help of median and standard deviation. The TEC data that does not locate within the specified limits are marked as an anomaly. According to the work, 9-day TEC anomalies (5-day of them belong to pre-earthquake) are detected. The anomalies observed on February 2, 3, 5, and 7 may relate to the Mid-Atlantic earthquake.