Ionospheric Products Research Articles

Precise point positioning (PPP) based on the machine learning-based ionospheric tomography

Undifferenced and Uncombined Precise Point Positioning (UPPP) currently stands as a prominent research area, where the integration of high-precision ionospheric products holds the potential to substantially enhance accuracy and convergence performance in UPPP. Presently, the majority of external ionospheric constraints for UPPP rely on Global Ionospheric Maps (GIM). However, the accuracy and resolution of GIM fall short, imposing significant limitations on the positioning performance of UPPP. Consequently, this paper introduces the application of Computerized Ionospheric Tomography based on Machine Learning (CIT-ML) to enhance UPPP performance (PPP-CIT-ML). In this approach, we convert the three-dimensional electron density of CIT-ML into vertical total electron content (VTEC), and then it is compiled into ionospheric grid files essential for UPPP. Simultaneously, the traditional ionospheric tomography methods based on the improved algebraic reconstruction technique (IART) is also employed for UPPP (PPP-CIT-IART), alongside the ionospheric grid files broadcasted by the Center for Orbit Determination in Europe (CODE) for UPPP (PPP-CODE), where PPP-CIT-IART and PPP-CODE are used as the reference methods to test the performance of PPP-CIT-ML. In Static and Kinematic UPPP, compared to PPP-CODE, PPP-CIT-IART demonstrated average improvements in positioning accuracy and convergence performance by over 10% and 7%, respectively. PPP-CIT-ML showed average improvements in positioning accuracy and convergence performance by over 26% and 28%, respectively. The extrapolated ionospheric electron density (IED) applied to UPPP (ECIT-ML-PPP) and compared with PPP-CODE displayed average improvements in positioning accuracy by over 21% and convergence performance by over 26%. Compared with PPP-CIT-IART, ECIT-ML-PPP displayed average improvements in positioning accuracy and convergence performance by over 6% and 21%, respectively. These findings highlight that ionospheric error correction information obtained through ionospheric tomography significantly enhances the positioning accuracy and convergence performance of UPPP. Moreover, the performance enhancement achieved through machine learning-based ionospheric tomography is more pronounced. This study provides preliminary validation for the feasibility of applying machine learning-based ionospheric tomography results to navigation positioning.

Triple Ionosophere PhotoMeter onboard Fengyun-3E satellite: Data validation and ionosphere information extraction

The Triple Ionosophere PhotoMeter (TRIPM) is designed to make the disk observations of the Earth airglow emissions at OI 135.6 nm and N2 Lyman-Birge-Hopfield (LBH) band that flew aboard the newly launched early morning satellite Fengyun-3E (FY-3E) which was launched on July 5th, 2021. This is the first attempt to obtain the global twilight airglow data in the far ultraviolet band using Ionosophere PhotoMeter. The seasonal behavior of the twilight airglow at the OI 135.6 nm and N2LBH band is exhibited and interpreted in the paper. Validity of the TRIPM data is analyzed by comparison with the simulation results of the Global Airglow (GLOW) and the simulation results reveal good consistency with the observational results. Furthermore, based on the ionospheric contributions calculated by GLOW model, we extract the ionospheric information from the TRIPM 135.6 nm emissions. This means the ionospheric signature in the 135.6 nm radiance at the dawn and dusk times can be provided. Comparison between the ionospheric 135.6 nm radiance due to O+ and electron radiative recombination with GPS TEC data shows a similar morphology of equatorial arcs and seasonal variation of the ionosphere.

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.

The Ionospheric Exploration Based on TJU#01 Meteorological Microsatellite Mission: Initial Results

AbstractThe global navigation satellite system radio occultation technique is achieving increasing significance and extensive promotion in the remote sensing of near‐earth atmosphere, climate, and ionosphere in recent three decades. Nowadays, many communities become interested in developing commercial mode in occultation measurement by launching massive nano‐ or cube satellite constellations at low costs. The TJU#01 is the first meteorological satellite of Tianjin Yunyao Aerospace Technology Co., Ltd. which was successfully launched into space on 7 December 2021. The occultation antenna onboard the satellite is able to probe both the ionosphere and atmosphere at multi‐frequencies including GPS, GLONASS, and BEIDOU systems. The microsatellite applies the same occultation antenna to receive both the atmospheric and ionospheric occultations at 100 and 1 Hz respectively. 100 Hz narrow band power and wide band power data are downloaded to the ground to generate high‐rate signal‐to‐noise ratio (SNR) series and applied to ionospheric scintillation exploration. In this paper, the primary parameters of the satellite and occultation antenna are introduced, as well as the ionospheric data processing methodologies. The initial ionospheric products of TJU#01 are evaluated after one‐month in orbit. The TJU#01 results have a good agreement with the co‐located ground ionosonde data and COSMIC‐2 observations. The new satellite and its follow‐on missions have great capabilities and potentials in the ionospheric and space weather researches.

Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting

AbstractAccurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting.

Assessment of GRAS Ionospheric Measurements for Ionospheric Model Assimilation

We conducted a study to assess the GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding (GRAS) ionospheric data quality by processing Radio Occultation (RO) observations of ionospheric products. The main objective of the study is to validate ionospheric data generated at EUMETSAT, such as ionospheric bending angle profiles, amplitude and phase scintillations, topside Total Electron Content (TEC) from MetOp-A GRAS instrument as well as generating and validating new ionospheric products derived from GRAS RO observations such as the TEC, rate of TEC and vertical electron density profiles. The assessment is conducted by comparing and evaluating the systematic differences between similar products from other Low Earth Orbit (LEO) satellite missions or from ground-based ionospheric measurements. The study confirms that the GNSS topside and RO observations recorded by the GRAS instrument onboard MetOp satellites are of good quality and are a valuable source of data for ionospheric research.

A Novel Method of Ionospheric Inversion Based on Horizontal Constraint and Empirical Orthogonal Function

Tomographic inversion of the ionosphere is a rank-deficient problem. To overcome the above problem, an algebraic reconstruction technique (ART) based on adaptive horizontal constraint and empirical orthogonal function (ARTHCEOF) is proposed. The new algorithm avoids the difficulty of vertically constrained matrix construction and resolves the description of the ionospheric vertical structure by using EOF. To confirm the feasibility and validate the ascendancy of the ARTHCEOF, three algorithms are first tested by using the emulated scheme. The test results show that the ARTHCEOF surpasses the ART and the ART based on the horizontal constraint (ARTHC) in both the inversion accuracy and computational efficiency. Finally, the ARTHCEOF method is applied to invert electron density values using the GNSS measurements during different geomagnetic activities. The tomographic images validate that the ARTHCEOF can reflect ionospheric daily changes in the European region. The altitudinal profiles in a fixed location are illustrated according to the inversion results of ARTHCEOF. Compared with the profiles recorded by the ionosonde station, the altitudinal profiles of ARTHCEOF have a good consistency. In the meantime, the VTEC values are inverted using the CIT results. The differential VTEC values are calculated by means of the inverted VTEC values and ionospheric products of CODE. The differential results further identify the dependability of ARTHCEOF.

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.

Real-Time Single-Frequency Precise Point Positioning for Connected Autonomous Vehicles: A Case Study over Brazilian Territory

Intelligent Transportation Systems (ITS) and Connected Autonomous Vehicles (CAV) are extremely active areas of research, which have attracted the attention of users, companies, and development agencies worldwide. The Global Navigation Satellite System (GNSS) is a main enabling technology, as ITS/CAV applications are fundamentally dependent on precise positioning systems. Among the recent GNSS technology advances that have allowed high accuracy to be achieved is Real-Time Precise Point Positioning (RT-PPP). Even though the Real-Time Service (RTS) of the International GNSS Service (IGS) provides various correction streams for RT-PPP deployment, some of them (e.g., those associated with ionospheric delays) still have limited accuracy. This has motivated local organizations and research institutes to deploy their own RT-PPP products, based on a denser regional network of GNSS Continuously Operating Reference Stations (CORS). The purpose of this work is to evaluate the performance of a recently established regional ionosphere RT-PPP product, from the Argentine University of La Plata (UNLP), which serves all of South America, with extensions to the Caribbean and Antarctica Peninsula. The main contributions of the work are: we show that RT-PPP using UNLP products outperforms RT-PPP using products exclusively from IGS-RTS; we benchmark positioning strategies, such as Post-Processed PPP (PP-PPP) and Relative Global Positioning System (RGPS); and, demonstrate the ability of Brazilian RT-PPP users employing Single-Frequency (SF) GPS code measurements and UNLP ionosphere products to achieve the CAV lane-level specification (i.e., 1-meter horizontal positioning accuracy at 95% probability). The article includes results from both static and moving experimental tests.

A New GNSS TEC Neural Network Prediction Algorithm With the Data Fusion of Physical Observation

This work models and predicts anomalous jumps in GNSS total electron content (TEC) caused by geomagnetic disturbance or solar radiation by introducing F10.7 and regional Dst indices, which are used to characterize solar radiation and geomagnetic disturbance status, respectively. We then introduced long-distance constraint stations to restrain ionospheric variations during high solar activity, which reduced the influence of unmodelled factors on prediction accuracy. In our experimental work, Global Ionosphere Map (GIM) TEC data from 11 stations located at different latitudes were used for comparison during maximum (2014) and minimum (2018) of solar cycle 24. Sample data for >27 days were used for learning, and then ionospheric changes for the whole year were predicted using the sliding window method. The results showed that, the standard deviation for the global ionospheric prediction products could be guaranteed to be within 2 TECU. The proximity station constraint method proposed in this paper and the Dst high-order polynomial correction method during geomagnetic disturbance control the prediction error within -4.4 TECU, while the change of adjacent days is -51.4 TECU in 2014. The new algorithm reduced the TEC jump effect in predictions, and the prediction of ionospheric products provided by this algorithm can meet the basic requirements of ionospheric delay correction in high-precision positioning.

COSMIC-2 Mission Summary at Three Years in Orbit

We summarize the status of the FORMOSAT-7/COSMIC-2 (COSMIC-2) mission which has completed its first three years in orbit. COSMIC-2 is a joint U.S./Taiwan program consisting of six satellites in low-inclination orbits with the following payloads: Global Navigation Satellite System radio occultation, in-situ ion velocity meter, and tri-band radio frequency beacon. The constellation is in its final orbit configuration and reached mission full operating capability in September 2021. An extensive calibration/validation campaign has to date enabled the release of all baseline neutral atmosphere products and nearly all baseline ionosphere products. The mission is providing usually more than 5000 neutral atmosphere RO profiles per day with a precision better than 2 μrad from 30–60 km altitude. Each day, nearly 12,000 combined total electron content occultations and arcs are generated with absolute accuracy of better than 3 TECU. IVM density precision is at or below the 1% requirement. Neutral atmosphere and ionosphere latency, measured from time of observation to product creation time, is below 30 min median. Data products are delivered in near real-time to operational weather and space weather centers and made available openly to the research community. New ionosphere products specifying the presence and absence of scintillation are under development and planned for future release.

Introduction of TROPS ionospheric TEC products for FORMOSAT-7/COSMIC-2 mission

Six low Earth orbit (LEO) satellites were launched on June 25th, 2019 for a radio occultation (RO) mission for the FORMOSAT-7/COSMIC-2 (F7/C2) program. The GPS and GLONASS RO signals received by these F7/C2 satellites can be used to retrieve atmospheric and ionospheric parameter profiles for atmospheric and ionospheric research. In order to process the received RO signal, the processing system named Taiwan Radio Occultation Processing System (TROPS) is built. TROPS is developed by National Space Organization, Taiwan Analysis Center for COSMIC, and GPS Science and Application Research Center in Taiwan. The ionospheric products of TROPS are electron density profile, ionospheric scintillation index (S4 index), and absolute total electron content (TEC). S4 index has been calculated on board the satellites and other two products are retrieved by TROPS after the observation data downlink to ground. TEC is the linear integration of electron density along the signal propagation path. The electron density profile is retrieved from the relative TEC when the elevation angle of GNSS satellite is negative from F7/C2 satellite. The absolute TEC is the TEC from GNSS satellite to F7/C2 satellite. The difference between absolute and relative TEC is the TEC with/without differential code bias (DCB) correction. Currently, the data for the electron density profile and absolute TEC are provided by TROPS. Users can obtain the products freely from the internet. In this study, the retrieval method and the preliminary F7/C2 ionospheric TEC products retrieved by TROPS are presented in detail.

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

AbstractThe ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short‐term memory (bi‐LSTM) method and observations from 257 ground‐based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi‐LSTM‐based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI‐2016, artificial neural network and LSTM‐based models, correspondingly. The bi‐LSTM‐based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi‐LSTM‐based model. Moreover, the TEC maps resulting from the bi‐LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

IGS ROTI Maps: Current Status and Its Extension towards Equatorial Region and Southern Hemisphere

The International GNSS Service (IGS) diurnal ROTI maps ionospheric product was developed to characterize ionospheric irregularities occurrence over the Northern hemisphere and has been available for the community since 2014. Currently, the diurnal ROTI maps database hosted by NASA CDDIS covers the period from 2010 to now. Here, we report the ROTI maps product operational status and important changes in the product availability and access. Apart from actual ROTI maps product production, we work on the extension of ROTI maps to cover not only the Northern hemisphere but also the area of the Southern hemisphere and equatorial/low latitude region. Such extended ROTI maps are important for ionospheric irregularities climatology research and ionospheric responses to space weather. We present recent development toward the new ROTI maps product and the updated data format. To evaluate extended the ROTI maps performance, we analyzed the ability to represent key features of ionospheric irregularity occurrence over the Southern hemisphere and low latitudes. For auroral and midlatitudes, we present the cross-comparison of ROTI-derived irregularities patterns over the Northern and Southern hemispheres. For low latitudes, we examined the sensitivity of the resulted ROTI maps to detect plasma irregularities associated with equatorial plasma bubbles development for low, middle, and high solar activity periods.

CNES real-time ionospheric product accuracy analysis based on empirical orthogonal function decomposition

CNES real-time ionospheric product accuracy analysis based on empirical orthogonal function decomposition

Estimating ion escape from unmagnetized planets

Abstract. We propose a new method to estimate ion escape from unmagnetized planets that combines observations and models. Assuming that upstream solar wind conditions are known, a computer model of the interaction between the solar wind and the planet is executed for different ionospheric ion production rates. This results in different amounts of mass loading of the solar wind. We then obtain the ion escape rate from the model run that best fits observations of the bow shock location. As an example of the method, we estimate the heavy-ion escape from Mars on 1 March 2015 to be 2×1024 ions s−1, using a hybrid plasma model and observations by the Mars Atmosphere and Volatile Evolution (MAVEN) and Mars Express (MEX) missions. This method enables studies on how escape depends on different parameters as well as studies on escape rates during extreme solar wind conditions; moreover, the technique is applicable to studies of escape in the early solar system and at exoplanets.

The Accuracy Assessment and Scientific Research Progress of Ionospheric Radio Occultation Products Observed by Fengyun-3 GNOS

Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a novel detection technique that can provide global ionospheric products with high vertical resolution, high precision, and low cost. In recent years, China has launched the FY3 series of meteorological satellites carrying the first RO payload to simultaneously receive GPS (Global Positioning System) and BDS (BeiDou Navigation System) signals. In the accuracy assessment of RO products observed by GNOS (GNSS Occultation Sounder), the maximum F2-layer electron density (NmF2) of GPS occultation and BDS occultation have a standard deviation (std) of less than 20% in comparison with that of ionosondes. The std of F-layer worst-case ionospheric scintillation index ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S4_{max}^F$</tex-math></inline-formula> ) between COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3/GNOS is less than 0.1. The above results prove the high precision of FY3 ionospheric RO products. The RO products have been applied to preliminary scientific research and applications, e.g., the process of main phase and recovery phase of magnetic storms revealed by the NmF2 observed by FY3C/GNOS, the pre-midnight dynamics of F-layer strong scintillation during magnetic storms revealed by GNOS scintillation data, the ionospheric perturbation driven by Tonga volcano eruption revealed by FY3/GNOS, applications of the RO data for the research of sporadic E layers, evaluation of IRI model in statistics and ionospheric climatological characteristics, etc. With the successive network observation and continuous deployment of FY3 meteorological satellites, the continuous improvement of GNOS payload and the BDS system, massive high-precision ionospheric RO products will be developed and show more significant value.

Near Real-Time Global Ionospheric Modeling Based on an Adaptive Kalman Filter State Error Covariance Matrix Determination Method

Aiming at the urgent demands on (near) real-time ionosphere products, we study the near real-time (NRT) modeling of the global ionospheric total electron content (TEC) by IGS hourly data and introduce the Kalman filter (KF) to solve the model parameters. The main objective of this article is to propose an adaptive method for determining the KF process noise covariance matrix. This method can reflect the change regularity of spherical harmonic (SH) between epochs and consider the impact of the current ionosphere level on SH. It can adaptively adjust the KF process noise covariance matrix of each epoch to improve the accuracy of NRT global ionosphere maps (GIMs). We analyze the effects of different initial values of the state vector and its covariance matrix on the SH coefficients and propose a method to avoid repeated filter initialization. The results show that for different initial values, the filter can reach the state of convergence within 6 h, but a high-precision initial value can significantly accelerate the KF convergence speed. Compared with Global Navigation Satellite System (GNSS) differential slant TEC (dSTEC) observables, the rms of our NRT products xrtg during quiet and magnetic storms are 1.47 and 1.56 TECU, respectively, larger than the postprocessed GIMs, but significantly smaller than those of real-time GIMs. Compared with Jason VTEC, the results also show that the accuracy of xrtg is even better than European Space Agency (ESA) final products during the magnetic storm.

Real‐Time Monitoring of Ionosphere VTEC Using Multi‐GNSS Carrier‐Phase Observations and B‐Splines

AbstractMonitoring the Vertical Total Electron Content (VTEC) of the ionosphere is important for applications ranging from navigation to detection of space weather events. Therefore, emerging efforts have been made by several analysis centers to estimate the VTEC using different approaches in real‐time. Global Navigation Satellite Systems (GNSS) is a crucial technology for ionosphere modeling due to its worldwide distributed receivers, high temporal resolution, and low latency data dissemination capability. The selection of a convenient approach to extract ionosphere information from GNSS and the representation of VTEC by an appropriate mathematical model are essential factors for providing fast and accurate ionosphere products. Contrarily to the widespread phase‐leveling method, which uses noisy and erroneous code measurements, the modeling concept in this paper utilizes pure carrier‐phase measurements. Measurements acquired through the International GNSS Service (IGS) real‐time service in Radio Technical Commission for Maritime Services format are from GPS, GLONASS, and GALILEO. The measurement biases, including the ambiguity of carrier‐phase measurements, are simultaneously estimated along with VTEC model parameters. In our approach, VTEC is represented by B‐spline expansions embedded into a Kalman filter. Due to their localizing feature, B‐splines form a highly sparse structure in the filter measurement model. Thus, matrix operations for large‐scale problems can be performed fast using sparse matrix operations, as is done in this study. The differential slant total electron content (dSTEC) analysis and the comparison with Jason‐3 altimetry VTEC were performed for validation within selected periods in 2019. The dSTEC analysis shows that the quality of the generated real‐time VTEC maps slightly outperforms those provided by the other IGS analysis centers.

Accuracy of Global Ionosphere Maps in Relation to Their Time Interval

Global ionosphere maps (GIMs) representing ionospheric total electron content (TEC) are applicable in many scientific and engineering applications. However, the GIMs provided by seven Ionosphere Associated Analysis Centers (IAACs) are generated with different temporal resolutions and using different modeling techniques. In this study, we focused on the influence of map time interval on the empirical accuracy of these ionospheric products. We investigated performance of the high-resolution GIMs during high (2014) and low (2018) solar activity periods as well as under geomagnetic storms (19 February 2014 and 17 March 2015). In each of the analyzed periods, GIMs were also assessed over different geomagnetic latitudes. For the evaluation, we used direct comparison of GIM-derived slant TEC (STEC) with dual-frequency GNSS observations obtained from 18 globally distributed stations. In order to perform a comprehensive study, we also evaluated GIMs with respect to altimetry-derived vertical TEC (VTEC) obtained from the Jason-2 and Jason-3 satellites. The study confirmed the influence of GIMs time interval on the provided TEC accuracy, which was particularly evident during high solar activity, geomagnetic storms, and also at low latitudes. The results show that 120-min interval contributes significantly to the accuracy degradation, whereas 60-min one is sufficient to maintain TEC accuracy.