International GNSS Service Stations Research Articles

A simulation study of the Argo program-enhanced global ionospheric modeling

The ionospheric total electron content (TEC) is a critical parameter for space weather and Global Navigation Satellite System (GNSS) applications. Since 1998, the Ionospheric Working Group of the International GNSS Service (IGS) has been generating global ionospheric maps using data from hundreds of GNSS stations. However, the uneven geographical distribution of these observations has resulted in significant variations in TEC map accuracy, particularly in marine areas where observations are scarce or absent. In this study, we introduce Argo data as additional observations to enhance the accuracy of global ionospheric modeling. Argo data consists of measurements collected by more than 1,000 robotic floats deployed across the world's oceans. By utilizing the NeQuick model and considering the geographic coordinates of both IGS stations and Argo buoys, we simulate the ionospheric Slant Total Electron Content (STEC). This approach allows us to examine the impact of integrating Argo data into global ionospheric modeling and evaluate its effectiveness. The results indicate a significant improvement in the overall accuracy of the model after incorporating Argo data. Furthermore, by increasing the model's degree, a better global fit is achieved. It eliminates noticeable overestimation or underestimation of TEC, especially evident at a degree of 30. As the availability and uniform distribution of observations improve, the model demonstrates higher accuracy and enhanced global performance.

Research on the performance of the PPP-AR technique under different FCB product services

Researchers and manufacturers use the Precise Point Positioning (PPP) technique. After the phase biases of the satellites are resolved from GNSS networks, they are broadcast to users as Fractional Cycle Bias (FCB). In this study, FCB products provided by WUHAN, CODE, CNES and JAXA services were downloaded and PPP Ambiguity Resolution solutions were realised by Net_Diff software. For ten days in 2022, 24-hour observations were collected from 40 International GNSS Service stations. As a result, it has been observed that WUHAN and CODE services provided the ±1 cm or ±5 cm accuracy level by converging in short-term observations.

A Precise Zenith Hydrostatic Delay Calibration Model in China Based on the Nonlinear Least Square Method

Abstract Zenith hydrostatic delay (ZHD) is a crucial parameter in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology. Since the Saastamoinen ZHD model has a larger error in China, it is significant to improve the Saastamoinen ZHD model. This work first estimated the Saastamoinen model using the integrated ZHD as reference values obtained from radiosonde data collected at 73 stations in China from 2012 to 2016. Then, the residuals between the reference values and the Saastamoinen modeled ZHDs were calculated, and the correlations between the residuals and meteorological parameters were explored. The continuous wavelet transform method was used to recognize the annual and semiannual characteristics of the residuals. Because of the nonlinear variation characteristics of residuals, the nonlinear least squares estimation method was introduced to establish an improved ZHD model—China Revised Zenith Hydrostatic Delay (CRZHD)—adapted for China. The accuracy of the CRZHD model was assessed using radiosonde data and International GNSS Service (IGS) data in 2017; the radiosonde data results show that the CRZHD model is superior to the Saastamoinen model with a 69.6% improvement. The three IGS stations with continuous meteorological data present that the BIAS and RMSE are decreased by 2.7 and 1.5 (URUM), 5.9 and 5.3 (BJFS), and 9.6 and 8.8 mm (TCMS), respectively. The performance of the CRZHD model retrieving PWV was discussed using radiosonde data in 2017. It is shown that the CRZHD model retrieving PWV (CRZHD-PWV) outperforms the Saastamoinen model (SAAS-PWV), in which the precision is improved by 44.4%. The BIAS ranged from −1 to 1 mm and RMSE ranged from 0 to 2 mm in CRZHD-PWV account for 89.0% and 95.9%, while SAAS-PWV account for 46.6% and 58.9%. Significance Statement Zenith hydrostatic delay (ZHD) is one of the most important parameters in Global Navigation Satellite System (GNSS) navigation and positioning and GNSS meteorology, which can be derived from a precise ZHD model due to its stability. This research established an improved ZHD model for China to obtain accurate ZHD, which is a prerequisite for pinpoint precipitable water vapor (PWV) retrieval. And the PWV value is beneficial to analyze the change in precipitation in some regions, forecast the short-term rainfall, and monitor the climate.

Regional/Single Station Zenith Tropospheric Delay Combination Prediction Model Based on Radial Basis Function Neural Network and Improved Long Short-Term Memory

Atmospheric water vapor is an essential source of information that predicts global climate change, rainfall, and disaster-natured weather. It is also a vital source of error for Earth observation systems, such as the global navigation satellite system (GNSS). The Zenith Tropospheric Delay (ZTD) plays a crucial role in applications, such as atmospheric water vapor inversion and GNSS precision positioning. ZTD has specific temporal and spatial variation characteristics. Real-time ZTD modeling is widely used in modern society. The conventional back propagation (BP) neural network model has issues, such as local, optimal, and long short-term memory (LSTM) model needs, which help by relying on long historical data. A regional/single station ZTD combination prediction model with high precision, efficiency, and suitability for online modeling was proposed. The model, called K-RBF, is based on the machine learning algorithms of radial basis function (RBF) neural network, assisted by the K-means cluster algorithm (K-RBF) and LSTM of real-time parameter updating (R-LSTM). An online updating mechanism is adopted to improve the modeling efficiency of the traditional LSTM. Taking the ZTD data (5 min sampling interval) of 13 international GNSS service stations in southern California in the United States for 90 consecutive days, K-RBF, R-LSTM, and K-RBF were used for regions, single stations, and a combination of ZTD prediction models regarding research, respectively. Real-time/near real-time prediction results show that the root-mean-square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and training time consumption (TTC) of the K-RBF model with 13 station data are 8.35 mm, 6.89 mm, 0.61, and 4.78 s, respectively. The accuracy and efficiency of the K-RBF model are improved compared with those of the conventional BP model. The RMSE, MAE, R2, and TTC of the R-LSTM model with WHC1 station data are 6.74 mm, 5.92 mm, 0.98, and 0.18 s, which improved by 67.43%, 66.42%, 63.33%, and 97.70% compared with those of the LSTM model. The comparison experiments of different historical observation data in 24 groups show that the real-time update model has strong applicability and accuracy for the time prediction of small sample data. The RMSE and MAE of K-RBF with 13 station data are 4.37 mm and 3.64 mm, which improved by 47.70% and 47.20% compared to K-RBF and by 28.48% and 31.29% compared to R-LSTM, respectively. The changes in the temporospatial features of ZTD are considered, as well, in the combination model.

Troposphere Delay Remote Sensing Using Single GPS Receiver

The most prominent spatially correlated errors in GNSS observations are well known to be atmospheric effects. The ionosphere and troposphere are the two main layers of the Atmosphere that cause delays in GNSS observations. A linear combination of the dual-frequency data can be used to reduce ionospheric delay. Unlike the ionospheric delay, the tropospheric delay cannot be eliminated using the same methods. The troposphere is primarily associated with GPS. The delay it causes in GPS signals is regarded as one of the primary sources of errors that must be eliminated to determine accurate positions. This paper's main purpose is to develop a new source code that can estimate the effect of tropospheric delay over any GPS station. The tropospheric delay in this proposed code is estimated utilizing sequential least-squares adjustment using a model depending on Niell Mapping Function (NMF). This model, known as the Tropospheric Delay Estimation program, was created in the MATLAB® environment (TDE). This research presents the results of tropospheric delay during DOY 2, 2020 of actual data from ten ground-based IGS stations distributed over Antarctica, China, Canada, Fiji, Russia, Greenland, and Portugal IGS stations worldwide. For validation of the proposed code results, they were compared with troposphere delay results of the International GNSS Service (IGS). Good agreement and high correlation were found between both results. In comparison to IGS, the proposed code's standard deviations range from 0.0000525 m to 0.008154 m, indicating how accurate this study is in terms of agreement of solutions provided by IGS. Finally, the MATLAB software can accurately estimate troposphere delay with an adaptable temporal resolution for GPS users.

ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity

AbstractFor global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio () based SIGMA‐ model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ model.

Progress of global ARAIM availability of BDS-2/BDS-3 with TGD and ISB

Advanced RAIM (ARAIM) is an extension of Receiver Autonomous Integrity Monitoring (RAIM) by enhancing the vertical navigation capabilities, so it can be applied to higher-level precision applications. But when the observation data of the second-generation BDS (BDS-2) and the third-generation BDS (BDS-3) are combined to evaluate the ARAIM availability, the evaluation be affected by the Timing Group Delay (TGD) of different satellite signals and the Inter-system Bias (ISB) between BDS-2 and BDS-3, which is ignored by the existing literature. Therefore, in this article, we deduced the mathematical model of ARAIM and the calculation formula of Vertical Protection Level (VPL) by considering the combined influence of TGD and ISB. We used the BDS observation data and ephemeris files of 374 International GNSS Service (IGS) stations around world on July 29, 2021 to calculate the VPL of these stations and evaluate the global ARAIM availability. The results show that the number of visible BDS satellites is 8.033 and the Geometric Dilution of Precision (GDOP) is 1.721, which can meet the basic needs of global ARAIM calculations. Furthermore, when considering the combined influence of TGD and ISB, the evaluation of ARAIM availability by Multiple Hypothesis Solution Separation (MHSS) is significantly better than that without considering the combined TGD and ISB influence or their individual influence. The average VPL values at these IGS stations in four cases are 124.925 m, 13.882 m, 32.610 m and 9.962 m, and the corresponding ARAIM availability percentages are 90.839%, 95.241%, 96.751% and 99.660%, respectively. Finally, the minimum coverage of ARAIM availability based on BDS can reach 90.839%, which can meet the aviation demand for ARAIM availability in most regions and times.

Farklı troposfer modellerinin gerçek zamanlı çoklu-GNSS PPP performansına etkisi

With the initialization of IGS (International GNSS Service) real-time service products, real-time Precise Point Positioning (PPP) applications have been a popular topic within the GNSS (Global Navigation Satellite Systems) users. The impact of the troposphere on GNSS signals is one of the most crucial error sources regarding the real-time PPP solution. In the PPP technique, the dry component of tropospheric delay is usually corrected by means of empirical models, while its wet component is estimated as an unknown parameter in the adjustment process. Hence, the troposphere model employed in the PPP solution has a considerable impact on the performance of the obtained solution. Therefore, the main objective of this study is to investigate the impact of different troposphere models on the performance of real-time multi-GNSS PPP. As a part of this study, four different troposphere models, where Saastamoinen and Hopfield models that are most frequently used in GNSS solutions are used together with GPT (Global Pressure and Temperature) 2 and 3 models separately, were constituted. In this context, the observation dataset acquired from a total of 16 different IGS stations over a ten-day period of December 19-28, 2021, were processed utilizing four different troposphere models. In addition to the positioning performance, PPP solutions were also analyzed in terms of zenith total delay (ZTD) estimation. The results show that the best positioning performance can be obtained when the Saastamoinen model is used in combination with GPT3. The three-dimensional positioning accuracy acquired from the corresponding solution is 2.72 cm, which is better than the closest solution by a ratio of 9.2%. Besides, in terms of the ZTD estimation, the best performance is achieved again in the case when the Saastamoinen model is used in combination with GPT3. For the related solution, the accuracy of ZTD estimation is calculated as 1.24 cm and this value indicates a better performance by a ratio of 10.2% compared with the closest solution.

BDS-3/GNSS multi-frequency precise point positioning ambiguity resolution using observable-specific signal bias

With the completion of the BeiDou Global Navigation Satellite System (BDS-3), more available frequencies have brought opportunities for precise point positioning ambiguity resolution (PPP-AR). This research theoretically deduced the multi-frequency PPP-AR model and the rule of observable-specific signal bias (OSBs) transformation. Then 34 International GNSS Service stations were selected to investigate the multi-frequency BDS-3 and multi-GNSS PPP-AR. The results suggested that the OSBs products provided by Centre National D'Etudes Spatiales (CNES) have absorbed the impact of inter-frequency clock bias and could be directly added to the original observations to restore the integer characteristics of the multi-frequency ambiguity. Meanwhile, the antenna phase center correction should keep consistent when using the CNES OSBs to restore the Melbourne-Wübbena ambiguity. The BDS-3 quad-frequency ambiguity-fixed convergence time was 39.0 and 25.1 min for kinematic and static PPP, which was shortened by nearly 15.0 and 3.4 min than the dual-frequency ambiguity-fixed solution. The quad-system multi-frequency PPP-AR showed the optimal state, which was 0.66, 0.76 and 2.66 cm for kinematic mode while 0.31, 0.31 and 1.02 cm for static mode in east, north, and up directions, respectively.

Analysis and improvement of the Bancroft algorithm for GNSS satellite orbit determination

Satellite orbit information is crucial for ensuring that global navigation satellite systems (GNSSs) provide appropriate positioning, navigation and timing services. Typically, users can obtain access to orbit information of a specific accuracy level from navigation messages or precise ephemeris products. Without this information, a system will not be able to provide normal service. In response to this problem, initial orbit information of a certain level of precision must be obtained to support subsequent applications, such as broadcasting or precise ephemeris calculations, thereby ensuring the successful subsequent operation of the navigation system. One of two ways to calculate the initial orbit of a GNSS satellite is to utilize ground tracking stations to observe satellite vector information in the geocentric inertial system; the second way is to utilize GNSS range observations and known orbit information from other satellites. For the second approach, some researchers use the Bancroft algorithm combined with receiver clock offset to determine the initial orbit of GNSS satellites. Because this method requires an additional known receiver clock offset, we study the dependence of the Bancroft algorithm on clock offset in GNSS orbit determination. By assessing the impact of errors of different magnitude on the accuracy of the orbit results, we obtain experimental conclusions. After comprehensively analyzing various errors, we determine the accuracy level that the Bancroft algorithm can achieve for orbit determination without considering receiver clock correction. Dual-frequency and single-frequency pseudorange data from International GNSS Service stations are used in orbit determination experiments. When a small receiver clock offset is considered and no correction is made, the deviations in the calculated satellite positions in three dimensions are approximately 979.3 and 1118.1 m (dual and single frequency); with a satellite clock offset, these values are approximately 928.8 and 1062.7 m (dual and single frequency).

A New Adaptive Absolute Method for Homogenizing GNSS‐Derived Precipitable Water Vapor Time Series

AbstractThe inhomogeneities, the so‐called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS‐PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS‐PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth‐generation European center for medium‐range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long‐term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA‐PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization.

Evaluation of Zenith Tropospheric Delay Derived from Ray-Traced VMF3 Product over the West African Region Using GNSS Observations

The growing demand for Global Navigation Satellite System (GNSS) technology has necessitated the establishment of a vast and ever-growing network of International GNSS Service (IGS) tracking stations worldwide. The IGS provides highly accurate and highly reliable daily time-series Zenith Tropospheric Delay (ZTD) products using data from the member sites towards the use of GNSS for precise geodetic, climatological, and meteorological applications. However, if for reasons like poor internet connectivity, equipment failure, and power outages, the IGS station is inaccessible or malfunctioning, and gaps are created in the data archive resulting in degrading the quality of the ZTD and precipitable water vapour (PWV) estimation. To address this challenge as a means of providing an alternative data source to improve the continuous availability of ZTD data and as a backup data in the event that the IGS site data are missing or unavailable in West Africa, this paper compares the sitewise operational Vienna Mapping Functions 3 (VMF3) ZTD product with the IGS final ZTD product over five IGS stations in West Africa. Eight different statistical evaluation metrics, such as the mean bias (MB), mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient (r), coefficient of determination (r2), refined index of agreement (IAr), Nash–Sutcliffe coefficient of efficiency (NSE), and the fraction of prediction within a factor of two (FAC2), are employed to determine the degree of agreement between the VMF3 and IGS tropospheric products. The results show that the VMF3-ZTD product performed excellently and matches very well with the IGS final ZTD product with an average MB, MAE, RMSE, r, r2, NSE, IAr, and FAC2 of 0.38 cm, 0.87 cm, 1.11 cm, 0.988, 0.976, 0.967, 0.992, and 1.00 (100%), respectively. This result is an indication that the VMF3-ZTD product is accurate enough to be used as an alternative source of ZTD data to augment the IGS final ZTD product for positioning and meteorological applications in West Africa.

Improved atmospheric weighted mean temperature modeling using sparse kernel learning

As a crucial parameter in the process of converting the zenith wet delay into precipitable water vapor at Global Navigation Satellite System (GNSS) stations, the weighted mean temperature (Tm) influences the accuracy of GNSS-based water vapor retrievals. We propose an improved atmospheric Tm modeling method by introducing sparse kernel learning to obtain high-accuracy and high-temporal-resolution Tm data. To establish the model, the temporal variation characteristics of Tm time series are first analyzed by spectral analysis using the Lomb–Scargle periodogram. Second, the sparse kernel learning method is introduced to model the residuals from spectral analysis, for which the Gauss radial basis function kernel, L1-norm regularization, and the highly efficient fast iterative shrinkage thresholding algorithm are employed. To verify the performance of the proposed method, we used ERA5 hourly data from China’s 9 International GNSS Service stations produced by the European Centre for Medium-Range Weather Forecasts. ERA5 Tm data with a temporal resolution of 12 h are used as the training data, and the ERA5 hourly Tm data (excluding the data employed for modeling) are used for testing. The results show that compared with the spectral analysis accuracy, the accuracy of the calculated Tm can be improved by approximately 48.3% when the residual sparse kernel learning method is used. Thus, this proposed method for GNSS-based water vapor retrievals can provide high-accuracy and high-temporal-resolution Tm data.

Impact of flex power on GPS Block IIF differential code biases

GPS Block IIF satellites are able to redistribute the transmit power between the signal components. This ability is called flex power, and it has been developed as a remedy against jamming. Since it is operationally not possible to increase the transmit power for all signal components simultaneously, a redistribution between them is necessary under certain operational situations. Flex power has been active on Block IIF satellites since January 2017 over a specific regional area and has an impact on differential code bias estimation as well as the signal-to-noise density ratio. A network of the International GNSS Service stations containing only Septentrio PolaRx5 and PolaRx5TR receivers between August 1 and November 21, 2019 has been used for differential code bias estimation using GPS L1 C/A, L1 P(Y), L2 P(Y), and L2C signals with and without consideration of the flex power in the estimation process for Block IIF satellites. The estimation results are compared with the German Aerospace Center as well as the Chinese Academy of Sciences DCB products to validate the results.

Unequal‐weighted TCAR algorithm for long‐baseline static triple‐frequency ambiguity resolution

On account of the different parameters of delay-locked loop, multipath effect and code bandwidth, the Global Navigation Satellite System (GNSS) pseudorange noise of each frequency is different. An unequal-weighted three-carrier ambiguity resolution (TCAR) algorithm for long-baseline static triple-frequency ambiguity resolution is proposed. The algorithm is conducted in cascaded steps. When calculating the coefficients of the extra-wide-lane (EWL), wide-lane (WL) and narrow-lane (NL) combinations, the difference of pseudorange noise is considered. Real triple-frequency BeiDou Navigation Satellite System and Global Positioning System observations from four International GNSS Service stations from 13 January to 19 January 2019 are analysed to evaluate the performance of the proposed algorithm. Results show that compared to the classical equal-weighted algorithm, the proposed algorithm shows a comparable performance for the EWL and WL ambiguity resolution and much better performance for the NL ambiguity resolution.

Analyzing the long-term changes in China’s National Height Datum

China’s National Height Datum was determined by the mean sea level at the Qingdao tide gauge station. Research was conducted to study the long-term changes in China’s National Height Datum. The first step was to analyze the long-term changes in the Qingdao Huanghai mean sea level relative to the crust by analyzing the tide data from 1952 to 2011. The frequencies and amplitudes of main tides, including 180 short-period tides and 6 long-period tides, were obtained by fast Fourier transform. Then, the changes in the mean sea level at the Qingdao tide gauge from 1952 to 2011 were studied using harmonic analysis based on least-squares estimation and the 18.61-year shifting average of the tidal heights. The results of the first step showed that the mean sea level at the Qingdao tide gauge was at the rate of 1.07 mm/yr from 1952 to 1980 and increased to 1.62 mm/yr from 1980 to 2011. The second step was to ascertain the long-term changes in the crustal subsidence by analyzing the daily data from the Qingdao global navigation satellite system (GNSS) from 2005 to 2015. Initially, the daily relax solutions for coordinates of this station were computed in a combined adjustment involving a global GNSS network of about 50 International GNSS Service stations and a regional network of 43 Crustal Monitoring Observation network of China stations. Then, a time series spanning 10 years for station coordinates aligned to the international terrestrial reference frame (ITRF) 2014 was obtained. The time series of height components was analyzed to detect entities such as outliers, offsets, frequencies, and seasonal periodic parts. Through statistical detection, we found that the vertical velocity of the crust in the Qingdao GNSS station was zero. Based on the combined analysis of tide data and Qingdao GNSS data, it can be concluded that the mean sea level at epoch 2019.0 is 247.446 cm, which is 4.556 cm higher than the 1985 National Height Datum.

Detection of possible ionospheric precursor caused by Papua New Guinea earthquake (Mw 7.5).

Ionospheric total electron content (TEC) variations estimated from dual-frequency receivers of global positioning systems (GPS) can be used as a tool to detect possible seismo-ionospheric anomalies before strong earthquakes. In this study, possible seismo-ionospheric anomalies before the 2018 Papua New Guinea earthquake (6.0684°S, 142.7678°E, February 25, 2018, 00:44:43 UTC, Mw = 7.5) was investigated by using GPS-TEC variations from four International GNSS Service (IGS) stations (KAT1, TOW2, SOLO and ALIC) near the epicenter and four IGS station (USUD, SHAO, WIND and ZAMB) outside of the EPZ. The earthquake (EQ) is a local phenomenon and space weather conditions (SWCs) are a global phenomenon so that we selected four IGS stations away from the earthquake epicenter, which is at the same latitude and the same longitude with the earthquake to figure out a clear SWCs' effects on the GPS-TEC anomalies. In order to see these effects on ionospheric TEC variations, 10 different SWC indices were used. An analysis of occurrence time and duration of the following conclusions was made: both positive and negative anomalies are ensuing, and earthquake-related ionospheric anomalies revealed that they occurred 1 to 7days before the associated earthquake. At the end of the study, the potential causes of these results were examined. Analysis results revealed that anomalies occurred, and the possible ionospheric GPS-TEC anomalies may be the earthquake precursors obtained 4 to 9days before the Papua New Guinea earthquake. The increase in scientific research to analyze the seismo-ionospheric anomalies that occur before earthquakes will become an important warning tool to alert people to the devastating environmental effects of earthquakes.

Global Ionospheric Model Accuracy Analysis Using Shipborne Kinematic GPS Data in the Arctic Circle

The global ionospheric model built by the International Global Navigation Satellite System (GNSS) Service (IGS) using GNSS reference stations all over the world is currently the most widely used ionospheric product on a global scale. Therefore, analysis and evaluation of this ionospheric product’s accuracy and reliability are essential for the practical use of the product. In contrast to the traditional way of assessing global ionospheric models with ground-based static measurements, our study used shipborne kinematic global positioning system (GPS) measurements collected over 18 days to perform a preliminary analysis and evaluation of the accuracy of the global ionospheric models; our study took place in the Arctic Circle. The data from the International GNSS Service stations near the Arctic Circle were used to verify the ionospheric total electron contents derived from the kinematic data. The results suggested that the global ionospheric model had an approximate regional accuracy of 12 total electron content units (TECu) within the Arctic Circle and deviated from the actual ionospheric total electron content value by about 4 TECu.

Trend estimation of zenith total delays at IGS stations by using nonparametric methods

Global Navigation Satellite System (GNSS) observations play an important role in climate studies because of the advantages they provide. An analysis of the GNSS observations can be used to obtain the zenith total delay (ZTD) parameter, which represents the effect of weather conditions. In this study, the trends of the time series of IGS (International GNSS Service) Repro1 ZTD data recorded between 1995 and 2010 at 19 GNSS stations throughout Turkey and Europe reprocessed in the framework of COST Action ES1206 were investigated. The trends of the time series were estimated by using the Mann–Kendall rank correlation test in addition to Spearman’s rho test and Sen’s slope method. In addition, the Run (Swed–Eisenhart) test was performed to test for homogeneity. The aim of this study was to evaluate the advantages and disadvantages of the applications of different methods to the estimation of trends in the ZTD time series. According to the Mann–Kendall rank correlation test, the ANKR, GRAS, HERS and MAS1 stations exhibited an increasing trend. Using the Spearman rho test, increasing trends were observed at the ANKR, EBRE and MAS1 stations, while decreasing trends were observed at the BRUS and GRAS stations. Finally, negative slope values were obtained at the BRUS, GRAS, GRAZ, HERS, JOZE, PENC and WTZR stations by using the Sen slope method.

Validation and application of optimal ionospheric shell height model for single-site estimation of total electron content

Abstract. We recently proposed a method to establish an optimal ionospheric shell height model based on the international GNSS service (IGS) station data and the differential code bias (DCB) provided by the Center for Orbit Determination in Europe (CODE) during the time from 2003 to 2013. This method is very promising for DCB and accurate total electron content (TEC) estimation by comparing to the traditional fixed shell height method. However, this method is basically feasible only for IGS stations. In this study, we investigate how to apply the optimal ionospheric shell height derived from IGS station to non-IGS stations or isolated GNSS receivers. The intuitive and practical method to estimate TEC of non-IGS stations is based on optimal ionospheric shell height derived from nearby IGS stations. To validate this method, we selected two dense networks of IGS stations located in regions in the US and Europe. Two optimal ionospheric shell height models are established by two reference stations, namely GOLD and PTBB, which are located at the approximate center of two selected regions. The predicted daily optimal ionospheric shell heights by the two models are applied to other IGS stations around these two reference stations. Daily DCBs are calculated according to these two optimal shell heights and compared to respective DCBs released by CODE. The validation results of this method are as follows. (1) Optimal ionospheric shell height calculated by IGS stations can be applied to its nearby non-IGS stations or isolated GNSS receivers for accurate TEC estimation. (2) As the distance away from the reference IGS station becomes larger, the DCB estimation error becomes larger. The relation between the DCB estimation error and the distance is generally linear.