Global Navigation Satellite System Stations Research Articles

Multilayer Ionospheric Model Constrained by Physical Prior Based on GNSS Stations

The need for accurate modeling of the ionosphere plays an important role in the global navigation satellite system (GNSS) positioning. The traditional multi-layer VTEC model without prior has been used for modeling the ionospheric delay error. However, it is assumed that the electron density of the ionosphere is compressed into multiple thin layers at fixed heights in the lack of capturing ionospheric physics. In this paper, the data enhancement method by virtual observations is proposed to build the constrained multi-layer VTEC model to capture physical features from empirical ionospheric models. The extraction methods of physical knowledge have been developed by prior VTEC based on principal component analysis and model coefficients based on emulated basis function. The constrained multi-layer modeling has been verified based on simulation and real measurement of GNSS data in Yunnan, China collected from Qianxun Ground-based Augmentation System on November 3, 2021. The receiver DCB error estimated by the multi-layer model with prior constraint is significantly lower than that of the single-layer model and the traditional multi-layer model. The experimental test shows that the constrained multi-layer model achieves the accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.5 \,\rm {TECU}$</tex-math></inline-formula> for the independent reference station. The dSTEC of the proposed two multi-layer models are significantly lower than those of the single-layer model for low elevation angles, and the RMSE of dSTEC is reduced by 63 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> with the cutoff elevation angle of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{\circ }$</tex-math></inline-formula> . The spatial distribution of the multi-layer VTEC model shows consistency with the tomography model to verify vertical feature capturing capability. Compared with the Un-differenced and Un-combined Precise Point Positioning (UCPPP) without ionospheric constraint, the multi-layer constrained model based on the test data improves the convergence time approximately by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$36.55\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$18.78\%$</tex-math></inline-formula> in the horizontal (H), up (U) directions, respectively. These results demonstrate that the proposed multi-layer models not only improve ionospheric delay estimation precision but also can obtain the VTEC distribution capturing the physical characteristics of the ionosphere. The proposed multi-layer models may be valuable for the ionospheric delay modeling of satellite navigation systems in harsh variable ionospheric conditions.

High-Precision ZTD Model of Altitude-Related Correction

Zenith tropospheric delay (ZTD) is one of the main error sources in space geodesy. The existing regional or global models, such as Global Pressure and Temperature 3 (GPT3), Global Tropospheric model, Global Hopfield, and Shanghai Astronomical observatory tropospheric delay model models, have good performance. However, the precision of these models is relatively low in regions with a large height difference, which becomes the focus of this article. A high-precision ZTD model considering the height effect on tropospheric delay is proposed, and China is selected as study area due to its large height difference, which is called the high-precision ZTD model for China (CHZ). The initial ZTD value is calculated on the basis of the GPT3 model, and the periodic terms of ZTD residual between the global navigation satellite system (GNSS) and GPT3 model, such as annual, semiannual, and seasonal periods, are determined by the Lomb–Scargle periodogram method in different subareas of China. The relationship between the ZTD periodic residual term and the height of the GNSS station is further analyzed at different seasons, and linear ZTD periodic residual models are obtained. A total of 164 GNSS stations derived from the Crustal Movement Observation Network of China and 87 radiosonde stations are selected to validate the proposed CHZ model, and hourly ZTD data derived from GNSS are used to establish the CHZ model. Statistical result shows that the averaged root mean square and Bias of the CHZ model are 21.12 and −2.51 mm, respectively, in the whole of China. In addition, the application of CHZ model in precision point positioning (PPP) show that the convergence time is improved by 34%, 15%, and 35%, respectively, in N, E, and U components when compared to GPT3-based PPP.

Characteristic differences in tropospheric delay between Nevada Geodetic Laboratory products and NWM ray-tracing

Numerical weather models (NWMs) are important data sources for space geodetic techniques. Additionally, the Global Navigation Satellite System (GNSS) provides many observations to continuously improve and enhance the NWM. Existing comparative analysis experiments on NWM tropospheric and GNSS tropospheric delays suffer from being conducted in highly specific regions with limited spatial coverage; furthermore, the length of time for the experiment is too short for analyzing seasonal characteristics, and the insufficient number of stations limits spatial density, making it difficult to obtain the equipment-dependent distribution characteristics. After strict quality control and data preprocessing, we have calculated and compared the bias and standard deviation of tropospheric delay for approximately 7000 selected Nevada Geodetic Laboratory (NGL) GNSS stations in 2020 with the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) hourly ray-traced tropospheric delay for the same group of stations. Characterizations in time, space, and linkage to receivers and antennas reveal positive biases of approximately 4 mm in the NGL zenith tropospheric delay (ZTD) relative to the NWM ZTD over most of the globe; moreover, there is a seasonal amplitude reaching 6 mm in the bias, and an antenna-related mean bias of approximately 1.6 mm in the NGL tropospheric delay. The obtained results can be used to provide a priori tropospheric delays with appropriate uncertainties; additionally, they can be applied to assess the suitability of using NWMs for real-time positioning solutions.

Analyzing the Spatiotemporal Characteristics of Extreme Rainfall Using CAPE and GNSS-Derived ZTD Across China

The power-law relationship between precipitable water vapor (PWV)/convective available potential energy (CAPE), and extreme rainfall (ER) has been explored. However, the retrieval of PWV is reliant on the zenith total delay (ZTD) of the Global Navigation Satellite System (GNSS), and errors are introduced when converting ZTD to PWV. In this study, we propose the ZCER model, a comprehensive analysis model that integrates ZTD, CAPE, and ER, for investigating long-term variation in ER intensity and frequency, which expresses their close relationships in a novel approach and expands the application area of GNSS-derived ZTD. Daily ZTD, CAPE, and rainfall data were collected from 219 GNSS stations in China from 2011 to 2020 (10 years). Time- and frequency-domain information encapsulated in the three variables was extracted using wavelet coherence, proving that both ZTD and CAPE contributed to rainfall. The relationships between ZTD/log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (CAPE) and log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> (ER) were investigated at the annual, seasonal, and monthly scales. The results revealed that the contribution of ZTD/CAPE to ER varied spatially and temporally. Furthermore, the synergistic contributions of ZTD and CAPE to ER were further investigated. Statistical results showed that CAPE and ZTD not only complemented each other on the geographical scales to ER in China but also on the seasonal and monthly scales. Moreover, the qualitative relationships between the ZTD, CAPE, and ER frequency were elucidated. Our findings validate the strong links between high ZTD, CAPE, and ER intensity and frequency in China on geographical and temporal scales.

Analysis of Polar Ionosphere Anomaly of Interplanetary Shock Events Based on GNSS TEC

Using large-scope and high-accuracy Global Navigation Satellite System (GNSS) ionospheric monitoring, this study considered the polar ionosphere anomaly morphology caused by an interplanetary shock event that occurred on September 12, 2014. First, we used the continuous observation data of GNSS stations in the Antarctic region to estimate the carrier TEC epochs variation(dTEC) that reflects ionospheric electron density changes, and the abnormal signals were extracted. Through sequence data analyses of longitudinal chain stations, we found that the influence range of this anomaly lies in the geomagnetic region between 65 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> and 80 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> S. Furthermore, latitude chain analyses revealed that with the continuous fluctuation of the solar wind flow pressure, the polar ionospheric geomagnetic 75 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> S annular observation sequence gives a corresponding continuous fluctuation response with a range of 0.05–0.4 TECU and a westward anomaly trigger position. The average speed was close to the satellite speed during the study period, and it was determined that the anomaly occurred within an altitude region of 150–300 km. The physical mechanism of this anomaly is discussed in this paper. Our results show that the GNSS dTEC and multi-source data can accurately reflect the regional intensity, influence region, trigger source location, and velocity of the ionosphere anomaly.

Derived precipitable water vapour from GNSS and radiosonde data using time series and spatial least-square

ABSTRACT Precipitable water vapour (PWV) plays an important role in rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In this paper, radiosonde techniques are used to compute PWV’s spatial and temporal variations and GNSS (Global Navigation Satellite Systems) using in spatial only. GNSS data (GPS and GLONASS) from eight Egyptian stations were processed for the year 2014. Five radiosonde stations for the period from 2005 to 2016 were used. Time series is constructed using the daily surface measurements of radiosonde stations. The linear trend is estimated by straight line fit over 12 years of seasonally adjusted PWV time series. The PWV in Egypt has a positive trend in time series at more than five radiosonde sites with a rate of 0.3 mm/year. The monthly cycle is a near sine curve and the stochastic errors are from 0% to 5.4% over 12 years. The comparison between PWV estimated from GNSS data using the PPP approach and radiosonde data for each station in year 2014 was done in the near station. The nearest two stations, GNSS station “MTRH” and radiosonde station “62,306”, get a bias of 0.66 mm. Three common interpolation techniques (Inverse Distance Weighting, Kriging, and Minimum Curve) are used. The biases of the three used methods were 1.65 mm, 1.96 mm and 0.61 mm, respectively. The statistical methods of Minimum Curve interpolation are found superior to other methods with mean error at Mersa-Matrouh, Aswan and Al-Arish stations reaching 0.1 mm, 1.0 mm and 0.30 mm, respectively. The minimum curve technique is recommended in spatial interpolation for the prediction of PWV amount.Abbreviations: PWV: precipitable water vapour; PPP: precise point positioning; GNSS: global navigation satellite system; ZPD: tropospheric zenith path delay; ZWD: zenith wet delay; IDW: inverse distance weighting; MC: minimum curvature; IGS: International GNSS service.

BitTorrent is Apt for Geophysical Data Collection and Distribution

This article covers a nouveau idea of a way to collect and handle geophysical data with a peer-to-peer network in near real-time. The text covers a brief introduction to the cause, the technology, and the particular case of collecting data from Global Navigation Satellite System (GNSS) stations. We describe the proof-of-concept implementation that has been tested. The test was conducted with an experimental GNSS station and a data aggregation facility. In the test, original raw GNSS signal measurements were transferred to the data aggregation center and subsequently to the consumer. Our implementation utilized the BitTorrent protocol to communicate and transfer data. The solution could be used to enable the majority of data aggregation centers for fast, reliable, and transparent real-time data handling experience for the benefit of the scientific community.

A New Approach for Improving GNSS Geodetic Position by Reducing Residual Tropospheric Error (RTE) Based on Surface Meteorological Data

Positioning error components related to tropospheric and ionospheric delays are caused by the atmosphere in positioning determined by global navigation satellite systems (GNSS). Depending on the user’s requirements, the position error caused by tropospheric influences, which is commonly referred to as zenith tropospheric delay (ZTD), must be estimated during position determination or determined later by external tropospheric corrections. In this study, a new approach was adopted based on the reduction of residual tropospheric error (RTE), i.e., the unmodeled part of the tropospheric error that remains included in the total geodetic position error, along with other unmodeled systematic and random errors. The study was performed based on Global Navigation Satellite System (GLONASS) positioning solutions and accompanying meteorological parameters in a defined and harmonized temporal-spatial frame of three locations in the Republic of Croatia. A multidisciplinary approach-based analysis from a navigational science aspect was applied. The residual amount of satellite positioning signal tropospheric delay was quantitatively reduced by employing statistical analysis methods. The result of statistical regression is a model which correlates surface meteorological parameters with RTE. Considering the input data, the model has a regional character, and it is based on the Saastamoinen model of zenith tropospheric delay. The verification results show that the model reduces the RTE and thus increases the geodetic accuracy of the observed GNSS stations (with horizontal components of position accuracy of up to 3.8% and vertical components of position of up to 4.37%, respectively). To obtain these results, the Root Mean Square Error (RMSE) was used as the fundamental parameter for position accuracy evaluation. Although developed based on GLONASS data, the proposed model also shows a considerable degree of success in the verification of geodetic positions based on Global Positioning System (GPS). The purpose of the research, and one of its scientific contributions, is that the proposed method can be used to quantitatively monitor the dynamics of changes in deviations of X, Y, and Z coordinate values along coordinate axes. The results show that there is a distinct interdependence of the dynamics of Y and Z coordinate changes (with almost mirror symmetry), which has not been investigated and published so far. The resultant position of the coordinates is created by deviations of the coordinates along the Y and Z axes—in the vertical plane of space, the deviations of the coordinate X (horizontal plane) are mostly uniform and independent of deviations along the Y and Z axes. The proposed model shows the realized state of the statistical position equilibrium of the selected GNSS stations which were observed using RTE values. Although of regional character, the model is suitable for application in larger areas with similar climatological profiles and for users who do not require a maximum level of geodetic accuracy achieved by using Satellite-Based Augmentation Systems (SBAS) or other more advanced, time-consuming, and equipment-consuming positioning techniques.

Wet tropospheric correction for satellite altimetry using SIRGAS-CON products

Abstract The wet tropospheric correction (WTC) is a required correction to satellite altimetry measurements, mainly due to the atmospheric water vapor delay. On-board microwave radiometers (MWR) provide information for WTC estimation but fail in coastal zones and inland waters. In view to recover the WTC in these areas, the Global Navigation Satellite System (GNSS)-derived Path Delay Plus (GPD+) method, developed by the University of Porto, uses Zenith Tropospheric Delays from GNSS global and regional networks’ stations combined with other sources of information, providing a WTC solution for all along-track altimeter points. To densify the existing dataset used by GPD+, it is necessary to add new GNSS stations, mainly in the southern hemisphere, in regions such as South America, Africa and Oceania. This work aims to exploit the SIRGAS-CON data and its potential for densification of the GPD+ input dataset in Latin America and to improve GPD+ performance. The results for the three analyzed satellites (Sentinel-3A, Sentinel-3B and CryoSat-2) show that, when compared with the WTC from GNSS and radiosondes, the densified GPD+ WTC leads to a reduction in the RMS of the WTC differences with respect to the non-densified GPD+ solution, up to 2 mm for the whole region and up to 5 mm in some locations.

A new method for retrieving urban heat island intensity using GNSS-derived ZTD and atmospheric empirical model

ABSTRACT The urban heat island (UHI) effect is one of the typical characteristics of urban climate, which makes a lot of harmful gases accumulate and cannot be diffused. The UHI effect will cause air pollution, affecting the health and living conditions of humans. In this paper, a new method for calculating UHI intensity (UHII) by using the Global Navigation Satellite Systems (GNSS) derived zenith tropospheric delay (GNSS-ZTD) dataset and atmospheric empirical model was proposed and tested by using UHII observations from ground meteorological sensors for monitoring UHII in the Hong Kong city. The method requires accurate GNSS-ZTD datasets, priori atmospheric parameters (i.e. ground pressure and water vapour partial pressure) and the coefficient of the refractive function as inputs. The GNSS-ZTD datasets were obtained by using observations from the Hong Kong GNSS continuously operating reference stations network, and priori atmospheric parameters were obtained from a prior atmospheric model based on ERA5 (fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting) dataset and the coefficient of the refractive function can be obtained from the historical radiosondes data. The ground refractivity, pressure and water vapour partial pressure can be estimated by using the above-mentioned datasets, then the time series of UHII can be obtained by using a refractivity model proposed by Smith and Weintraub (SW-UHII), or Thayer (Thayer-UHII model). The results showed that the accuracy of the SW-UHII is better than the accuracy of the Thayer-UHII, and the root mean square (RMS) of the SW-UHII is 14% lower than the RMS of the Thayer-UHII. The mean bias, RMS and standard deviation of the SW-UHII compared with meteorological data derived UHII over GNSS stations are 1.82°C, 1.9°C and 1.41°C in all seasons, respectively, and the largest RMS is in winter (day of year 1–61 and 331–365). All the above results indicated that the GNSS technique has a certain prospect for monitoring UHII.

Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS

Kunming city is located in the middle of Yunnan Province. Due to large-scale groundwater exploitation and urban development in recent years, this area has been affected by surface subsidence. In this paper, Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) data are used to monitor the surface subsidence in Kunming city area for better analysis and understanding. The study used data of Sentinel-1A from 2018 to 2020 with atmospheric correction based on GACOS to calculate the average annual subsidence rate in Kunming city area, and the results show that the maximum subsidence rate is 48 mm/year. The subsidence obtained by InSAR is compared with the vertical deformation information obtained by eight GNSS stations in continuous operation in the study area. The subsidence rate trend show by the two methods is consistent, which further verifies the validity of InSAR data to reflect the local deformation. Experimental results shown that the eastern and northeastern Dianchi lake areas were affected by underground resources mining, and the induced surface subsidence characteristics were obvious, with the surface subsidence rate reachde 48 mm/year and 37 mm/year respectively. The Kunyang Phosphate Mine also had different degrees of mining subsidence disaster, with the maximum subsidence rate reached 36 mm/year. The subsidence rate of InSAR and GNSS has the same trend on the whole. However, GNSS sites are generally located in stable areas, the settlement amount obtained in the same time period is somewhat different from that of InSAR.

Analysis of BDS-3 Real-Time Satellite Clock Offset Estimated in Global and Asia-Pacific and the Corresponding PPP Performances

The quality of satellite clock offset affects the performances of positioning, navigation and timing services, and thus it is essential to the Global Navigation Satellite System (GNSS). This research focuses on the estimation of BeiDou Navigation Satellite System (BDS) real-time precise satellite clock offset by using GNSS stations located in the Global and Asia-Pacific region based on the mixed-difference model. The precision of the estimated BDS clock corrections is then analyzed with the classification of the orbit types, satellite generations, and atomic clock types. The results show that the precision of the BDS clock offset estimated in the Asia-Pacific for Geosynchronous Earth Orbit (GEO), Inclined Geosynchronous Satellite Orbit (IGSO) and Medium Earth Orbit (MEO) satellites are 0.204 ns, 0.077 ns and 0.085 ns, respectively, as compared to those of clock offsets estimated in globally distributed stations. The average precision of the BDS-3 satellites clock offset estimated in global region is 0.074 ns, which is much better than the 0.130 ns of BDS-2. Furthermore, analyzing the characteristics of the corresponding atomic clocks can explain the performance of the estimated satellite clock offset, and the stability and accuracy of various parameters of the Passive Hydrogen Maser (PHM) atomic clocks are better than those of Rubidium (Rb) atomic clocks. In the positioning domain, the real-time clocks estimated in the global/Asia-Pacific have been applied to BDS kinematic Precise Point Positioning (PPP) in different regions. The Root Mean Square (RMS) of positioning results in global real-time kinematic PPP is within 4 cm in the horizontal direction and about 6 cm in the vertical direction. Hence, the BDS real-time clock offset can supply the centimeter-level positioning demand around the world.

Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Abstract. Tropospheric water vapor is one of the most important trace gases of the Earth's climate system, and its temporal and spatial distribution is critical for the genesis of clouds and precipitation. Due to the pronounced dynamics of the atmosphere and the nonlinear relation of air temperature and saturated vapor pressure, it is highly variable, which hampers the development of high-resolution and three-dimensional maps of regional extent. With their complementary high temporal and spatial resolutions, Global Navigation Satellite Systems (GNSS) meteorology and Interferometric Synthetic Aperture Radar (InSAR) satellite remote sensing represent a significant alternative to generally sparsely distributed radio sounding observations. In addition, data fusion with collocation and tomographical methods enables the construction of detailed maps in either two or three dimensions. Finally, by assimilation of these observation-derived datasets with dynamical regional atmospheric models, tropospheric water vapor fields can be determined with high spatial and continuous temporal resolution. In the following, a collection of basic and processed datasets, obtained with the above-listed methods, is presented that describes the state and course of atmospheric water vapor for the extent of the GNSS Upper Rhine Graben Network (GURN) region. The dataset contains hourly 2D fields of integrated water vapor (IWV) and 3D fields of water vapor density (WVD) for four multi-week, variable season periods between April 2016 and October 2018 at a spatial resolution of (2.1 km)2. Zenith total delay (ZTD) from GNSS and collocation and refractivities are provided as intermediate products. InSAR (Sentinel-1A/B)-derived double differential slant total delay phases (ddSTDPs) and GNSS-based ZTDs are available for March 2015 to July 2019. The validation of data assimilation with five independent GNSS stations for IWV shows improving Kling–Gupta efficiency (KGE) scores for all seasons, most notably for summer, with collocation data assimilation (KGE = 0.92) versus the open-cycle simulation (KGE = 0.69). The full dataset can be obtained from https://doi.org/10.1594/PANGAEA.936447 (Fersch et al., 2021).

Accuracy Assessment for GNSS Precise Point Position Using Online Service and Offline Software

There are many applications of Global Navigation Satellite System (GNSS) (GPS, GLONASS, Galileo, and BeiDou) such as civilian (agriculture, mapping, road and traffic safety) and military services. Precise point positioning (PPP) solution is considered as a main geodetic surveying task in post processing of GNSS observations. In last years (PPP) solution package are developed as online services and offline software’s. This study aims to introduce a comparative study between solutions of post processing free online processing (CSRS-PPP) and free offline software (PPPH). Field observations were carried out on 9 IGS stations utilizing a static GNSS observation method and an observation RINEX 3 format for period of 24hr for 3 consecutive days in January and 3 consecutive days in March 2022. Post-processing of the obtained data was done using online and offline software. The coordinates from online and offline compared with coordinates from IGS station to assess their respective differences and accuracy. The minimum difference 0 cm for both solutions and the maximum difference 1.568 cm for online and 1.404 cm for offline. Online GNSS processing services can be used for engineering and geodetic applications since they are simple to use like PPPH, and are easy to execute.

High-resolution surface mass loads in the Amazon Basin combining GRACE and river routing model

SUMMARYUnderstanding water redistribution on Earth's surface is essential to hydrological applications and water management. Variations in water mass loads have been observed by the Gravity Recovery and Climate Experiment (GRACE), but the low spatial resolution of GRACE limits determination of their distribution in detail. Hydrologic models provide higher spatial resolution water mass loads, but may include larger uncertainties. In this study, we develop high-resolution surface mass loads over the Amazon basin using forward modelling by combining GRACE data and a hydrologic model. River routing discharge is also included as a priori information because of the large water volume changes on relatively narrow channels in the Amazon basin. These high-resolution surface mass loads constrained by river routing agree with GRACE observations when spatially smoothed. Vertical deformation estimated from these high-resolution loads agree with Global Navigation Satellite System (GNSS) observations, at both seasonal and inter-annual timescales. In particular the most improved agreement is obtained at the NAUS GNSS station, close to the main channel of the Amazon, relative to predictions made using GRACE data. At two other stations (APSA and MAPA) near the main channel, the estimated vertical deformations apparently differ from observation, but much of the discrepancy is reduced when river path is corrected in river-routing model, indicating the importance of water loads on river channel to understand crustal displacement in the area.

Quasi-4-dimension ionospheric modeling and its application in PPP

Ionospheric delay modeling is not only important for Global Navigation Satellite System (GNSS) based space weather study and monitoring, but also an efficient tool to speed up the convergence time of Precise Point Positioning (PPP). In this study, a novel model, denoted as Quasi-4-Dimension Ionospheric Modeling (Q4DIM) is proposed for wide-area high precision ionospheric delay correction. In Q4DIM, the Line Of Sight (LOS) ionospheric delays from a GNSS station network are divided into different clusters according to not only the location of latitude and longitude, but also satellite elevation and azimuth. Both Global Ionosphere Map (GIM) and Slant Ionospheric Delay (SID) models that are traditionally used for wide-area and regional ionospheric delay modeling, respectively, can be regarded as the special cases of Q4DIM by defining proper grids in latitude, longitude, elevation, and azimuth. Thus, Q4DIM presents a resilient model that is capable for both wide-area coverage and high precision. Four different sets of clusters are defined to illustrate the properties of Q4DIM based on 200 EUREF Permanent Network (EPN) stations. The results indicate that Q4DIM is compatible with the GIM products. Moreover, it is proved that by inducting the elevation and azimuth angle dependent residuals, the precision of the 2-dimensional GIM-like model, i.e., Q4DIM 2-Dimensional (Q4DIM-2D), is improved from around 1.5 Total Electron Content Units (TECU) to better than 0.5 TECU. In addition, treating Q4DIM as a 4-dimensional matrix in latitude, longitude, elevation, and azimuth, whose sparsity is less than 5%, can result in its feasibility in a bandwidth-sensitive applications, e.g., satellite-based Precising Point Positioning Real-Time Kinematic (PPP-RTK) service. Finally, the advantages of Q4DIM in PPP over the 2-dimensional models are demonstrated with the one month's data from 30 EPN stations in both high solar activity year 2014 and low solar activity year 2020.

Leveraging the CYGNSS Spaceborne GNSS‐R Observations to Detect Ionospheric Irregularities Over the Oceans: Method and Verification

AbstractThe Global Navigation Satellite System (GNSS) has significantly facilitated the detection of ionospheric irregularities. Nevertheless, the ground‐based GNSS stations are inhomogeneously distributed around the world with large gaps, especially in the vast ocean regions. This adversely affects the interpretation of the spatio‐temporal variation characteristics of the ionospheric irregularities. In this study, ionospheric irregularities over the regions of the equatorial, and low‐latitude ocean during a G4‐class strong geomagnetic storm were investigated by utilizing the spaceborne GNSS reflectometry (GNSS‐R) technique. We derived and validated the ionospheric regularities index S4R from the signal‐to‐noise ratio measurements of the spaceborne GNSS‐R. Meanwhile, we utilized the ionospheric irregularities data from ground‐based (GNSS and ionosonde) and space‐based (Swarm and FORMOSAT‐3/COSMIC) instruments to evaluate and analyze the CYGNSS‐derived S4R. All comparisons and validations conducted within this study illustrate that the spaceborne GNSS‐R measurements provide complementary data and realize the timely observation as well as confirmation of the ionospheric irregularities at the same location over the oceans. It is expected to benefit the application of spaceborne GNSS‐R in ionospheric irregularities monitoring and applications over the ocean area.

The Influence of Sediments, Lithosphere and Upper Mantle (Anelastic) With Lateral Heterogeneity on Ocean Tide Loading and Ocean Tide Dynamics

AbstractOcean tide loading (OTL) and ocean tide dynamics (OTD) are known to be affected by Earth's internal structures, with the latter being affected by the self‐attraction and loading (SAL) potential. Combining the 3D earth models Lyon and LITHO1.0, we construct a hybrid model to quantify the coupled effect of sediments, oceanic and continental lithosphere, and anelastic upper mantle on OTL and OTD. Compared to PREM, this more realistic 3D model produces significantly larger vertical OTL displacement by up to 3.9, 2.6, and 0.1 mm for the M2, K1, and Mf OTL, respectively. Moreover, it shows a smaller vector difference of 0.1 mm and a smaller amplitude difference of 0.2 mm than PREM with OTL observations at 663 Global Navigation Satellite System stations, a confirmation of the cumulative effect due to these earth features. On the other hand, we find a resonant impact of wider extent and larger magnitude on OTD, especially for the M2 and K1 tides. Specifically, this impact is concentrated in the ranges 0–6 mm and 0–1.5 mm for M2 and K1, respectively, which is considerably larger than the impact on SAL (mostly in the ranges 0–2 mm and 0–1.0 mm, respectively). Since the effect on vertical displacement is at a similar level compared to the accuracy of modern data‐constrained ocean tide models that require correction of the geocentric tide by loading induced vertical displacements, we regard its consideration to be potentially beneficial in OTD modeling.

Machine Learning Models Applied to a GNSS Sensor Network for Automated Bridge Anomaly Detection

Structural health monitoring (SHM) based on global navigation satellite systems (GNSS) is an interesting solution to provide absolute positions at different locations of a structure in a global reference frame. In particular, low-cost GNSS stations for large-scale bridge monitoring have gained increasing attention these last years because recent experiments showed the ability to achieve a subcentimeter accuracy for continuous monitoring with adequate combinations of antennas and receivers. Technical solutions now allow displacement monitoring of long bridges with a cost-effective deployment of GNSS sensing networks. In particular, the redundancy of observations within the GNSS network with various levels of correlations between the GNSS time series makes such monitoring solution a good candidate for anomaly detection based on machine learning models, using several predictive models for each sensor (based on environmental conditions, or other sensors as input data). This strategy is investigated in this paper based on GNSS time series, and an anomaly indicator is proposed to detect and locate anomalous structural behavior. The proposed concepts are applied to a cable-stayed bridge for illustration, and the comparison between multiple tools highlights recurrent neural networks (RNN) as an effective regression tool. Coupling this tool with the proposed anomaly detection strategy enables one to identify and localize both real and simulated anomalies in the considered data set.

Water-Vapour Monitoring from Ground-Based GNSS Observations in Northwestern Argentina

The Central Andes in northwestern Argentina are characterized by steep topographic and climatic gradients. The humid foreland areas at 1 km asl elevation rapidly rise to over 5 km in the eastern Cordillera, and they form an orographic rainfall barrier on the eastern windward side. This topographic setting combined with seasonal moisture transport through the South American monsoon system leads to intense rainstorms with cascading effects such as landsliding and flooding. In order to better quantify the dynamics of water vapour transport, we use high-temporal-resolution global navigation satellite system (GNSS) remote sensing techniques. We are particularly interested in better understanding the dynamics of high-magnitude storms with high water vapour amounts that have destructive effects on human infrastructure. We used an existing GNSS station network with 12 years of time series data, and we installed two new ground stations along the climatic gradient and collected GNSS time series data for three years. For several stations we calculated the GNSS signal delay gradient to determine water vapour transport direction. Our statistical analysis combines in situ rainfall measurements and ERA5 reanalysis data to reveal the water vapour transport mechanism for the study area. The results show a strong relationship between altitude and the water vapour content, as well as between the transportation pathways and the topography.