The growing interest of the GNSS community in computing GNSS series using the iPPP (Precise Point Positioning with integer ambiguity resolution) mode with the GINS software and the products of the CNES/CLS analysis center (GRG products) culminated in 2022 with the SPOTGINS project. This initiative enables several research laboratories to cooperate in processing a global network of stations, benefiting from the expertise of the IGS analysis center and the advanced quality of the GRG products.

This standardization and collaboration require a unified computational strategy, involving the same version of the GINS software, identical configurations (products, corrections, models, constellations), and consistent metadata. Currently, daily station positions are computed using GPS and Galileo constellations, using the “G20” orbits and clocks from the IGS CNES-CLS based on ITRF2020, FES2014b ocean tidal loading and VMF1 mapping functions.

SPOTGINS started in 2022 with the collaboration between the OMP in Toulouse, the EOST in Strasbourg and the LIENSs in La Rochelle, whose massive calculations with GINS were already being done for several years. In 2023 and 2024, other groups expressed interest in joining SPOTGINS: the GeF/Cnam in Le Mans, the IPGP/IGN in Paris and the OSUG/ISTerre in Grenoble. Each member pursues different scientific objectives, but all contribute  collectively to the dissemination of PPP series for the community through the Geodesy Plotter of the FormaTerre data and service hub.

How to cite: Boy, J.-P., Fériol, F., Gravelle, M., Janex, G., Loyer, S., Nahmani, S., Nicolas, J., Pollet, A., Sakic, P., Santamaría-Gómez, A., and Tsapong-Tsague, A.-B.: SPOTGINS: A New Global GNSS Daily iPPP Solution Derived Using GINS software , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12545, https://doi.org/10.5194/egusphere-egu25-12545, 2025.

PRIDE PPP-AR is a Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) software specifically designed for applications in Earth sciences. Since its initial release in 2019, the software has been updated to version 3.1. The core strength of PRIDE PPP-AR lies in its PPP-AR processing capabilities with arbitrary dual-frequency ionosphere-free combinations. It is also featured by low Earth orbit satellite solutions, multipath mitigation, epoch-by-epoch constraints in segmented processing mode, multi-day continuous processing, and ocean tide loading corrections without ocean tide coefficients. Furthermore, PRIDE PPP-AR is compatible with the three major operating systems: Linux, Windows, and macOS, providing great convenience for early-career researchers, facilitating easier learning and usage.

Tests conducted using 511 IGS reference stations in January 2018 showed that, when compared with IGS solutions, PRIDE PPP-AR can achieve millimeter-level static solutions and tropospheric delays, and centimeter-level dynamic solutions, with an average fix rate of 95%. In the fields of geodesy and Earth sciences, the high-precision positioning capabilities of PRIDE PPP-AR make it a powerful tool for studying Earth dynamics, crustal deformation, and seismic monitoring. Additionally, its performance on high-dynamic platforms such as aerial photogrammetry and shipborne gravimetry is outstanding, providing technical support for scientific research and engineering practices in related fields. The new version of PRIDE PPP-AR will be able to fully utilize the advantages of GNSS modernization, enhancing high-precision positioning accuracy in a broader range of research areas.

How to cite: Geng, J. and Wen, Q.: PRIDE PPP-AR: mitigating multipath and day-boundary discontinuities for geodesy and geophysics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4789, https://doi.org/10.5194/egusphere-egu25-4789, 2025.

The BDS-3 provides signals at six different frequency bands, which are expected to improve the precision and convergence speed of Precise Point Positioning (PPP). To fully utilize the full-frequency data from BDS-3, three PPP function models were constructed: the six-frequency undifferenced and non-combined model (UC6), the single-layer six-frequency ionosphere-free model (IF6-1), and the six-frequency ionosphere-free dual-combination model (IF6-2).For the six-frequency undifferenced and non-combined model (UC6), the IFB (Inter-frequency Bias) processing strategy was studied, and the impact of three commonly used processing strategies on IFB estimation was analyzed. The results showed that when using constant models and random walk models, the estimated IFB values were similar, while the white noise model, despite slight fluctuations in the estimated IFB, exhibited a trend consistent with the other two models.The positioning experimental results indicated that the positioning accuracy after static convergence for the UC6, IF6-1, and IF6-2 models was similar. The horizontal mean accuracy was 1.36, 1.35, and 1.39 cm, respectively, and the vertical accuracy was 1.42, 1.45, and 1.63 cm. In dynamic mode, the horizontal accuracy was 4.04, 4.10, and 4.12 cm, and the vertical accuracy was 5.52 cm, 5.19 cm, and 5.02 cm.In terms of convergence time, the IF6-1 model showed superior static and dynamic convergence performance compared to the UC6 and IF6-2 models. Further analysis compared the receiver clock offsets, inter-frequency biases, and zenith wet delays between the different models. The receiver clock offset time series from all three models were consistent, and the inter-frequency bias for the same receiver model remained stable throughout the day. Additionally, the zenith wet delay estimates from all three models tended to converge after the process reached steady-state.

How to cite: Wang, L., Ma, C., Cui, B., Huang, G., and Zhang, Q.: Performance Analysis and Comparison of BDS-3 All-frequency Precise Point Positioning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8092, https://doi.org/10.5194/egusphere-egu25-8092, 2025.

Multipath continues to pose a significant challenge for the Global Navigation Satellite System (GNSS) technique in achieving precise Precise Point Positioning (PPP). Sidereal Filtering (SF) and multipath hemispherical map (MHM) represent common methods for mitigating multipath by leveraging satellite temporal and spatial repeatability. However, the effectiveness of these approaches relies heavily on the quality of the multipath correction model derived from preceding PPP residuals, which often contains product and parameter estimation errors. These errors can notably compromise the accuracy and reliability of multipath mitigation efforts. We propose an innovative method for extracting multipath based on a refined error separation strategy and mitigating multipath with SF. This method involves decomposing PPP residuals into multiple reconstructed components (RCs) using the multi-channel singular spectrum analysis (MSSA) technique and subsequently isolating multipath by reconstructing RCs exhibiting strong temporal repeatability. Extensive experiments with a 28-day dataset from 20 multi-GNSS stations demonstrated the effectiveness of our method. Compared to the conventional wavelet-based SF method, the proposed approach improved PPP accuracy by 23%, 20%, and 18% in the East, North, and Up directions, respectively, and by 18%, 16%, and 15% compared to the MHM method. Results also reveal that PPP residuals can be decomposed into multipath, high-frequency noise, common-mode error (CME), and site-specific errors. The last two components, which are non-repeatable in time and space, pose limitations on the effectiveness of conventional SF and MHM strategies in addressing multipath effects. 

How to cite: Li, X., Wang, L., Ding, X., Zhang, Q., Qu, X., and Shu, B.: New Insights of Accurate Multipath Mitigation for Multi-GNSS PPP Using Refined Multipath Extraction Strategy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2304, https://doi.org/10.5194/egusphere-egu25-2304, 2025.

Despite the increasing number of satellites in multi-constellation GNSS, signal availability and quality remain significant problems in urban and forested environments. Field obstacles such as buildings and dense vegetation can lead to severe multipath issues. Various methods have been developed to mitigate multipath effects on measurement results, including optimizing antenna placement, selecting appropriate antenna and receiver types, and employing advanced post-processing techniques. However, these efforts have been unable to completely eliminate multipath interference, which can greatly affect positioning accuracy. The authors of this presentation have developed a tool that helps identify and remove reflected signals from measurement data sets. This tool, called GNSS MPD, was developed to predict satellite signal obstructions. It considers Line of Sight (LOS) vectors between specific locations and satellite positions and obstacle models derived from airborne LiDAR data. The LiDAR data is automatically acquired from geoportal.gov.pl, enabling the generation of an approximate terrain cover model. Satellite obstructions are validated using a ray-casting method. As part of testing the developed platform, the authors designed two experiments. The first experiment is a comparative analysis between satellite visibility scenarios obtained from GNSS MPD calculations and hemispherical photography. The second study involves performing positioning using information regarding the satellite visibility from GNSS MPD software. As part of this study, five 24-hour measurement sessions were conducted in a highly urbanized area.  Based on the receiver's approximate position, satellite visibility scenarios are generated using the developed platform. Static positioning measurements were performed in the experiment, yielding two sets of results: one based on raw receiver observations and the other incorporating visibility scenarios from the platform to adjust the observation files. The test results demonstrate improvements in both accuracy and the success rate of position determination.

How to cite: Tomaszewski, D., Rapiński, J., Janowski, A., and Pelc-Mieczkowska, R.: Enhancing GNSS Positioning Accuracy in Challenging Environments: Development and Validation of a Multipath Prediction Tool, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2818, https://doi.org/10.5194/egusphere-egu25-2818, 2025.

As urban populations continue to grow, the demand for precise positioning has become increasingly critical for various applications, including navigation, location-based services (LBS), transportation, delivery, and logistics. Global Navigation Satellite Systems (GNSS) are widely utilized for positioning; however, they encounter significant challenges in urban canyons, where signal obstructions and reflections can degrade positioning accuracy by tens to hundreds of meters.

To address these challenges, existing consistency check methods have been developed to detect and exclude erroneous observations from multiple GNSS constellations, thereby improving performance in open or low-density urban environments. Nevertheless, high-density urban areas present difficulties, as the majority of GNSS signals are often compromised by non-line-of-sight (NLOS) reception and multipath interference. While the integration of inertial sensors with GNSS technology has shown effectiveness in addressing GNSS outages, the accumulation of drift errors in pedestrian dead reckoning (PDR) still hinder performance in dense urban settings where GNSS solutions are consistently unreliable.

In this study, we propose a novel approach that tightly integrates PDR and GNSS data in the measurement domain to effectively identify fault-free measurements amidst a backdrop of contaminated signals. We introduce a multi-epoch smoothing algorithm designed to enhance positioning accuracy. Our method employs a two-stage consistency check algorithm to mitigate multipath effects, incorporating both satellite quality assessments and grid quality evaluations based on raw GNSS observations and inertial sensor data. Notably, we leverage time-series residuals from multi-epoch GNSS observations to identify fault-free measurements, moving beyond the limitations of single-epoch data. Additionally, grid quality is evaluated based on the discrepancies in residuals among high-quality satellites. To bolster the robustness and reliability of positioning, our algorithm integrates a positioning scheme that utilizes weight smoothing based on multi-epoch grid mapping and outlier mitigation through density-based spatial clustering of applications with noise (DBSCAN) clustering.

Field experiments conducted in typical urban environments in Hong Kong, utilizing a standard smartphone as the receiver, demonstrated substantial improvements over conventional consistency check methods and chip outputs. Our findings reveal that traditional consistency check methods underperformed compared to chip outputs in dense urban areas. In contrast, the proposed method significantly enhanced positioning accuracy across all trials, achieving accuracies ranging from 2m to 10m, compared to chip outputs that varied from 5m to 58m. The proposed approach yielded an improvement rate of 50% to 88% across different urban densities.

This innovative method is compatible with most consumer-grade devices, requiring no additional hardware, thereby offering enhanced convenience and intelligence for urban residents. Its ease of implementation across various brands and real-time operation with low computational load make it a versatile solution for improving positioning accuracy in complex urban environments.

How to cite: luo, H., Yang, Y., Mi, X., Ding, M., and Chen, W.: Enhancing GNSS Positioning Accuracy in Urban Canyons: A Multi-Epoch Residual-Based Consistency Check and IMU-GNSS Tight Coupling Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14893, https://doi.org/10.5194/egusphere-egu25-14893, 2025.

In precise point positioning, the fixed ambiguity can significantly improve the positioning accuracy of PPP and accelerate the convergence speed to a certain extent. The quality of phase deviation products directly determines the ambiguity resolution effect, and then affects the positioning performance of PPP-AR. At present, CNES is the only analysis center that publicly provides real-time observable-specific signal bias products (OSB). In order to promote the practical application of RT PPP-AR, it is necessary to evaluate the data quality and PPP-AR positioning performance of real-time OSB products provided by CNES. In this paper, the observation data of 42 MGEX stations from May 1, 2023 (DOY 121) to May 10, 2023 (DOY 130) are selected, and the positioning performance of PPP-AR is analyzed and evaluated after OSB product correction. In terms of OSB product quality, the data availability (DA) of GPS satellite is greater than 94%, that of Galileo satellite is less than 56%, and the standard deviations (STD) of GPS and Galileo satellite are 0.053 and 0.075, 0.031 and 0.044 respectively. After correction by OSB products, the percentage of wide lane (WL) residuals of GPS and Galileo systems exceeds 90% and 83% within 0.25 cycle, and the percentage of narrow lane (NL) residuals of the two systems within 0.25 cycle is 83% and 81% respectively. As far as the positioning performance of PPP-AR is concerned, the positioning errors of GPS+Galileo dynamic PPP-AR in E, N and U directions are 1.45 cm, 1.51 cm and 4.16cm, respectively, and the ambiguity fixing rate is about 97%. Compared with PPP, the convergence time of PPP-AR corrected by OSB products can be shortened by more than 59%. However, due to the lack of phase deviation, there is a phenomenon of re-convergence in the positioning process. In this paper, Grey model is used to predict OSB data, which has certain reliability and significantly improves the situation of re-convergence.

How to cite: Huang, G., Yang, N., and Wang, L.: Performance analysis of undifferenced and uncombined PPP based on CNES OSB products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9469, https://doi.org/10.5194/egusphere-egu25-9469, 2025.

This research involved an analysis aimed at determining the accuracy of results derived from the Trimble RTX web-based post-processing service for Multi-GNSS solutions. To achieve this, nine stations situated on the Eurasian plate were selected from the MGEX network set up by the IGS. A GNSS dataset from 2022 was processed using Trimble RTX. The data from these GNSS stations were analyzed as three satellite combinations: GPS-only, GPS-GLONASS, and GPS-GLONASS-Galileo.

Initially, a 3D transformation was applied to investigate statistically significant differences among the 24-hour processing results for the three combinations. Upon confirming that the differences in coordinates were statistically insignificant, the GNSS data was divided into two 12-hour datasets and three 8-hour datasets, which were then processed using Trimble RTX. The results from the 24-hour analysis were accepted as accurate for all three satellite combinations, and the differences between the 12-hour and 8-hour processing results were examined. The results indicated that, while the differences in coordinate components were insignificant, the use of all three satellite combination datasets contributed to both the Cartesian coordinate components and the standard deviation values.

How to cite: Oz Demir, D.: Statistical Analysis of Trimble RTX Service Processing Results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9510, https://doi.org/10.5194/egusphere-egu25-9510, 2025.

This study examines the stability and temperature variations of SAPOS reference stations across Lower Saxony and neighboring federal states over a four-year period (2017–2020). SAPOS, the Satellite Positioning Service of the German National Survey, provides high-precision geodetic
reference points essential for applications such as surveying, navigation, and geographic information systems (GIS). Data from 43 SAPOS stations in Lower Saxony, ten SAPOS stations in adjacent regions, and 54 German Weather Service (DWD) stations were analysed using custom programs in Python
and MATLAB. The findings demonstrate the high SAPOS availability and station quality, though systematic effects were identified in the cleaned time series of topocentric coordinates for certain stations. These effects relate to multipath sensitivity, assessed using the Multipath Indicator (MPI),
which reflects signal reflection interference, confirming the high quality of SAPOS stations. The study corrected these effects via drift and offset adjustments and outlier removal. The analysis investigated five categories of deviations, revealing recurring patterns influenced by station type and installation characteristics. Notably, unilateral solar exposure and temperature fluctuations at the Braunschweig station caused deviations of up to 5 mm in the north and east components of topocentric coordinates.

How to cite: Zhu, Y., Schön, S., and Jahn, C. H.: Analysis of Stability and Temperature Variations in SAPOS Reference Stations in Lower Saxonyand Adjacent Regions between 2017 and 2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21876, https://doi.org/10.5194/egusphere-egu25-21876, 2025.

The high latitudes are widely recognized as the most disturbed region of the ionosphere. The reason for that is the shape of the geomagnetic field and, related to this,  the precipitation of energetic particles originating from the solar wind. The consequence of the strong plasma variation in the polar and auroral ionosphere is the deterioration of GNSS signal amplitude and phase, which may lead to losses of phase lock and cycle slips. The latter impact results in challenging scenarios for GNSS precise positioning based on phase data,  but it may also be used to indicate the high-latitude ionosphere conditions. Since there is a correlation between GNSS signals scintillation and cycle slips, the analysis of the second parameter may support the climatological investigations for this area. Such an assumption allows us to use conventional GNSS data from permanent networks (of 1-30 s sampling rate), significantly extending the spatiotemporal distribution of measurements. Nevertheless, the adoption of cycle-slips number as a parameter describing ionospheric activity has to be preceded by cross-evaluation of its behaviour for different receivers and signals. This step is crucial, considering the increasing number of GNSS constellations and signals.

Motivated by such developments, we investigated the occurrence of cycle slips in GNSS phase data recorded by stations located at northern high latitudes. The basis for the analysis was multi-GNSS observations (GPS, GLONASS, Galileo, BDS) collected by permanent IGS/EPN stations. As a test period, we selected two major geomagnetic storms in 2024, which took place in May and October. The analysis confirms significant differences between the number of cycle slips for a particular system and signal. The discrepancies are also observed for collocated stations equipped with different receivers. The results indicate a need for unifying multi-GNSS cycle-slip numbers for climatological ionospheric studies.

How to cite: Sieradzki, R. and Paziewski, J.: The cycle-slips occurrence at high-latitude GNSS stations during geomagnetic storms – inter-receiver and inter-signal comparison, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12253, https://doi.org/10.5194/egusphere-egu25-12253, 2025.

Recently, we have observed remarkable progress in algorithms aimed at precise point positioning (PPP) based on uncombined GNSS observations, also with an integer ambiguity resolution. Despite the high potential of such a model, its performance still depends on several factors, among which is the a priori information on slant total electron content (STEC), which is crucial. The ionospheric corrections may be obtained by converting VTEC from global ionosphere maps (GIM) using the selected ionospheric mapping function (MF). Thus, the accuracy of such STECs depends on the uncertainties introduced by both GIMs and MFs. Considering the latter, the most common approach is using a single-layer model (SLM) with a zenith angle as a parameter. However, it may be less effective for regions with strong TEC gradients that depend on azimuth. Improving such areas seems feasible with the support of ionosphere models and multi-layered mapping functions.

In this study, we evaluate the new multi-layered mapping functions. The new functions were developed, taking the Neustrelitz TEC Model as a basis. In this case, the ionosphere comprises numerous thin shells, and the ratio of aggregated slant and vertical TEC values provides the modeled mapping factors. Such derived MFs were validated based on PPP positioning performance. Also, an agreement analysis using the geometry-free linear combination was performed to assess the MFs in the GNSS observation domain.  The preliminary tests involved GNSS data from several globally distributed stations, corresponding to different daily patterns of the ionosphere. The analysis provides the statistics for the low solar activity period (year 2019).  According to the results, we can report a slight benefit from applying the multi-layered mapping function for PPP performance compared to the standard SLM approach. The advancement is the most noticeable for the positioning initialization period and, therefore, is reflected in the convergence time. The analysis performed with the geometry-free linear combination is consistent with PPP results, and it highlights the highest potential of the multi-layered mapping function for the equatorial region.

How to cite: Wielgosz, P., Sieradzki, R., Paziewski, J., Hoque, M., Frauenberger, O., Dhital, N., Nykiel, G., Milanowska, B., and Orus Perez, R.: Applicability of multi-layered mapping function to STEC/VTEC conversion – validation in PPP positioning and GNSS observation domains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18016, https://doi.org/10.5194/egusphere-egu25-18016, 2025.

The tropospheric effect is considered as one of the most significant sources of error limiting GNSS positioning accuracy. In addition, atmospheric mass loading, which is seasonally variable, can also affect the repeatability of daily GNSS positions to a minor level. Therefore, the correct modelling of tropospheric and atmospheric loading effects is crucial to achieve the suitable accuracy, especially in high-accuracy GNSS applications. The aim of this work is to study the impact of tropospheric modelling and atmospheric mass loading on the repeatability of daily GNSS solutions. The research methodology is based on the analysis of GNSS data obtained from the IGS network, incorporating multiple processing scenarios including different mapping functions, elevation masks, and atmospheric mass loading. The analysis studies data from contrasting geographical locations (mid-latitude and polar regions), and accounts for seasonal variations by analysing measurements taken during both summer and winter periods, enabling a comprehensive assessment of how these various factors influence GNSS data processing outcomes, especially the daily position repeatabilities. The obtained results show that the efficiency of the mapping functions varies from one region to another. Furthermore, the consideration of atmospheric mass loading affects the performance of the mapping functions.

How to cite: Dekkiche, H., Namaoui, H., and Bouaoula, W.: Impact of tropospheric and atmospheric loading models on the repeatability of GNSS solutions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17541, https://doi.org/10.5194/egusphere-egu25-17541, 2025.

Seismic monitoring depends on accurately identifying P-wave and S-wave arrivals, which are critical for earthquake localization and Earthquake Early Warning (EEW). In EEW networks, a Global Navigation Satellite System (GNSS)-driven geodetic component enhances lead-time estimation and ground-shaking assessment, particularly for large earthquakes (Mw > 6.0). Advancing S-wave detection algorithms is essential to providing fast and reliable warnings to communities.

This study presents a sliding-window-based algorithm designed to detect the first time of arrival (ToA) of S-waves in high-rate (<1 Hz) GNSS instantaneous velocity time series without frequency-domain pre-filtering. The algorithm employs a three-phase process: (1) preprocessing, (2) statistical analysis and hypothesis testing for extracting ground-shaking disturbances, and (3) S-wave picking. It is implemented in an open-source Python-based toolbox, which also provides auxiliary seismic data, including ground-shaking duration, component-wise Peak Ground Velocity (PGV), and waveform energy.

The algorithm’s performance was evaluated using data from the 2016 Mw 6.2 Norcia and 2023 Mw 7.7 Kahramanmaraş-Gaziantep earthquakes. Results showed root mean square errors (RMSE) of 1.8 seconds and 3.8 seconds, respectively, when compared to ground-truth S-wave arrivals derived from P-wave readings on seismic waveforms recorded within 5 km of the GNSS sensors using the auxiliary Pphase-Picker software. The P-wave readings were extrapolated to GNSS sensor locations assuming equal P-wave velocities and a P-to-S-wave velocity ratio of 1.5.

Severe ground-shaking durations of up to 80 and 250 seconds, along with short S-P times of 3-5 seconds for the town of Norcia and the city of Gaziantep, highlight the severity of these events. This study demonstrates the new algorithm’s potential to enhance GNSS-based S-wave detection.

How to cite: Lapadat, A. M., Pierzchala, H., Kudłacik, I., Cai, Y., Aponte, O., Nastase, E. I., and Lackowski, M.: Automated S-Wave Arrival Timing in GNSS Instantaneous Velocity Data Without Frequency-Domain Pre-Filtering, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15685, https://doi.org/10.5194/egusphere-egu25-15685, 2025.

The southwest Pacific Region regularly produces tsunamis triggered by strong earthquakes of magnitude up to M8.1 associated to its complex tectonics and subduction processes. According to the NOAA/NCEI database, these tsunamis represent about one quarter of the tsunamis recorded worldwide. Some of them have been catastrophic for coastal populations in terms of the number of dead and injured and destructions of infrastructures or agriculture land. For example, the 2007 Solomon Islands & the 2009 Samoa tsunamis killed 50 and 192 people, respectively and did hundreds of millions of US dollars in damage. The present study focuses on the Solomon Islands which has suffered from several recent destructive tsunamis, including the tsunami in 2007, and a doublet tsunami in 2016. Only a few tide gauges and 2 Australian DART captured the tsunami signal, demonstrating the need for more densely spaced observations and direct measurements from the ocean, in order to improve the warning procedures, reducing the alert timing. One way to increase this observing capacity is to fill the geodetic observation gap in the ocean using a network of cargo-ships equipped with GNSS systems tracking anomalous variations of the sea-level. These measurements can potentially detect tsunamis of different origins. To complete the few available studies focusing on the Solomon Islands tsunamis, the project aims (i) to model the 2007 and 2016 tsunamis using the records/observations on land or close to the shore (e.g., seismic network, land-based GNSS and tide-gauges data), (ii) to compare their source and impact on population and infrastructure, (iii) to analyze what a constantly moving cargo-ship GNSS network might experience in terms of tsunami travel time and tsunami predicted amplitudes, and (iv) to determine how useful such a cargo-ship GNSS network would be to increase our ability to detect and respond to these hazards through local early warning. By exploring the relationship between tsunami sources, travel times and amplitudes using ships’ locations, the study seeks to determine the ability of a defined regional ship network to function as a low-cost method to improve the detection of tsunamis, and to improve effective warnings and hazard mitigation for coastal areas and the exposed communities in the region.

How to cite: Thomas, B. E. O., Roger, J., and Foster, J.: Evaluation of the ability of a cargo-ship GNSS network to detect tsunamis in the Solomon Islands region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13055, https://doi.org/10.5194/egusphere-egu25-13055, 2025.

This study addresses the scientific question of the applicability of low-cost antennas to the most precise GNSS applications. First of all, we inspect the implications of the availability and quality of low-cost antenna PCC models for precise positioning. From this point of view, we analyze the selected performance indicators of multi-constellation positioning with the representative set of low-cost mass-market GNSS receiver antennas. The processing strategy was based on the relative positioning model, considered the most reliable and precise one. To isolate the antenna-related errors from atmospheric propagation ones, we conducted an experiment based on an ultra-short baseline. As the main indications of low-cost antenna performance, we considered distance and height residuals, defined as the difference between benchmarks and the retrieved from GNSS measurements. We found that the low-cost antenna's PCV effect may significantly affect the final results. On the other hand, the results obtained using certain configurations of low-cost antennas were characterized by only slightly higher standard deviations and discrepancies with respect to benchmark values than those obtained with surveying or geodetic equipment. We identify several sets of low-cost antennas where distance residuals do not exceed 4 mm and height residuals do not exceed 6 mm, which shows the low-cost antenna performance comparable to those achieved using high-grade antennas. On this basis, we conclude that selected low-cost antennas can meet the requirements of high-precision surveying applications.

How to cite: Dawidowicz, K., Paziewski, J., Krzan, G., and Stępniak, K.: On the applicability of low-cost GNSS antennas to precise surveying applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18548, https://doi.org/10.5194/egusphere-egu25-18548, 2025.

To meet the demanding requirements in terms of accuracy and availability, GPS has introduced the signals on L5 that are compatible to Galileo E5a signals. The L5 signal was designed to mitigate the multipath and poor performance in harsh environments such as indoor, forests, and areas affected by jamming. As the L2 signal will become obsolete in the future, action must be taken to take advantage of the modern signal type which is currently broadcast by 19 out of GPS satellites. This is particularly important for some future LEO satellites (e.g. EPS-SG) which will rely exclusively on L1/L5. CODE (Center for Orbit Determination in Europe) is building up a prototype processing chain to generate L1/L5-based clock and bias products in addition to the classic L1/L2-based processing chain. First results regarding analysis product consistency are presented.

How to cite: Kalarus, M., Schaer, S., Dach, R., Arnold, D., and Jaeggi, A.: Towards new CODE analysis products based on GPS L1/L5 signals, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17355, https://doi.org/10.5194/egusphere-egu25-17355, 2025.

Global navigation satellite systems (GNSS) are used for various applications in the Earth and atmospheric sciences, navigation, surveying and mapping, as well as in early warning systems for geo-hazards. Solutions are often required to not only be of high accuracy, integrity, and continuity, but also to be available in real-time with a delay of only a few seconds. A prerequisite for real-time precise point positioning (PPP) are precise satellite orbit and clock products. While satellite orbits can be predicted with high precision, at least for a few hours, the satellite clocks have to be estimated using real-time GNSS data from a global network of reference stations and distributed via real-time data streams to the user. GFZ is operating a real-time GNSS analysis center, which is contributing to the Real-Time Service (RTS) of the International GNSS Service (IGS). In this contribution, we introduce the new GFZ in-house real-time GNSS network analysis software that is currently being developed and provide an initial assessment of the generated products.

In the first development stage that is presented in this contribution, the generated products contain satellite orbits and satellite clocks referring to the ionosphere-free code observations. The orbits are taken from the predicted part of the operational GFZ IGS ultra-rapid GPS, GLONASS, and Galileo solution, which are updated every three hours. The associated satellite clocks are estimated every five seconds using a recursive least-squares estimator from globally recorded real-time dual-frequency code and phase observations, together with receiver clock parameters, tropospheric zenith delay parameters, inter-system biases, and carrier-phase ambiguities.

Important aspects are the data cleaning to obtain high-quality results and an efficient implementation of the estimation filter to satisfy the delay requirements of the products – less than five seconds for the IGS. For the data cleaning and cycle-slip detection, the concept of single-receiver, single-channel integrity is used, in which the uniformly most powerful invariant test statistics are evaluated separately for each satellite-receiver link using its code and phase observations of two consecutive epochs. For the estimation filter, a sequential Kalman filter implementation using the standard covariance form is used. ‘Sequential’ refers to the strategy that the scalar observations of the same epoch are processed sequentially one at a time, leading to a more efficient operation of the filter compared to the case that the entire vector of measurements is processed at once. With this strategy, the processing time per epoch is around two seconds.

An initial evaluation of this real-time satellite orbit and clock product will be presented by means of a direct comparison to post-processed multi-GNSS reference products and a comparison of PPP analyses using in addition also broadcast navigation data and real-time products of other analysis centers.

How to cite: Brack, A., He, S., Du, S., and Wickert, J.: Real-time GNSS for geosciences: Initial assessment of new data products from GFZ, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10805, https://doi.org/10.5194/egusphere-egu25-10805, 2025.

After Google’s release of Android Nougat (Android 7) in 2016, raw GNSS observations of Android smartphones have been freely accessible to the GNSS research community. Using smartphone raw GNSS measurements, numerous theories and practical methods were developed to achieve high precision in positioning, navigation, timing, and GNSS remote sensing. However, due to the chipset and built-in antenna quality, smartphone observations have more powerful noises compared to the geodetic grade GNSS receivers. Besides, frequent interruptions and cycle slips are present in smartphone observations. Therefore, it is difficult to achieve high positioning accuracy with smartphones, since it requires more sophisticated data processing methods. Smartphone stochastic modeling can be included in these methods since the variation of observation noise follows behaviors that are difficult to represent as a function of satellite elevation angle, while carrier-to-noise ratio (C/N0) representation provides a more suitable weighting scheme e.g., for smartphone Precise Point Positioning (PPP).
In addition to the current developments, analyzing the time-correlation behavior of smartphone observation noise can help to develop resilient algorithms for positioning, such as the detection of cycle slips, outliers, and adaptive filtering methods. In this contribution, we present noise analysis of smartphone raw GNSS measurements from different perspectives, namely stand-alone, double-differenced, and based on PPP residuals. To eliminate systematic effects such as the multipath effect from smartphone observations, we use a Kalman Filter algorithm and we compare it with Least Squares Harmonic Estimation (LS-HE). To investigate the time-correlation property of filtered observations, we use autocorrelation and Allan Deviation (AD) methods. Results showed that even if the strong periodicities of the multipath effect are filtered, there are residual multipath effects that remain in smartphone observations and residuals. Furthermore, AD analysis showed that smartphone observations/residuals contain white noise and time-correlated noise that exhibits similar characteristics to a Gauss–Markov process. Using these insights, our study discusses the influence of the weighting scheme and time-correlated errors on smartphone PPP and recently developed adaptive filtering techniques with the goal of improving PPP performance. We aim to provide a foundation for further advancements in modeling and mitigating multipath and time-correlated smartphone observation noises.

How to cite: Gul, C. and Warnant, R.: Stand-alone, double differenced, and residual-based noise analysis of smartphone raw GNSS observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8500, https://doi.org/10.5194/egusphere-egu25-8500, 2025.

GNSS campaigns are still in use today especially for the monitoring of natural hazards such as landslides and local subsidence. Studying the velocity uncertainty of GNSS campaign deformation monitoring is a challenge among scientists cause the velocity error cannot always be characterized as pure white noise.  We test the velocity uncertainty of GNSS campaign measurements referring to the mathematical theory developed to find the velocity uncertainty of GNSS continuous coordinate time series. GPS coordinate time series have been downloaded from the archive of NASA JPL. The time series have been decimated to annual synthetic GNSS campaigns because researchers usually can handle GNSS campaigns once every year in three or more consecutive days. Using the continuous GNSS coordinate campaigns we estimated the spectral indices for the 30 IGS stations spread across the globe. Then using these spectral indices and the formulation given in Zhang et al.1997, Mao et al. 1999, Dixon et al. 2000, and Williams 2003 we have developed a practical methodology to estimate noise amplitudes and velocity errors for the data of synthetically derived annual GNSS campaigns. The methodology avoids large matrix computations and consumes only a little PC memory. The velocity error derived using the above-mentioned approach has been cross validated by the velocity error derived from the sample of annual GPS campaign time series constituted employing the continuous GPS time series. 10-12 synthetically derived independent GPS annual campaign time series were formed from the continuous data of each of the thirty stations we used from the IGS network. Then the velocity error has been computed as the inter-quartile range of the velocities derived from those of the annually sampled time series. The inter-quartile range values are comparable with the velocity errors computed using the methodology described in this study. The significancy of the differences were also tested referring to Wilcoxon's Signed Rank hypothesis testing.  The method has been found to be promising for the velocity error estimation of GNSS campaign measurements.

How to cite: Sanli, D. U., Ercoban, M., Aras, M. C., and Uysal, E.: A practical methodology to scale velocity uncertainties derived from GNSS campaign time series, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9699, https://doi.org/10.5194/egusphere-egu25-9699, 2025.

Global Satellite Navigation System (GNSS) has been widely used in our daily life, due to its high-precision positioning ability. However, in urban environments, GNSS signals are prone to obstructions from tall buildings, leading to Non-Line-of-Sight (NLOS) errors and a significant decline in positioning accuracy. Machine learning (ML) techniques for NLOS detection has emerged as a significant research hotspot, thanks to its advantages, such as requiring no hardware modifications, achieving high accuracy, and offering practical applicability, etc. However, the existing research on ML-based NLOS detection usually relies on single models, which are prone to becoming trapped in local optima during the optimization process. To overcome this limitation, we propose a GNSS NLOS detection method based on the Stacking Ensemble Learning (SEL) model. Initially, the fisheye camera is utilized to generate NLOS labels, and five GNSS signal features are extracted: elevation angle, carrier-to-noise ratio (C/N₀), measurement residual, C/N₀ rate of change, and pseudorange standard deviation. Subsequently, the SEL model is designed with a two-layer structure. The first layer consists of basic ML classification models, including Convolutional Neural Network (CNN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM). The second layer employs Logistic Regression (LR) as the meta-learning model to integrate the outputs from the first layer. Finally, the trained SEL model processes the five GNSS signal features in real time for detecting smartphone GNSS NLOS signals, and incorporates a weighted model for SPP positioning. Several smartphone-based vehicle experiments were conducted in different urban areas of Wuhan, China, to validate the effectiveness of the proposed method. Experimental results demonstrate that the SEL method achieves GNSS NLOS detection accuracies exceeding 90%, with detection performance improvements ranging from 15.6% to 32.7%, compared with the single ML methods such as CNN, GBDT, RF, and SVM. Furthermore, the SEL method enhances 3D positioning accuracy, with improvements ranging from 26.7% to 39.6%. Particularly, in dense urban canyon areas, the vertical positioning accuracy is improved by up to 73.1%, effectively mitigating the impact of NLOS signals. This method requires no additional improvements to low-cost receiver hardware, thus offers potential for widespread application across various GNSS terminals, and provides new ideas for navigation and positioning in smart cities.

How to cite: liu, L., li, Z., and jiang, W.: An Enhanced Method for NLOS Signal Detection in Urban Environments based on Stacking Ensemble Learning for Smartphone Positioning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5389, https://doi.org/10.5194/egusphere-egu25-5389, 2025.