G1.2

How to cite: Steigenberger, P., Hauschild, A., and Montenbruck, O.: Orbit, clock and attitude analysis of QZS-1R, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4504, https://doi.org/10.5194/egusphere-egu22-4504, 2022.

The use of zero-difference processing schemes becomes more and more popular within the GNSS (Global Navigation Satellite Systems) community. This change from double- to zero-difference approaches increases the demand of PPP-AR (Ambiguity Resolution for Precise Point Positioning) enabling products. Those products can be created in two ways, either estimate the geometrical part (orbits) based on a double-difference global network solution with a separate zero-difference solution for the clocks and phase biases, or in a combined zero-difference solution. The latter one allows a more flexible approach; however, the challenge lies in the handling of the increased number of parameters and ambiguity resolution.

The estimation of combined orbit and clock zero-difference enabling products needs a thought-out design of the processing strategy, where the elimination and back-substitution steps are vital to deal with the large number of parameters. Nonetheless, the amount of ambiguity parameters dramatically grows with an increasing size of the network, posing some computational limitations, since they should not be eliminated for a successful ambiguity resolution. Such a restriction originates from fixing float orbits: their accuracy does not allow to estimate reliable ambiguity parameters. To cope with that, we propose a new algorithm capable to decouple them from the orbits, allowing to fix between-satellite ambiguities in a later station-wise parallelisation.

On the poster, we describe selected details on the ambiguity resolution strategy that we have developed. The obtained results are characterized and compared to other solutions using classical ambiguity resolution schemes.

How to cite: Calero Rodríguez, E. J., Villiger, A., Schaer, S., Dach, R., and Jäggi, A.: Combined orbit and clock zero-difference solution at CODE: ambiguity resolution strategy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11628, https://doi.org/10.5194/egusphere-egu22-11628, 2022.

Although the full operational capability of the Galileo system has not been officially announced as yet, the European GNSS, Galileo, has already remarkably contributed to geodesy, positioning, navigation, timing, and fundamental physics. Galileo metadata with the details on the satellite construction and surface properties allow for the development of the high-accuracy satellite macro-models and precise orbit determination. Two integrated onboard observation techniques – satellite laser ranging (SLR) and microwave GNSS – allow for the integration of space geodetic techniques and co-location in space. Calibrated satellite and receiver antenna offsets allow for scale realization and scale transfer for the reference frames.

GNSS orbits of superior quality constitute the basis for other geodetic products, such as Earth rotation parameters, station coordinates, geocenter motion, international terrestrial reference frames, tropospheric and ionospheric delays. Moreover, the high-quality orbits and clocks installed on a pair of Galileo satellites launched onto eccentric orbits allow for studying effects emerging from general relativity, both related to the time redshift, as well as to orbital Schwarzschild, Lense-Thirring, and de Sitter effects constituting the essential issues of fundamental physics. Finally, high-quality and frequently-updated broadcast orbits together with very stable clocks onboard Galileo assure the superior accuracy of the real-time positioning when compared to other GNSS.

We discuss the advantages and limitations of the Galileo system in terms of its applicability to geodesy, concentrating on daily and sub-daily Earth rotation parameters – polar motion and length-of-day variability, station coordinates, and geocenter motion. We address the system-specific errors discovered in GPS, GLONASS, and Galileo time series due to different satellite revolution periods, aliasing effects, tidal constituents, and orbit modeling issues. Some orbit modeling issues related, e.g., to thermal effects, remain unresolved, however, their impact may be mitigated by estimating empirical parameters and the combination of laser and microwave observations. The co-location in space onboard Galileo paves new opportunities for the realization of the reference frames tied in space, onboard GNSS satellites. We provide results on the recent developments of precise orbit determination and co-location in space based on integrated SLR and GNSS observations. Eventually, we discuss the latest applications of high-accurate orbits of Galileo satellites in near-circular and eccentric orbits toward the verification of the effects emerging from general relativity.

How to cite: Sośnica, K., Zajdel, R., Bury, G., Kazmierski, K., Hadaś, T., Mikoś, M., Lackowski, M., and Strugarek, D.: Contribution of the Galileo system to space geodesy and fundamental physics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2477, https://doi.org/10.5194/egusphere-egu22-2477, 2022.

Signal biases are hardware delays that occur during the transmission and reception of GNSS signals. On the satellite side, there is a delay between the generation of a signal and its transmission at the antenna. The same is the case on the receiver side, where a delay occurs between signal reception at the antenna and the actual measurement of a specific signal in the receiver. As the name suggests, code biases refer to the delays affecting code observations. Similarly, phase observations are affected by phase biases. In general, signal biases differ per constellation, satellite, frequency, signal attribute, as well as receiver hardware and settings.

The main issue with signal biases is that they are usually not known. Therefore, they have to be estimated during GNSS processing. However, the relative nature of GNSS observations prevents the estimation of absolute signal biases. This results in several rank deficiencies in the normal equation system when signal biases are estimated together with other geodetic parameters in a global multi-GNSS processing.

We present a general approach based on eigenvalue analysis to solve these rank deficiencies. Therefore, the co-estimation of pseudo-absolute transmitter and receiver signal biases in our multi-GNSS processing becomes possible. This approach also enables ambiguity resolution of GLONASS phase obervations.

How to cite: Strasser, S., Mayer-Gürr, T., Süsser-Rechberger, B., and Dumitraschkewitz, P.: Estimable phase and code biases in the frame of global multi-GNSS processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12557, https://doi.org/10.5194/egusphere-egu22-12557, 2022.

Global navigation satellite systems (GNSS) are integral to a wide array of scientific and commercial applications. Precise orbit determination of satellites in low Earth orbit relies on high-quality GNSS products. Examples of such satellites are those of the Copernicus Earth observation program of the European Union or the satellite gravimetry missions GRACE/GRACE-FO and GOCE. Numerous ground-based applications also require these products, for example: estimation of terrestrial water storage variations, earthquake monitoring, GNSS reflectometry, tropospheric and ionospheric research, surveying, or civil engineering. Furthermore, GNSS-derived station coordinates play an important role in the determination of the International Terrestrial Reference Frame. The analysis centres of the International GNSS Service (IGS) generate such products by processing observations from a global network of ground stations to one or more GNSS constellations.

So far, this kind of processing only incorporates elevation-dependent a priori modelling of observation variances and disregards temporal correlations. Meanwhile, numerous studies have shown the positive impact the incorporation of sophisticated stochastic modelling has on GNSS processing and resulting products. However, there have not been any large-scale investigations regarding the impact of stochastic modelling of observation noise on global GNSS processing.

In this contribution, we discuss a post-fit residuals approach for deriving temporal correlations in global multi-GNSS processing and their limitations. We used several years of observations and a selected IGS network of ground stations. Based on this data we analysed the post-fit residuals and the derived temporal correlations per station with respect to their seasonal effects, specific used receivers, antennas, and different transmitter signal types.

How to cite: Dumitraschkewitz, P., Mayer-Gürr, T., and Strasser, S.: Empirical stochastic modeling of observation noise in global GNSS network processing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2566, https://doi.org/10.5194/egusphere-egu22-2566, 2022.

For highly precise and accurate positioning and navigation solutions with GNSS, it is mandatory to take all error sources – including phase center corrections (PCC) – adequately into account. These corrections are provided by different calibration facilities and are published in the official IGS antenna exchange format (ANTEX) file for several geodetic antennas.

Currently, the IGS antenna working group (AWG) is discussing which metrics should be used as a basis for accepting new calibration facilities as an official IGS calibration facility. To this end, requirements have to be set for comparing different sets of PCC for the same type of antenna.

Mostly, characteristic values of difference patterns (dPCC) are analysed, e.g. maximum deviations, RMS of dPCC, or percentage of dPCC values that are smaller than 1 mm. For users and station providers, however, it is most interesting to investigate the impact of dPCC on geodetic parameters, e.g. topocentric coordinate deviations and troposphere estimates. Since the impact is not only depending on the antenna in use and the station’s location but also on the applied processing strategies, a standardized comparison strategy is needed.

In this contribution, we present the impact of different PCC values on geodetic parameters using a standardized simulation approach. We show results for several globally distributed stations using different processing strategies and their respective impact on the geodetic parameters. This includes the application of different elevation cut-off angles, observation weightings w.r.t satellite coverages and elevation angles as well as use of different frequencies and linear combinations. The obtained results are analysed in detail, repeated behaviours are grouped and compared to widely used characteristic values of dPCC. Thus, an overall conclusion of the similarity of different PCC models can not only be drawn on the pattern level, but also their impact on geodetic parameters can be assessed.

How to cite: Kröger, J., Kersten, T., Breva, Y., Brekenkamp, M., and Schön, S.: Impact of Different Phase Center Correction Values on Geodetic Parameters: A Standardized Simulation Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1146, https://doi.org/10.5194/egusphere-egu22-1146, 2022.

Large earthquakes strongly shake the upper atmosphere, leaving distinctive signatures in total electron content (TEC) measured using GNSS trans-ionospheric monitoring. The ionosphere is particularly sensitive to brutal uplift motions of the ground or sea surface, triggering upward propagating mechanical waves. In specific conditions that we will detail in this presentation, GNSS-TEC measurements contain critical information on the immediate consequences of an earthquake. If accurate and provided rapidly, independent knowledge of the sea surface deformation extent and distribution could feed tsunami early warning systems.

Radio waves emitted by GNSS satellites integrate the ionospheric electron density wavefield along their propagation path. At ground level, GNSS receivers can only sense the TEC, which contains the contribution of the ionospheric wavefronts. These wavefronts are destructively or constructively integrated, depending on the involved geometry of observation. In some conditions, even a close station will not sense the TEC perturbation, while a station located 200 km away will sense large TEC fluctuations. This complex behavior mainly depends on the line-of-sight 3D geometry crossing the electron density perturbation. To study how this geometry can affect the estimation of the generating motion, we first build TEC sensitivity maps and highlight more blind or sensitive zones at the Earth’s surface. We apply the procedure to past tsunamigenic earthquakes at mid and low latitudes. Those are the 2010 Mw 7.6 Mentawaii earthquake (Indonesia), the 2016 Mw 7.8 Kaikoura earthquake (New Zealand), and the 2010 Mw 8.8 Maule earthquake (Chile). The TEC sensitivity maps allow us to investigate how the reciprocal locations of the available GNSS stations and satellites can affect the localization of the origin of the ionospheric disturbances. In a second step, we build localization maps with a full waveform method (IonoSeis software) and, where possible, with a time delay fitting method. We compare the resulting maps with the Earth’s surface deformation distribution estimated by more conventional seismo-geodetic methods. We finally show how the extension and densification of GNSS networks with multi-GNSS low-cost receivers and enhanced ionosphere monitoring could help mitigate tsunamis better.

How to cite: Rolland, L., Munaibari, E., Zedek, F., Anthony, S., Mikesell, T. D., Pierdavide, C., and Bertrand, D.: Rapid characterization of tsunami sources with GNSS-TEC ionospheric monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11421, https://doi.org/10.5194/egusphere-egu22-11421, 2022.

    Global Navigation Satellite System (GNSS) is a well-recognized observation technique in studies on the ionosphere due to its sensitivity to the total electron content (TEC). The era of modern smartphones, running on Android version 7.0 and higher, facilitates the acquisition of raw dual-frequency GNSS measurements, paving the way for the GNSS community data to be potentially exploited in geoscience applications. One can assume that the continuous progress in this domain may result in future in a performance of those smart devices reaching the level of GNSS receivers (and antennas) used for atmospheric monitoring. The prospective utilization of a very large number of GNSS-capable smartphones, as a dynamic crowdsourcing receiver network, could form thus an attractive source of complementary GNSS data, allowing to significantly increase the spatial resolution of observations available for the analysis and cover areas of the globe where GNSS receivers are not yet present. The enormous volume of prospective GNSS community data brings, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest, also in near-real time. The same applies to the analysis of such huge and heterogeneous data sets, requiring a dedicated approach in order to exploit the data in a thorough manner and fully benefit from such a concept.
Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) is an ongoing ESA NAVISP project with activities covering acquisition of GNSS observations from modern smartphones and development of the dedicated infrastructure regarding GNSS processing and machine learning at scale. An Android application, developed within that project, is utilized to retrieve code and phase observations from the modern generation of smartphones. The acquired user-specific data is available to the user in the form of RINEX3-compliant files and can be uploaded by the user to the central server for subsequent processing.
This contribution highlights the CAMALIOT project in relation to the ionosphere and provides information on the developed Android application, data ingestion and processing, complemented with methodology and initial results related to the TEC retrieval based on smartphone data collected in the vicinity of geodetic GNSS receivers, with the latter used for deriving reference time series. Concerning the smartphone data, the amount and quality of observations are much lower compared to the high-grade GNSS equipment and a dedicated pre-processing stage is needed in order to discard bad observations in a proper manner. An apparent correlation between the data quality, utilized frequency bands and satellite constellation involved is visible too. This area of GNSS still suffers from the limitations related mainly to the components comprising the smartphone, resulting in the lower quality of the acquired GNSS observations, compared to those obtained with the use of high-grade GNSS receivers and antennas. This translates to a greater susceptibility to multipath as well as a much more frequent occurrence of observation gaps and cycle slips, affecting the data availability and continuity of the carrier-phase measurements.

How to cite: Kłopotek, G., Soja, B., Awadaljeed, M., Crocetti, L., Rothacher, M., See, L., Weinacker, R., Sturn, T., McCallum, I., and Navarro, V.: Total Electron Content Monitoring Complemented with Crowdsourced GNSS Observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5780, https://doi.org/10.5194/egusphere-egu22-5780, 2022.

The adverse effects of ionospheric delays limit the positioning accuracy of single-frequency GNSS users. To mitigate these effects, GNSS system providers make several ionospheric delays models available for their global users. For example, the GPS has offered the Klobuchar model from the beginning. More recently, Galileo users can use the NeQuick G model. In the meantime, several independent models available for real-time navigation have emerged. Recent examples are the NTCM (Neustrelitz Total Electron Content Model) correction model provided by the German Aerospace Center (DLR) and real-time global ionosphere maps (RT-GIMs) provided by the National Centre for Space Studies (CNES).

In this contribution, we evaluate the performance of several global ionospheric delay correction models in SPP mode. We used single-frequency pseudorange data from 12 GNSS stations distributed globally, covering different latitudes for the evaluation. The test data includes GNSS observations from DOY 93/2020 to DOY 80/2021, covering almost one full year of increasing solar activity. We validated the performance of the NTCM-G model driven by the Galileo Az parameters against the Klobuchar, NeQuick 2, NeQuick G, and CNES RT GIMs models. Finally, we compared the results to reference solutions obtained with CODE GIM and also using the ionosphere-free linear combination. We showed that NTCM-G corrections presented accuracy comparable with the NeQuick G model and better than the Klobuchar one.

How to cite: Milanowska, B., Wielgosz, P., Hoque, M., Tomaszewski, D., Jarmołowski, W., Krypiak-Gregorczyk, A., Krzykowska-Piotrowska, K., and Rapiński, J.: Evaluation of NTCM-G ionospheric delay correction model for single-frequency SPP users., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5477, https://doi.org/10.5194/egusphere-egu22-5477, 2022.

During the last decade, GNSS interferometric reflectometry (GNSS-IR) has shown great potential for sea level monitoring. In combination with geodetic positioning, GNSS-IR provides a possibility to directly link the sea level measurements to the global terrestrial reference frame. However, many error sources can still be better modeled, and the accuracy of GNSS-IR sea level measurements can be improved. Specifically, we revise the tropospheric error model in ground-based GNSS-IR for sea level applications. Unlike GNSS positioning applications, in GNSS-IR the bending effect is as important as the delay effect. Also, usually very low elevation angle observations are used in GNSS-IR, which makes the atmospheric impact even more important. For the bending effect, we propose a new calculation which takes into account the water vapour content and utilizes the widely used mapping function approach to account for the elevation dependence. For the GNSS-IR atmospheric delay, we revise the geometry of the GNSS signal path for the case of coastal GNSS-IR where the antenna is within < 100 m from the sea surface. The atmospheric delay for the reflected signal is separately evaluated at the surface specular reflection point. The delay from the satellite to the reflection point and the direct signal can both be derived from the zenith delay and mapping function, at their respective local coordinates. The delay from the reflection point to the antenna is obtained assuming an average layer refractivity. We validated our model with ray-tracing radiosonde data. At 2° elevation angle, the new method can correct > 98 % of the atmospheric bending effect, compared to about 88 % with the previously adopted approach. With fewer approximations than the previous approach (directly using the mapping function), the new delay error model is also more accurate but with less absolute improvement of about 3 % compared to the previously existing model.

How to cite: Feng, P., Haas, R., Elgered, G., and Strandberg, J.: Calibrating tropospheric errors on ground-based GNSS reflectometry: calculation of bending and delay effects, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12698, https://doi.org/10.5194/egusphere-egu22-12698, 2022.

The aim of the study is to investigate the quality of the tropospheric estimates obtained with the use of the latest dual-frequency low-cost GNSS receivers. We aim to verify if the low-cost receivers may provide information on the parameters that describe the state of the troposphere with accuracy and reliability close to that of high-grade receivers. In this way, we address a scientific question on the potential usability of such receivers for climate applications. We investigate selected GNSS tropospheric estimates such as zenith tropospheric delays (ZTDs) and horizontal gradients. ZTD accuracy is validated in comparison to ERA5, which is the fifth generation reanalysis for the global climate and weather produced by European Centre for Medium-Range Weather Forecasts (ECMWF).

The experiment is based on GNSS data collected during two measurement campaigns. The 1^st campaign was carried out over three days in the winter 2020; the 2^nd one was held over three days in the summer 2021. Three collocated stations equipped with u-blox ZED F9P receivers and one station with a high-grade Trimble Alloy receiver were used. Receivers were connected to two different types of GNSS antennas: a surveying-grade Leica AR10 and a patch ANN-MB antenna. Collected GNSS data were processed using Bernese GNSS Software v.5.2 in Precise Point Positioning (PPP) mode based on dual-frequency ionosphere-free model.

The presented results confirm that the tropospheric solutions based on low-cost receivers data can achieve high accuracy. Low-cost equipment provides tropospheric parameters with precision and reliability only slightly lower than that of high-grade one. We also show that an application of a surveying-grade antenna to a low-cost receiver may noticeably enhance the accuracy of the tropospheric estimates derived with such receivers. Finally, validation against the ERA5 climate reanalysis confirms that both sets can provide high-quality, accurate tropospheric estimates, which can be further used in climate applications.

How to cite: Stępniak, K. and Paziewski, J.: Validation of low-cost receiver derived tropospheric products against ERA5 reanalysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7899, https://doi.org/10.5194/egusphere-egu22-7899, 2022.

In recent years, dual-frequency GNSS chipsets became available on the mass market. The ongoing developments in sensor and processing technologies steadily improve the positioning performance so that nowadays sub-decimeter accuracies can be achieved with such devices, even in real-time. Thus these sensors become a powerful, inexpensive choice for equipping or densifying existing GNSS monitoring networks. Station densification can be of significant added value for earthquake early warning systems, assimilation of GNSS water vapor estimates into numerical weather prediction models and the detection of severe weather events. Even if somewhat noisier, smartphone data can be used for GNSS-based remote sensing purposes as well.

This contribution is twofold, and focusses on both, the current capabilities and the perspectives of these GNSS low-cost technologies for such remote sensing applications. In the first part we highlight the accuracy of PPP-enabled seismic and tropospheric monitoring using low-cost loggers and stations developed in-house. We show that differential smartphone GNSS observations on short- and medium-length baselines can be used to sense the state of the regional troposphere. In the second part, we present first results on the performance of the u-blox D9S application board, which enables highest precision by PPP-RTK with ambiguity resolution, and the feasibility of high-precision positioning is assessed for long baselines involving smartphone data as well. Finally, we briefly discuss the potential of data-driven methods for mitigating multipath, which is still one of the main error sources when using equipment of low quality. Concerning the GNSS processing, we rely on further-developed versions of open-source and commercial GNSS software packages. Regarding sensor technology, u-blox chips -- which are currently deployed in our self-sufficient GNSS stations -- are used together with different low-cost and medium-grade GNSS antennas (both, patch and recent helical-type low-cost antennas).

We conclude that low-cost GNSS sensor technology is on the way to satisfy the same demands in accuracy as geodetic-grade equipment -- centimeter-level accuracy can be obtained, even in real-time. New possibilities for station densifications arise by employing low-cost, autonomous stations or by crowdsourcing of GNSS data with smartphones. These observations can aid in resolving small-scale structures in the atmosphere, or for a quick detection and localization of geohazards.

How to cite: Hohensinn, R., Stauffer, R., Herrera Pinzón, I. D., Möller, G., Aichinger-Rosenberger, M., Rossi, Y., Pan, Y., Kłopotek, G., Soja, B., and Rothacher, M.: Low-cost and smartphone GNSS sensors: current capabilities and perspectives for seismic and tropospheric monitoring applications, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9079, https://doi.org/10.5194/egusphere-egu22-9079, 2022.

The 2022 Tonga event highlight the necessity to have more and more knowledge about the activity
of volcanoes. To this point, it is well known that volcanoes explosion can trigger ionospheric
perturbation detectable through the Global Navigation Satellite System (GNSS) signal [1].
The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm has been
successfully applied to detection of ionospheric perturbations in several real-time scenarios [2, 3].
VARION, thus, estimates sTEC (slant total electron content) variations starting from the single time
differences of geometry-free combinations of GNSS carrier-phase measurements.
The aim of this work is to analyse some Etna explosions occurred in 2021 with the VARION algorithm
in order to better study the coupling between volcanoes and ionosphere. This study can pave the
way to a real-time ionospheric monitoring of Etna volcano.
[1] Manta, Fabio, et al. "Correlation between GNSS‐TEC and eruption magnitude supports the use
of ionospheric sensing to complement volcanic hazard assessment." Journal of Geophysical
Research: Solid Earth 126.2 (2021): e2020JB020726.
[2] Ravanelli, Michela, et al. "GNSS total variometric approach: first demonstration of a tool for
real-time tsunami genesis estimation." Scientific Reports 11.1 (2021): 1-12.
[3] Savastano, Giorgio, et al. "Advantages of geostationary satellites for ionospheric anomaly
studies: Ionospheric plasma depletion following a rocket launch." Remote Sensing 11.14 (2019):
1734.

How to cite: Ravanelli, M., Ferrara, F., Fuso, F., Cannata, A., Crespi, M., and Occhipinti, G.: The VARION approach to volcanoes: case study on 2021 Etna eruptions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13439, https://doi.org/10.5194/egusphere-egu22-13439, 2022.

Ambiguity fixing on the geometry free combination presents some desirable characteristics. In particular, it does not require precise ephemeris, modelling of station displacement motion or tropospheric modelling or estimation. For such reasons, it can be particularly interesting in the case where such data and models are not available or if simpler processing is wanted. Such fixing procedure has been studied in the past for dual-frequency and triple frequency cases. Unfortunately, especially in the two frequency case, this procedure is not practical due to the long observation period needed to reliable fix a correct integer set. In this contribution, we review the fixing performances of the “geometry free” model using an undifferenced uncombined approach. Furthermore, we present the case to four and five frequency cases using Galileo and Beidou observations showing that reliable fixing in a reduced time span is possible. All analyses presented are performed using real GNSS data from the IGS permanent network. Finally, some possible applications are presented with a focus on ionospheric studies.

How to cite: Tagliaferro, G.: Ambiguity fixing on geometry free like model using modernized GNSS signals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4881, https://doi.org/10.5194/egusphere-egu22-4881, 2022.

Global Navigation Satellite Systems (GNSSs) are widely used for Earth system monitoring, e.g., solid earth and atmosphere. However, the time series of station coordinates and zenith tropospheric delay derived using GNSS are inherently affected by several technique-specific errors that influence the interpretation of geophysical processes and phenomena. GPS plays a crucial role and is most often used in interdisciplinary studies. However, the multiplicity of navigation systems, including fully operational GLONASS and Galileo, allowed us to assess system-specific high-frequency signals and inconsistencies arising from using different constellations.

This work shows that using different GNSS constellations leads to the appearance of various artificial signals with amplitudes up to several millimeters in the series of station coordinates. The presence of the GNSS system-specific artifacts and inter-system disagreements are demonstrated using the 2-year long series of station coordinates and zenith total delay parameters for 15 stations using Precise Point Positioning algorithms. Finally, we assessed the benefit of using GPS, GLONASS, and Galileo jointly.

We identified the origin of the spurious signals in orbital errors. The most dominant orbital artifacts for Galileo appear with periods of 14.08 h, 17.09 h, 34.20 h, 2.49 d, ~3.4 d. The corresponding signals for GLONASS appear with periods of 5.63 h, 7.36 h, 10.64 h, 21.26 h, 3.99 d, and ~8 d. Moreover, when estimating discrete 24-hour solutions from high-rate GNSS data, high-frequency signatures are under-sampled, resulting in long-term aliased periodic signals. The GPS orbital signals arise at the periods corresponding to the harmonics of the K₁ tide, which leads to the inconsistency of the GPS-based products with ocean tidal loading models reaching on average 12 mm for the K₂ tidal term in the Up component. The magnitude of the orbital signals varies between different site locations and depends on the GNSS observation geometry and dominant direction of satellites' flybys. For example, because of the high inclination of the GLONASS orbital planes, the stations located in absolute low latitudes observe mostly North-South satellite flybys; thus, the estimated East component of the coordinates is exposed to the orbital artifacts.

Galileo is less vulnerable to the orbital signals than GPS or GLONASS. The difference is visible mainly for the East coordinate component. The Galileo-based daily estimates are up to 55% and 36% better than those delivered from GLONASS and GPS. Finally, using a combination of GPS and Galileo increases the precision of estimates by 10% compared with the best-case Galileo-only solution and remarkably reduces the system-specific errors in station coordinate time series.

How to cite: Zajdel, R., Kazmierski, K., and Sośnica, K.: Inconsistency in Precise Point Positioning products from GPS, GLONASS and Galileo, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2327, https://doi.org/10.5194/egusphere-egu22-2327, 2022.

At present, significant development of the positioning methods using the Global Navigation Satellite System (GNSS) can be seen. One of the most developed methods is the absolute Precise Point Positioning (PPP) method. This can be particularly seen using multi-GNSS measurements. The development of multi-GNSS increases the number of satellites observed and increases the accuracy of the products, but also creates new requirements for observation modeling. Obtaining the correct values, ​​of the estimated parameters, requires the appropriate determination of the deterministic model as well as the stochastic model. Currently, the deterministic model is well known. In contrast, the stochastic model is not fully known and still requires a number of studies. Stochastic modeling is based on determining the covariance matrix and which can be modeled using a weighting function that takes into account the elevation angle of the observed satellite. ​

In our analysis, we focus on studies on the weighting functions of GNSS observations for the PPP method. Analysis was performed on the Multi-GNSS Pilot Project (MGEX) stations which were characterized by global distribution and various equipment in 2021. Studies were conducted for the GPS‑only, Galileo-only, and GPS+Galileo constellations, with particular emphasis on the Galileo observations, which has achieved significant progress in recent years. Eight different observation weighting models have been selected for analysis: one of them assumes that all observations have the same precision, without dependence on the elevation angle; for the other used functions, the observation precision value depends on the elevation angle. Parameters such as accuracy, convergence time, zenith path delay (ZPD), and inter-system bias (ISB) are analyzed.

Based on the tests performed, we show that, depending on the solutions adopted (i.e. GPS-only, Galileo-only, GPS+Galileo), the best results were obtained for different weighting functions. We have shown that using different weighting functions have no impact on the horizontal component but a visible impact on the vertical component,  the tropospheric delay, and the convergence time. Also, we choose the best functions for GPS-only and Galileo-only and used them for the GPS+Galileo solution. For this new approach obtained a shorter convergence time and higher accuracy of the ZPD. More information and results will be presented at the conference.

How to cite: Kiliszek, D., Araszkiewicz, A., and Kroszczyński, K.: Analysis of different weighting functions of observations for GPS and Galileo PPP, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7489, https://doi.org/10.5194/egusphere-egu22-7489, 2022.

The use of theoretical modeling algorithms to compute the satellite altitude causes some errors which are eventually absorbed by the satellite clocks. This adversely reduces the fixed positioning performance in global navigation satellite system (GNSS) precise point positioning (PPP). Currently, different International GNSS service (IGS) analysis centers (ACs) provide satellite altitude quaternions which are an auxiliary dataset necessary in PPP fixed solutions. Hence, this study aims at a comprehensive evaluation of the effect of accounting for the BeiDou satellite attitude quaternions in PPP. The quaternions provided by different ACs are applied to BeiDou PPP using different weighting schemes suitable for handling satellites in three distinct orbits. The obtained numerical results indicate that considering the quaternions in BeiDou PPP reduces the observation residuals, improves the ambiguity fixing, and enhances positioning performance.

How to cite: Suya, R. G., Chen, Y.-T., Kwong, C.-F., and Zhang, P.: Considering Satellite Attitude Quaternions in BeiDou Precise Point Positioning Performance, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12926, https://doi.org/10.5194/egusphere-egu22-12926, 2022.

The European Plate Observing System (EPOS) is a very large and complex European e-infrastructure that provides pre-operational access to a first set of datasets and services for Solid Earth research. The EPOS-GNSS Data Gateway provides, through an Application Program Interface (API) and a web portal, access to GNSS (Global Navigation Satellite Systems) RINEX data from a distributed infrastructure of data nodes. Currently, ten EPOS-GNSS nodes have been installed, and three of them are still in the pre-operational phase. To monitor the long-term data quality of EPOS-GNSS stations at the nodes level, ROB is developing a new service. The first step of this service is a web portal (www.gnssquality-epos.oma.be) that provides access to data quality metrics of the RINEX data available from the different EPOS-GNSS nodes.

The web portal presents plots of the long-term tracking performance of more than 1000 EPOS-GNSS stations. The plots focus on several data quality metrics such as the number of observed versus expected observations, the number of missing epochs, the number of observed satellites, the number of cycle slips, and multipath values on code observations. These metrics have been computed at the node level using GLASS and Anubis Software (https://gnutsoftware.com/software/anubis). The metrics provide helpful information for node managers or station users to assess the EPOS-GNSS station’s performance and detect potential degradation of the RINEX data quality. The outlook of this work is to investigate the possible usage of data quality metrics to detect data unsuitable for high-precision GNSS analysis for geophysical or meteorological applications. Here, we will present the newly developed web portal, the considered data quality metrics, and some preliminary results of this ongoing work.

How to cite: Bamahry, F., Legrand, J., Bruyninx, C., and Fabian, A.: First experience with GNSS data quality monitoring in the distributed EPOS e-infrastructure, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7927, https://doi.org/10.5194/egusphere-egu22-7927, 2022.