Global Navigation Satellite System (GNSS) products are an integral part of a wide range of scientific and commercial applications. The creation of such products requires processing software capable of solving a combined station position and GNSS satellite orbit estimation by least squares adjustment, also known as global GNSS processing. Such processing is routinely performed by the International GNSS Service (IGS) and its Analysis Centers. For the IGS Reprocessing Campaign 3 (repro3), Graz University of Technology (TUG) participated as an AC using the raw observation approach, which uses all measurements as observed by the receivers. However, a common feature of almost all global multi-GNSS processing strategies is the use of diagonal covariance matrices as stochastic models for simplicity. This implies that any spatial or temporal correlations are ignored. However, numerous studies have shown that GNSS processing is indeed affected by spatial and temporal correlations. For global GNSS processing, research on stochastic modeling and its challenges is rather scarce. In this work, a detailed insight into the problems of stochastic modeling in global GNSS processing using the raw observation approach is given along with a detailed overview of the intended TUG approach. An analysis of the impact of temporal correlation modeling on the resulting GNSS products and GNSS frame estimation is also given.

The project CASPER is an interdisciplinary collaboration between the Institute of Geodesy and the Institute of Physics and is funded by the Austrian Research Promotion Agency. It deals with the influence of solar eruptions such as coronal mass ejections and solar flares on the Earth's neutral atmosphere and ionosphere. In this study we focus on the most severe geomagnetic storms that occurred in solar cycle 25. This includes the effects of the extreme "Gannon storms" on 10/11 May, as well as solar eruptions that occurred in August and October in 2024. We present a detailed analysis of various interplanetary and terrestrial measurements to allegorise the individual solar storms. For the thermospheric variations, the analysis is based on observations from the GRACE-FO and SWARM satellite missions using accelerometer and kinematic orbit data. On this basis, we are able to present changes in the Earth's neutral in terms of satellite orbit decays. Regarding the ionosphere, the part of the Earth’s atmosphere where solar radiation ionizes the atoms and molecules, we present the latest implementations in our software GROOPS. The slant total electron content (STEC) parameter is determined using a least-squares adjustment approach. To enhance the accuracy, high-order ionospheric correction terms are integrated into the estimation process. This information allows the derivation of additional ionospheric parameters, such as vertical total electron content (VTEC), for various altitude layers to be comparable with neutral density estimates. Finally, in terms of the predictability of the impact of solar eruptions on the satellite altitude, we also present the current status of the SODA forecast service which is part of the ESA's Space Safety Programme Ionospheric Weather Expert Service Centre (I.161).

Global Navigation Satellite System (GNSS) products are an integral part of a wide range of scientific and commercial applications such as precise orbit determination for low Earth orbit satellites, earthquake monitoring, GNSS reflectometry, tropospheric and ionospheric research, surveying and many more. These products, consisting of GNSS orbits, clocks, phase biases and more, are generated by the International GNSS Service (IGS) analysis centres by processing observations from a global network of ground stations to one or more GNSS constellations. The processing consists of a combined station position and GNSS satellite orbit determination using a least squares approach donated as global multi-GNSS processing. Within the IGS 3rd reprocessing (repro3) campaign for the new release of the International Terrestrial Reference Frame (ITRF), Graz University of Technology (TUG), Working Group Satellite Geodesy has contributed as an Analysis Centre (AC) for global multi-GNSS processing. TUG has demonstrated high quality results on par with other ACs using its self-developed geodetic processing software Gravity Recovery Object Oriented Programming System (GROOPS). Within GROOPS the global multi-GNSS processing uses the raw observation approach. The raw observation approach uses all measurements as observed by the receivers without explicitly creating any linear combinations or differences. This allows the information contained in each individual observation to be fully exploited. GROOPS has been shown to be capable of global multi-GNSS processing using GPS, Galileo and GLONASS. With more publicly available metadata for the BeiDou system, GROOPS has been further developed to use BeiDou within global multi-GNSS processing using the raw observation approach. Therefore, in this contribution we present the improvements in GROOPS global multi-GNSS processing using BeiDou and discuss the quality of the resulting orbit, station position time series, clock and phase bias products.

Combined satellite-only global gravity field models represent a combined solution of gravity field observations from multiple geodetic measurement principles and satellite missions. The advantage of such a combination is that it compensates for the weaknesses of individual observing techniques. However, when combining solutions estimated by different institutions, inconsistencies may arise due to the different algorithms and models used in the actually available software, leading to a deterioration in performance. To mitigate such performance degradation, it is advantageous to perform all evaluations with a uniform software package. Since our in-house Gravity Recovery Object Oriented Programming System (GROOPS) software tool has become a widely accepted tool in the scientific community, we have now also incorporated the Satellite Laser Ranging (SLR) functionality to ensure the continued development of the software. On this basis, the opportunity arises to uniformly determine all the contributions to the combined gravity field using a single software package. This contribution is intended as a preliminary study for the next Gravity Observation Combination (GOCO) model. In this regard, we present low-degree solutions based on SLR observations using GROOPS and compare them with findings from other research groups (e.g., Cheng et al. 2013, Krauss et al. 2019) as well as solutions based on the satellite mission GRACE and GRACE-FO.

In this study a regional modelling framework for water mass changes is developed. The approach can introduce geodetic observation types of varying temporal and spatial resolution including their correlated error information. For this purpose a Kalman filter process was set up using a regional parameterisation by space-localising radial basis functions and a process model based on stochastic prediction. The feasibility of the approach is confirmed in a closed-loop simulation experiment using gridded water storage estimates derived from simulated monthly solutions of the GRACE satellite gravimetry mission and considering realistic error patterns. The resulting mass change time series exhibit strongly reduced noise and a very high agreement with the reference model. The modelling framework is designed to flexibly allow a future extension towards combining satellite gravimetry with other geodetic observations such as GNSS station displacements or terrestrial gravimetry.

In 2019 the Combination Service for Time-variable Gravity fields (COST-G) started its operation with the first release of combined monthly GRACE gravity field models. Meanwhile almost five years have passed, while new experience was gained with the operational combination of the monthly gravity field models of the successor mission GRACE-FO, which has triggered a review of the weighting scheme and consequently a second release of GRACE-FO models in 2023. Moreover, the COST-G consortium has been in close cooperation with new GRACE/GRACE-FO analysis centers from China since spring 2020, which recently provided time-series of unconstrained models, covering the whole GRACE period, as it is requested by the COST-G processing standards. After careful evaluation of all individual time-series the whole GRACE time-series has now been recombined based on the new weighting scheme and also taking into account the contributions of the new COST-G analysis centers APM-SYSU, HUST, SUSTech and Tongji. We present the COST-G GRACE RL02 and also show latest results of the operational GRACE-FO combination.

Satellites in Earth orbit are exposed to Earth radiation, consisting of reflected solar and emitted thermal radiation, thereby exerting a radiation pressure force that causes acceleration and affects the orbits. Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) mission aiming to retrieve the Earth gravity potential is critically dependent on accounting for all non-gravitational forces, including the Earth radiation. Although weather-of-the-day; e.g., clouds and their properties, has a major role in Earth radiation pressure, only climatology has been used so far to represent this force. Using climatological data doesn’t account for orbit perturbations owing to weather-related transient changes in the Earth radiation pressure. We show here that the top-of-atmosphere radiation fluxes computed with a numerical weather prediction model explain most of the measured variations in the radial acceleration of the GRACE-FO satellite. Our physics-based modelling corrects a hitherto unexplained lack of power spectral density in the measured accelerations. For example, we can accurately model the accelerations associated with a tropical storm in Indian Ocean in December 2020, which would not be possible when using climatological data. Our results demonstrate that using a global numerical weather prediction model significantly improves the simulation of non-gravitational effects in the satellites’ orbit. This advancement will allow more precise gravity retrieval and its applications in Earth sciences.

Global models of the Earth's static gravity field are crucial for geophysical and geodetic applications such as oceanography, tectonics, and global reference systems. The combined global gravity field model GOCO2025 is the latest release of the GOCO* series and is derived solely from satellite observations. Data from the dedicated satellite gravimetry missions GRACE, GRACE-FO, and GOCE as well as satellite laser ranging observations and kinematic orbits of geodetic satellites are used to determine a static gravity field together with a regularized trend, an annual and a semi-annual oscillation. Starting with GRACE in 2002 the time series spans more than 20 years of data from satellite missions with complementary observation principles, which allows for the determination of a high-accuracy gravity field model with the best possible spatial resolution.The unique instrumentation on GRACE-FO with two independent inter-satellite ranging systems (laser ranging interferometer (LRI) and K/Ka band ranging instrument (KBR)) that operate simultaneously allows for the determination of a stochastic model taking the cross-correlation between the instruments into account by using variance component estimation. This modeling approach results in formal errors of the spherical harmonic coefficients that align well with empirical estimates which is crucial for a combination with other data types and uncertainty propagation. The significantly higher measurement precision of the LRI compared to the KBR is especially beneficial for determining high-degree spherical harmonics. Proper stochastic modeling of all the input data results in realistic accuracy information for the derived combined gravity field solution (represented by a full variance-covariance matrix) that is crucial for further combination with, for example, terrestrial gravity data.Consistent and up-to-date background models are used in the combination process of the different satellite data sets. A combined ocean tide model (GOT5.6 + FES2022 + TIME22) and additional corrections to this model, estimated over the entire GRACE/GRACE-FO time series, are employed and uncertainty information of the Atmosphere and Ocean De-Aliasing Level-1B product (AOD1BRL07) is incorporated in the estimation of short time gravity variations within the least-squares adjustment.

The Combination Service for Time-variable Gravity fields (COST-G) of the IAG looks back at an eventful and very successful year. The operational combination of the monthly GRACE-FO gravity fields now comprises eight analysis centers, providing high-quality solutions with short latency on a regular basis. When the new release 06.3 of the GRACE-FO Science Data System (SDS) time-series became available in September 2024, COST-G generated test combinations and could confirm the quality gain compared to the former release 06.2. Meanwhile, release 06.3 is routinely incorporated in the operational combination.The number of analysis centers providing complete time-series of monthly gravity fields of the GRACE mission to COST-G has more than doubled compared to the original COST-G GRACE RL01, published in 2019. The current COST-G GRACE RL02 is aweighted combination of 11 time-series, where the weighting scheme was adapted to be consistent with the operational GRACE-FO combination. The quality gain of the new combination is most pronounced during the early and late GRACE mission period, when data quality issues and environmental conditions were challenging.

Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) is a gravity field retrieval mission consisting of two identical satellites orbiting the Earth since 2018. Each satellite is equipped with a Global Positioning System (GPS) receiver, a microwave ranging system, and an accelerometer (ACC) to measure non-gravitational accelerations. The data from these instruments, alongside the estimations of gravitational force models, are used to determine the satellites' orbits. Soon after launching, one of the satellite’s ACC degraded, and its data was replaced with ACC data transplant, a synthetic data derived from simulations and the other twin satellite’s ACC data. The default data used for orbit determination of GRACE-FO satellites includes Clouds and Earth's Radiant Energy Systems (CERES) climatology for ACC data transplant and Vienna Mapping Function 3 (VMF3) for tropospheric error correction in GPS observation processing. In this study, we propose a novel approach that uses the Open Integrated Forecasting System (OpenIFS) weather model to account for both ACC transplant and tropospheric delay correction, and in the end, assessed the accuracy of GRACE-FO satellites’ orbits. Orbit determination for GPS satellites was conducted with tropospheric slant delays derived from OpenIFS, using data from 256 stations. Then, the precise orbits of GRACE-FO satellites were estimated using the GPS precise orbits as well as the gravitational and non-gravitational forces acting on the satellites. The ACC transplant was performed by the Technical University of Graz, using the OpenIFS-derived simulated non-gravitational accelerations. This method demonstrates an overall 4 cm improvement in orbit accuracy compared to the traditional method, as it offers a more realistic ACC simulation by explaining the rapid changes of atmosphere affecting the GRACE-FO orbit. OpenIFS provides a greater temporal resolution compared to VMF3 and compensates for the asymmetric atmosphere; thus, the GPS orbit is more precise. The largest accuracy improvement occurred in the along-track direction. This is likely due to a more accurate representation of the radiative effects caused by the satellite entering and exiting Earth's shadow, which typically has the most significant effect on along-track accelerations and, consequently, on the satellite's orbit in this direction.

Tidal variability originating from the orbital dynamics of the Sun and the Moon can be observed in virtually all subsystems of the Earth. The evoked tidal phenomena in the atmosphere, the solid Earth, and the world oceans cause a large-scale redistribution of masses, primarily on daily and sub-daily time scales. The implied tidal variability impacts geodetic measurements. For example, the induced mass transport induces temporal changes in the Earth's gravity field which impact the orbits of artificial satellites. However, observations of a single satellite are generally insufficient to precisely estimate tidal signatures, resulting in a decreased accuracy of the Precise Orbit Determination (POD) of near-Earth satellites. Therefore, a priori prediction of tidal signals, especially ocean tidal signatures, by tidal atlases is necessary to exploit the full potential of geodetic data sets. The most accurate ocean tide atlases are produced by incorporating satellite altimetry observations into the modeling process. However, limitations arising from the ambient signal-to-noise level have hindered their ability to accurately estimate small signals associated with minor tidal constituents. For those minor constituents, data-unconstrained ocean tide models can yield valuable constraints. For processing satellite altimetry data, initial experiments have been undertaken to integrate empirical and numerical models, aiming to deliver comprehensive tidal corrections (Hart-Davis et al., 2021, doi: 10.3390/rs13163310). It has been proposed that experimentation is necessary across all geodetic applications to determine the preferred model for specific tidal constituents and the optimal approach for merging models. This also includes the possibility of including minor ocean tides only implicitly, by deriving their admittance function from suitable neighboring tidal constituents.

Representing the tropospheric slant delays in geodesy can get complicated due to the inhomogeneity and fast variations of the weather. Mapping functions are the most common used tool for this task, but due to the lack of information when calculating the parameters of the mapping functions, relevant errors could appear. The errors in the zenithal direction come from the limitations of the mapping functions, and in the azimuthal direction come from the asymmetries in the sight-field of the receiver. New representations, as the full skyviews representation made by University of Helsinki, have proven to lead to better results in the computation of GNSS products using orbit processing softwares, but these are expensive, both computationally and in size. In this study, we apply the mapping functions approach using the Least Travel Time ray-tracer with larger amounts of mapping functions per receiver, and a 1-hour update of all the parameters. We believe that a more precise use of the slant delays would lead to a better computaiton of GNSS products, along with a important data assimilation to the weather forecast from the residuals obtained in the Least Squares Adjustment used in the processing. The results show that the error induced when using mapping functions converges quickly to a minimum when we increase the amount of mapping functions used per receiver. The most efficient number of mapping functions is 10, being equidistant (one mapping functions every 36 degrees in azimuth).

The Global Gravity-based Groundwater Product (G3P) has evolved with a new version (V1.12), bringing substantial enhancements to our satellite-based groundwater storage anomaly dataset—a prototype for a future product within the EU Copernicus Climate Change Service. Groundwater as the world's largest distributed freshwater storage, is a vital resource for human, industrial, and agricultural needs. Despite its significance, Copernicus lacks a service delivering operational, observation-based, and globally comprehensive data on changing groundwater resources. G3P could serve as a pivotal extension to the Copernicus portfolio. Leveraging the unique capabilities of GRACE and GRACE-FO satellite gravimetry, G3P monitors subsurface mass variations employing a mass balance approach. This involves subtracting the satellite-based and partly model-based water storage compartments (WSCs) snow water equivalent, root-zone soil moisture, glacier mass and surface water storage from GRACE/GRACE-FO monthly terrestrial water storage anomalies (TWSA). Ensuring a consistent subtraction of individual WSCs from GRACE-TWSA involves filtering them similarly to GRACE-TWSA, using filters whose type and parametrization had to be derived by spatial correlation analyses. The G3P dataset spans more than two decades (from 2002 to 2023) with a monthly resolution and global coverage at 0.5-degree spatial resolution. Notable updates in V1.12 compared to previous versions include an extended data time period until September 2023, modifications of the methodology of several WSCs, and the incorporation of new evaluation results. This study has received funding from the European Union’s Horizon 2020 research and innovation programme for G3P (Global Gravity-based Groundwater Product) under grant agreement nº 870353.

For decades, the residual terrain model (RTM) concept (Forsberg and Tscherning in J Geophys Res Solid Earth 86(B9):7843–7854, https://doi.org/10.1029/JB086iB09p07843 , 1981) has been widely used in regional quasigeoid modeling. In the commonly used remove-compute-restore (RCR) framework, RTM provides a topographic reduction commensurate with the spectral resolution of global geopotential models. This is usually achieved by utilizing a long-wavelength (smooth) topography model known as reference topography. For computation points in valleys this neccessitates a harmonic correction (HC) which has been treated in several publications, but mainly with focus on gravity. The HC for the height anomaly only recently attracted more attention, and so far its relevance has yet to be shown also empirically in a regional case study. In this paper, the residual spherical-harmonic topographic potential (RSHTP) approach is introduced as a new technique and compared with the classic RTM. Both techniques are applied to a test region in the central European Alps including validation of the quasigeoid solutions against ground-truthing data. Hence, the practical feasibility and benefits for quasigeoid computations with the RCR technique are demonstrated. Most notably, the RSHTP avoids explicit HC in the first place, and spectral consistency of the residual topographic potential with global geopotential models is inherently achieved. Although one could conclude that thereby the problem of the HC is finally solved, there remain practical reasons for the classic RTM reduction with HC. In this regard, both intra-method comparison and ground-truthing with GNSS/leveling data confirms that the classic RTM (Forsberg and Tscherning 1981; Forsberg in A study of terrain reductions, density anomalies and geophysical inversion methods in gravity field modeling. Report 355, Department of Geodetic Sciences and Surveying, Ohio State University, Columbus, Ohio, USA, https://earthsciences.osu.edu/sites/earthsciences.osu.edu/files/report-355.pdf , 1984) provides reasonable results also for a high-resolution (degree 2160) RTM, yet neglecting the HC for the height anomaly leads to a systematic bias in deep valleys of up to 10–20 cm.

In the third reprocessing campaign (repro3) initiated by the International GNSS Service (IGS), 11 analysis centers (ACs) reanalyzed GPS/GLONASS/Galileo observations spanning 1994–2020 for station coordinates, satellite orbits, clocks, biases and attitudes. To improve the robustness of satellite products, the IGS AC Coordinator (ACC) carried out the satellite orbit combination, and the reference satellite attitudes were computed by the Technical University of Graz (TUG). The clock/bias combination was performed by Wuhan University via the IGS “Precise Point Positioning with Ambiguity Resolution” (PPP-AR) Pilot Project using the PRIDE ckcom software. This article aims at reporting the clock/bias combination results in the repro3. In particular, the consistencies for the combined GPS P1–P2/Galileo C1–C5 differential code biases (DCBs) and the GPS/Galileo uncalibrated phase delays (UPDs) among contributing ACs are all better than 0.1 ns and 0.05 cycles, respectively. As a result, the consistencies for the combined GPS/Galileo satellite clocks/biases are better than 10 ps, equating about 3 mm which is very close to the nominal precision of carrier-phase. In general, the Hadamard deviation and PPP-AR results confirm the higher robustness of the combined satellite clock/bias products over their original AC-specific counterparts. This is because the combined satellite clock/bias products harvest the merits of AC-specific contributions by identifying and excluding outlier solutions from the combination process.

The satellite missions GRACE and GRACE Follow-On have undoubtedly been the most important sources to observe mass transport on global scales. Within the Combination Service for Time-Variable Gravity Fields (COST-G), gravity field solutions from various processing centers are being combined to improve the signal-to-noise ratio and further increase the spatial resolution. The time series of monthly gravity field solutions suffer from a data gap of about one year between the two missions GRACE and GRACE Follow-On among several smaller data gaps. We present an intermediate technique bridging the gap between the two missions allowing (1) for a continued and uninterrupted time series of mass observations and (2) to compare, cross-validate and link the two time series. We focus on the combination of high-low satellite-to-satellite tracking (HL-SST) of low-Earth orbiting satellites by GPS in combination with satellite laser ranging (SLR), where SLR contributes to the very low degrees and HL-SST is able to provide the higher spatial resolution at an lower overall precision compared to GRACE-like solutions. We present a complete series covering the period from 2003 to 2022 filling the gaps of GRACE and between the missions. The achieved spatial resolution is approximately 700 km at a monthly temporal resolutions throughout the time period of interest. For the purpose of demonstrating possible applications, we estimate the low degree glacial isostatic adjustment signal in Fennoscandia and North America. In both cases, the location, the signal strength and extend of the signal coincide well with GRACE/GRACE-FO solutions achieving 99.5% and 86.5% correlation, respectively.

<p>Although a satellite mission to observe Earth’s magnetic field, the Swarm satellites also collect GPS data with sufficient accuracy to observe Earth’s gravity field with a spatial resolution of roughly 1500 km. These monthly models are available from 2014 to the present and do not rely on any other source of gravimetric data nor any a priori information in, for example, the form of temporal and spatial correlations. This time series covers the gap between the GRACE and GRACE-FO missions, as well as any other short gaps in their time series. Given the healthy state of the Swarm satellites, it is also likely that it will provide gravimetric information during possible gaps in the GRACE-FO data, and future dedicated gravimetric satellite missions.</p> <p>We are a consortium of international research institutes, composed of the Astronomical Institute of the University of Bern, the Astronomical Institute of the Czech Academy of Sciences, the Delft University of Technology, the Institute of Geodesy of the Graz University of Technology, and the School of Earth Sciences of the Ohio State University. These activities are supported by the European Space Agency and the International Combination Service for Time-variable Gravity Fields (COST-G). We publish the models every 3 months at ESA’s Swarm Data Accessserver (https://swarm-diss.eo.esa.int) as well at the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm). Each institute exploits different gravity inversion strategies, thus producing independent solutions, which are combined at the solution level using weights derived with Variance Component Estimation. In this way, we ensure the published models are not biased towards particular strategies or assumptions.</p> <p>We illustrate the geophysical signal captured by Swarm’s GPS receivers over large hydrological basins, the errors represented by the variability of the models over the oceans and the agreement with GRACE and GRACE-FO. All analyses span the GRACE/GRACE-FO gap, to illustrate the importance of the Swarm satellites to bridge the absence of low-low satellite-to-satellite tracking data.</p>

<p>The Gravity Recovery Object Oriented Programming System (GROOPS) is meanwhile a highly sophisticated and well accepted tool in the scientific community which enables the user to perform core geodetic tasks. The key features of the software include gravity field recovery from satellite and terrestrial data, the processing of global navigation satellite system (GNSS) constellations and ground station networks and the determination of satellite orbits from GNSS measurements. In the course of the continuous development of GROOPS, we will extend the software by satellite laser ranging (SLR) as an additional feature. Satellite laser ranging offers the possibility to measure distances from low earth orbiting (LEO) satellites with an accuracy in the mm and cm range. A major advantage of this observation technique is that only a passive space technology, called laser retroreflectors are required onboard. Satellite laser ranging can be used as a tool for independent validation of precise determined LEO satellite orbits. Thus, through the extension of GROOPS by SLR we will also be able to perform a validation of our own determined satellite orbits, which we publish regularly. In this work we will give an overview of the integration of the SLR processing, and how corrections regarding the satellites centre of mass, antenna offsets, laser retroreflector offsets and satellite/station specific range biases are handled. Additionally, we will show the orbit validation results of several satellite missions with laser retroreflectors onboard.</p>

Global navigation satellite systems (GNSS) products are integral to a wide array of scientific and commercial applications such as pecise orbit determination of low Earth orbit satellites, earthquake monitoring, GNSS reflectomrety, tropospheric and ionospheric research, surveying and much more. These products consisting of GNSS orbit, clock, phase biases and more are generated by the analysis centres of the International GNSS Service (IGS) by processing observations from a global network of ground stations to one or more GNSS constellations. The processing consists of a combined station position and GNSS satellite orbit determination through a least squares approach donated as global multi-GNSS processing.Within global multi-GNSS processing it is assumed that the observation noise is elevation-dependent and any spatial and temporal correlations are disregarded. Within numerous studies it has been shown that this assumption is incorrect while several studies additional pointed out that a sophisticated stochastic modelling has a positiv impact on GNSS processing and resulting products. In past reseach we have shown to exploit the post-fit residuals to derive temporal correlations for a sophisticated stochastic modeling. However, there have not been any large-scale investigations regarding the impact of stochastic modelling of observation noise on global GNSS processing products. Furthermore, to guarantee the quality of the GNSS products global multi-GNSS processing requires a sophisticated cycle slip detection and repairing algorithm. Cycle slips are discontinuities in the phase observations and if not corrected can lead to degrading quality of GNSS products. We present our advancements in global multi-GNSS processing by exploiting post-fit residuals for stochastic modeling and cycle slip detection. We used several years of observations and a selected IGS network of ground stations to generate GNSS products. Based on this data we analysed the impact our newly integrated approaches have on GNSS products such as orbits, clocks, phase biases and station coordinate time series.

<p>The influence of ocean tides plays a major role for geodetic space methods in the modeling of satellite orbits, Earth rotation, and short-period station motions.</p> <p>Current ocean tide models are published with a large number of individual constituents that are representative for the entire tidal spectrum. The analysis requires interpolation of the constituents onto the spectrum using admittance theory. The application of admittance is non-trivial, as it explicitly and implicitly refers to historical conventions that are hard to retrace for users. Further linear admittance theory is non-unique and requires some insight into the principles of ocean tidal dynamics.</p> <p>Nonetheless, the error-prone implementation of admittance is mostly left to the user, which can easily induce confusion and errors. For example, the IERS2010 conventions describe only one method for the outdated FES2004 model that does not apply directly to current models. While there is a conventional routine for calculating the ocean loading displacement, it needs to consider recent developments and therefore does not exploit the full potential of current ocean tide models.</p> <p>In this presentation, a unified approach is presented for discussion to exploit the full potential of current ocean tide models in satellite orbit calculation and for station displacements. This approach aims to set up a framework for tidal correction that is userfriendly, model-independent, and applies to many geophysical observables.</p>