Paul Rebischung

A new antenna model for BeiDou-3 satellites in medium Earth orbit (MEO) and inclined geosynchronous orbit (IGSO) is presented. This model enables consistent processing of BeiDou-3 with other GNSSs in the IGS20 reference frame and has been adopted for use within the International GNSS Service (IGS) in February 2026. The model introduces estimated phase patterns for four different blocks of BeiDou-3 satellites and provides satellite- and frequency-specific phase center offsets (PCOs) for the B1, B2, and B3 bands. B1C/B2a phase patterns and PCOs are obtained as weighted averages of individual estimates from up to nine IGS analysis centers (ACs) to minimize the dependence on AC-specific models and processing standards. In order to support users of the B1I/B3I legacy signals, satellite-specific PCOs for this linear combination have been determined by two ACs. As part of the antenna model development, temporal variations of PCOs at the few decimeter level were observed over the 2021–2024 time frame for selected satellites built by the Shanghai Engineering Center for Microsatellites (SECM), which are handled as piece-wise constant PCOs in the new IGS antenna model for the affected satellites. The new phase center model is aligned to the IGS20 reference frame and ensures consistency between GPS, GLONASS, Galileo, and BeiDou-3 in multi-GNSS processing. A precise point positioning validation of the new antenna model with B1C/B2a observations confirms a 0.11 ppb scale consistency of BeiDou-3 with the IGS20 reference frame.

Long-term deformation of the Earth’s surface inferred from Global Navigation Satellite Systems (GNSS) station position time series plays a central role in high-precision geodetic and geophysical applications, such as terrestrial reference frame realization, crustal deformation and strain rate estimation, or vertical land motion estimates for improved sea level change assessment. GNSS station position time series contain stochastic variations, often referred to as noise, that exhibit spatial correlations over distances of thousands of kilometers. The impact of these spatial correlations on the uncertainty of longterm station velocity estimates, however, remains poorly modeled and is therefore largely neglected. To address this limitation, we build on recent advances in the characterization and modeling of the spatio-temporal correlations of the noise in GNSS station position time series. We develop a multi-scale spatio-temporal noise correlation model and use it to derive the full covariance matrix of errors in a global station velocity field. Our results show that the spatio-temporally correlated noise induces significant and non-trivial correlations across station velocity errors, which depend not only on the distance between stations but also on the temporal overlap of the station position time series. Accounting for these spatial correlations could significantly improve the detection and uncertainty assessment of subtle long-term deformation signals. Plain Language Summary Position time series of Global Navigation Satellite Systems (GNSS) stations provide essential information about the long-term deformation of the Earth’s surface. However, the detection of subtle long-term deformation signals is limited by the presence of time-correlated noise in GNSS station position time series. Part of this noise has a largescale spatial structure, meaning that stations separated by large distances can experience similar noise patterns. This is usually ignored when estimating long-term deformation rates and their associated uncertainties. In this study, we describe how to model and account for spatially correlated noise in GNSS station position time series. This leads to more realistic uncertainty estimates for GNSS-derived long-term deformation and can benefit a variety of high-precision geodetic and geophysical applications.

The ITRF2020 marked considerable innovations compared with previous versions of the International Terrestrial Reference Frame (ITRF), by modeling nonlinear station motions (seasonal signals and post-seismic deformation for sites subject to major earthquakes). It was also demonstrated that the accuracy and long-term stability of the frame origin, based on the Earth’s center of mass as sensed by Satellite Laser Ranging (SLR), are, since ITRF2005, at the level of or better than 5 mm at epoch 2015.0 and 0.5 mm/yr. Moreover, for the first time in the ITRF history, the scale agreement between the SLR and Very Long Baseline Interferometry (VLBI) input solutions was at the level of 1 mm at the equator at epoch 2015.0, with no drift. Motivated by these results, and for a number of reasons exposed in this paper, the International Terrestrial Reference System (ITRS) Center decided to regularly (yearly) update ITRF2020. The first two ITRF2020 updates, named ITRF2020-u2023 and ITRF2020-u2024, were released in 2024 and 2025, respectively. They are aligned to ITRF2020, thus preserving the same frame parameters (origin, scale, and orientation), while providing improved and up-to-date station trajectory models. The results of the first two ITRF2020 updates are discussed, with a special focus on the evaluation of the uncertainties of the alignment over the frame physical parameters (origin and scale), as well as on Earth Orientation Parameters. We show in particular that the addition of 4 years of extended data from the four space geodetic techniques impacts the transfer of origin and scale between techniques at the level of only 1 mm, demonstrating a high-level of consistency between the frame parameters of ITRF2020 and its updates.

We evaluate the quality of zenith tropospheric delay (ZTD) estimates from four IGS Analysis Centres (ACs) – COD, GFZ, GRG, and TUG – participating in the third IGS reprocessing campaign (repro3). This study focuses on outliers, defined as estimates outside the 1–3 meter range. A small yet significant fraction of estimates fall outside this range for three ACs that did not constrain this parameter. Most outliers originate from a limited number of stations and frequently occur in clusters or during specific periods (e.g., 2000–2003). We find that 95% of outliers are unique to individual ACs, indicating dependence on their specific processing procedures. Outliers are typically linked to poor data quality, including large multipath effects, cycle slips, and/or strong data editing during pre-processing or processing. They often coincide with suboptimal ambiguity fixing statistics. Other parameters, such as station positions, may also be poorly estimated. outliers can be mitigated through robust screening of estimates. Screening based solely on formal errors may be insufficient, as some datasets processed with a zero-difference network strategy contain outliers despite small formal errors. As an alternative, screening using an independent reference ZTD data from atmospheric reanalysis is recommended.

All geodetic technique observations (DORIS, GNSS, SLR and VLBI) have been processed up to the end of 2024 in order to compute the second update of the International Terrestrial Reference Frame 2020, namely ITRF2020-u2024 (https://itrf.ign.fr/en/solutions/ITRF2020-u2024). Following the IERS conventions, no environmental loading corrections have been applied besides ocean tides.We also compute daily GNSS solution using the GINS software in iPPP (precise point positioning with integer ambiguity resolution) for the 2000-2025 period, and orbit/clock products from the CNES/CLS analysis center.In parallel, the IERS Global Geophysical Fluid Center has provided atmospheric, induced oceanic and hydrological loading estimates for all permanent stations based on the latest ECWMF reanalysis (ERA5) and the barotropic ocean model TUGO-m (http://loading.u-strasbg.fr/ITRF2020/).In this paper, we present a comparison of both the combined ITRF2020-u2024 and our daily GNSS residual displacements to environmental (atmosphere, ocean and continental hydrology) loading estimates. In more details, we show that the ERA5-based reanalyzes are in better agreement with the geodetic observations than the MERRA2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) reanalysis. We also show the improvement of the ERA5-land, a re-run of the land component of the ECMWF ERA5 climate reanalysis, versus the original ERA5 hydrological component.Finally, we also show that a dynamic ocean response to pressure and wind is more suitable to model high frequency ocean non-tidal loading effects than the classical inverted barometer (IB) approximation.

A comparison of the three-dimensional annual motions of the global Navigation satellite system (GNSS) stations in two different solutions – the latest reprocessing campaign of the International GNSS Service (IGS), and the station position time series provided by the Nevada Geodetic Laboratory (NGL) – reveals large-scale differences with amplitudes of about 1 mm in horizontal and 3 mm in vertical. We show that these differences are largely explained, in the vertical component, by differences between the alignment strategies of both solutions to the terrestrial reference frame. Further comparisons with the annual displacements predicted by a global loading deformation model suggest that true annual station motions are less subject to aliasing, hence better preserved with the IGS alignment strategy. Considering these results, we urge providers of GNSS station position time series to take measures to minimize the aliasing of geophysical station motions that occur when aligning their daily station position estimates to the reference frame and propose different such measures.

The International GNSS Service (IGS) requires advanced multi-GNSS orbit combination strategies to replace current GPS/GLONASS-focused operations with consistent products covering GPS, GLONASS, Galileo, and BDS. We developed an enhanced orbit combination methodology using a modified Förstner Variance Component Estimation (VCE) scheme that optimizes weighting strategies through data clustering approaches, including individual satellite weighting, satellite-type grouping, and machine-learning-generated clusters. Our novel approach incorporates a priori knowledge from Satellite Laser Ranging (SLR) orbit validations and sequential weight information from previous combinations to refine Analysis Center (AC) weights. Sequential weight estimation significantly reduces day boundary orbit misclosures and stabilizes temporal AC weight variability. The combined solutions demonstrate exceptional inter-consistency with RMS values below 3–5 mm for GPS and Galileo, while GLONASS and BDS show higher variability (10–15 mm), highlighting the importance of satellite grouping strategies. Intermediate grouping approaches based on IGS metadata or hierarchical clustering provide optimal balance between constellation-level oversimplification and satellite-specific day-to-day variability. SLR-based knowledge incorporation offers targeted improvements, particularly for challenging high and low β angle conditions, demonstrating the effectiveness of external validation in multi-GNSS orbit combination.

Precise knowledge of geocenter motion, i.e., the relative motion between the Earth's center of mass (CM) and the center of figure of the Earth's surface (CF), is crucial to high-stakes geodetic applications such as sea level rise monitoring with satellite altimetry or the establishment of regional and global mass budgets with satellite gravimetry. The computation of the latest release of the International Terrestrial Reference Frame, ITRF2020, involved the estimation of a field of seasonal motions for a global network of geodetic stations, expressed with respect to CM, as sensed by Satellite Laser Ranging, from which the translational part represents seasonal geocenter motion. This paper presents two different methods to isolate seasonal geocenter motion from the field of ITRF2020 seasonal station motions, among which a new method based on a direct weighted average of seasonal station motions, with station-specific weights chosen so as to provide a better approximation of CF than the standard network shift approach. The ITRF2020 annual geocenter motion model thus obtained is then compared with other recent geodetic and geophysical estimates. Although different subgroups of estimates with relatively good internal consistency may be identified, the overall scatter of recent geodetic estimates remains at the level of several mm, i.e., close to the amplitude of annual geocenter motion itself. Efforts toward reconciling seasonal geocenter motion estimates therefore still appear necessary. Meanwhile, it would seem safe to assume that seasonal geocenter motion models, in particular those currently used in satellite altimetry and satellite gravimetry, are still uncertain.