We describe the development and assessment of a new terrestrial reference frame (TRF) based on combining GPS, SLR and VLBI at the observation level over the period 2010–2022. Included in the solution, in addition to station coordinates and precise orbit solutions for all participating satellites, are Earth orientation parameters (EOP) and low-degree zonal coefficients (J2 and J3) of the geopotential. The overall solution concept grew from earlier efforts to realize a TRF using GPS data alone, capitalizing on GPS receivers on the ground and in low-Earth orbit (LEO). Here we add observations from both the SLR and VLBI techniques, which provide the foundation for traditional realizations of the TRF.In linking the GPS and SLR techniques, our approach dispenses with traditional ground survey ties, relying exclusively on space ties from the GRACE and Jason LEO missions. In addition to SLR from these satellites, we include observations from the dedicated LAGEOS satellites, which prove particularly important for recovering low-degree gravity. A major evolution of our approach is the addition of VLBI at the observation level. Lacking a robust tie in Earth orbit for VLBI observations, we apply as constraints the published ground survey ties to nearby GPS stations, enforcing inclusion of the corresponding tracking data in the solutions. The VLBI effort is in the exploratory phase, and further tuning of the strategy is needed to better exploit collocations with both GPS and SLR. About 40% of the participating solution arcs (spanning 2010–2022) now include VLBI and support accurate recovery of UT1 as part of the EOP solution.Though the resulting TRF solution is based on only 12.6 years of data, it is competitive with ITRF2020 in terms of fundamental frame parameters (origin and scale) and their temporal evolution, both linear and seasonal. The relative rates of origin (3D) and scale (at Earth's surface) are 0.2 mm yr-1 and 0.1 mm yr-1 respectively. Absolute scale (at epoch 2015.0) and 3D origin both differ by 2 mm. One advantage of our technique is that precise orbit solutions for both GRACE and Jason missions, defined in the realized TRF, are byproducts of the overall solution. We use the Jason orbit solutions to characterize the impact of contemporary TRF errors on sea level variations (both global and regional) and discuss the implications of these results.

In recent years, new determinations of the ITRF based on full-blown reanalyses of frame inputs from the four space-geodetic techniques have been produced at intervals of 3-6 years. Between frame determinations, ITRF users must rely on predictions of station positions of the reference stations included in the frame whose accuracy rapidly degrades over time, thus causing errors in the products derived from such predictions. JTRF2020 is the most recent TRF solution computed at JPL by assimilating the frame input data submitted by IGS, IVS, ILRS, and IDS for ITRF2020. Determined with a square-root information filter and Dyer-McReynolds smoother algorithm, JPL frame products lend themselves to being updated rather easily as long as frame inputs from the four technique centers consistent with the frame-defining data set are readily available. In this presentation, we will discuss and test SREF (Square-root Reference frame Estimation Filter) updating capabilities in relation to JTRF2020. We will upload state estimate and its covariance computed at the last step of JTRF2020, and update them by assimilating at daily intervals the extended frame inputs made available by IGS (Repro3 extension), IVS (BKG operational combined series with loading effects restored using loading information from the NASA GSFC solution), ILRS (v170 and v171), and IDS (wd20) from 2021 through the end of 2022. Discussions will focus on the peculiarities of the extended frame inputs in relation to the data submitted for the ITRF2020 computation, and in particular on the data pre-processing and transformations we’ve applied to the extended frame inputs in order to ensure consistency with JTRF2020. We’ll also assess the quality of the JTRF2020 updates in terms of frame-defining parameters.

We describe the development and assessment of a new terrestrial reference frame (TRF) based on combining GPS, SLR and VLBI at the observation level over the period 2010–2022. Included in the solution, in addition to station coordinates and precise orbit solutions for all participating satellites, are Earth orientation parameters (EOP) and low-degree zonal coefficients (J2 and J3) of the geopotential. The overall solution concept grew from earlier efforts to realize a TRF using GPS data alone, capitalizing on GPS receivers on the ground and in low-Earth orbit (LEO). Here we add observations from both the SLR and VLBI techniques, which provide the foundation for traditional realizations of the TRF. In linking the GPS and SLR techniques, our approach dispenses with traditional ground survey ties, relying exclusively on space ties from the GRACE and Jason LEO missions. In addition to SLR from these satellites, we include observations from the dedicated LAGEOS satellites, which prove particularly important for recovering low-degree gravity. A major evolution of our approach is the addition of VLBI at the observation level. Lacking a robust tie in Earth orbit for VLBI observations, we apply as constraints the published ground survey ties to nearby GPS stations, enforcing inclusion of the corresponding tracking data in the solutions. The VLBI effort is in the exploratory phase, and further tuning of the strategy is needed to better exploit collocations with both GPS and SLR. About 40% of the participating solution arcs (spanning 2010–2022) now include VLBI and support accurate recovery of UT1 as part of the EOP solution. Though the resulting TRF solution is based on only 12.6 years of data, it is competitive with ITRF2020 in terms of fundamental frame parameters (origin and scale) and their temporal evolution, both linear and seasonal. The relative rates of origin (3D) and scale (at Earth's surface) are 0.2 mm yr-1 and 0.1 mm yr-1 respectively. Absolute scale (at epoch 2015.0) and 3D origin both differ by 2 mm. One advantage of our technique is that precise orbit solutions for both GRACE and Jason missions, defined in the realized TRF, are byproducts of the overall solution. We use the Jason orbit solutions to characterize the impact of contemporary TRF errors on sea level variations (both global and regional) and discuss the implications of these results.

We describe the development and assessment of a new terrestrial reference frame (TRF) based on a combination of geodetic techniques at the observation level over the period 2010-2022. Included in the solution are observations from the Global Positioning System (GPS), Satellite Laser Ranging (SLR) and Very Long Baseline Interferometry (VLBI). A key feature of our solution strategy is the use of space ties in low-Earth orbit to connect SLR to GPS. Though the resulting TRF solution is based on only 12.6 years of data, it is competitive with the international (ITRF2020) standard in terms of fundamental frame parameters (origin and scale) and their temporal evolution, both linear and seasonal. The relative rates of origin (3D) and scale (at Earth's surface) are 0.2 mm yr-1 ${\text{yr}}<^>{-1}$ and 0.1 mm yr-1 ${\text{yr}}<^>{-1}$ respectively. Absolute scale and 3D origin (at epoch 2015.0) both differ by 2-3 mm. In addition to station positions and velocities, our combined solution includes Earth orientation parameters (EOP), low-degree zonal coefficients (J2 and J3) of the geopotential and precise orbit solutions for all participating satellites (GPS, GRACE and GRACE Follow-on tandems, Jason 2 and 3, and LAGEOS 1 and 2). We discuss potential benefits of our solution strategy and characterize the impacts of our new TRF on estimates of geocenter motion and sea level change from satellite altimetry.

&lt;p&gt;JTRF2020 is the latest TRF solution computed at JPL by assimilating frame input data submitted by the IVS, IGS, ILRS, and IDS for ITRF2020. Determined with SREF (Square-root Reference frame Estimation Filter), a computational code based on a square-root information filter and Dyer-McReynolds smoother algorithm, JTRF2020 adopts a time-series based representation: Its daily time series determined from observations of a multi-technique space-geodetic network of 931 stations describe, through their Cartesian coordinates, the deformation of solid Earth as a function of time. Such coordinates are inherently expressed in Earth&amp;#8217;s Center of Mass as sensed by SLR and give access to the quasi-instantaneous scale implied by the underlying frame.&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &lt;/span&gt;JTRF2020, like its predecessor JTRF2014, traditionally adopts a scale relying on VLBI and SLR observations only. In this presentation, we will guide the readers through the process we adopted to define and construct the scale of JTRF2020, from the determination and analysis of the scale differences between VLBI and SLR to the way in which the time-variable scale bias between VLBI and SLR is handled within the J2020 assimilation.&lt;span class=&quot;Apple-converted-space&quot;&gt;&amp;#160; &amp;#160; &amp;#160;&lt;/span&gt;&lt;/p&gt;

JTRF2020 is a recent contribution by NASA Jet Propulsion Laboratory to IERS as part of their effort to determine ITRF2020. JTRF2020 is estimated daily using a sequential computational method (called "SREF") based on the SRIF and DMCS algorithms which are respective variants of the Kalman filter and smoother. The filtering algorithm allows us to make timely update of the reference frame, as newer geodetic analysis data become available beyond those ingested into JTRF2020. Benefits of such frame updating are examined using hindcasting experiments, which are presented here. Also, some of the standard formulas used in terrestrial reference frame realizations are not immediately suitable for sequential formulation required by the algorithms like the Kalman filter. We thus describe our sequential re-formulation of these formulas, including the intrinsic constraints, week-long EOP arcs, and lack of initial conditions.

Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

GeoGateway (http://geo-gateway.org) is a web-based interface for analysis and modeling of geodetic imaging data and to support response to related disasters. Geodetic imaging data product currently supported by GeoGateway include Global Navigation Satellite System (GNSS) daily position time series and derived velocities and displacements and airborne Interferometric Synthetic Aperture Radar (InSAR) from NASA's UAVSAR platform. GeoGateway allows users to layer data products in a web map interface and extract information from various tools. Extracted products can be downloaded for further analysis. GeoGateway includes overlays of California fault traces, seismicity from user selected search parameters, and user supplied map files. GeoGateway also provides earthquake nowcasts and hazard maps as well as products created for related response to natural disasters. A user guide is present in the GeoGateway interface. The GeoGateway development team is also growing the user base through workshops, webinars, and video tutorials. GeoGateway is used in the classroom and for research by experts and non-experts including by students.

We present a data‐driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et al., 2013, https://doi.org/10.1785/0220130039 ). Desired features of the data can be selected for clustering, including some subset of displacement or velocity components, uncertainty estimates, station location, and other relevant information. Based on those selections, the clustering procedure autonomously groups the GNSS stations according to a selected clustering method. We have implemented this approach as a Python application, allowing us to draw upon the full range of open source clustering methods available in Python's scikit‐learn package (Pedregosa et al., 2011, https://doi.org/10.5555/1953048.2078195 ). The application returns the stations labeled by group as a table and color coded KML file and is designed to work with the GNSS information available from GeoGateway (Donnellan et al., 2021, https://doi.org/10.1007/s12145-020-00561-7 ; Heflin et al., 2020, https://doi.org/10.1029/2019ea000644 ) but is easily extensible. We demonstrate the methodology on California and western Nevada. The results show partitions that follow faults or geologic boundaries, including for recent large earthquakes and post‐seismic motion. The San Andreas fault system is most prominent, reflecting Pacific‐North American plate boundary motion. Deformation reflected as class boundaries is distributed north and south of the central California creeping section. For most models a cluster boundary connects the southernmost San Andreas fault with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass.

Abstract This paper describes the methods used to estimate positions, velocities, breaks, and seasonal terms from daily Global Navigation Satellite System (GNSS) measurements. Break detection and outlier removal have been automated so that decades of daily measurements from thousands of stations can be processed in a few hours. New measurements are added, and parameters are updated every week. Model parameters allow separation of interseismic, annual, coseismic, and postseismic signals. Tools available through GeoGateway (http://geo-gateway.org) allow rapid visualization and analysis of these terms for results that can be subsetted in time or space. Results show highly variable and nonlinear motion for GPS stations in southern California. The variable motion is related to seasonal motions, distributed tectonic motion, earthquakes, and postseismic motions that can continue for years. In some areas results suggest that additional processes are responsible for the observed motions. In general, following earthquakes, stations return to their long‐term motions after 2–3 years, though some exceptions occur. The use of the tools shows nonlinear motion in the Salton Trough of southern California related to the 2010 M7.2 El Mayor‐Cucapah earthquake, 2012 Brawley earthquake swarm, and a creep event on the Superstition Hills fault in 2017.

The JTRF2014 realization of terrestrial reference frame has adopted a weekly time series representation that can track dominant non-linear station motions including periodic and random variations. The realization is based on the Kalman filter and smoother algorithms whose time sequential nature would also be suitable for continuous updating of an existing frame as soon as new geodetic data become available.As a part of preparation for the next reference frame realization, we have been examining alternative filter and smoother algorithms based on the square-root information filter (SRIF), known generally for improved numerical accuracy of the covariance matrix represented by a square-root matrix.The new algorithms offer a number of other advantages over the conventional filter/smoother algorithms used in JTRF2014. Namely, the new approach allows us to (1) avoid using some fictitious covariance matrix to initialize the filter, (2) avoid the random-walk constraints for the Helmert parameter sequences, and (3) handle cross-temporal EOP data such as the week-long segments reported by the SLR and DORIS networks. We have also been enhancing the stochastic models of the station position motion to be used by the filter and smoother, including models for non-tidal deformation.

Spatiotemporal coverage gaps and the nature of distant signature sources in Global Navigation Satellite System (GNSS) and The Gravity Recovery and Climate Experiment (GRACE) data require global inversions of data combinations for cross validation and complete and continuous monitoring of surface water mass variations. For spectral completeness, accurate data‐based degree‐1 surface mass variation coefficient or equivalent geocenter motion estimates are also desired but very difficult to obtain. To improve the reliability of such estimates, we carry out a new effort to combine different space geodetic technique data in a Kalman filter and time series approach to Terrestrial Reference Frame realization using an advanced formulation. With origin at the center of mass of the Earth system, the new parameterization now includes site displacements in the state vector explicitly for realistic assessment of their covariance matrices. More robust geocenter motion results are achieved when the displacement information is combined further with GRACE gravity data in a unified inversion. Previously, various systematic effects in GNSS data resulted in rather large discrepancies in global surface mass change estimates when compared with those from GRACE gravity data. Here, we show that significantly improved GNSS data after reprocessing and the refined Estimating the Circulation and Climate of the Ocean (ECCO) bottom pressure model reveal a global surface mass variation pattern that has been largely reconciled with that from GRACE data.

The ITRF2014 candidate solutions DTRF2014 and JTRF2014 provide time-dependent station coordinates accounting for irregular station motions. DTRF2014 by DGFI-TUM expands the secular coordinate model via non-tidal loading corrections caused by changes in the atmosphere and continental water storage. JTRF2014 by JPL follows a time series approach to TRF determination based on Kalman filtering, providing weekly updates to station coordinates. The process noise model of the Kalman filter is derived from non-tidal loading deformations. Global features in station displacements have been studied in the past by determining coefficients of spherical harmonics. So far, studies have mostly focused on individual coordinate components at a time. Typically, the vertical coordinate component is of most interest, since it most often contains the largest signals. In this work, we apply the concept of vector spherical harmonics (VSH) to study temporal variations in station displacements of DTRF2014 and JTRF2014. The advantage of VSH compared to scalar spherical harmonics is that all three coordinate components can be considered at the same time. We estimate VSH coefficients up to degree-2, which includes dipole and quadrupole deformations. Degree-1 deformations represent translations and rotations of the frame, while degree-2 terms contain, inter alia, information on the oblateness of the Earth. We use VSH to analyze station displacements of DTRF2014 and JTRF2014 individually and to conduct comparisons between the two frames. Furthermore, since the temporal variations in both DTRF2014 and JTRF2014 are linked to non-tidal loading deformations, our analysis of temporal variations in VSH coefficients allows for geophysical interpretation.

We review the main concepts underlying the determination of terrestrial reference frames (TRFs) through a recursive algorithm based on Kalman Filtering and Rauch-Tung-Striebel (RTS) smoothing which is currently adopted at Jet Propulsion Laboratory (JPL) to compute sub-secular frame products (JTRFs). We contextualize the TRF determination in the state-space framework and we emphasize connections between frame state, its observability through space-geodetic frame inputs and the similarity transformation which is central to frame definition. We elaborate on the notion of sub-secular frame, enabled by our approach, in constrast to standard TRF products which, secular by construction, are designed to represent the long-term mean physical properties of the frame. Comparisons of JTRF solutions to standard products such as the International Terrestrial Reference Frame (ITRF) suggest high-level consistency in a long-term sense with time derivatives of the Helmert transformation parameters connecting the two TRFs below 0.18 mm/yr. We discuss advantages and limitations of JPL approach to TRF determination and outline lines of inquiries that are currently being researched as part of JTRF development plan.

GipsyX/RTGx is the Jet Propulsion Laboratory?s (JPL) next generation software package for positioning, navigation, timing, and Earth science using measurements from three geodetic techniques: Global Navigation Satellite Systems (GNSS), Satellite Laser Ranging (SLR), and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS); with Very Long Baseline Interferometry (VLBI) under development. The software facilitates combined estimation of geodetic and geophysical parameters using a Kalman filter approach on real or simulated data in both post -processing and in real-time. The estimated parameters include station coordinates and velocities, satellite orbits and clocks, Earth orientation, ionospheric and tropospheric delays. The software is also capable of full realiza- tion of a dynamic terrestrial reference through analysis and combination of time series of ground station coordinates. Applying lessons learned from its predecessors, GIPSY -OASIS and Real Time GIPSY (RTG), GipsyX/RTGx was re -designed from the ground up to offer improved precision, accuracy, usability, and operational flexibility. We present some key aspects of its new archi- tecture, and describe some of its major applications, including Real-time orbit determination and ephemeris predictions in the U.S. Air Force Next Generation GPS Operational Control Segment (OCX), as well as in JPL?s Global Differential GPS (GDGPS) System, sup- porting User Range Error (URE) of <5 cm RMS; precision post -processing GNSS orbit determination, including JPL?s contributions to the International GNSS Service (IGS) with URE in the 2 cm RMS range; Precise point positioning (PPP) with ambiguity resolution, both statically and kinematically, for geodetic applications with 2 mm horizontal, and 6.5 mm vertical repeatability for static positioning; Operational orbit and clock determination for Low Earth Orbiting (LEO) satellites, such as NASA?s Gravity Recovery and Climate Experiment (GRACE) mission with GRACE relative clock alignment at the 20 ps level; calibration of radio occultation data from LEO satellites for weather forecasting and climate studies; Satellite Laser Ranging (SLR) to GNSS and LEO satellites, DORIS-based and multi -technique orbit determination for LEO; production of terrestrial reference frames and Earth rotation parameters in support of JPL?s contribution to the International Terrestrial Reference Frame (ITRF). (c) 2020 COSPAR. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

We review the main steps that led to the construction of JTRF2014, the frame solution the Jet Propulsion Laboratory (JPL) submitted for the realization of ITRF2014. In the first part of this technical note, we recall the main concepts underlying the determination of terrestrial reference frames through Kalman Filtering and Rauch-Tung-Striebel smoothing, currently adopted at JPL to compute sub-secular frame products, and we discuss the main results associated with JTRF2014. In the second part, we adopt a comparative approach and, elaborating on metrics associated with Earth Orientation and Helmert transformation parameters, we analyze JTRF2014 in relation to ITRF2014, the official combined frame computed at the Laboratoire de Recherche en Géodésie of the Institut National de l’Information Géographique et Forestière (IGN) and DTRF2014, produced at the Deutsches Geodätisches Forschungsinstitut of the Technische Universität München. Introduction We present and discuss JTRF2014, the Terrestrial Reference Frame (TRF) the Jet Propulsion Laboratory constructed by combining spacegeodetic inputs from Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), Global Navigation Satellite Systems (GNSS) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) submitted for the realization of ITRF2014. Determined through a Kalman filter and RTS smoother assimilating position observations, Earth Orientation Parameters (EOPs), and local ties, JTRF2014 is a sub-secular, time series-based TRF whose origin is at the quasi instantaneous Center of Mass (CM) as sensed by SLR and whose scale is determined by the quasi instantaneous VLBI and SLR scales. JTRF2014 is rotationally aligned to ITRF2008 through the implementation of a No-Net-Rotation (NNR) condition applied at each step of the filter. The kinematic evolution of the station positions is described by superimposing a secular motion, annual, and semi-annual periodic modes. Site-dependent variances based on the analysis of loading displacements induced by mass redistributions of terrestrial fluids have been used to control the extent of random walk adopted in the combination. This technical note is conceptually articulated in two parts: The first recalls the main concepts underlying the JPL approach to TRF combination and illustrates the main products associated with JTRF2014;

A Kalman filter and time series approach to the International Terrestrial Reference Frame (ITRF) realization (KALREF) has been developed and used in JPL. KALREF combines weekly or daily SLR, VLBI, GNSS and DORIS data and realizes a terrestrial reference frame in the form of time-variable geocentric station coordinate time series. The origin is defined at nearly instantaneous Center-of-Mass of the Earth system (CM) sensed by weekly SLR data and the scale is implicitly defined by the weighted averages of those of weekly SLR and daily VLBI data. The standard KALREF formulation describes the state vector in terms of time variable station coordinates and other constant parameters. Such a formulation is fine for station positions and their uncertainties or covariance matrices at individual epochs. However, coordinate errors are strongly correlated over time given KALREF’s unique nature of combining different technique data with various frame strengths through local tie measurements and co-motion constraints and its use of random walk processes. For long time series and large space geodetic networks in the ITRF, KALREF cannot keep track of such correlations over time. If they are ignored when forming geocentric displacements for geophysical inverse or network shift geocenter motion studies, the covariance matrices of coordinate differences cannot adequately represent those of displacements. Consequently, significant non-uniqueness and inaccuracies would occur in the results of studies using such matrices. To overcome this difficulty, an advanced KALREF formulation is implemented that features explicit displacement parameters in the state vector that would allow the Kalman filter and smoother to compute and return covariance matrices of displacements. The use of displacement covariance matrices reduces the impact of time correlated errors and completely solves the non-uniqueness problem. However, errors in the displacements are still correlated in time. Further calibrations are needed to accurately assess covariance matrices of derivative quantities such as averages, velocities and accelerations during various time periods. We will present KALREF results of the new formulation and their use along with newly reprocessed RL06 GRACE gravity data in a new unified inversion for geocenter motion.

GRACE and GRACE Follow-On (FO) Level 2 data provide quasi-monthly, band-limited estimates of Stokes (geopotential, spherical harmonic) coefficients mostly reflecting surface mass variability due to non-tidal atmosphere, ocean, and continental hydrology. Although space gravimetry does not directly provide CM-related degree-1 Stokes coefficients, GRACE data have been successfully used over the years to complement time series of station positions from global space-geodetic (SG) network when inverting for Center-of-Mass to Center-of-Network (CM-CN) displacements (Wu et al, 2006). Surficial mass variability observed through GRACE/GRACE-FO can be conveniently converted into load-induced (ENU) deformations at SG observing sites by adopting a spectral (i.e. load Love-number based) formalism and assuming Earth’s response is fully elastic and isotropic. GRACE-derived elastic displacements at observing sites would represent, if accurate, band-limited (degree 2 to 96, or higher if Mascon solutions are adopted) load-induced deformations that can be removed from SG-derived station displacements in order to more accurately recover degree-1 surface deformation signature (and therefore geocenter motion). In this study, we adopt GRACE JPL Mascon RL06 data in conjunction with Preliminary Reference Earth Model-derived load Love numbers to infer elastic displacement at SG sites and remove them from SLR inherently geocentric time series of station positions. In so doing, the residual SLR station displacements, consistently expressed in a geocentric frame, would in principle reflect a degree-1 deformation signature that can be recovered via either surface deformation (Chanard et al, 2018) or translational approach. We will compare the SLR/GRACE (CM-CN) determined in this study to standard estimates of geocenter motion such as ILRS’s and JTRF2014’s estimated via translational approach and spectrally inverted solutions (CM-CF). References Chanard K et al, (2018). JGR-Sol Ea doi:10.1002/2017JB015245 Wu X et al, (2006). JGR-Sol Ea doi:10.1029/2005JB004100.