The IGS contribution to ITRF2014

Rebischung, Paul; Altamimi, Z.; Ray, Jim; Garayt, B.

doi:10.1007/s00190-016-0897-6

Cited by 202 publications

(162 citation statements)

References 29 publications

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“…Temporal variations of WRMS provide an indication of the intrinsic quality of each single‐technique frame as a function of time. The WRMS analyses conducted on the edited SG inputs to JTRF2014 proved substantially consistent with the most recent investigations presented in the literature [see, e.g., Moreaux et al , ; Rebischung et al , ]. Due to the improvement over time of data quality and the observing systems as well, we observe the WRMSs for all of the SG techniques tend to decrease monotonically as a function of time.…”

Section: Precombination Analysessupporting

confidence: 89%

“…Also, the intrinsic geocenter information conveyed by repro2 has been removed via application of a NNT condition to IGb08 (ITRF2008). Although it is feasible to restore the intrinsic GNSS‐derived geocenter by removing the translational constraints applied to repro2, the resulting GNSS translations turn out to be contaminated by draconitic signatures making the seasonal geocenter motion from repro2 unreliable [ Rebischung et al , ]. Nonconventional global GPS solutions based upon an independent satellite antenna calibration and making use of observations from low Earth orbiters exist and proved effective in providing independent scale information as well as high‐quality estimates of the equatorial component ( T x and T y ) of the geocenter motion.…”

Section: Jtrf2014 Multitechnique Combinationmentioning

confidence: 99%

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JTRF2014, the JPL Kalman filter and smoother realization of the International Terrestrial Reference System

et al. 2017

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We present and discuss JTRF2014, the Terrestrial Reference Frame (TRF) the Jet Propulsion Laboratory constructed by combining space‐geodetic inputs from very long baseline interferometry (VLBI), satellite laser ranging (SLR), Global Navigation Satellite Systems (GNSS), and Doppler orbitography and radiopositioning integrated by satellite submitted for the realization of ITRF2014. Determined through a Kalman filter and Rauch‐Tung‐Striebel smoother assimilating position observations, Earth orientation parameters, and local ties, JTRF2014 is a subsecular, 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. The dynamical evolution of the positions accounts for a secular motion term, annual, and semiannual 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. With differences in the amplitude of the annual signal within the range 0.5–0.8 mm, JTRF2014‐derived center of network‐to‐center of mass (CM‐CN) is in remarkable agreement with the geocenter motion obtained via spectral inversion of GNSS, Gravity Recovery and Climate Experiment (GRACE) observations and modeled ocean bottom pressure from Estimating the Circulation and Climate of the Ocean (ECCO). Comparisons of JTRF2014 to ITRF2014 suggest high‐level consistency with time derivatives of the Helmert transformation parameters connecting the two frames below 0.18 mm/yr and weighted root‐mean‐square differences of the polar motion (polar motion rate) in the order of 30 μas (17 μas/d).

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Section: Precombination Analysessupporting

confidence: 89%

Section: Jtrf2014 Multitechnique Combinationmentioning

confidence: 99%

JTRF2014, the JPL Kalman filter and smoother realization of the International Terrestrial Reference System

et al. 2017

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“…The UNR and JPL IGS08 time series are derived from a global network, and therefore, scale estimates from these analyses are less sensitive to the average height variations of the North America region. There are, however, global scale variations seen in the IGS analysis, and at least some part of these variations are believed to arise from common snow and hydrological loading across the landmasses of the Northern hemisphere [ Dong et al , ; Argus et al , ; Rebischung et al , , ]. Some portion of these seasonal scale changes may also result from modeling errors in the analyses of global GPS data.…”

Section: Analysis Of the Resultsmentioning

confidence: 99%

Plate Boundary Observatory and related networks: GPS data analysis methods and geodetic products

Herring

Melbourne

Murray

et al. 2016

Reviews of Geophysics

212

217

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The Geodesy Advancing Geosciences and EarthScope (GAGE) Facility Global Positioning System (GPS) Data Analysis Centers produce position time series, velocities, and other parameters for approximately 2000 continuously operating GPS receivers spanning a quadrant of Earth's surface encompassing the high Arctic, North America, and Caribbean. The purpose of this review is to document the methodology for generating station positions and their evolution over time and to describe the requisite trade‐offs involved with combination of results. GAGE GPS analysis involves formal merging within a Kalman filter of two independent, loosely constrained solutions: one is based on precise point positioning produced with the GIPSY/OASIS software at Central Washington University and the other is a network solution based on phase and range double‐differencing produced with the GAMIT software at New Mexico Institute of Mining and Technology. The primary products generated are the position time series that show motions relative to a North America reference frame and secular motions of the stations represented in the velocity field. The position time series themselves contain a multitude of signals in addition to the secular motions. Coseismic and postseismic signals, seasonal signals from hydrology, and transient events, some understood and others not yet fully explained, are all evident in the time series and ready for further analysis and interpretation. We explore the impact of analysis assumptions on the reference frame realization and on the final solutions, and we compare within the GAGE solutions and with others.

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“…On average, 70 satellites were available: 32 GPS, 24 GLONASS, and 14–18 Galileo satellites. Each solution is a modification of GNSS POD processing, where TRF is realized (Rebischung & Schmid, ; Rebischung et al, ), which is directly aligned to the ITRF2014 (Altamimi et al, ) and includes station‐specific corrections for handling antenna calibration differences.…”

Section: Methodsmentioning

confidence: 99%

Network Effects and Handling of the Geocenter Motion in Multi‐GNSS Processing

et al. 2019

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Both, the network configuration and the way of terrestrial reference frame (TRF) realization, affect the global geodetic products delivered from the Global Navigation Satellite Systems (GNSS) data processing. The purpose of this study is to analyze the differences in GNSS products, such as station coordinates, Earth rotation parameters, geocenter coordinates (GCC), and satellite orbits delivered from the double‐difference multi‐GNSS (GPS, GLONASS, and Galileo) processing, which may arise from (1) using a homogeneous and inhomogeneous network of multi‐GNSS stations, (2) different approaches to the TRF realization using minimum constraint conditions, and (3) different approaches to handling of GCC in GNSS global processing. The questionable quality of GCC delivered from the global GNSS solutions is described with a special attention to network effects and system‐specific parameters. We found that Galileo can provide GCC, whose quality corresponds to the GPS series. Moreover, the GCC from Galileo is of a better quality than those based on GLONASS data, despite the same number of nominal orbital planes and a much lower number of active satellites. When the No‐Net‐Translation constraint is not applied on the GNSS network, the station coordinate repeatability is worsened by about 70%, 55%, and 25% for the north, east, and up components, respectively, compared to the solution when applying No‐Net‐Translation and when having the network origin consistent with the international TRF. We thus infer that the No‐Net‐Translation condition is mandatory in global GNSS solutions.

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The IGS contribution to ITRF2014

Cited by 202 publications

References 29 publications

JTRF2014, the JPL Kalman filter and smoother realization of the International Terrestrial Reference System

JTRF2014, the JPL Kalman filter and smoother realization of the International Terrestrial Reference System

Plate Boundary Observatory and related networks: GPS data analysis methods and geodetic products

Network Effects and Handling of the Geocenter Motion in Multi‐GNSS Processing

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