Contribution of Starlette, Stella, and AJISAI to the SLR-derived global reference frame

Sośnica, Krzysztof; Jäggi, Adrian; Thaller, Daniela; Beutler, Gerhard; Dach, Rolf

doi:10.1007/s00190-014-0722-z

Cited by 60 publications

(55 citation statements)

References 27 publications

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“…the lowest spread of formal errors 1 / s p y,c ). Ajisai appears to contribute the most to the observability of the Z geocentre coordinate rather than Stella, as argued by Sośnica et al (2014). This result is partly due to the large number of Ajisai observations, but confirms through real data analysis the finding of Kuang et al (2015) that a polar orbit is the least favourable for the determination of the Z geocentre coordi- nate.…”

Section: Lageos-starlette-stella-ajisai Combined Solutionssupporting

confidence: 63%

“…It is customary to set up daily (Cheng and Tapley 1999;Moore et al 2005;Maier et al 2012;Sośnica et al 2014) or even 12-hourly (Lejba and Schillak 2011;Cheng et al 2013) drag coefficients for LEO orbits to reduce the RMS of the observation residuals and hence improve the orbital fit. Solving for a high number of drag coefficients does, however, lead to increased correlations between parameters and potential collinearity issues.…”

Section: Standard Parameterisationmentioning

confidence: 99%

“…With the exception of geopotential coefficients, the geodetic products derived from Starlette, Stella and Ajisai data are typically of lower quality than their LAGEOS-derived equivalents (e.g. Lejba and Schillak 2011), but recent LAGEOS-Starlette-StellaAjisai data combinations involving complex modelling or LEO orbits yielded promising results (Cheng et al 2013;Sośnica et al 2014). However, to compensate for the effects of non-conservative forces and to improve the orbital fit to LAGEOS-like values, a large number of empirical parameters need to be solved for.…”

Section: Lageos-starlette-stella-ajisai Combined Solutionsmentioning

confidence: 99%

“…1 The LEOs Starlette, Stella and Ajisai have been predominantly used, in conjunction with LAGEOS-1 and 2, for recovering low degree and order (d/o) geopotential coefficients (Cheng et al 1997;Cheng and Tapley 1999;Moore et al 2005;Maier et al 2012). Recent studies (Cheng et al 2013;Sośnica et al 2014) reported geo-centre coordinates from combined LAGEOS-LEO data, but no conclusive evidence that multi-satellite solutions improve the observability of the geocentre coordinates compared to LAGEOS-1 and 2 solutions has been provided. The higher sensitivity of LEOs to the long-wavelength harmonics of the gravity field and the quasi-polar orbit of Stella are often speculatively quoted as arguments for the potentially superior quality of geocentre motion time series derived from multi-satellite data (Angermann and Müller 2008;Sośnica et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies (Cheng et al 2013;Sośnica et al 2014) reported geo-centre coordinates from combined LAGEOS-LEO data, but no conclusive evidence that multi-satellite solutions improve the observability of the geocentre coordinates compared to LAGEOS-1 and 2 solutions has been provided. The higher sensitivity of LEOs to the long-wavelength harmonics of the gravity field and the quasi-polar orbit of Stella are often speculatively quoted as arguments for the potentially superior quality of geocentre motion time series derived from multi-satellite data (Angermann and Müller 2008;Sośnica et al 2014). LARES data analysis results have yet to be widely reported due to the short tracking history of the satellite launched on February 13, 2012, but its inclusion in ILRS products is envisaged for the near future.…”

Section: Introductionmentioning

confidence: 99%

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Collinearity assessment of geocentre coordinates derived from multi-satellite SLR data

Spatar

Moore

Clarke

2015

J Geod

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Of the three satellite geodetic techniques contributing to the International Terrestrial Reference Frame (ITRF), Satellite Laser Ranging (SLR) is generally held to provide the most reliable time series of geocentre coordinates and exclusively defines the ITRF origin. Traditionally, only observations to the two LAser GEOdynamics Satellite (LAGEOS) and Etalon pairs of satellites have been used for the definition of the ITRF origin. Previous simulation studies using evenly sampled LAGEOS-like data have shown that only the Z component of geocentre motion suffers minor collinearity issues, which may explain its lower quality compared to the equatorial components. Using collinearity diagnosis, this study provides insight into the actual capability of SLR to sense geocentre motion using the existing geographically unbalanced ground network and real observations to eight spherical geodetic satellites. We find that, under certain parameterisations, observations to the low Earth orbiters (LEOs) Starlette, Stella, Ajisai and LAser RElativity Satellite (LARES) are able to improve the observability of the geocentre coordinates in multi-satellite solutions compared to LAGEOS-only solutions. The higher sensitivity of LEOs to geocentre motion and the larger number of observations are primarily responsible for the improved observability. Errors in the modelling of Starlette, Stella and Ajisai orbits may contaminate the geocentre motion estimates, but do not disprove the intrinsic strength of LEO tracking data. The sporadically observed Etalon satellites fail to make a significant beneficial contribution to the observability of the geocentre coordinates derived via the network shift approach and can be safely omitted from SLR

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Section: Lageos-starlette-stella-ajisai Combined Solutionssupporting

confidence: 63%

Section: Standard Parameterisationmentioning

confidence: 99%

Section: Lageos-starlette-stella-ajisai Combined Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Collinearity assessment of geocentre coordinates derived from multi-satellite SLR data

Spatar

Moore

Clarke

2015

J Geod

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Optimizing estimates of annual variations and trends in geocenter motion and J₂ from a combination of GRACE data and geophysical models

2016

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The focus of the study is optimizing the technique for estimating geocenter motion and variations in J2 by combining data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission with output from an Ocean Bottom Pressure model and a Glacial Isostatic Adjustment (GIA) model. First, we conduct an end‐to‐end numerical simulation study. We generate input time‐variable gravity field observations by perturbing a synthetic Earth model with realistically simulated errors. We show that it is important to avoid large errors at short wavelengths and signal leakage from land to ocean, as well as to account for self‐attraction and loading effects. Second, the optimal implementation strategy is applied to real GRACE data. We show that the estimates of annual amplitude in geocenter motion are in line with estimates from other techniques, such as satellite laser ranging (SLR) and global GPS inversion. At the same time, annual amplitudes of C10 and C11 are increased by about 50% and 20%, respectively, compared to estimates based on Swenson et al. (2008). Estimates of J2 variations are by about 15% larger than SLR results in terms of annual amplitude. Linear trend estimates are dependent on the adopted GIA model but still comparable to some SLR results.

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Contribution of Multi‐GNSS Constellation to SLR‐Derived Terrestrial Reference Frame

Sośnica

Bury

Zajdel

2018

Geophysical Research Letters

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All satellites of new Global Navigation Satellite Systems (GNSS) are equipped with laser retroreflectors dedicated to Satellite Laser Ranging (SLR). This paper demonstrates the contribution of SLR tracking of multi‐GNSS constellations to the improved SLR‐derived reference frame and scientific products. We show a solution strategy with estimating satellite orbits, SLR station coordinates, geocenter coordinates, and Earth rotation parameters using SLR observations to 2 Laser Geodynamics Satellites (LAGEOS) and 55 GNSS satellites: 1 GPS, 31 Globalnaya Navigatsionnaya Sputnikovaya Sistema, 18 Galileo, 3 BeiDou Inclined Geosynchronous Orbit, 1 BeiDou Medium Earth Orbit, and 1 Quasi‐Zenith Satellite System satellite for the period 2014.0–2017.4. Due to a substantial number of GNSS observations, the number of weekly solutions for some SLR stations, for example, Arkhyz, Komsomolsk, Altay, and Brasilia, is larger up to 41% in the combined LAGEOS + GNSS solution when compared to the LAGEOS‐only solution. The SLR observations to GNSS can transfer the orientation of the reference frame from GNSS to SLR solutions. As a result, the SLR‐derived pole coordinates and length‐of‐day estimates become more consistent with GNSS microwave‐based results. The root‐mean‐square errors of length‐of‐day are reduced from 122.5 μs/d to 43.0 μs/d, whereas mean offsets are reduced from −81.6 μs/d to 0.5 μs/d in LAGEOS only and in the combined LAGEOS + GNSS solutions, respectively.

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Contribution of Starlette, Stella, and AJISAI to the SLR-derived global reference frame

Cited by 60 publications

References 27 publications

Collinearity assessment of geocentre coordinates derived from multi-satellite SLR data

Collinearity assessment of geocentre coordinates derived from multi-satellite SLR data

Optimizing estimates of annual variations and trends in geocenter motion and J₂ from a combination of GRACE data and geophysical models

Contribution of Multi‐GNSS Constellation to SLR‐Derived Terrestrial Reference Frame

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Contribution of Starlette, Stella, and AJISAI to the SLR-derived global reference frame

Cited by 60 publications

References 27 publications

Collinearity assessment of geocentre coordinates derived from multi-satellite SLR data

Collinearity assessment of geocentre coordinates derived from multi-satellite SLR data

Optimizing estimates of annual variations and trends in geocenter motion and J2 from a combination of GRACE data and geophysical models

Contribution of Multi‐GNSS Constellation to SLR‐Derived Terrestrial Reference Frame

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Product

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Optimizing estimates of annual variations and trends in geocenter motion and J₂ from a combination of GRACE data and geophysical models