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• Sorted by Date • Sorted by Last Name of First Author •
Yu, Hongjuan, Zhang, Yong, Sun, Yu, and Sosnica, Krzysztof, 2025. Unveiling GRACE-based estimation techniques: insights from multichannel singular spectrum analysis of geocentre motion. Geophysical Journal International, 242(1):ggaf002, doi:10.1093/gji/ggaf002.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025GeoJI.242....2Y,
author = {{Yu}, Hongjuan and {Zhang}, Yong and {Sun}, Yu and {So{\'s}nica}, Krzysztof},
title = "{Unveiling GRACE-based estimation techniques: insights from multichannel singular spectrum analysis of geocentre motion}",
journal = {Geophysical Journal International},
year = 2025,
month = jul,
volume = {242},
number = {1},
eid = {ggaf002},
pages = {ggaf002},
abstract = "{This study aims to provide valuable scientific insights into various
estimation techniques of geocentre motion (GCM) from the
perspective of signal analysis, thereby enhancing Gravity
Recovery and Climate Experiment (GRACE) users' understanding and
application of GCM. Initially, it utilizes the satellite laser
ranging (SLR) technique with the network shift approach to
estimate over 30 yr of weekly GCM time-series from 1994 to 2024.
Subsequently, we employ two approaches to estimate three types
of monthly GCM time-series spanning more than 20 yr from 2002 to
2023: combining GRACE data with an ocean bottom pressure model
(GRACE-OBP approach), the fingerprint approach (FPA), and the
fingerprint approach with satellite altimetry data (FPA-SA, up
to 2022). The former is referred to as SLR-based GCM estimates,
while the latter, which uses GRACE Earth's gravity field models,
is termed GRACE-based GCM estimates. Furthermore, this study
pioneers the use of multichannel singular spectrum analysis
(MSSA) for GCM analysis, especially focusing on the latest
GRACE-based GCM estimates from the GRACE-OBP and FPA/FPA-SA
approaches, marking the first comprehensive analysis of GCM
estimated by various techniques. The results show that MSSA can
effectively extract common signals from the three components of
the GCM time-series. The seasonal components extracted from
GRACE-based GCM estimates using MSSA are consistent with those
from SLR-based GCM estimates, although the former exhibit
slightly larger amplitudes of the annual and semi-annual
signals. After correcting the atmosphere-ocean dealiasing, the
amplitudes of the SLR-based estimates correspondingly decrease,
remaining slightly larger but becoming closer to those of the
GRACE-based estimates. However, a periodic signal with an
approximate 160-d period is detectable in all GRACE-based GCM
estimates, but it is absent in SLR-based GCM estimates. Further
investigation using MSSA into higher degree spherical harmonic
(SH) coefficients of the Earth's gravity field models reveals
that these SH coefficients contain a 160-d periodic signal. This
finding suggests that the signal detected in GRACE-based GCM
estimates originates from systematic errors in these SH
coefficients, offering new insights for improving the accuracy
of GRACE Earth's gravity field solutions. Additionally, GRACE-
based GCM estimates show significant secular non-zero trends,
notably larger than those in SLR-based GCM estimates, which are
not expected to exhibit any trends. However, the reliance of
GRACE-based GCM estimates on geophysical models (e.g. glacier
melting, glacial isostatic adjustment and hydrological models)
limits the accuracy of their trends, underscoring the need for
further validation. Overall, this study highlights new
challenges regarding the accuracy of GRACE-based GCM estimates
and emphasizes the necessity for further validation in mass
change research.}",
doi = {10.1093/gji/ggaf002},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025GeoJI.242....2Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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