Mon Aug 24, F. Flechtner
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Zhang, Lin, Shen, Yunzhong, Sneeuw, Nico, Saemian, Peyman, Ji, Kunpu, Chen, Qiujie, and Wang, Fengwei, 2026. A combination of time–variable gravity field solutions from multi–satellite datasets (1993–2024) via constrained collocation model. Earth System Science Data, 18(7):5167–5186, doi:10.5194/essd-18-5167-2026.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026ESSD...18.5167Z,
author = {{Zhang}, Lin and {Shen}, Yunzhong and {Sneeuw}, Nico and {Saemian}, Peyman and {Ji}, Kunpu and {Chen}, Qiujie and {Wang}, Fengwei},
title = "{A combination of time-variable gravity field solutions from multi-satellite datasets (1993--2024) via constrained collocation model}",
journal = {Earth System Science Data},
year = 2026,
month = jul,
volume = {18},
number = {7},
pages = {5167-5186},
abstract = "{Time-variable gravity field solutions from GRACE and GRACE-FO have been
successfully applied in hydrological and geophysical studies;
however, inter- and intra-mission gaps and limited record length
constrain their broader utility. Current approaches involve
hydrometeorological-forced machine-learning reconstructions and
satellite-tracking-observation combinations; however, the former
is constrained by the accuracy and completeness of data inputs,
while the latter requires additional filtering due to limited
spectral sensitivity, resulting in filtering-dependent
solutions. Both approaches neglect covariance information of
observation noise and signal, precluding optimal solutions. To
address these limitations, this study develops gapless monthly
solutions up to degree/order 60 spanning January 1993 to
December 2024 using Constrained Collocation Model (CCM) based on
Tikhonov regularization, which integrates combination and
denoising processes of gravity field solutions without explicit
filtering. CCM-based Combined Solutions (CCM-CS) integrates
trends, annual and semi-annual variations, and non-seasonal
signals from multi-satellite observations (GRACE/-FO, Low Earth
Orbit satellites, and Satellite Laser Ranging) without external
hydrometeorological inputs, while incorporating covariance
matrices of observation errors and combined signals to optimally
balance error reduction and signal preservation. Evaluation
results indicate that CCM-CS significantly eliminates striping
noise and high-degree coefficient noise while effectively
preserving low-degree gravity signals (e.g., C20 and C30) and
achieving high signal-to-noise ratios. Comparison with three
reconstructed products (IGG-SLR-DORIS, RESDCAE, BNML) shows that
CCM-CS achieves the lowest sea level budget misclosures, with
reductions of 40 \%, 2.9 \%, and 49 \%, respectively. Across 52
major basins, CCM-CS achieves lower water balance errors in 98.1
\%, 82.7 \%, and 63.5 \% of the basins, respectively. For
Antarctic and Greenland ice sheet mass changes, CCM-CS closely
match IMBIE (Ice Sheet Mass Balance Inter-comparison Exercise)
estimates, with trend consistency improvements of 46.8 \% and
32.7 \% over IGG-SLR-DORIS and 48.6 \% and 67.4 \% over RESDCAE,
respectively. The combined monthly gravity field solutions are
available at 10.5281/zenodo.18589507 (Zhang et al., 2026).}",
doi = {10.5194/essd-18-5167-2026},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026ESSD...18.5167Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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