Fri Apr 10, F. Flechtner
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Gao, Geng, Zhang, Shoujian, Zhao, Yongqi, Liu, Haifeng, and Zhong, Luping, 2026. Impacts of Line–of–Sight Kinematic and Dynamic Empirical Parameters on GRACE–FO Orbit Determination and Gravity Field Recovery. Remote Sensing, 18(5):695, doi:10.3390/rs18050695.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026RemS...18..695G,
author = {{Gao}, Geng and {Zhang}, Shoujian and {Zhao}, Yongqi and {Liu}, Haifeng and {Zhong}, Luping},
title = "{Impacts of Line-of-Sight Kinematic and Dynamic Empirical Parameters on GRACE-FO Orbit Determination and Gravity Field Recovery}",
journal = {Remote Sensing},
keywords = {dynamic empirical parameter, kinematic empirical parameter, orbit determination, gravity field recovery, GRACE},
year = 2026,
month = feb,
volume = {18},
number = {5},
eid = {695},
pages = {695},
abstract = "{What are the main findings? Dynamic and kinematic empirical
parameterizations reduce low-frequency KBR residuals by
\raisebox{-0.5ex}\textasciitilde20\%, acting as effective
temporal filters. The combined DYN+KIN scheme increases oceanic
EWH noise by \raisebox{-0.5ex}\textasciitilde16\% and amplifies
striping. Dynamic and kinematic empirical parameterizations
reduce low-frequency KBR residuals by
\raisebox{-0.5ex}\textasciitilde20\%, acting as effective
temporal filters. The combined DYN+KIN scheme increases oceanic
EWH noise by \raisebox{-0.5ex}\textasciitilde16\% and amplifies
striping. What are the implications of the main findings? All
dynamic and kinematic parameterization strategies consistently
improve GNSS-based GRACE-FO orbit accuracy. Over-
parameterization in DYN+KIN damps the 160-day C$_{2,0}$ signal,
revealing a trade-off between noise suppression and geophysical
signal fidelity. All dynamic and kinematic parameterization
strategies consistently improve GNSS-based GRACE-FO orbit
accuracy. Over-parameterization in DYN+KIN damps the 160-day
C$_{2,0}$ signal, revealing a trade-off between noise
suppression and geophysical signal fidelity. The dynamic
approach integrates Global Positioning System and K-band range-
rate (KRR) observations to enable precise orbit determination
(POD) and gravity field recovery. However, background model
uncertainties and temporal aliasing introduce frequency-
dependent noise into the post-fit KRR residuals, thereby
degrading overall solution accuracy. To mitigate these effects,
empirical signals are typically modeled using either dynamic
(DYN) or kinematic (KIN) parameterization strategies.
Nevertheless, the combined use of DYN and KIN parameterizations
remains largely unassessed, and their potential synergistic
impact on POD and gravity field recovery merits systematic
evaluation. This study evaluates the individual and joint
impacts of DYN and KIN (DYN+KIN) on The Gravity Recovery and
Climate Experiment (GRACE) Follow-On orbit accuracy and monthly
gravity field recovery using nearly one year of 2019 data
(excluding February due to severe data gaps). The refined
solutions act as empirical temporal filters, effectively
suppressing low-frequency components in KRR residuals,
particularly below 1-cycle-per-revolution. Relative to nominal
ambiguity-fixed reduced-dynamic orbits, the refined solutions
mainly enhance the cross-track component, with DYN+KIN showing
the largest improvement, while along-track precision experiences
only minor (sub-millimeter) degradation. Overall three-
dimensional orbit accuracy improves from 3.8 cm to 3.0 cm (DYN),
2.8 cm (KIN), and 2.8 cm (DYN+KIN). In terms of gravity field
recovery, the DYN+KIN solution begins to exhibit more pronounced
deviations from the other solutions beyond degree and order 30.
Over oceanic regions, residual mass anomaly analysis shows that
the DYN+KIN solution is associated with an approximately 16\%
higher noise level compared to the individual DYN and KIN
strategies, which exhibit modest noise reductions relative to
the nominal solution. The DYN+KIN also exhibits a dampened
\raisebox{-0.5ex}\textasciitilde160-day periodicity in the
temporal evolution of low-degree coefficients (e.g., C$_{2,0}$),
likely due to spectral overlap between empirical parameter
frequencies and low-degree gravity signal components. These
results indicate that over-parameterization introduces spectral
redundancy and absorbs geophysical signals, underscoring the
need to balance parameter flexibility and signal fidelity in
gravity recovery strategies.}",
doi = {10.3390/rs18050695},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026RemS...18..695G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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