@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
@COMMENT https://ui.adsabs.harvard.edu/
@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}
}