Publications related to the GRACE Missions (no abstracts)

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Improved GRACE/GRACE–FO Monthly Gravity Field Estimation by Modeling Sub–Monthly Mass Change Aliasing Signals

Chen, Qiujie, Shen, Zhanglin, Shen, Yunzhong, and Zhang, Xingfu, 2026. Improved GRACE/GRACE–FO Monthly Gravity Field Estimation by Modeling Sub–Monthly Mass Change Aliasing Signals. Journal of Geophysical Research (Solid Earth), 131(3):e2025JB031602, doi:10.1029/2025JB031602.

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@ARTICLE{2026JGRB..13131602C,
       author = {{Chen}, Qiujie and {Shen}, Zhanglin and {Shen}, Yunzhong and {Zhang}, Xingfu},
        title = "{Improved GRACE/GRACE-FO Monthly Gravity Field Estimation by Modeling Sub-Monthly Mass Change Aliasing Signals}",
      journal = {Journal of Geophysical Research (Solid Earth)},
     keywords = {satellite gravimetry, GRACE/GRACE-FO, monthly gravity field, sub-monthly mass change signals},
         year = 2026,
        month = mar,
       volume = {131},
       number = {3},
          eid = {e2025JB031602},
        pages = {e2025JB031602},
     abstract = "{Considering the inadequate modeling of sub-monthly mass change aliasing
        signals in monthly gravity field estimation, a joint modeling
        approach that simultaneously estimates gravity field parameters
        and aliasing components was proposed. The effectiveness of the
        dealiasing strategy was validated using a closed-loop full-scale
        simulation and real data processing analysis. Consequently, this
        method effectively mitigates the treatment of sub-monthly
        aliasing variations as high-degree noise in gravity field
        processing, leading to approximately 9.5\% improvement in the
        accuracy of gravity field estimation. Using this method, we
        developed the Tongji-Grace2022 monthly gravity field solutions
        from Gravity Recovery and Climate Experiment/GRACE Follow-On
        Level-1B observations. Comprehensive analyses conducted in the
        spectral, temporal, and spatial domains demonstrate that the
        Tongji-Grace2022 solution outperforms other gravity field
        solutions without modeling sub-monthly aliasing signals,
        achieving an average noise reduction rate of approximately
        9.5\%. Compared with models that do not account for sub-monthly
        aliasing effects, the signal-to-noise ratio (SNR) values
        obtained with Tongji-Grace2022 are consistently higher across
        most regions of 40 global river basins. Additionally,
        comparisons with the official monthly models (e.g., CSR RL06.3,
        GFZ RL06.3, and JPL RL06.3) and ITSG-Grace2018 solutions show
        that the mass change signals from Tongji-Grace2022 are in close
        agreement with those from the other four models. Notably, both
        Tongji-Grace2022 and ITSG-Grace2018 exhibit lower noise levels
        than the three official models at the global scale. In
        particular, the average root-mean-square (RMS) values over ocean
        regions indicate that Tongji-Grace2022 achieves noise reduction
        rates of approximately 40.6\% and 10.5\%, respectively, in
        comparison to CSR RL06.3 and ITSG-Grace2018 when exclusively
        utilizing P4M6 decorrelation filtering.}",
          doi = {10.1029/2025JB031602},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026JGRB..13131602C},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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