Publications related to the GRACE Missions (no abstracts)

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A Signal–to–Noise Ratio Filter by Incorporating Spectral Characteristics of Temporal Gravity Field Signals and Varying GRACE Observation Conditions

Xuan, Jianhao, Chen, Qiujie, Zhang, Xingfu, and Shen, Yunzhong, 2025. A Signal–to–Noise Ratio Filter by Incorporating Spectral Characteristics of Temporal Gravity Field Signals and Varying GRACE Observation Conditions. IEEE Transactions on Geoscience and Remote Sensing, 63:TGRS.2025, doi:10.1109/TGRS.2025.3637084.

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@ARTICLE{2025ITGRS..63S7084X,
       author = {{Xuan}, Jianhao and {Chen}, Qiujie and {Zhang}, Xingfu and {Shen}, Yunzhong},
        title = "{A Signal-to-Noise Ratio Filter by Incorporating Spectral Characteristics of Temporal Gravity Field Signals and Varying GRACE Observation Conditions}",
      journal = {IEEE Transactions on Geoscience and Remote Sensing},
     keywords = {Filter, Gravity Recovery and Climate Experiment (GRACE), signal-to-noise ratio (SNR), temporal gravity field},
         year = 2025,
        month = jan,
       volume = {63},
          eid = {TGRS.2025},
        pages = {TGRS.2025},
     abstract = "{Unconstrained monthly gravity field solutions of the Gravity Recovery
        and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO)
        predominantly contain correlated and high-frequency noise. To
        mitigate the effect of this noise, this article proposes a
        signal-to-noise ratio (SNR) filter (SF) that incorporates the
        spectral characteristics of a priori monthly gravity field
        signals and varying observation conditions throughout the entire
        GRACE period. The performance of the SF filter was evaluated
        through a comparative analysis with the DDK filter and a
        combination filter of Gaussian and P4M6 (Gauss+P4M6). Compared
        to DDK3 and Gauss+P4M6 filters, the SF filter exhibits an
        improved SNR in mass change estimation under observation
        conditions characterized by poor data quality, repeat ground
        track, and normal observation periods. In global scale analysis,
        SF filtering exhibits a noise reduction of 51\% and 81\% while
        retaining stronger amplitude and trend signals than DDK3 and
        Gauss+P4M6 filtering. Especially in Greenland, Central Africa,
        and the Amazon river basin, higher SNR is achieved by the
        proposed SF filtering method. In small-scale regions like
        subbasins of Greenland and other river basins worldwide, mass
        changes estimated using SF filtering demonstrate a better
        agreement with those from the Center for Space Research (CSR)
        mascon solutions or Global Land Data Assimilation System (GLDAS)
        models. For extended analysis, the SF filter was further applied
        to GRACE-FO monthly solutions, including CSR RL06.2, ITSG-
        Grace\_op, and COST-G GRACE-FO RL02, consistently achieving
        improved SNR in mass change estimation with respect to the other
        two filters.}",
          doi = {10.1109/TGRS.2025.3637084},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025ITGRS..63S7084X},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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