Publications related to the GRACE Missions (no abstracts)

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Enhanced Denoising of GRACE/GRACE–FO Signals for Antarctic Ice Sheet Mass Change Retrieval Using Savitzky\&Golay Filtering and Variational Mode Decomposition

Wu, Haojian, Hao, Weifeng, Zhang, Chuanyin, Ma, Chao, Li, Dandan, Yang, Yidi, Shi, Zhuoya, Xu, Fang, and Li, Fei, 2026. Enhanced Denoising of GRACE/GRACE–FO Signals for Antarctic Ice Sheet Mass Change Retrieval Using Savitzky\&Golay Filtering and Variational Mode Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 64:4303115–4303115, doi:10.1109/TGRS.2026.3723519.

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@ARTICLE{2026ITGRS..64R3115W,
       author = {{Wu}, Haojian and {Hao}, Weifeng and {Zhang}, Chuanyin and {Ma}, Chao and {Li}, Dandan and {Yang}, Yidi and {Shi}, Zhuoya and {Xu}, Fang and {Li}, Fei},
        title = "{Enhanced Denoising of GRACE/GRACE-FO Signals for Antarctic Ice Sheet Mass Change Retrieval Using Savitzky\&Golay Filtering and Variational Mode Decomposition}",
      journal = {IEEE Transactions on Geoscience and Remote Sensing},
     keywords = {Antarctic ice sheet (AIS), climate drivers, empirical orthogonal function (EOF), gravity recovery and climate experiment (GRACE), signal denoising, three-cornered hat (TCH)},
         year = 2026,
        month = jan,
       volume = {64},
        pages = {4303115-4303115},
     abstract = "{The gravity recovery and climate experiment (GRACE) and its successor
        GRACE follow-on (GRACE-FO) have provided an unprecedented
        perspective on the mass change of the Antarctic ice sheet (AIS).
        However, the accuracy of mass balance estimates remains
        constrained by noise and processing errors in satellite gravity
        data. Traditional filtering methods cannot adequately balance
        noise suppression and signal fidelity. Therefore, we propose an
        improved denoising method using the Savitzky\&Golay filter and
        variational mode decomposition (S\&G-VMD). The generalized
        three-cornered hat (TCH) method is applied to quantify the
        uncertainty in AIS mass change estimates and calculate the
        signal-to-noise ratio (SNR). The S\&G-variational mode
        decomposition (VMD) method reduces the average uncertainty by
        68.72\% compared to the combination of decorrelation and
        Gaussian filter and by 83.68\% compared to the decorrelation and
        denoising kernel (DDK) filter, while increasing the SNR by
        28.80\% and 78.72\%, respectively. These improvements are the
        most pronounced in dynamically active and coastal regions.
        Empirical orthogonal function (EOF) analysis of AIS mass change
        reveals three dominant modes that account for 24.80\%, 13.03\%,
        and 10.98\% of the total variance. These modes are closely
        linked to major climate indices, with the leading mode
        correlated with the Southern Annular Mode (SAM) (R = 0.70), the
        second with El Ni{\~n}o--Southern Oscillation (R = 0.72), and
        the third with the Antarctic Oscillation (AAO) (R = 0.76).
        Further analysis indicates that extreme weather events, such as
        extreme snowfall and anomalous temperature changes,
        significantly affect short-term mass fluctuations in dynamically
        active Antarctic regions. These findings provide crucial
        insights into the responses of the AIS to climate change and the
        short-term effects of extreme weather events on regional ice
        sheet variability.}",
          doi = {10.1109/TGRS.2026.3723519},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026ITGRS..64R3115W},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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