Publications related to the GRACE Missions (no abstracts)

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An Enhanced Parameter Filtering Approach for Postprocessing GRACE Monthly Gravity Field Models

Zhang, Lin, Shen, Yunzhong, Ji, Kunpu, and Chen, Qiujie, 2025. An Enhanced Parameter Filtering Approach for Postprocessing GRACE Monthly Gravity Field Models. IEEE Geoscience and Remote Sensing Letters, 22:LGRS.2025, doi:10.1109/LGRS.2025.3575197.

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@ARTICLE{2025IGRSL..22L5197Z,
       author = {{Zhang}, Lin and {Shen}, Yunzhong and {Ji}, Kunpu and {Chen}, Qiujie},
        title = "{An Enhanced Parameter Filtering Approach for Postprocessing GRACE Monthly Gravity Field Models}",
      journal = {IEEE Geoscience and Remote Sensing Letters},
     keywords = {Gauss-Markov process, gravity recovery and climate experiment (GRACE), parameter filtering (PF), signal extraction},
         year = 2025,
        month = jan,
       volume = {22},
          eid = {LGRS.2025},
        pages = {LGRS.2025},
     abstract = "{An effective filtering approach is essential for accurately interpreting
        gravity recovery and climate experiment (GRACE) monthly gravity
        field models. The improved parameter filtering (IPF) (Zhang et
        al., 2024) simultaneously estimates signal components with
        deterministic harmonic parameters and time-variable irregular
        signals through Kalman filtering (KF), followed by signal
        denoising based on their signal and noise covariance matrices.
        However, it has two critical limitations: 1) excessive
        computational burden due to redundant dynamic calculations of
        deterministic parameters in KF and 2) signal attenuation
        resulting from a suboptimal two-step estimation framework. For
        this reason, this letter proposes an enhanced parameter
        filtering (EPF) based on a more rigorous parameter estimation
        criterion, which independently resolves deterministic parameters
        and irregular signals, thereby avoiding dynamic estimations of
        deterministic parameters while effectively integrating the two-
        step procedures of IPF. Here, we employ EPF to denoise the ITSG-
        Grace2018 model at degree 96 from April 2002 to December 2023,
        comparing its performance with IPF. Results demonstrate that the
        computational efficiency of EPF is improved by 92.3\%, with
        fitting errors reduced by 62.4\% and signal-to-noise ratios
        enhanced by 4.4\%. Spatial analysis of filtered global
        Terrestrial Water Storage Anomalies (TWSAs) shows EPF better
        matches center for space research Mascon (CSRM) RL06, jet
        propulsion laboratory Mascon (JPLM) RL06, and NOAH products,
        with average Nash-Sutcliffe Coefficients increased by 1.7\%,
        2.9\%, and 8.3\%, respectively. Further comparisons of TWSAs
        across 30 global basins, mass changes in Greenland and
        Antarctica, and co-seismic gravity signals of the 2004 Sumatra-
        Andaman and 2010 Chile earthquakes, reveal the superior
        performance of EPF over IPF and four recently proposed filters.}",
          doi = {10.1109/LGRS.2025.3575197},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025IGRSL..22L5197Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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