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An integrated postprocessing approach for extracting time variable signals from GRACE monthly gravity field models

Zhang, Lin, Shen, Yunzhong, Ji, Kunpu, Wang, Fengwei, and Chen, Qiujie, 2025. An integrated postprocessing approach for extracting time variable signals from GRACE monthly gravity field models. Journal of Hydrology, 661:133552, doi:10.1016/j.jhydrol.2025.133552.

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@ARTICLE{2025JHyd..66133552Z,
       author = {{Zhang}, Lin and {Shen}, Yunzhong and {Ji}, Kunpu and {Wang}, Fengwei and {Chen}, Qiujie},
        title = "{An integrated postprocessing approach for extracting time variable signals from GRACE monthly gravity field models}",
      journal = {Journal of Hydrology},
     keywords = {GRACE, Integrated Postprocessing Approach, Signal Extraction, Filtering},
         year = 2025,
        month = nov,
       volume = {661},
          eid = {133552},
        pages = {133552},
     abstract = "{Since significant north{\textendash}south striped noise exists in the
        GRACE (Gravity Recovery and Climate Experiment) monthly gravity
        field models, various filtering approaches are employed before
        extracting time variable signals with a harmonic fitting model
        that includes trends, annual and semiannual terms, leading to
        signal attenuation and leakage. This study develops an
        Integrated Postprocessing Approach (IPA) to iteratively estimate
        time variable signals and filter noise using a harmonic model
        plus a non-seasonal signal term. The stochastic characteristics
        of non-seasonal signals are described using an exponential
        function. To improve computational efficiency, the non-seasonal
        signals are represented by a Gauss{\textendash}Markov process
        and estimated by Kalman filtering. The Terrestrial Water Storage
        Anomaly (TWSA) signals across 22 global basins from April 2002
        to December 2023 estimated by IPA exhibit high spatial
        resolution, closely matching Combined Mascon (CM) solutions.
        Relative to CM solutions, IPA reduces the latitude-weighted
        RMSEs of spatial TWSA signals by 8.0\% while increases the Nash-
        Sutcliffe Efficiency (NSE) of TWSA series by 2.1\% compared to
        Improved Parameter Filtering (IPF; Zhang et al. 2024) approach.
        Additionally, IPA estimates significantly enhanced trends and
        strong annual amplitudes of TWSA signals in the Amazon and
        Parana basins, along with the volume variances of Three-Gorges
        Reservoir and Poyang Lake, as well as 2.1 and 4.2-year
        interannual volume variance signals in Victoria Lake. The Water
        Storage Drought Index (WSDI) constructed by IPA aligns well with
        SPEI-6 in detecting the duration and intensity of severe
        droughts and floods in typical basins. Further experiments
        indicate the IPA outperforms the other eight common spectral
        filters.}",
          doi = {10.1016/j.jhydrol.2025.133552},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025JHyd..66133552Z},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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