Publications related to the GRACE Missions (no abstracts)

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Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins

Sun, Zhangli, Long, Di, Yang, Wenting, Li, Xueying, and Pan, Yun, 2020. Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins. Water Resources Research, 56(4):e2019WR026250, doi:10.1029/2019WR026250.

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@ARTICLE{2020WRR....5626250S,
       author = {{Sun}, Zhangli and {Long}, Di and {Yang}, Wenting and {Li}, Xueying and {Pan}, Yun},
        title = "{Reconstruction of GRACE Data on Changes in Total Water Storage Over the Global Land Surface and 60 Basins}",
      journal = {Water Resources Research},
     keywords = {GRACE, spherical harmonics, mascons, machine learning, data gaps, reconstruction},
         year = 2020,
        month = apr,
       volume = {56},
       number = {4},
          eid = {e2019WR026250},
        pages = {e2019WR026250},
     abstract = "{Launched in May 2018, the Gravity Recovery and Climate Experiment
        Follow-On mission (GRACE-FO){\textemdash}the successor of the
        erstwhile GRACE mission{\textemdash}monitors changes in total
        water storage, which is a critical state variable of the
        regional and global hydrologic cycles. However, the gap between
        data of the two missions is breaking the continuity of the
        observations and limiting its further application. In this
        study, we used three learning-based models, that is, deep neural
        network, multiple linear regression (MLR), and seasonal
        autoregressive integrated moving average with exogenous
        variables, and six GRACE solutions (i.e., Jet Propulsion
        Laboratory spherical harmonics (JPL-SH), Center for Space
        Research SH (CSR-SH), GeoforschungsZentrum Potsdam SH (GFZ-SH),
        JPL mass concentration blocks (mascons) (JPL-M), CSR mascons
        (CSR-M), and Goddard Space Flight Center mascons (GSFC-M)) to
        reconstruct the missing monthly data at a grid cell scale.
        Evaluation showed that the three learning-based models were
        reliable for the reconstruction of GRACE data in areas with
        humid and no/low human interventions. The deep neural network
        models slightly outperformed the seasonal autoregressive
        integrated moving average with exogenous variables models and
        significantly outperformed the multiple linear regression models
        in most of 60 basins studied. The three GRACE mascon data sets
        performed better than the SH data sets at the basin scale. The
        models with SH solutions showed similar performance, but the
        models with the mascon solutions varied markedly in some basins.
        Results of this study are expected to provide a reference for
        bridging the data gaps between the GRACE and GRACE-FO satellites
        and for selecting suitable GRACE solutions for regional
        hydrologic studies.}",
          doi = {10.1029/2019WR026250},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2020WRR....5626250S},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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