Publications related to the GRACE Missions (no abstracts)

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Near-Real-Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts

Mo, Shaoxing, Schumacher, Maike, Dijk, Albert I. J. M., Shi, Xiaoqing, Wu, Jichun, and Forootan, Ehsan, 2025. Near-Real-Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts. Geophysical Research Letters, 52(7):2024GL112677, doi:10.1029/2024GL112677.

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@ARTICLE{2025GeoRL..5212677M,
       author = {{Mo}, Shaoxing and {Schumacher}, Maike and {Dijk}, Albert I.~J.~M. and {Shi}, Xiaoqing and {Wu}, Jichun and {Forootan}, Ehsan},
        title = "{Near-Real-Time Monitoring of Global Terrestrial Water Storage Anomalies and Hydrological Droughts}",
      journal = {\grl},
     keywords = {GRACE, Bayesian convolutional neural network, hydrological drought, terrestrial water storage anomaly, data latency, deep learning},
         year = 2025,
        month = apr,
       volume = {52},
       number = {7},
        pages = {2024GL112677},
     abstract = "{Global terrestrial water storage anomaly (TWSA) products from the
        Gravity Recovery and Climate Experiment (GRACE) and its Follow-
        On mission (GRACE/FO) have an approximately three-month latency,
        significantly limiting their operational use in water management
        and drought monitoring. To address this challenge, we develop a
        Bayesian convolutional neural network (BCNN) to predict TWSA
        fields with uncertainty estimates during the latency period. The
        results demonstrate that BCNN provides near-real-time TWSA
        estimates that closely match GRACE/FO observations, with median
        correlation coefficients of 0.92{\textendash}0.95, Nash-
        Sutcliffe efficiencies of 0.81{\textendash}0.89, and root mean
        squared errors of 1.79{\textendash}2.26 cm for one- to three-
        month ahead predictions. More importantly, the model advances
        global hydrological drought monitoring by enabling detection up
        to three months before GRACE/FO data availability, with median
        characterization mismatches below 16.4\%. This breakthrough in
        early warning capability addresses a fundamental constraint in
        satellite-based hydrological monitoring and offers water
        resource managers critical lead time to implement drought
        mitigation strategies.}",
          doi = {10.1029/2024GL112677},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2025GeoRL..5212677M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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