GRACE and GRACE-FO Related Publications (no abstracts)

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Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap

Mo, Shaoxing, Zhong, Yulong, Forootan, Ehsan, Mehrnegar, Nooshin, Yin, Xin, Wu, Jichun, Feng, Wei, and Shi, Xiaoqing, 2022. Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap. Journal of Hydrology, 604:127244, doi:10.1016/j.jhydrol.2021.127244.

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@ARTICLE{2022JHyd..60427244M,
       author = {{Mo}, Shaoxing and {Zhong}, Yulong and {Forootan}, Ehsan and {Mehrnegar}, Nooshin and {Yin}, Xin and {Wu}, Jichun and {Feng}, Wei and {Shi}, Xiaoqing},
        title = "{Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap}",
      journal = {Journal of Hydrology},
     keywords = {GRACE, Bayesian convolutional neural network, Gap filling, ERA5, Deep learning},
         year = 2022,
        month = jan,
       volume = {604},
          eid = {127244},
        pages = {127244},
          doi = {10.1016/j.jhydrol.2021.127244},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2022JHyd..60427244M},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

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