GRACE and GRACE-FO Related Publications (no abstracts)

Sorted by DateSorted by Last Name of First Author

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.

Downloads

from the NASA Astrophysics Data System  • by the DOI System  •

BibTeX

@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}
}

Generated by bib2html_grace.pl (written by Patrick Riley modified for this page by Volker Klemann) on Wed Feb 07, 2024 16:33:16

GRACE

Wed Feb 07, F.Flechtner