Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2923-2022
https://doi.org/10.5194/hess-26-2923-2022
Research article
 | 
14 Jun 2022
Research article |  | 14 Jun 2022

Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning

Lei Xu, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen

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Cited articles

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Short summary
Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here, an integrated predictive uncertainty modeling framework is proposed by jointly considering data and model uncertainties through an uncertainty propagation theorem. The results indicate an effective predictive uncertainty estimation for precipitation forecasting, indicating the great potential for uncertainty quantification of numerous predictive applications.