Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2923-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2923-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jun 2022
Research article |
| 14 Jun 2022
| 14 Jun 2022
Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning
National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
Nengcheng Chen
National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
State Key Laboratory of Information Engineering in Surveying, Mapping,
and Remote Sensing, Wuhan University, Wuhan 430079, China
Chao Yang
CORRESPONDING AUTHOR
National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
Hongchu Yu
School of Navigation, Wuhan University of Technology, Wuhan 430063,
China
Sanya Science and Education Innovation Park of Wuhan University of
Technology, Sanya, China
Zeqiang Chen
National Engineering Research Center for Geographic Information
System, School of Geography and Information Engineering, China University of
Geosciences (Wuhan), Wuhan 430074, China
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Cited
14 citations as recorded by crossref.
- Deep learning model for flood probabilistic forecasting considering spatiotemporal rainfall distribution and hydrologic uncertainty X. Xiang et al. 10.1016/j.jhydrol.2025.132879
- Classification of precipitation types in Poland using machine learning and threshold temperature methods Q. Pham et al. 10.1038/s41598-023-48108-2
- Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model M. Najafi & V. Kuchak 10.1002/joc.8586
- Artificial intelligence for modeling and understanding extreme weather and climate events G. Camps-Valls et al. 10.1038/s41467-025-56573-8
- Incorporating spatial autocorrelation into deformable ConvLSTM for hourly precipitation forecasting L. Xu et al. 10.1016/j.cageo.2024.105536
- Quantifying uncertainty in soil moisture retrieval using a Bayesian neural network framework Y. Li et al. 10.1016/j.compag.2023.108414
- Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach X. Yan et al. 10.1002/hyp.15040
- DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties B. Harnist et al. 10.5194/gmd-17-3839-2024
- Short-term forecasting of fecal coliforms in shellfish growing waters N. Chazal et al. 10.1016/j.marpolbul.2024.116053
- Pentad-mean air temperature prediction using spatial autocorrelation and attention-based deep learning model L. Xu et al. 10.1007/s00704-023-04763-z
- PP-Loss: An imbalanced regression loss based on plotting position for improved precipitation nowcasting L. Xu et al. 10.1007/s00704-024-04984-w
- Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting A. Ferchichi et al. 10.1007/s00521-025-11026-7
- Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China D. Sun et al. 10.1029/2023GL104406
- Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks T. Zhang et al. 10.5194/hess-27-1945-2023
14 citations as recorded by crossref.
- Deep learning model for flood probabilistic forecasting considering spatiotemporal rainfall distribution and hydrologic uncertainty X. Xiang et al. 10.1016/j.jhydrol.2025.132879
- Classification of precipitation types in Poland using machine learning and threshold temperature methods Q. Pham et al. 10.1038/s41598-023-48108-2
- Ensemble‐based monthly to seasonal precipitation forecasting for Iran using a regional weather model M. Najafi & V. Kuchak 10.1002/joc.8586
- Artificial intelligence for modeling and understanding extreme weather and climate events G. Camps-Valls et al. 10.1038/s41467-025-56573-8
- Incorporating spatial autocorrelation into deformable ConvLSTM for hourly precipitation forecasting L. Xu et al. 10.1016/j.cageo.2024.105536
- Quantifying uncertainty in soil moisture retrieval using a Bayesian neural network framework Y. Li et al. 10.1016/j.compag.2023.108414
- Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach X. Yan et al. 10.1002/hyp.15040
- DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties B. Harnist et al. 10.5194/gmd-17-3839-2024
- Short-term forecasting of fecal coliforms in shellfish growing waters N. Chazal et al. 10.1016/j.marpolbul.2024.116053
- Pentad-mean air temperature prediction using spatial autocorrelation and attention-based deep learning model L. Xu et al. 10.1007/s00704-023-04763-z
- PP-Loss: An imbalanced regression loss based on plotting position for improved precipitation nowcasting L. Xu et al. 10.1007/s00704-024-04984-w
- Evidential uncertainty quantification with multiple deep learning architectures for spatiotemporal drought forecasting A. Ferchichi et al. 10.1007/s00521-025-11026-7
- Deep Learning Improves GFS Wintertime Precipitation Forecast Over Southeastern China D. Sun et al. 10.1029/2023GL104406
- Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks T. Zhang et al. 10.5194/hess-27-1945-2023
Latest update: 09 Mar 2025
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.
Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here,...
