Articles | Volume 27, issue 10
https://doi.org/10.5194/hess-27-1945-2023
https://doi.org/10.5194/hess-27-1945-2023
Research article
 | 
22 May 2023
Research article |  | 22 May 2023

Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks

Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li

Related authors

Two-dimensional Differential-form of Distributed Xinanjiang Model
Jianfei Zhao, Zhongmin Liang, Vijay P. Singh, Taiyi Wen, Yiming Hu, Binquan Li, and Jun Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-377,https://doi.org/10.5194/hess-2024-377, 2025
Preprint under review for HESS
Short summary
Multicriteria assessment framework of flood events simulated with vertically mixed runoff model in semiarid catchments in the middle Yellow River
Dayang Li, Zhongmin Liang, Yan Zhou, Binquan Li, and Yupeng Fu
Nat. Hazards Earth Syst. Sci., 19, 2027–2037, https://doi.org/10.5194/nhess-19-2027-2019,https://doi.org/10.5194/nhess-19-2027-2019, 2019
Short summary
Connections between meteorological and hydrological droughts in a semi-arid basin of the middle Yellow River
Binquan Li, Changchang Zhu, Zhongmin Liang, Guoqing Wang, and Yu Zhang
Proc. IAHS, 379, 403–407, https://doi.org/10.5194/piahs-379-403-2018,https://doi.org/10.5194/piahs-379-403-2018, 2018
Short summary
Non-stationary hydrological frequency analysis based on the reconstruction of extreme hydrological series
Y. M. Hu, Z. M. Liang, X. L. Jiang, and H. Bu
Proc. IAHS, 371, 163–166, https://doi.org/10.5194/piahs-371-163-2015,https://doi.org/10.5194/piahs-371-163-2015, 2015
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
High-resolution land surface modelling over Africa: the role of uncertain soil properties in combination with forcing temporal resolution
Bamidele Oloruntoba, Stefan Kollet, Carsten Montzka, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 29, 1659–1683, https://doi.org/10.5194/hess-29-1659-2025,https://doi.org/10.5194/hess-29-1659-2025, 2025
Short summary
Investigating the global and regional response of drought to idealized deforestation using multiple global climate models
Yan Li, Bo Huang, Chunping Tan, Xia Zhang, Francesco Cherubini, and Henning W. Rust
Hydrol. Earth Syst. Sci., 29, 1637–1658, https://doi.org/10.5194/hess-29-1637-2025,https://doi.org/10.5194/hess-29-1637-2025, 2025
Short summary
Distribution, trends, and drivers of flash droughts in the United Kingdom
Iván Noguera, Jamie Hannaford, and Maliko Tanguy
Hydrol. Earth Syst. Sci., 29, 1295–1317, https://doi.org/10.5194/hess-29-1295-2025,https://doi.org/10.5194/hess-29-1295-2025, 2025
Short summary
Are dependencies of extreme rainfall on humidity more reliable in convection-permitting climate models?
Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025,https://doi.org/10.5194/hess-29-1201-2025, 2025
Short summary
Leveraging a radar-based disdrometer network to develop a probabilistic precipitation phase model in eastern Canada
Alexis Bédard-Therrien, François Anctil, Julie M. Thériault, Olivier Chalifour, Fanny Payette, Alexandre Vidal, and Daniel F. Nadeau
Hydrol. Earth Syst. Sci., 29, 1135–1158, https://doi.org/10.5194/hess-29-1135-2025,https://doi.org/10.5194/hess-29-1135-2025, 2025
Short summary

Cited articles

Amini, A., Dolatshahi, M., and Kerachian, R.: Adaptive precipitation nowcasting using deep learning and ensemble modeling, J. Hydrol., 612, 128197, https://doi.org/10.1016/j.jhydrol.2022.128197, 2022. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. 
Bremnes, J. B.: Probabilistic forecasts of precipitation in terms of quantiles using NWP model output, Mon. Weather Rev., 132, 338–347, https://doi.org/10.1175/1520-0493(2004)132<0338:PFOPIT>2.0.CO;2, 2004. 
Buizza, R., Milleer, M., and Palmer, T. N.: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteorol. Soc., 125, 2887–2908, https://doi.org/10.1002/qj.49712556006, 1999. 
Download
Short summary
We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Share