Articles | Volume 27, issue 10
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

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Cited articles

Amini, A., Dolatshahi, M., and Kerachian, R.: Adaptive precipitation nowcasting using deep learning and ensemble modeling, J. Hydrol., 612, 128197,, 2022. 
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55,, 2015. 
Bremnes, J. B.: Probabilistic forecasts of precipitation in terms of quantiles using NWP model output, Mon. Weather Rev., 132, 338–347,<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,, 1999. 
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.