Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 10
Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2737-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-2737-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 10
Model experiment description paper
 | 
23 May 2023
Model experiment description paper |  | 23 May 2023

CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting

Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi

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Cited articles

Austin, G. and Bellon, A.: The use of digital weather radar records for short-term precipitation forecasting, Q. J. Roy. Meteor. Soc., 100, 658–664, https://doi.org/10.1002/qj.49710042612, 1974. a
Ayzel, G., Heistermann, M., and Winterrath, T.: Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1), Geosci. Model Dev., 12, 1387–1402, https://doi.org/10.5194/gmd-12-1387-2019, 2019. a, b
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644, https://doi.org/10.5194/gmd-13-2631-2020, 2020. a, b, c, d
Bowler, N. E., Pierce, C. E., and Seed, A.: Development of a precipitation nowcasting algorithm based upon optical flow techniques, J. Hydrol., 288, 74–91, https://doi.org/10.1016/j.jhydrol.2003.11.011, 2004. a
Bowler, N. E., Pierce, C. E., and Seed, A. W.: STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP, Q. J. Roy. Meteor. Soc., 132, 2127–2155, https://doi.org/10.1256/qj.04.100, 2006. a, b
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Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
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