Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2737-2023
https://doi.org/10.5194/gmd-16-2737-2023
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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Latest update: 14 Jul 2024
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Short summary
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.