Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9723-2025
https://doi.org/10.5194/gmd-18-9723-2025
Development and technical paper
 | 
08 Dec 2025
Development and technical paper |  | 08 Dec 2025

Improving the fine structure of intense rainfall forecast by a designed generative adversarial network

Zuliang Fang, Qi Zhong, Haoming Chen, Xiuming Wang, Zhicha Zhang, and Hongli Liang

Cited articles

Agarap, A. F.: Deep Learning using Rectified Linear Units (ReLU), arXiv [preprint], https://doi.org/10.48550/arXiv.1803.08375, 2019. a
Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein Generative Adversarial Networks, Proceedings of Machine Learning Research, 70, 214–223, https://proceedings.mlr.press/v70/arjovsky17a.html (last access: 2 December 2025), 2017. a
Ayzel, G., Heistermann, M., and Winterrath, T.: 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
Bihlo, A.: Key factors for quantitative precipitation nowcasting using deep learning, Geosci. Model Dev., 16, 5895–5916, https://doi.org/10.5194/gmd-16-5895-2023, 2023. a
Boeing, G.: Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction, Systems, 4, 37, https://doi.org/10.3390/systems4040037, 2016. a
Download
Short summary
We developed a deep learning model based on Generative Adversarial Networks (GANs) to improve rainfall forecasts in northern China. Traditional models struggle with accuracy, especially for heavy rain. Our model merges data from multiple forecasts, capturing detailed rainfall patterns and offering more reliable short-term predictions.
Share