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.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2737-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
23 May 2023
Model experiment description paper |
| 23 May 2023
| 23 May 2023
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
Yan Ji
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Michael Langguth
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Amirpasha Mozaffari
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Xiefei Zhi
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
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Cited
21 citations as recorded by crossref.
- Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall J. Ritvanen et al. https://doi.org/10.5194/gmd-18-1851-2025
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
- Spatiotemporal rainfall rorecasting through GAN-based deep learning with enhanced temporal coherence R. Fredyan & K. Setiawan https://doi.org/10.1016/j.procs.2025.08.289
- A probabilistic approach to wildfire spread prediction using a denoising diffusion surrogate model W. Yu et al. https://doi.org/10.5194/gmd-19-1027-2026
- TriPhysGAN-Attn: A Physics-Informed Generative Model for Radar Echo Forecasting via Triple Mechanism Decomposition and Attention Fusion Y. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3658947
- Multimodel ensemble heavy precipitation forecast with U-Net deep learning model integrating the spatial FSS loss function H. Qi et al. https://doi.org/10.1016/j.atmosres.2026.108931
- MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation Y. Pei et al. https://doi.org/10.3390/rs16193597
- MT-GAN: A Multitask GAN for Severe Convective Weather Nowcasting L. Fan et al. https://doi.org/10.1109/LGRS.2024.3522463
- TPDTC-Net-DRA: Enhancing Nowcasting of Heavy Precipitation via Dynamic Region Attention X. Qi et al. https://doi.org/10.3390/rs18030490
- CNN-Based Time Series Decomposition Model for Video Prediction J. Lee & G. Kim https://doi.org/10.1109/ACCESS.2024.3458460
- Review on deep learning quantitative precipitation nowcasting: Advances and challenges D. Li et al. https://doi.org/10.1016/j.eswa.2025.130775
- Coupling strategies for precipitation nowcasting in China Q. Luo et al. https://doi.org/10.1080/02626667.2025.2587687
- Bidirectional motion-aware GAN for future frame prediction in autonomous driving X. Wang et al. https://doi.org/10.1016/j.ins.2025.122887
- Enhancing Multiple Precipitation Data Integration Across a Large-Scale Area: A Deep Learning ResU-Net Framework Without Interpolation G. Noh & K. Ahn https://doi.org/10.1109/TGRS.2025.3538829
- ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data M. Lu et al. https://doi.org/10.3390/rs16203911
- Generation of Synthetic Advanced Microwave Scanning Radiometer-2 23.8 GHz Dual-Polarization Measurements From Global Precipitation Measurement Microwave Imager Observations H. Ryu et al. https://doi.org/10.1109/TGRS.2025.3555803
- A Practical Online Incremental Learning Framework for Precipitation Nowcasting C. Luo et al. https://doi.org/10.1109/TGRS.2023.3330303
- Intelligent English Writing Tutoring System Using Generative Adversarial Networks and Collaborative Filtering for Personalized Feedback and Style Enhancement L. Zhou & Z. Yang https://doi.org/10.1142/S0218126626500702
- The Evolution of Generative AI: Trends and Applications M. Trigka & E. Dritsas https://doi.org/10.1109/ACCESS.2025.3574660
- Toward a Variation-Aware and Interpretable Model for Radar Image Sequence Prediction X. Huang et al. https://doi.org/10.1109/TII.2024.3399401
- Evaluation of IMERG Precipitation Product Downscaling Using Nine Machine Learning Algorithms in the Qinghai Lake Basin K. Lei et al. https://doi.org/10.3390/w17121776
21 citations as recorded by crossref.
- Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall J. Ritvanen et al. https://doi.org/10.5194/gmd-18-1851-2025
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
- Spatiotemporal rainfall rorecasting through GAN-based deep learning with enhanced temporal coherence R. Fredyan & K. Setiawan https://doi.org/10.1016/j.procs.2025.08.289
- A probabilistic approach to wildfire spread prediction using a denoising diffusion surrogate model W. Yu et al. https://doi.org/10.5194/gmd-19-1027-2026
- TriPhysGAN-Attn: A Physics-Informed Generative Model for Radar Echo Forecasting via Triple Mechanism Decomposition and Attention Fusion Y. Zhang et al. https://doi.org/10.1109/JSTARS.2026.3658947
- Multimodel ensemble heavy precipitation forecast with U-Net deep learning model integrating the spatial FSS loss function H. Qi et al. https://doi.org/10.1016/j.atmosres.2026.108931
- MAFNet: Multimodal Asymmetric Fusion Network for Radar Echo Extrapolation Y. Pei et al. https://doi.org/10.3390/rs16193597
- MT-GAN: A Multitask GAN for Severe Convective Weather Nowcasting L. Fan et al. https://doi.org/10.1109/LGRS.2024.3522463
- TPDTC-Net-DRA: Enhancing Nowcasting of Heavy Precipitation via Dynamic Region Attention X. Qi et al. https://doi.org/10.3390/rs18030490
- CNN-Based Time Series Decomposition Model for Video Prediction J. Lee & G. Kim https://doi.org/10.1109/ACCESS.2024.3458460
- Review on deep learning quantitative precipitation nowcasting: Advances and challenges D. Li et al. https://doi.org/10.1016/j.eswa.2025.130775
- Coupling strategies for precipitation nowcasting in China Q. Luo et al. https://doi.org/10.1080/02626667.2025.2587687
- Bidirectional motion-aware GAN for future frame prediction in autonomous driving X. Wang et al. https://doi.org/10.1016/j.ins.2025.122887
- Enhancing Multiple Precipitation Data Integration Across a Large-Scale Area: A Deep Learning ResU-Net Framework Without Interpolation G. Noh & K. Ahn https://doi.org/10.1109/TGRS.2025.3538829
- ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data M. Lu et al. https://doi.org/10.3390/rs16203911
- Generation of Synthetic Advanced Microwave Scanning Radiometer-2 23.8 GHz Dual-Polarization Measurements From Global Precipitation Measurement Microwave Imager Observations H. Ryu et al. https://doi.org/10.1109/TGRS.2025.3555803
- A Practical Online Incremental Learning Framework for Precipitation Nowcasting C. Luo et al. https://doi.org/10.1109/TGRS.2023.3330303
- Intelligent English Writing Tutoring System Using Generative Adversarial Networks and Collaborative Filtering for Personalized Feedback and Style Enhancement L. Zhou & Z. Yang https://doi.org/10.1142/S0218126626500702
- The Evolution of Generative AI: Trends and Applications M. Trigka & E. Dritsas https://doi.org/10.1109/ACCESS.2025.3574660
- Toward a Variation-Aware and Interpretable Model for Radar Image Sequence Prediction X. Huang et al. https://doi.org/10.1109/TII.2024.3399401
- Evaluation of IMERG Precipitation Product Downscaling Using Nine Machine Learning Algorithms in the Qinghai Lake Basin K. Lei et al. https://doi.org/10.3390/w17121776
Saved (final revised paper)
Latest update: 05 Jun 2026
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.
Formulating short-term precipitation forecasting as a video prediction task, a novel deep...
