Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-5967-2022
© Author(s) 2022. 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-15-5967-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
01 Aug 2022
Model description paper |
| 01 Aug 2022
| 01 Aug 2022
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
Suyeon Choi
Department of Civil and Environmental Engineering, Yonsei University,
Seoul 03722, Korea
Department of Civil and Environmental Engineering, Yonsei University,
Seoul 03722, Korea
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Cited
29 citations as recorded by crossref.
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. https://doi.org/10.1016/j.eswa.2024.126301
- Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning D. Han et al. https://doi.org/10.1080/15481603.2023.2203363
- Enhanced radar echo extrapolation for precipitation nowcasting quality using the convolutional Kolmogorov–Arnold networks Q. Cheng et al. https://doi.org/10.1016/j.jhydrol.2025.134134
- Deep learning and generative models to predict Manning resistance I. Loukam et al. https://doi.org/10.1080/00221686.2026.2623511
- A Strategy to set up Test Dataset and Evaluation Benchmark for Radar Nowcasting of Precipitation in Italy C. Annella et al. https://doi.org/10.1109/JSTARS.2025.3568185
- 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
- NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes A. Albu et al. https://doi.org/10.3390/rs14163890
- 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
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim https://doi.org/10.1088/1748-9326/ad8bdc
- Spatiotemporal Graph Neural Networks for PM2.5 Concentration Forecasting V. Chabalala et al. https://doi.org/10.3390/air4010002
- Evaluation of a Conditional Generative Adversarial Network Model for Retrieval of Instantaneous Rain Rates from INSAT-3D Outgoing Longwave Radiation Observations A. Deshpande & K. Gopalan https://doi.org/10.1007/s12524-026-02466-2
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. https://doi.org/10.5194/gmd-16-5895-2023
- RSG-GAN: A GAN-Based Precipitation Nowcasting Model Integrating Radar QPE, GOES-16 SWD, and GNSS ZTDs C. Lu et al. https://doi.org/10.1109/TGRS.2025.3587883
- PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images A. Salim Rasheed et al. https://doi.org/10.1109/ACCESS.2024.3404554
- Coupling strategies for precipitation nowcasting in China Q. Luo et al. https://doi.org/10.1080/02626667.2025.2587687
- An Effective Algorithm of Outlier Correction in Space–Time Radar Rainfall Data Based on the Iterative Localized Analysis Y. Kim et al. https://doi.org/10.1109/TGRS.2024.3366400
- Prediction of severe thunderstorm events with ensemble deep learning and radar data S. Guastavino et al. https://doi.org/10.1038/s41598-022-23306-6
- Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches J. Ha & J. Park https://doi.org/10.3389/feart.2023.1301523
- trajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning C. Ji & Y. Xu https://doi.org/10.1016/j.heliyon.2024.e36134
- Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow J. Ha & H. Lee https://doi.org/10.3390/rs15215169
- TU2Net-GAN: A temporal precipitation nowcasting model with multiple decoding modules X. Ling et al. https://doi.org/10.1016/j.patrec.2023.12.025
- Conditional Generative Adversarial Networks Enhance Atmospheric Thermodynamic Profile Retrieval from Ground-Based Microwave Radiometer Measurements D. Fu et al. https://doi.org/10.1007/s13351-025-5017-1
- Unveiling the Role of Weighted Loss Functions in Deep Learning-Based Nowcasting of Extreme Rainfall Events H. Choi et al. https://doi.org/10.1109/TGRS.2025.3592853
- SR2-GAN: A Novel Generative Adversarial Network for Radar Image Sequence Nowcasting H. Son et al. https://doi.org/10.1007/s11036-026-02532-6
- Flood forecasting based on radar precipitation nowcasting using U-net and its improved models J. Li et al. https://doi.org/10.1016/j.jhydrol.2024.130871
- Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness M. Piran et al. https://doi.org/10.1016/j.jag.2024.103962
- A novel spatiotemporal GAN for radar nowcasting with multi-branch intensity separation and 3D temporal attention H. Son et al. https://doi.org/10.1007/s10791-026-10285-9
- Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach Y. Wang et al. https://doi.org/10.1109/JSTARS.2024.3383397
- Physics-Guided Latent Diffusion Model for Intensity- and Extremity-Aware VIL Nowcasting C. Gao et al. https://doi.org/10.1109/JSTARS.2026.3694114
29 citations as recorded by crossref.
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. https://doi.org/10.1016/j.eswa.2024.126301
- Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning D. Han et al. https://doi.org/10.1080/15481603.2023.2203363
- Enhanced radar echo extrapolation for precipitation nowcasting quality using the convolutional Kolmogorov–Arnold networks Q. Cheng et al. https://doi.org/10.1016/j.jhydrol.2025.134134
- Deep learning and generative models to predict Manning resistance I. Loukam et al. https://doi.org/10.1080/00221686.2026.2623511
- A Strategy to set up Test Dataset and Evaluation Benchmark for Radar Nowcasting of Precipitation in Italy C. Annella et al. https://doi.org/10.1109/JSTARS.2025.3568185
- 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
- NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes A. Albu et al. https://doi.org/10.3390/rs14163890
- 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
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim https://doi.org/10.1088/1748-9326/ad8bdc
- Spatiotemporal Graph Neural Networks for PM2.5 Concentration Forecasting V. Chabalala et al. https://doi.org/10.3390/air4010002
- Evaluation of a Conditional Generative Adversarial Network Model for Retrieval of Instantaneous Rain Rates from INSAT-3D Outgoing Longwave Radiation Observations A. Deshpande & K. Gopalan https://doi.org/10.1007/s12524-026-02466-2
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. https://doi.org/10.5194/gmd-16-5895-2023
- RSG-GAN: A GAN-Based Precipitation Nowcasting Model Integrating Radar QPE, GOES-16 SWD, and GNSS ZTDs C. Lu et al. https://doi.org/10.1109/TGRS.2025.3587883
- PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images A. Salim Rasheed et al. https://doi.org/10.1109/ACCESS.2024.3404554
- Coupling strategies for precipitation nowcasting in China Q. Luo et al. https://doi.org/10.1080/02626667.2025.2587687
- An Effective Algorithm of Outlier Correction in Space–Time Radar Rainfall Data Based on the Iterative Localized Analysis Y. Kim et al. https://doi.org/10.1109/TGRS.2024.3366400
- Prediction of severe thunderstorm events with ensemble deep learning and radar data S. Guastavino et al. https://doi.org/10.1038/s41598-022-23306-6
- Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches J. Ha & J. Park https://doi.org/10.3389/feart.2023.1301523
- trajPredRNN+: A new approach for precipitation nowcasting with weather radar echo images based on deep learning C. Ji & Y. Xu https://doi.org/10.1016/j.heliyon.2024.e36134
- Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow J. Ha & H. Lee https://doi.org/10.3390/rs15215169
- TU2Net-GAN: A temporal precipitation nowcasting model with multiple decoding modules X. Ling et al. https://doi.org/10.1016/j.patrec.2023.12.025
- Conditional Generative Adversarial Networks Enhance Atmospheric Thermodynamic Profile Retrieval from Ground-Based Microwave Radiometer Measurements D. Fu et al. https://doi.org/10.1007/s13351-025-5017-1
- Unveiling the Role of Weighted Loss Functions in Deep Learning-Based Nowcasting of Extreme Rainfall Events H. Choi et al. https://doi.org/10.1109/TGRS.2025.3592853
- SR2-GAN: A Novel Generative Adversarial Network for Radar Image Sequence Nowcasting H. Son et al. https://doi.org/10.1007/s11036-026-02532-6
- Flood forecasting based on radar precipitation nowcasting using U-net and its improved models J. Li et al. https://doi.org/10.1016/j.jhydrol.2024.130871
- Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness M. Piran et al. https://doi.org/10.1016/j.jag.2024.103962
- A novel spatiotemporal GAN for radar nowcasting with multi-branch intensity separation and 3D temporal attention H. Son et al. https://doi.org/10.1007/s10791-026-10285-9
- Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach Y. Wang et al. https://doi.org/10.1109/JSTARS.2024.3383397
- Physics-Guided Latent Diffusion Model for Intensity- and Extremity-Aware VIL Nowcasting C. Gao et al. https://doi.org/10.1109/JSTARS.2026.3694114
Saved (final revised paper)
Latest update: 27 Oct 2026
Short summary
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to...
