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
17 citations as recorded by crossref.
- Prediction of severe thunderstorm events with ensemble deep learning and radar data S. Guastavino et al. 10.1038/s41598-022-23306-6
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. 10.1016/j.eswa.2024.126301
- Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning D. Han et al. 10.1080/15481603.2023.2203363
- Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches J. Ha & J. Park 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 10.1016/j.heliyon.2024.e36134
- NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes A. Albu et al. 10.3390/rs14163890
- Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow J. Ha & H. Lee 10.3390/rs15215169
- TU2Net-GAN: A temporal precipitation nowcasting model with multiple decoding modules X. Ling et al. 10.1016/j.patrec.2023.12.025
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim 10.1088/1748-9326/ad8bdc
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
- Flood forecasting based on radar precipitation nowcasting using U-net and its improved models J. Li et al. 10.1016/j.jhydrol.2024.130871
- Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness M. Piran et al. 10.1016/j.jag.2024.103962
- Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach Y. Wang et al. 10.1109/JSTARS.2024.3383397
- PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images A. Salim Rasheed et al. 10.1109/ACCESS.2024.3404554
- An Effective Algorithm of Outlier Correction in Space–Time Radar Rainfall Data Based on the Iterative Localized Analysis Y. Kim et al. 10.1109/TGRS.2024.3366400
- Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains S. Choi & Y. Kim 10.5194/gmd-15-5967-2022
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
15 citations as recorded by crossref.
- Prediction of severe thunderstorm events with ensemble deep learning and radar data S. Guastavino et al. 10.1038/s41598-022-23306-6
- Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting S. An et al. 10.1016/j.eswa.2024.126301
- Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning D. Han et al. 10.1080/15481603.2023.2203363
- Advancing very short-term rainfall prediction with blended U-Net and partial differential approaches J. Ha & J. Park 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 10.1016/j.heliyon.2024.e36134
- NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes A. Albu et al. 10.3390/rs14163890
- Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow J. Ha & H. Lee 10.3390/rs15215169
- TU2Net-GAN: A temporal precipitation nowcasting model with multiple decoding modules X. Ling et al. 10.1016/j.patrec.2023.12.025
- Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model H. Seo & Y. Kim 10.1088/1748-9326/ad8bdc
- Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning D. Han et al. 10.5194/gmd-16-5895-2023
- Flood forecasting based on radar precipitation nowcasting using U-net and its improved models J. Li et al. 10.1016/j.jhydrol.2024.130871
- Precipitation nowcasting using transformer-based generative models and transfer learning for improved disaster preparedness M. Piran et al. 10.1016/j.jag.2024.103962
- Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach Y. Wang et al. 10.1109/JSTARS.2024.3383397
- PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images A. Salim Rasheed et al. 10.1109/ACCESS.2024.3404554
- An Effective Algorithm of Outlier Correction in Space–Time Radar Rainfall Data Based on the Iterative Localized Analysis Y. Kim et al. 10.1109/TGRS.2024.3366400
2 citations as recorded by crossref.
- Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains S. Choi & Y. Kim 10.5194/gmd-15-5967-2022
- Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities Z. Ma et al. 10.1007/s10462-024-10764-9
Latest update: 05 Feb 2025
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...
