Articles | Volume 15, issue 4
https://doi.org/10.5194/gmd-15-1467-2022
© 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-15-1467-2022
© 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
| 
18 Feb 2022
Model description paper |
| 18 Feb 2022
| 18 Feb 2022
GAN–argcPredNet v1.0: a generative adversarial model for radar echo extrapolation based on convolutional recurrent units
School of Geography and Information Engineering, China University of
Geosciences, Wuhan 430074, China
Yan Liu
CORRESPONDING AUTHOR
School of Geography and Information Engineering, China University of
Geosciences, Wuhan 430074, China
Jinbiao Zhang
Guangdong Meteorological Observation Data center, Guangzhou 510080, China
Cong Luo
Guangdong Meteorological Observatory, Guangdong 510080, China
Siyu Tang
Guangdong Meteorological Observatory, Guangdong 510080, China
Huihua Ruan
Guangdong Meteorological Observation Data center, Guangzhou 510080, China
Qiya Tan
School of Geography and Information Engineering, China University of
Geosciences, Wuhan 430074, China
Yunlei Yi
Wuhan Zhaotu Technology Co. Ltd., Wuhan 430074, China
Xiutao Ran
Wuhan Zhaotu Technology Co. Ltd., Wuhan 430074, China
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Cited
13 citations as recorded by crossref.
- RAP-Net: Region Attention Predictive Network for precipitation nowcasting Z. Zhang et al. 10.5194/gmd-15-5407-2022
- Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting J. Ritvanen et al. 10.1109/JSTARS.2023.3238016
- Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data M. Lu et al. 10.3390/rs14092200
- TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data Q. Huang et al. 10.3390/rs15010142
- SCECA-Net: A Deep Learning-Based Model for Precipitation Nowcasting L. Li et al. 10.1109/TGRS.2025.3534278
- GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement K. Zheng et al. 10.5194/gmd-17-399-2024
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. 10.1016/j.atmosres.2024.107380
- SepConv-ens: An ensemble of separable convolution-based deep learning models for weather radar echo temporal extrapolation G. Czibula et al. 10.1016/j.procs.2024.09.482
- Deep learning model based on multi-scale feature fusion for precipitation nowcasting J. Tan et al. 10.5194/gmd-17-53-2024
- TU2Net-GAN: A temporal precipitation nowcasting model with multiple decoding modules X. Ling et al. 10.1016/j.patrec.2023.12.025
- Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast M. Hung et al. 10.3390/atmos14081201
- A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation J. Yao et al. 10.1109/ACCESS.2023.3280932
- A Cross-Modal Spatiotemporal Joint Predictive Network for Rainfall Nowcasting K. Zheng et al. 10.1109/TGRS.2024.3452767
13 citations as recorded by crossref.
- RAP-Net: Region Attention Predictive Network for precipitation nowcasting Z. Zhang et al. 10.5194/gmd-15-5407-2022
- Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting J. Ritvanen et al. 10.1109/JSTARS.2023.3238016
- Monitoring Lightning Location Based on Deep Learning Combined with Multisource Spatial Data M. Lu et al. 10.3390/rs14092200
- TSRC: A Deep Learning Model for Precipitation Short-Term Forecasting over China Using Radar Echo Data Q. Huang et al. 10.3390/rs15010142
- SCECA-Net: A Deep Learning-Based Model for Precipitation Nowcasting L. Li et al. 10.1109/TGRS.2025.3534278
- GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement K. Zheng et al. 10.5194/gmd-17-399-2024
- Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells Y. Cheng et al. 10.1016/j.atmosres.2024.107380
- SepConv-ens: An ensemble of separable convolution-based deep learning models for weather radar echo temporal extrapolation G. Czibula et al. 10.1016/j.procs.2024.09.482
- Deep learning model based on multi-scale feature fusion for precipitation nowcasting J. Tan et al. 10.5194/gmd-17-53-2024
- TU2Net-GAN: A temporal precipitation nowcasting model with multiple decoding modules X. Ling et al. 10.1016/j.patrec.2023.12.025
- Assessments of Use of Blended Radar–Numerical Weather Prediction Product in Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) for Quantitative Precipitation Forecast of Tropical Cyclone Landfall on Vietnam’s Coast M. Hung et al. 10.3390/atmos14081201
- A Forecast-Refinement Neural Network Based on DyConvGRU and U-Net for Radar Echo Extrapolation J. Yao et al. 10.1109/ACCESS.2023.3280932
- A Cross-Modal Spatiotemporal Joint Predictive Network for Rainfall Nowcasting K. Zheng et al. 10.1109/TGRS.2024.3452767
Latest update: 22 Feb 2025
Short summary
In extrapolation methods, there is a phenomenon that causes the extrapolated image to be blurred and unrealistic. The paper proposes the GAN–argcPredNet v1.0 network model, which aims to solve this problem through GAN's ability to strengthen the characteristics of multi-modal data modeling. GAN–argcPredNet v1.0 has achieved excellent results. Our model can reduce the prediction loss in a small-scale space so that the prediction results have more detailed features.
In extrapolation methods, there is a phenomenon that causes the extrapolated image to be blurred...
