GAN–argcPredNet v1.0: a generative adversarial model for radar echo extrapolation based on convolutional recurrent units

Zheng, Kun; Liu, Yan; Zhang, Jinbiao; Luo, Cong; Tang, Siyu; Ruan, Huihua; Tan, Qiya; Yi, Yunlei; Ran, Xiutao

doi:https://doi.org/10.5194/gmd-15-1467-2022

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.

https://doi.org/10.5194/gmd-15-1467-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 4

Model description paper

|

18 Feb 2022

Model description paper |

| 18 Feb 2022

GAN–argcPredNet v1.0: a generative adversarial model for radar echo extrapolation based on convolutional recurrent units

Kun Zheng, Yan Liu, Jinbiao Zhang, Cong Luo, Siyu Tang, Huihua Ruan, Qiya Tan, Yunlei Yi, and Xiutao Ran

Model code and software

GAN-argcPredNet and argcPredNet models Kun Zheng https://github.com/LukaDoncic0/GAN-argcPredNet

pretrained weights of GAN-argcPredNet and argcPredNet Kun Zheng and Yan Liu https://doi.org/10.5281/zenodo.4765575

GAN-argcPredNet and argcPredNet models Kun Zheng and Yan Liu https://doi.org/10.5281/zenodo.5035201

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.