Submitted as: model description paper
16 Jan 2023
Submitted as: model description paper |  | 16 Jan 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

GAN-argcPredNet v2.0: A Radar Echo Extrapolation Model based on Spatiotemporal Process Intensification

Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng

Abstract. Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is the common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between real rainfall evolution and generated images. The evolution information is easily lost during extrapolation, which is reflected as echoes attenuation. Existing models, including Generative Adversarial Network (GAN)-based models, are all difficult to reduce loss and curb attenuation, which results in insufficient rainfall prediction accuracy. Aim to the problem, a Spatiotemporal Process Intensification Network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 is designed. GAN-argcPredNet v2.0 reduces the loss by intensifying the influence of the previously input evolution information. A Spatiotemporal Information Changes Prediction (STIC-Prediction) network is designed as generator. With the intensification of echo feature sequence, the generator focuses on the spatiotemporal variation and generates more accurate images. Furthermore, discriminator is a Channel-Spatial Convolution (CS-Convolution) network. The discriminator intensifies the discrimination of echoes information by strengthening spatial information of single image. Identification results are fed back to the generator, which reduces the loss of important evolutionary information. The experiments are based on the radar dataset of South China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with baseline, the Probability of Detection (POD), the Critical Success Index (CSI), and the Heidke Skill Score (HSS) increase by 24.8 %, 22.2 % and 21.5 % respectively. The False Alarm Ratio (FAR) decreases by 3.76 %.

Kun Zheng et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2022-265', Long He, 16 Feb 2023
    • AC3: 'Reply on CC1', Kun Zheng, 10 Apr 2023
  • RC1: 'Comment on gmd-2022-265', Anonymous Referee #1, 26 Feb 2023
    • AC1: 'Reply on RC1', Kun Zheng, 10 Apr 2023
  • RC2: 'Comment on gmd-2022-265', Anonymous Referee #2, 10 Mar 2023
    • AC2: 'Reply on RC2', Kun Zheng, 10 Apr 2023

Kun Zheng et al.

Kun Zheng et al.


Total article views: 492 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
396 78 18 492 3 2
  • HTML: 396
  • PDF: 78
  • XML: 18
  • Total: 492
  • BibTeX: 3
  • EndNote: 2
Views and downloads (calculated since 16 Jan 2023)
Cumulative views and downloads (calculated since 16 Jan 2023)

Viewed (geographical distribution)

Total article views: 455 (including HTML, PDF, and XML) Thereof 455 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 01 May 2023
Short summary
Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by enhancing the influence of real rainfall information on the extrapolation, especially for heavy rainfall. The results show that our model has better performance, which is useful for urban operation and flood prevention.