RAP-Net: Region Attention Predictive Network for Precipitation Nowcasting
- 1Dept. of Computer Science, Harbin Institute Technology, Shenzhen, China
- These authors have contributed equally to this work.
- 1Dept. of Computer Science, Harbin Institute Technology, Shenzhen, China
- These authors have contributed equally to this work.
Abstract. Natural disasters caused by heavy rainfall often cause huge loss of life and property. Hence, the task of precipitation nowcasting is of great importance. To solve this problem, several deep learning methods have been proposed to forecast future radar echo images and then the predicted maps are converted to the distribution of rainfall. The prevailing spatiotemporal sequence prediction methods apply ConvRNN structure which combines the Convolution and Recurrent neural network. Although ConvRNN methods achieve remarkable success, they ignore capturing both local and global spatial features simultaneously, which degrades the nowcasting in regions of heavy rainfall. To address this issue, we propose a Region Attention Block (RAB) and embed it into ConvRNN to enhance forecasting in the area with strong rainfall. Besides, the ConvRNN models are hard to memorize longer historical representations with limited parameters. To this end, we propose Recall Attention Mechanism (RAM) to improve the prediction. By preserving longer temporal information, RAM contributes to the forecasting, especially in the middle rainfall intensity. The experiments show that the proposed model Region Attention Predictive Network (RAP-Net) significantly outperforms state-of-the-art methods.
Zheng Zhang et al.
Status: closed
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RC1: 'Comment on gmd-2022-19', Anonymous Referee #1, 02 Mar 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-19/gmd-2022-19-RC1-supplement.pdf
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RC2: 'Reply on RC1', Anonymous Referee #2, 17 Mar 2022
The paper describes a RAP-Net network that can be used for radar echo extrapolation. Experiments demonstrate the effectiveness of this method. The authors are suggested to supplement the experimental comparison of high-intensity echoes.
- AC2: 'Reply on RC2', Chuyao Luo, 05 Apr 2022
- AC1: 'Reply on RC1', Chuyao Luo, 05 Apr 2022
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RC2: 'Reply on RC1', Anonymous Referee #2, 17 Mar 2022
-
RC3: 'Comment on gmd-2022-19', Anonymous Referee #3, 19 Mar 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-19/gmd-2022-19-RC3-supplement.pdf
- AC3: 'Reply on RC3', Chuyao Luo, 05 Apr 2022
Status: closed
-
RC1: 'Comment on gmd-2022-19', Anonymous Referee #1, 02 Mar 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-19/gmd-2022-19-RC1-supplement.pdf
-
RC2: 'Reply on RC1', Anonymous Referee #2, 17 Mar 2022
The paper describes a RAP-Net network that can be used for radar echo extrapolation. Experiments demonstrate the effectiveness of this method. The authors are suggested to supplement the experimental comparison of high-intensity echoes.
- AC2: 'Reply on RC2', Chuyao Luo, 05 Apr 2022
- AC1: 'Reply on RC1', Chuyao Luo, 05 Apr 2022
-
RC2: 'Reply on RC1', Anonymous Referee #2, 17 Mar 2022
-
RC3: 'Comment on gmd-2022-19', Anonymous Referee #3, 19 Mar 2022
The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-19/gmd-2022-19-RC3-supplement.pdf
- AC3: 'Reply on RC3', Chuyao Luo, 05 Apr 2022
Zheng Zhang et al.
Zheng Zhang et al.
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