Articles | Volume 28, issue 3
© Author(s) 2021. This work is distributed underthe Creative Commons Attribution 4.0 License.
Producing realistic climate data with generative adversarial networks
- Final revised paper (published on 30 Jul 2021)
- Preprint (discussion started on 16 Feb 2021)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
: Report abuse
RC1: 'Comment on npg-2021-6', Anonymous Referee #1, 17 Mar 2021
- AC1: 'Reply on RC1', Camille Besombes, 05 Jun 2021
RC2: 'Comment on npg-2021-6', Anonymous Referee #2, 25 Apr 2021
- AC2: 'Reply on RC2', Camille Besombes, 05 Jun 2021
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Camille Besombes on behalf of the Authors (08 Jun 2021)  Author's response Author's tracked changes Manuscript
ED: Publish as is (17 Jun 2021) by Takemasa Miyoshi
AR by Camille Besombes on behalf of the Authors (17 Jun 2021)  Author's response Manuscript
This paper introduces a novel method of producing realistic climate data using Generative Adversarial Networks. The paper is well written in general. The text is fluent. The scientific approach is sound and clear. The results are well presented.
1. How tolerant are the wrap padding layers to discontinuity? To which extent, the padding process may affect the training?
2. By looking at the inputs/outputs in figures 1 to 5, I assume that some convolutional blocks have pooling/ upsampling layers and others do not. Could you explain the reason for that and present these blocks differently in the figures?
3. Figure 2 and 3 present two different architectures of the residual blocks of the Critic. Which one was used?
4. The stopping criteria of the training is a maximum number of iterations. Could this condition be combined with the convergence of the loss function, for example loss function derivative very small or training set and validation set having almost equal loss?
5. I would suggest reducing the length of the paper as follows:
1. figure 10 and 11 could be merged in one figure
2. figure 12 and 13 present almost the same information. I would recommend keeping only one of them.
1. Please review the formatting of equations 5, 10, 13
2. Figure 2 and 3 have the same title
3. Please use the same title pattern in figure 5 as in figure 2
4. Figure 21, 22 and 24, please review the titles and explain what is a, b, c, d
5. Figure 29, please use the equation reference, (as in 28) instead of the equation itself.