Articles | Volume 25, issue 20
https://doi.org/10.5194/acp-25-13879-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Siberian wildfire smoke observations from space-based multi-angle imaging: a multi-year regional analysis of smoke particle properties, their evolution, and comparisons with North American boreal fire plumes
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- Final revised paper (published on 28 Oct 2025)
- Supplement to the final revised paper
- Preprint (discussion started on 05 Feb 2025)
- Supplement to the preprint
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
- RC1: 'Comment on egusphere-2025-395', Anonymous Referee #1, 18 Feb 2025
- RC2: 'Comment on egusphere-2025-395', Anonymous Referee #2, 04 Mar 2025
- AC1: 'Response to Reviewers on egusphere-2025-395', Katherine Junghenn Noyes, 27 Mar 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Katherine Junghenn Noyes on behalf of the Authors (27 Mar 2025)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (31 Mar 2025) by Stephanie Fiedler
RR by Anonymous Referee #1 (01 Apr 2025)
RR by Anonymous Referee #2 (01 Apr 2025)
RR by Anonymous Referee #3 (02 Sep 2025)
ED: Publish subject to technical corrections (10 Sep 2025) by Stephanie Fiedler
AR by Katherine Junghenn Noyes on behalf of the Authors (16 Sep 2025)
Manuscript
Using MISR and MERRA2 products, the authors extend previous analyses of Canada and Alaska wildfires to include those in Siberia. This is a generally excellent paper. It’s timely, insightful, and informative. But my recommendation to the editor is a straightforward rejection because of Code/Data Availability concerns (see my fuller comment below). If the authors address these concerns, I will be happy to take another look. I very much like the rest of the manuscript; if it weren’t for the Code/Data Availability, I would’ve recommended it for publication as-is (comments below are non-blocking).
Comments I wrote while (re)reading the manuscript:
L29: I think the word “however” is likely the wrong word here
L32: I think man-made may require qualification or even better a citation
L80: Still not really clear which study exactly examined the Canada and Alaska wildfires? Which one is it? Maybe just cite it here!
L86: And here!
L137: is the geometric standard deviation designated as well?
L140: citation (or link?) for may be needed for “publicly available”
L255: I didn’t check, but I assume that’s the same way you also defined it in the other study for the sake of consistency?
Figure 4: I found this figure hard to decipher (panels are too small, etc.). Consider improving. You can unify the titles (N=22, …) and labels (Forest, Woody, …) on top of the first row; you can use just on y-axis label (and include with it the a) median plume height, etc. info. Then, you may have enough space to showcase the figures themselves instead of the BIG words. See Figure 5, 6, 7 for inspiration :)
Table 3: I find tables in general hard to read. Is there a better way to showcase this data-rich info?
Table 4: this is a productive usage of the table format. Note a minor typo “but it’s AOD does” should be “but its AOD does” in the 4th row of the Fall column.
L511: The analysis and data are consistent across datasets/studies, right? If so, I would assure the readers here by stating that.
Section 3.6: I understand the authors plan to release more work with further and more in-depth analyses, but I think this manuscript will benefit from further contextualization and/or speculation. My comment here is vague, but it simply an invitation for the authors to do more here if they think it is warranted.
Code/Data Availability: After reading this manuscript, I got pretty excited about potentially using the data and/or taking a look the amazing underlying dataset. In my opinion, this Code/Data Availability section is unacceptable and as such I don’t think this work can be published without better disclosure of the underlying data AND some reproducibility code (to reproduce figures and tables in this manuscript). My request here, of course, does not apply to the noted propriety algorithm; you can keep that secret all you want. It applies to the raw data produced by this work, and especially the raw data used to make the scientific statements in this work. Also, please explicitly cite and point the reader to where they can find the "user-friendly" MINX. Anything short of full disclosure, the manuscript should be rejected and other reviewers shouldn’t waste time on it. I understand the authors wrote “[final URL is TBD]" but I am sorry, let’s not waste reviewers' time before it is “determined”
Supplement: same comment regarding tables and figures as I made regarding Figure 4 and Table 3 above.