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Article
Peer-Review Record

Evaluation and Global-Scale Observation of Nitrous Oxide from IASI on Metop-A

Remote Sens. 2022, 14(6), 1403; https://doi.org/10.3390/rs14061403
by Rémi Chalinel 1, Jean-Luc Attié 1,*, Philippe Ricaud 2, Jérôme Vidot 3, Yannick Kangah 4, Didier Hauglustaine 5 and Rona Thompson 6
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Remote Sens. 2022, 14(6), 1403; https://doi.org/10.3390/rs14061403
Submission received: 27 January 2022 / Revised: 25 February 2022 / Accepted: 4 March 2022 / Published: 14 March 2022
(This article belongs to the Special Issue Advances in Infrared Observation of Earth's Atmosphere)

Round 1

Reviewer 1 Report

Please see attached review. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

This study is well designed and motivated, as in measurement of N20 by IASI and ascertaining its performance using various sources of in-situ observations. The methodology is solid, and the analysis is thorough and clear of a cutting-edge instrument. I think this is a work of great relevance and interest, especially in the context of N20 being a greenhouse gas whose measurements have been a challenge. Given all the merits of this work and potential benefits, I recommend it for publication in Remote Sensing.  

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 3 Report

Dear authors,

I regret to inform you that your manuscript is lacking in numerous aspects, details of which you will find in the annotated text, in attachment. I am enumerating here the major ones, without implying that the rest in the text do not have to be addressed equally:

  1. You describe a new IASI N2O product. The major point that you have to make is why your product is an improvement to the operational N2O IASI product and/or other scientific products already in existence. This should be performed both in a detailed discussion, but also by comparing your product to the operational one.
  2. You do not provide details on the validation datasets you used. Such details should include references to the datasets, other validation studies that have used these datasets and what findings they had, the resolution/accuracy/precision of the auxiliary datasets. Without those, the reader cannot assess whether it makes sense to compare these to your new product.
  3. Your error analysis seems to me to be on the low side. You used initial error estimates on your algorithm input parameters which you do not justify and which, mathematically, result in too low values. Unless you claim that your product is far superior to the validation datasets, in which case, why validate?
  4. The manuscript lacks proper structure. I.e. you should first provide all the details on the analysis algorithm, explain the vertical resolution, the padding you performed - with Figures. Then you should show the product and THEN the validation.

I am not keen to reject this manuscript, however I am not convinced that you can address adequately all of its issues in the time frame allowed by the journal. You might consider withdrawing and resubmitting at your own pace. 

Best wishes.

Comments for author File: Comments.pdf

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Your manuscript is now much improved. All points addressed. Recommend publish as is. Congratulations.

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