Articles | Volume 16, issue 9
https://doi.org/10.5194/amt-16-2431-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.Satellite remote-sensing capability to assess tropospheric-column ratios of formaldehyde and nitrogen dioxide: case study during the Long Island Sound Tropospheric Ozone Study 2018 (LISTOS 2018) field campaign
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- Final revised paper (published on 16 May 2023)
- Supplement to the final revised paper
- Preprint (discussion started on 07 Sep 2022)
- Supplement to the preprint
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on amt-2022-237', Anonymous Referee #1, 01 Nov 2022
- AC1: 'Reply on RC1', Matthew S. Johnson, 06 Feb 2023
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RC2: 'Comment on amt-2022-237', Anonymous Referee #2, 08 Nov 2022
- AC2: 'Reply on RC2', Matthew S. Johnson, 06 Feb 2023
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Matthew S. Johnson on behalf of the Authors (07 Feb 2023)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (10 Feb 2023) by Folkert Boersma
RR by Anonymous Referee #1 (22 Feb 2023)

RR by Anonymous Referee #3 (07 Mar 2023)

ED: Publish subject to minor revisions (review by editor) (29 Mar 2023) by Folkert Boersma

AR by Matthew S. Johnson on behalf of the Authors (04 Apr 2023)
Author's response
Author's tracked changes
Manuscript
ED: Publish as is (17 Apr 2023) by Folkert Boersma

AR by Matthew S. Johnson on behalf of the Authors (17 Apr 2023)
Author's response
Manuscript
This study evaluates OMI and TROPOMI retrievals of NO2, HCHO and FNR using aircraft measurements during the LISTOS campaign. The manuscript is well-written, and it is a good for for AMT. See my comments below.
Specific Comments:
Abstract: The abstract is lengthy. I’d suggest the authors shorten the abstract to include only the core findings of this work. For example, the first paragraph may belong to introduction.
Line 370: What are the quality flags for? Is this the same quality flag as for TROPOMI? If so, why do you choose different thresholds? Better to include references here.
Table 2: I’d suggest include an estimate of the error, such as normalized mean standard errors. NMB doesn’t tell much about the precision of the retrievals.
Line 530: Maybe you could have a figure of the mean biases of HCHO to show where OMI or TROPOMI HCHO is biased high?
Line 600: I’m not sure if we could call this as a ‘high pollution’ day because ozone was actually low on this day. This could very well be a cold day when the lifetime of NO2 is long, and the the photolysis is low. I’m not sure how much value there is to evaluate FNR on this day. It’d be more interesting to add another day with both high ozone and high NO2.
Line 700: It is interesting to see that improved the a priori from CMAQ does not improve the retrieval performance of OMI. The authors attribute this to coarse resolution of OMI. Could this be due to the coarse resolution of cloud and surface albedo data used in the retrieval?
Line 835: While the low mean biases of FNR is low, the standard deviation is very large. The R sure is also low for FNR. Thus I don’t think the errors of HCHO and NO2 could cancel out. The errors in HCHO and NO2 can offset only if the errors are correlated. I’d suggest the authors make a scatter plot for errors of HCHO versus NO2, and see if they are correlated.