Improved inversion of aerosol components in the atmospheric column from remote sensing data

Zhang, Ying; Li, Zhengqiang; Chen, Yu; de Leeuw, Gerrit; Zhang, Chi; Xie, Yisong; Li, Kaitao

doi:https://doi.org/10.5194/acp-20-12795-2020

Articles | Volume 20, issue 21

https://doi.org/10.5194/acp-20-12795-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Satellite and ground-based remote sensing of aerosol optical,...

https://doi.org/10.5194/acp-20-12795-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 21

Research article

|

04 Nov 2020

Research article |

| 04 Nov 2020

Improved inversion of aerosol components in the atmospheric column from remote sensing data

Ying Zhang, Zhengqiang Li, Yu Chen, Gerrit de Leeuw, Chi Zhang, Yisong Xie, and Kaitao Li

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Final revised paper (published on 04 Nov 2020)
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Preprint (discussion started on 22 Jan 2020)
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review of Improved inversion of aerosol components in the atmospheric column from remote sensing data by Zhang et al.', Anonymous Referee #1, 19 Feb 2020
- AC1: 'Reply to reviewer #1', Ying Zhang, 15 Jun 2020
RC2: 'Review on acp-2019-1062 Improved inversion of aerosol components in the atmospheric column from remote sensing data by Zhang et al.', Anonymous Referee #3, 04 May 2020
- AC2: 'Reply to reviewer #3', Ying Zhang, 15 Jun 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Ying Zhang on behalf of the Authors (16 Jun 2020) Manuscript

ED: Referee Nomination & Report Request started (21 Jun 2020) by Stelios Kazadzis

RR by Anonymous Referee #3 (06 Jul 2020)

RR by Anonymous Referee #2 (15 Jul 2020)

Suggestions for revision or reasons for rejection

General comments:
This paper describes a method that estimates the aerosol components by calculating the refractive index of a aerosol mixture. Then the algorithm was applied to ground-based remote sensing measurements to retrieve the aerosol components in China. The information of aerosol component is important for the understanding of climate change, air quality, the interaction between aerosols and cloud, chemical transport model estimation, etc. Meanwhile, the concentration of aerosol components in the atmospheric column are quite difficult to measured. Therefore, the efforts on retrieval of aerosol component in this study are commendable and the work is meaningful. However, I have some comments on the current manuscript.

Major comments:
1. I think the authors should highlight the improvements of aerosol component retrieval in their study rather than some results that are well-known in many previous studies in the abstract, such as “the atmospheric columnar DU component is dominant in the northern region of China, whereas the AW is higher in the southern coastal region”. Because the title of “improved inversion of aerosol components in the atmospheric column from remote sensing data” emphasizes the new development of algorithm. I also suggest to show some comparisons of aerosol component retrievals between the improved algorithm and the previous algorithm. As Referee #3 mentioned in “Major comments 3: a comparison to a previous method could be presented, if, of course, such comparison could be done. For example, comparison with OM from Zhang 2018 could be performed to illustrate improvements (if any)”

2. Line 217-223: You mentioned that “The improved algorithm described here is more suitable for the calculation of the properties of a mixture of multiple water-soluble components …” and the previous algorithm had some limits. Could you provide the comparisons of aerosol component retrieval derived by these two algorithms? For example, aerosol water fraction. Because I wonder if these two approaches can obtain similar results for aerosol water fraction that is considered in both of two algorithms. The aerosol water fraction should have a good agreement between the improved algorithm and previous algorithm (Zhang, 2018).

3. Could you provide a table to show the statistics of fitting for each aerosol component in Figure 5?

4. I also read the paper (Zhang et al., 2018), which is you cited and mentioned in the current manuscript. The values of aerosol component density used in Zhang et al. (2018) (Table 1) are quite different to that used in the current manuscript (Table 2), but with same values of complex refractive index. Why? I suggest to use same values of density in the current manuscript as that in Zhang et al. (2018), because more uncertainty could be induced from the density. For example, WIOM and WSOM density in Zhang et al. (2018) is 1.0, whereas it is 1.547 in current manuscript. The uncertainty could be up to more than 50%. In this case, the results in Figure 8,9 and 10 may have some changes.

5. Could you provide some validation of aerosol component retrievals? For example, the validation for black carbon concentration with in situ measurements.

Minor comments:
1. The definition of “aerosol water” is not appropriate and it cannot describe exactly the aerosol component. I suggest to use “aerosol water content” in the paper.

2. Line 23: “aerosol particles scatter and absorb solar radiation” It is imprecise.

3. Please reword the sentence of “the detail of information depends on the technique used” (Line 27)

4. Line 217: “In that algorithm” should be “In previous algorithm”

5. Line 234: “in good agreement” should be “in a good agreement”

6. Line 230-231: “…the volume fraction of BC was constrained between 0 to 3.0%...”. Why?

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ED: Reconsider after major revisions (18 Jul 2020) by Stelios Kazadzis

AR by Ying Zhang on behalf of the Authors (10 Aug 2020) Manuscript

ED: Referee Nomination & Report Request started (28 Aug 2020) by Stelios Kazadzis

RR by Anonymous Referee #2 (18 Sep 2020)

ED: Publish as is (23 Sep 2020) by Stelios Kazadzis

AR by Ying Zhang on behalf of the Authors (24 Sep 2020)

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Short summary

Observation of atmospheric aerosol components plays an important role in reducing uncertainty in climate assessment. In this study, an improved remote sensing method which can better distinguish scattering components is developed, and the aerosol components in the atmospheric column over China are retrieved based on the Sun–sky radiometer Observation NETwork (SONET). The component distribution shows there could be a sea salt component in northwest China from a paleomarine source in desert land.