First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil

Round 1

Reviewer 1 Report

Minor corrections as requested in the uploaded annotated version

Comments for author File: Comments.pdf

Author Response

We would like to express our gratitude to reviewer for the careful revision of our
manuscript. All comments were very helpful to improve the manuscript quality. Our
comments and responses to all questions and comments are given in the attached file.

Author Response File: Author Response.pdf

Reviewer 2 Report

I believe that the authors’ response makes sense, and this article has been highly improved and could be published. But a few issues I want to remind the authors:

1) The redlined version of the revision is quite inconvenient to check the corrections somehow. It is quite messy, maybe because it is not done by Adobe Acrobat. I can not understand why somewhere is also highlighted, which looks to be irrelevant to the previous comments. You could just only highlight the major corrections and summary the language polishing in the response letter. Also, it would be better to emphasize how you make the corrections (just copy them or point out the line number) in the response letter for saving my time, if there is a next-round revision.

2) What I said is about the lidar ratios from CALIPSO are UNRELIABLE, and I wouldn’t say that they are WRONG. The development of CALIPSO algorithm has employed numerous data from sunphotometer (AERONET), Raman lidar (EARLINET), and high spectral resolution lidar (NASA airborne field campaign) to fit them. In those validated region, they would have a strong correlation of course, as they could always modify the algorithm to fit the results. But for those invalidated regions, given that you claim that there is no available measurement from sumphotometer or Raman lidar, the uncertainty of lidar ratios comes from two parts, the misclassification and the numerical fluctuation from the nature of aerosol type, especially for polluted dust. You show some literature with good agreement; however, there are also some results with poor agreement. I do not intend to pick up a debate about the CALIPSO data quality so I would not give any reference, as I THOUGHT that your ground-based Raman lidar should be available as my previous comments. Now you claim that Raman lidar data could not be accessed, I understand your situation as having something is better than nothing. But I still encourage you to employ your Raman lidar to validate the CALIPSO data if that is possible.

3) Please spell out the abbreviation in the abstract and the main text at first mention, e.g., CETEST, HYSPLIT, etc.

Author Response

We would like to express our gratitude to reviewer for the careful revision of our
manuscript. All comments were very helpful to improve the manuscript quality. Our
comments and responses to all questions and comments are given in the attached file.

Author Response File: Author Response.pdf

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

The authirs shour revise the manuscript considerably. They should not use the aersol extinction profiling, nor refer to AOD values, since both have very large uncertainties; instead they can use only the baer profiles.

This means that the CALIPSO data should provide information only on the aerosol typing and this should be used in an attentive manner; any uncertainties on this aerosol subtyping should be presented and cited.

The paper needs major revision (see attached reviewed file in yellow with corrections and comments) of the MS).

Comments for author File: Comments.pdf

Reviewer 2 Report

General comments:

I am glad to see that the authors report a lidar field campaign in Brazil, which is rarely seen in this area and should be encouraged. I could also notice that the authors devote respectful efforts in a lot of statistical results.

 

However, I cannot avoid to see some confusing materials and the inappropriate structure in this manuscript. The major problem is that the results part is seemingly accumulated by the local air quality data, the CALIPSO data, and the ground-based lidar data. What is the connection and relationship between those data, and what do the authors want to provide except some statistical results? It is hard for me to follow the logical of the presented context.

 

Also the structure could be improved: it is an arduous work to read the context about the ground-based lidar data and tease out what meaningful results the authors try to deliver. Because the ground-based lidar data was presented with different dates, different stations, and different conditions. It is quite annoying for me that I must often jump back to compare the previous parts and check the date of presented data. I cannot understand why the authors only present the results corresponding to specific days while they have longer observations. Do they reveal something remarkably, or just due to the limited data quality? I would recommend the authors to make some classifications with the results for better understanding, e.g., the clean days, the polluted days, the specials days with aerosol regional transportation, etc. Just naming some of the features from the observations of different stations is also optional, which might be not available since the authors claimed that two stations show similar patterns. Or combining both of them, it is your choice.

 

To my opinion, the manuscript does not exhibit enough merits to be published in Remote Sensing currently. Many aspects still need to be discussed and presented well. I would also recommend the authors  polish the paper and check the syntax, especially the abstract and the introduction. Please find some specific comments below. I suppose that major revisions will be required to consider the manuscript for acceptance.

 

Specific comments:

 

Abstract, Introduction and Conclusion: IPCC AR6 report did reveal that the increasing of greenhouse gases cause global warming mainly, but your observations are about aerosols. The significance of your lidar observations should be related to the particulate matter, not greenhouse gases. Generally, aerosols affect the climate by means of activation of clouds (cooling Earth) and absorbing the solar (heating Earth) mostly. Their effects are complex and needed to be quantified. You draft some sentences about the global warming in the introduction part, which is true but not related to your studies. The major merit of lidar observations is to provide vertical profiles which you should emphasize.

 

Abstract:

Line 3: recommend change “backscatter” to “backscattering”.

Line 4 and 6: change “were” to “was”.

Line 7: CALIPSO actually can not provide practical lidar ratios since it is an elastic lidar. Basically, CALIPSO algorithm identifies the aerosol types with profiles of attenuated backscattering and depolarization ratio at visible band and infrared band, then "guessing" the lidar ratio using the information of aerosol types, geometric places, etc. They are not reliable results.

Line 8-10: It is strange in “… patterns were verified …”. I can not understand how you verify the results like that. I suppose you should mean “… patterns were observed”.

Line 14: I recommend to delete “greenhouse gases”. The significance of your observations are measly for the quantification of greenhouse gases.

 

Introduction: It is unfavorable for me to read such a long paragraph without pause. Please divide it into several paragraphs with logical manner, e.g., a basic introduction to this field, more detailed background about previous works, how your main results add to previous knowledge, etc.

 

2.2.2 Lidar system: You mention that you have 607-nm channel. So it is a Raman lidar not just an elastic lidar. To the best of my knowledge, the commercial Raymetrics Raman lidar can observe the nighttime Raman signal at the very least. However, it seems like that it is only performed as a regular elastic lidar in this paper. What happen to the Raman channel? I could notice that the resolution here (100-s and 7.5-m) is too high for the Raman channel. It would be appropriate to employ a much lower resolution and show the Raman observations in the following results. More additional explanations about the lidar configuration are required.

 

2.2.3. CALIPSO satellite: Please keep in mind that stand-alone CALIPSO data cannot provide lidar ratio observations directly. The lidar ratio data from CALIPSO weakly support the aerosol type and it should not be considered for a statistically significant parameter. Misclassification in CALIPSO algorithm is quite normal due to its limited information and poor data quality. I would not recommend to use CALIPSO data to classify aerosol type. Since you have your own lidar and it is a Raman lidar, it has a highly possibility to use your own lidar data to do some classification. At the very least, it could be still meaningful for you to verify the CALIPSO classification via your results.

 

2.2.4. HYSPLIT air-masses trajectories: I would recommend you to follow the citation policy of the HYSPLIT (A. F. Stein, et. al, 2015, BAMS). Please cite their publications where appropriate.

 

3.Lidar retrieval methodology:

Line 216-217: You claim that “we first present a calculation of the expected elastic return signal from a pure molecular atmosphere”. I strongly doubt it and recommend you delete this sentence. Please refer classical lidar technical books.

 

Figure 2: The authors present a general view and the distribution of PM10 data, while I notice that the air quality stations provide PM2.5 data. Why not show simultaneous PM2.5 measurements to identify possible dust events since the authors claimed that one of the major aerosol types is polluted dust? Also the corresponding backscattering coefficients should be highly related to the PM10 and PM2.5 data. Showing a comparison between particulate matter concentration data and lidar data would be more significant. I also have a concern that the monthly average PM10 concentration are rarely exceed 40 ug/m^3 but the distribution of PM10 data shows an opposite pattern. Something is inconsistent here.

 

4.2 CALIPSO satellite retrieval: Again, the lidar ratio data from CALIPSO is almost meaningless. You have a Raman lidar so please retrieve the extinction coefficients directly. By the way, how long the distance between the CALIPSO footprint and the location you choose in the statistical results, e.g., 20 km or 200 km? The statistical results could be varied widely when employing different distance to count the CALIPSO data. Please specify the reason why you choose this distance and add more explanations.

 

Figure 6: You show that the aerosol types are dominant by the polluted continental aerosols and the polluted dust aerosols. Some insights can be shown by combining the air quality data and ground-based lidar data. For instance, you can show the corresponding PM2.5 and PM10 data to identify whether the dust aerosols identified by CALIPSO come from regional transportation or local emission. Or you can compare your profiles from ground-based observations with CALIPSO observations in dusty days.