the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Use of Lidar Aerosol Extinction and Backscatter Coefficients to Estimate Cloud Condensation Nuclei (CCN) Concentrations in the Southeast Atlantic
Lan Gao
Jens Redemann
Feng Xu
Sharon P. Burton
Brian Cairns
Ian Chang
Richard A. Ferrare
Chris A. Hostetler
Pablo E. Saide
Calvin Howes
Yohei Shinozuka
Snorre Stamnes
Mary Kacarab
Amie Dobracki
Jenny Wong
Steffen Freitag
Athanasios Nenes
Abstract. Accurately capturing cloud condensation nuclei (CCN) concentrations is key to understanding the aerosol-cloud interactions that continue to feature the highest uncertainty amongst numerous climate forcings. In situ CCN observations are sparse and most non-polarimetric passive remote sensing techniques are limited to providing column-effective CCN proxies such as total aerosol optical depth (AOD). Lidar measurements, on the other hand, resolve profiles of aerosol extinction and/or backscatter coefficients that are better suited for constraining vertically-resolved aerosol optical and microphysical properties. Here we present relationships between aerosol backscatter and extinction coefficients measured by the airborne High Spectral Resolution Lidar 2 (HSRL-2) and in situ measurements of CCN concentrations. The data were obtained during three deployments in the NASA ObseRvations of Aerosols above Clouds and their intEractionS (ORACLES) project, which took place over the Southeast Atlantic (SEA) during September 2016, August 2017, and September–October 2018.
Our analysis of spatiotemporally collocated in situ CCN concentrations and HSRL-2 measurements indicates strong linear relationships between both data sets. The correlation is strongest for supersaturations greater than 0.25 % and dry ambient conditions above the stratocumulus deck, where relative humidity (RH) is less than 50 %. We find CCN – HSRL-2 Pearson correlation coefficients between 0.95–0.97 for different parts of the seasonal burning cycle that suggest fundamental similarities in biomass burning aerosol (BBA) microphysical properties. We find that ORACLES campaign-average values of in situ CCN and in situ extinction coefficients are qualitatively similar to those from other regions and aerosol types, demonstrating overall representativeness of our data set. We compute CCN – backscatter and CCN – extinction regressions that can be used to resolve vertical CCN concentrations across entire above-cloud lidar curtains. These lidar-derived CCN concentrations can be used to evaluate model performance, which we illustrate using an example CCN concentration curtain from WRF-CAM5. These results demonstrate the utility of deriving vertically-resolved CCN concentrations from lidar observations to expand the spatiotemporal coverage of limited or unavailable in situ observations.
Emily D. Lenhardt et al.
Status: closed
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CC1: 'Comment on amt-2022-262', Goutam Choudhury, 10 Nov 2022
The correlation between extinction coefficients and CCN concentrations is intriguing. The findings support additional research into extinction-to-CCN parameterizations for various aerosol types that will be directly applicable to spaceborne lidar measurements. Here, I would like to draw the author's attention to an ongoing effort in the lidar community to obtain aerosol-type-specific CCN concentrations from ground-based and spaceborne lidar. For ground-based lidar, Mamouri and Ansmann (2016) presented a technique to convert the lidar-derived aerosol-type-specific extinction coefficients to number concentrations of aerosols, which were then used in parameterizations to estimate CCN concentrations at different supersaturations. Choudhury and Tesche (2022a) further developed a CCN-retrieval method specifically for application to spaceborne CALIPSO lidar measurements. The technique uses the normalized size distributions of the CALIPSO aerosol model and modifies it to reproduce the CALIPSO-derived extinction coefficients. The final modified size distributions are then used in CCN parameterizations, similar to Mamouri and Ansmann (2016), to compute CCN concentrations at different supersaturations. The resulting CCN concentrations were found to be consistent with airborne (Choudhury et al., 2022) and surface (Choudhury and Tesche, 2022b) in-situ measurements.
References
Choudhury, G. and Tesche, M.: Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements, Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, 2022a.
Choudhury, G., Ansmann, A., and Tesche, M.: Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements, Atmos. Chem. Phys., 22, 7143–7161, https://doi.org/10.5194/acp-22-7143-2022, 2022.
Choudhury G and Tesche M. Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements, Remote Sensing, 14(14), 3342, https://doi.org/10.3390/rs14143342, 2022b.
Mamouri, R.-E. and Ansmann, A.: Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters, Atmos. Chem. Phys., 16, 5905–5931, https://doi.org/10.5194/acp-16-5905-2016, 2016.
Citation: https://doi.org/10.5194/amt-2022-262-CC1 -
AC1: 'Reply on CC1', Emily Lenhardt, 19 Dec 2022
Thank you for pointing out the two additional papers (that we had not yet cited) about recent efforts to derive CCN concentrations using CALIPSO lidar measurements. It is encouraging to read that this method has proved successful in comparison to ground- and aircraft-based in situ CCN observations for multiple different aerosol types. We will continue to take into account the details of these and other similar physics-based retrieval methods of CCN concentration to assess which additional details and methodologies may be useful in our work. The capabilities in physics-based retrievals are noteworthy – we submit however, that some of these capabilities must be based on correlations rather than actual physical dependence of aerosol optics on aerosol number concentrations (and hence CCN concentrations), because some of the CCN are simply too small to be optically active, as we know the authors of this comment are aware. Therefore, we have taken a different approach by seeking to quantify these very correlations between aerosol optics and CCN, i.e., by directly relating lidar extinction and backscatter to in situ measured CCN concentrations and using resultant regression equations to estimate CCN from lidar profiles. There is a lot of implicit information in these correlations, especially in considering hygroscopicity of aerosols and how it impacts the lidar signal for specific aerosol types. We have discussed that it may be interesting in the future to see if our methodology is also applicable to CALIPSO observations, although we have concerns about the large uncertainties and hence limited information content in extensive aerosol optical properties derived from backscatter lidar systems. Therefore, we believe that there is merit in pursuing both physics-based retrievals and correlation analysis based on auxiliary information (either from correlative measurements or reanalysis data). We will clarify this point in our revised manuscript and we would again like to thank the authors of this comment for the thoughtful feedback.
Citation: https://doi.org/10.5194/amt-2022-262-AC1
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AC1: 'Reply on CC1', Emily Lenhardt, 19 Dec 2022
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RC1: 'Comment on amt-2022-262', Anonymous Referee #1, 14 Dec 2022
This paper uses a recent observational dataset to examine the relationships between lidar measurements (backscatter and extinction) and inlet-based in situ CCN concentrations, with the ultimate goal of evaluating model performance and HSRL-based CCN retrievals. While the paper is well-written, I worry the conclusions are somewhat simplistic and not fully supported by the data or uncertainty analysis as presented. I would like revision to clarify the below questions before the paper is published.
Major comments:
1) While there’s nothing inherently wrong about a straightforward approach, the basic methodology as I understand it (essentially, using a linear fit to estimate CCN concentration from HSRL data) merits a more detailed description. For example, which “best fit” lines are shown in the figures? I didn't see it described. Many standard statistics packages use ordinary least-squares, which presumes that the x-variable is a perfect measurement and all the error/uncertainty is in the y-variable, but for a regression between two observed variables, I don’t think that’s accurate. Surely HSRL-2 comes with uncertainty as well? How was this accounted for? It also seems like OLS may have been used since Figs 3 and 4 and 9 show RMSE, in units of CCN concentration, i.e. the y-variable. (Follow-up question: what’s the utility of this metric here? Is it to say that the CCN uncertainty associated with the linear regression is ~>100/cm3 in each case? Surely there’s more to it than just that? How does an estimate of uncertainty in the linear regression influence the later results, e.g. Fig 9, 8?)
1a) Specifically to the above: Line 360-361 mentions “higher uncertainties and coarser resolutions associated with the [HSRL-2] extinction coefficient”-- where is this incorporated or considered?
2) The authors also combine all three years (with different measurement times of each) into one plot, which could be fine, but in Figs 3, 4, and 6, it seems the goodness of the fit is likely strongly influenced by those cluster of high CCN, high X (X= the given HSRL-2 variable) which occur specifically in 2017. Plus the choice to force through 0-0. There are only 13 points in 2017, all >~900cm-3, which is almost an entirely different range than 2016, for example. Again, this probably could be fine, *if* the CCN/HSRL relationship(s) hold over the range of conditions as the months change. The authors consider different metrics f44, AI (if not AE) (mostly in Section 4.2), and changes in SS level, RH %, and measurement constraints for different years (Table 2), but with so few points from so few days, it’s hard to determine how consistent this relationship actually is in a multivariate sense, just from what has been presented.
2a) Further comments on Fig 6: am I reading it right that the thicker grey line is for 2018? The three diamond points I can see, do not appear to fit well to that line, since the two higher points are both below that fit and other two are right on it. Is that correct? How can that be the best fit to those four points? And are there really only two data points from 2017? Is that enough to draw conclusions from?
2b) Finally, I worry that the methodology of fitting through 0, while physically intuitive, is constraining the results in a way that’s not supported by the (somewhat limited) data which are shown. To take an extreme example, if one fits a line through (0,0) and 2 other data points which have some error associated, the fit will likely be hugely different compared to a fit through just those two data points themselves. The artificial (0,0) “data” would completely overwhelm the relationship between the actual observed datapoints, which is what the authors are trying to show. And obviously this would have a more dramatic effect for studies with fewer datapoints. How much does this fit depend on the (0,0) constraint? It seems it may have a big effect here.
3) I’m not clear on the purpose of Sec 4.3/Fig 8-9. First, is (or can) WRF-CAM5 be taken as a ground truth, or can HSRL-2? (I suspect neither, with the information presented). The two plots in Fig 8 are so different I’m having trouble understanding what’s the message here. The authors (Line 464) suggest that the lidar-derived method is “better” than the models. Based on what?
3a) Figure 9 has many points which suggest variability in in situ CCN which is not captured by the HSRL-2-derived product, as well as some which suggest artificial variability in HSRL-2 compared with in situ. Some cases seem to show a mismatch of an order of magnitude. What’s going on in these cases? Is this solely a function of the “expanded” dataset, i.e. those strong diversions are a result of greater mismatches in space/time? Does it have anything to do with the age/humidity/supersaturation? How does this fit into Fig 5, which at least for 2017 seems to show both young and old aerosol? Does this matter? As presented here it’s difficult to believe in the results of Fig 9.
3b) If the two above points are addressed, I’d suggest flipping Figs 8 and 9; 9 should establish the validity of the HSRL-derived CCN product and then 8 could show it in the context of one model result. Perhaps that’s more what the authors are intending.
4) Figure 5, Lines 396-8: this is not correct. The main source of the BBA in this region is the south african easterly jet (AEJ-S) which is most frequently present between 5-15S, not at the equator. See: Adebiyi et al 2016 DOI:10.1002/qj.2765; Ryoo et al 2021 https://doi.org/10.5194/acp-21-16689-2021
Minor comments:
For the “amount of data within +/- X% of the linear regression line,” X=10% in the figure captions (Figs 3, 4) and 20% in the text (lines 300, 327, 330).
The authors consider both extinction and AI and draw conclusions that the latter isn’t more representative because AE has minimal variation (paragraph on Line 365), but presumably the AE used to calculate AI was also determined from HSRL-2 measurements. Did you examine the AE variability directly to support this conclusion? It seems fairly straightforward to check (I’m considering this a “minor comment” because this is more of a curiosity rather than a major issue with the paper).
Line 136: a plausible and robust collocation and filtering is central and critical to the subsequent results; in other words, I’d remove the word “briefly” here.
Line 147,224: Fig 1 caption says the third deployment was only Oct 2018?
Line 170: “the exact temporal” … resolution?
Line 191: is this — here necessary? It seems a bit awkward. Maybe needs a comma?
Line 196: how do +/- 10% and 5-10 cm-3 compare to one another in absolute terms?
Line 200-1: revise, I don’t follow. Maybe missing the word “fraction”?
Figure 2b: this uses both \deltat and dt in different places (also line 231, 235)– should this be the same notation?
Figure 3a: the legend covers the 532nm datapoints; resize or shiftCitation: https://doi.org/10.5194/amt-2022-262-RC1 -
AC2: 'Reply on RC1', Emily Lenhardt, 11 Feb 2023
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2022-262/amt-2022-262-AC2-supplement.pdf
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AC2: 'Reply on RC1', Emily Lenhardt, 11 Feb 2023
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RC2: 'Comment on amt-2022-262', Anonymous Referee #2, 22 Dec 2022
Review of “Use of Lidar Aerosol Extinction and Backscatter Coefficients to Estimate Cloud Condensation Nuclei (CCN) Concentrations in the Southeast Atlantic” by Lenhardt et al., submitted to Atmospheric Measurement Techniques, 2022.
Overview:
This paper presents empirical relationships between remote sensing and in situ measurements of aerosol properties that were made during the NASA ORACLES project. The goal is to inform vertically-resolved CCN concentration retrieval algorithms that are heavily based on HRSL-2 data in the southeastern Atlantic airmasses dominated by smoke. The results presented in the form of correlation coefficients indicate that there is a strong relationship between HSRL-2 observations and the in situ CCN measurements from aircraft mounted sensors.
Review:
The paper is well organized and written. The figures complement the conclusions and are laid out appropriately. I do not find the conclusions to be overwrought because the authors state that the correlations described are limited to the SEA region and BBA type that was observed during ORACLES. However, there is a general reliance on the HSRL-2 observations without adequate caution. The authors are experienced with this system, so I recommend they include a more complete description of the limitations of the instrument on the airborne platform and the how the error propagates into the relationships derived herein, especially with regards to volume averaging extinction and backscatter coefficients. After the inclusion of such a discussion, I would find the paper suitable for publication.
Minor comments:
In the second line of the Figure 9 caption, “.0.5” should be replaced with “0.5”
Citation: https://doi.org/10.5194/amt-2022-262-RC2 -
AC3: 'Reply on RC2', Emily Lenhardt, 11 Feb 2023
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2022-262/amt-2022-262-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Emily Lenhardt, 11 Feb 2023
Status: closed
-
CC1: 'Comment on amt-2022-262', Goutam Choudhury, 10 Nov 2022
The correlation between extinction coefficients and CCN concentrations is intriguing. The findings support additional research into extinction-to-CCN parameterizations for various aerosol types that will be directly applicable to spaceborne lidar measurements. Here, I would like to draw the author's attention to an ongoing effort in the lidar community to obtain aerosol-type-specific CCN concentrations from ground-based and spaceborne lidar. For ground-based lidar, Mamouri and Ansmann (2016) presented a technique to convert the lidar-derived aerosol-type-specific extinction coefficients to number concentrations of aerosols, which were then used in parameterizations to estimate CCN concentrations at different supersaturations. Choudhury and Tesche (2022a) further developed a CCN-retrieval method specifically for application to spaceborne CALIPSO lidar measurements. The technique uses the normalized size distributions of the CALIPSO aerosol model and modifies it to reproduce the CALIPSO-derived extinction coefficients. The final modified size distributions are then used in CCN parameterizations, similar to Mamouri and Ansmann (2016), to compute CCN concentrations at different supersaturations. The resulting CCN concentrations were found to be consistent with airborne (Choudhury et al., 2022) and surface (Choudhury and Tesche, 2022b) in-situ measurements.
References
Choudhury, G. and Tesche, M.: Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements, Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, 2022a.
Choudhury, G., Ansmann, A., and Tesche, M.: Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements, Atmos. Chem. Phys., 22, 7143–7161, https://doi.org/10.5194/acp-22-7143-2022, 2022.
Choudhury G and Tesche M. Assessment of CALIOP-Derived CCN Concentrations by In Situ Surface Measurements, Remote Sensing, 14(14), 3342, https://doi.org/10.3390/rs14143342, 2022b.
Mamouri, R.-E. and Ansmann, A.: Potential of polarization lidar to provide profiles of CCN- and INP-relevant aerosol parameters, Atmos. Chem. Phys., 16, 5905–5931, https://doi.org/10.5194/acp-16-5905-2016, 2016.
Citation: https://doi.org/10.5194/amt-2022-262-CC1 -
AC1: 'Reply on CC1', Emily Lenhardt, 19 Dec 2022
Thank you for pointing out the two additional papers (that we had not yet cited) about recent efforts to derive CCN concentrations using CALIPSO lidar measurements. It is encouraging to read that this method has proved successful in comparison to ground- and aircraft-based in situ CCN observations for multiple different aerosol types. We will continue to take into account the details of these and other similar physics-based retrieval methods of CCN concentration to assess which additional details and methodologies may be useful in our work. The capabilities in physics-based retrievals are noteworthy – we submit however, that some of these capabilities must be based on correlations rather than actual physical dependence of aerosol optics on aerosol number concentrations (and hence CCN concentrations), because some of the CCN are simply too small to be optically active, as we know the authors of this comment are aware. Therefore, we have taken a different approach by seeking to quantify these very correlations between aerosol optics and CCN, i.e., by directly relating lidar extinction and backscatter to in situ measured CCN concentrations and using resultant regression equations to estimate CCN from lidar profiles. There is a lot of implicit information in these correlations, especially in considering hygroscopicity of aerosols and how it impacts the lidar signal for specific aerosol types. We have discussed that it may be interesting in the future to see if our methodology is also applicable to CALIPSO observations, although we have concerns about the large uncertainties and hence limited information content in extensive aerosol optical properties derived from backscatter lidar systems. Therefore, we believe that there is merit in pursuing both physics-based retrievals and correlation analysis based on auxiliary information (either from correlative measurements or reanalysis data). We will clarify this point in our revised manuscript and we would again like to thank the authors of this comment for the thoughtful feedback.
Citation: https://doi.org/10.5194/amt-2022-262-AC1
-
AC1: 'Reply on CC1', Emily Lenhardt, 19 Dec 2022
-
RC1: 'Comment on amt-2022-262', Anonymous Referee #1, 14 Dec 2022
This paper uses a recent observational dataset to examine the relationships between lidar measurements (backscatter and extinction) and inlet-based in situ CCN concentrations, with the ultimate goal of evaluating model performance and HSRL-based CCN retrievals. While the paper is well-written, I worry the conclusions are somewhat simplistic and not fully supported by the data or uncertainty analysis as presented. I would like revision to clarify the below questions before the paper is published.
Major comments:
1) While there’s nothing inherently wrong about a straightforward approach, the basic methodology as I understand it (essentially, using a linear fit to estimate CCN concentration from HSRL data) merits a more detailed description. For example, which “best fit” lines are shown in the figures? I didn't see it described. Many standard statistics packages use ordinary least-squares, which presumes that the x-variable is a perfect measurement and all the error/uncertainty is in the y-variable, but for a regression between two observed variables, I don’t think that’s accurate. Surely HSRL-2 comes with uncertainty as well? How was this accounted for? It also seems like OLS may have been used since Figs 3 and 4 and 9 show RMSE, in units of CCN concentration, i.e. the y-variable. (Follow-up question: what’s the utility of this metric here? Is it to say that the CCN uncertainty associated with the linear regression is ~>100/cm3 in each case? Surely there’s more to it than just that? How does an estimate of uncertainty in the linear regression influence the later results, e.g. Fig 9, 8?)
1a) Specifically to the above: Line 360-361 mentions “higher uncertainties and coarser resolutions associated with the [HSRL-2] extinction coefficient”-- where is this incorporated or considered?
2) The authors also combine all three years (with different measurement times of each) into one plot, which could be fine, but in Figs 3, 4, and 6, it seems the goodness of the fit is likely strongly influenced by those cluster of high CCN, high X (X= the given HSRL-2 variable) which occur specifically in 2017. Plus the choice to force through 0-0. There are only 13 points in 2017, all >~900cm-3, which is almost an entirely different range than 2016, for example. Again, this probably could be fine, *if* the CCN/HSRL relationship(s) hold over the range of conditions as the months change. The authors consider different metrics f44, AI (if not AE) (mostly in Section 4.2), and changes in SS level, RH %, and measurement constraints for different years (Table 2), but with so few points from so few days, it’s hard to determine how consistent this relationship actually is in a multivariate sense, just from what has been presented.
2a) Further comments on Fig 6: am I reading it right that the thicker grey line is for 2018? The three diamond points I can see, do not appear to fit well to that line, since the two higher points are both below that fit and other two are right on it. Is that correct? How can that be the best fit to those four points? And are there really only two data points from 2017? Is that enough to draw conclusions from?
2b) Finally, I worry that the methodology of fitting through 0, while physically intuitive, is constraining the results in a way that’s not supported by the (somewhat limited) data which are shown. To take an extreme example, if one fits a line through (0,0) and 2 other data points which have some error associated, the fit will likely be hugely different compared to a fit through just those two data points themselves. The artificial (0,0) “data” would completely overwhelm the relationship between the actual observed datapoints, which is what the authors are trying to show. And obviously this would have a more dramatic effect for studies with fewer datapoints. How much does this fit depend on the (0,0) constraint? It seems it may have a big effect here.
3) I’m not clear on the purpose of Sec 4.3/Fig 8-9. First, is (or can) WRF-CAM5 be taken as a ground truth, or can HSRL-2? (I suspect neither, with the information presented). The two plots in Fig 8 are so different I’m having trouble understanding what’s the message here. The authors (Line 464) suggest that the lidar-derived method is “better” than the models. Based on what?
3a) Figure 9 has many points which suggest variability in in situ CCN which is not captured by the HSRL-2-derived product, as well as some which suggest artificial variability in HSRL-2 compared with in situ. Some cases seem to show a mismatch of an order of magnitude. What’s going on in these cases? Is this solely a function of the “expanded” dataset, i.e. those strong diversions are a result of greater mismatches in space/time? Does it have anything to do with the age/humidity/supersaturation? How does this fit into Fig 5, which at least for 2017 seems to show both young and old aerosol? Does this matter? As presented here it’s difficult to believe in the results of Fig 9.
3b) If the two above points are addressed, I’d suggest flipping Figs 8 and 9; 9 should establish the validity of the HSRL-derived CCN product and then 8 could show it in the context of one model result. Perhaps that’s more what the authors are intending.
4) Figure 5, Lines 396-8: this is not correct. The main source of the BBA in this region is the south african easterly jet (AEJ-S) which is most frequently present between 5-15S, not at the equator. See: Adebiyi et al 2016 DOI:10.1002/qj.2765; Ryoo et al 2021 https://doi.org/10.5194/acp-21-16689-2021
Minor comments:
For the “amount of data within +/- X% of the linear regression line,” X=10% in the figure captions (Figs 3, 4) and 20% in the text (lines 300, 327, 330).
The authors consider both extinction and AI and draw conclusions that the latter isn’t more representative because AE has minimal variation (paragraph on Line 365), but presumably the AE used to calculate AI was also determined from HSRL-2 measurements. Did you examine the AE variability directly to support this conclusion? It seems fairly straightforward to check (I’m considering this a “minor comment” because this is more of a curiosity rather than a major issue with the paper).
Line 136: a plausible and robust collocation and filtering is central and critical to the subsequent results; in other words, I’d remove the word “briefly” here.
Line 147,224: Fig 1 caption says the third deployment was only Oct 2018?
Line 170: “the exact temporal” … resolution?
Line 191: is this — here necessary? It seems a bit awkward. Maybe needs a comma?
Line 196: how do +/- 10% and 5-10 cm-3 compare to one another in absolute terms?
Line 200-1: revise, I don’t follow. Maybe missing the word “fraction”?
Figure 2b: this uses both \deltat and dt in different places (also line 231, 235)– should this be the same notation?
Figure 3a: the legend covers the 532nm datapoints; resize or shiftCitation: https://doi.org/10.5194/amt-2022-262-RC1 -
AC2: 'Reply on RC1', Emily Lenhardt, 11 Feb 2023
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2022-262/amt-2022-262-AC2-supplement.pdf
-
AC2: 'Reply on RC1', Emily Lenhardt, 11 Feb 2023
-
RC2: 'Comment on amt-2022-262', Anonymous Referee #2, 22 Dec 2022
Review of “Use of Lidar Aerosol Extinction and Backscatter Coefficients to Estimate Cloud Condensation Nuclei (CCN) Concentrations in the Southeast Atlantic” by Lenhardt et al., submitted to Atmospheric Measurement Techniques, 2022.
Overview:
This paper presents empirical relationships between remote sensing and in situ measurements of aerosol properties that were made during the NASA ORACLES project. The goal is to inform vertically-resolved CCN concentration retrieval algorithms that are heavily based on HRSL-2 data in the southeastern Atlantic airmasses dominated by smoke. The results presented in the form of correlation coefficients indicate that there is a strong relationship between HSRL-2 observations and the in situ CCN measurements from aircraft mounted sensors.
Review:
The paper is well organized and written. The figures complement the conclusions and are laid out appropriately. I do not find the conclusions to be overwrought because the authors state that the correlations described are limited to the SEA region and BBA type that was observed during ORACLES. However, there is a general reliance on the HSRL-2 observations without adequate caution. The authors are experienced with this system, so I recommend they include a more complete description of the limitations of the instrument on the airborne platform and the how the error propagates into the relationships derived herein, especially with regards to volume averaging extinction and backscatter coefficients. After the inclusion of such a discussion, I would find the paper suitable for publication.
Minor comments:
In the second line of the Figure 9 caption, “.0.5” should be replaced with “0.5”
Citation: https://doi.org/10.5194/amt-2022-262-RC2 -
AC3: 'Reply on RC2', Emily Lenhardt, 11 Feb 2023
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2022-262/amt-2022-262-AC3-supplement.pdf
-
AC3: 'Reply on RC2', Emily Lenhardt, 11 Feb 2023
Emily D. Lenhardt et al.
Data sets
ORACLES Jens Redemann, Steve Howell, Athanasios Nenes, Chris Hostetler https://espoarchive.nasa.gov/archive/browse/oracles
Emily D. Lenhardt et al.
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