the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Mar 2024
The algorithm of microphysical parameter profiles of aerosol and small cloud droplets based on the dual wavelength Lidar data
Abstract. This study proposed an inversion method of atmosphere aerosol or cloud microphysical parameters based on dual wavelength lidar data. The matching characteristics between aerosol/cloud particle size distribution and Gamma distribution were studied using aircraft observation data. The feasibility of particle effective radius retrieval from lidar ratio and backscatter ratio was simulated and studied. A method for inverting the effective radius and number concentration of atmospheric aerosols or small cloud droplets using backscatter ratio was proposed, and the error sources and applicability of the algorithm were analyzed. This algorithm was suitable for the inversion of uniformly mixed and single property aerosol layers or small cloud droplets. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmosphere particles and has good robustness. For aerosol particles, the inversion range that this algorithm can achieve was 0.3–1.7 μm. For cloud droplets, it was 1.0–10 μm. An atmosphere observation experiment was conducted using the multi-wavelength lidar developed by Xi'an University of Technology, and a thin cloud formation process was captured. The microphysical parameters of aerosol and cloud during this process were retrieved. The results clearly demonstrate the growth of effective radius and number concentration.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-192', Anonymous Referee #3, 29 Mar 2024
This study proposed an inversion method of atmosphere aerosol or cloud microphysical parameters based on dual wavelength lidar data. However, several comments below should be settled.
- Line 31: an5bd?
- Part 2.2: This section needs to be improved by expanding to include more supportive figures and detailed descriptions.
- Figure 2: Not clear, Add more details on the description of the algorithm. For example, the look-up-table, etc.
- line 136: How to determine the boundary of the blue box?
- line 143: you claimed that the larger the value of b, themore pronounced the Gamma function describes the characteristics of large particles. Why did you choose b=6 for cloud droplets, and b=3 for aerosols?
- Figure 5: these figures do not match the description. The 3.2.2 part should be modified.
- Line 159-160: It is claimed that “According to Fig. 5, when the complex refractive index of particles changes, the color ratio curves will fluctuate, but they always monotonically decrease at 0.3 μm to 1.7 μm.”. However, the content displayed in some figures does not align with the aforementioned description. Furthermore, why is there a need to emphasize “0.3 μm to 1.7 μm”? How do the authors determine these two boundary values?
- Figure 6: The text of the legend of Figure6(a) and 6(b) such as Inversion value and True value should be modified.
- Part 3.3, line 182-183: It is claimed that the first three factors have been discussed earlier and this section focuses on the inversion error introduced by optical parameters. As the error analysis of the algorithm, the all factors which affected the inversion algorithm should be discussed.
- Part 4.2: the experimental observation of a cloud generation process was provided in this part. How can the experiment be confirmed as a test of the cloud generation process, rather than clouds drifting in from other locations?
- Figure 8: The description of the Figure 8 should be modified. Figure 8(b) is not the lidar observations.
- Figure 9 and Figure 10: What the error bars stand for?
- Only one experimental observation was provided. Could you provide more experiments?
Citation: https://doi.org/10.5194/egusphere-2024-192-RC1 -
CC2: 'Reply on RC1', Huige Di, 13 Apr 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. Please view the point-to-point replies in the attachment.
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AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. The following is our point-to-point replies.
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RC2: 'Comment on egusphere-2024-192', Anonymous Referee #1, 29 Mar 2024
A method for retrieval of aerosol and small cloud droplet microphysical parameters using the backscatter coefficient of two wavelengths (355nm and 1064nm) of lidar is proposed in this manuscript. The algorithm is derived in detail, and the sources of error and applicable conditions of the algorithm are discussed. This algorithm only requires two wavelengths to achieve effective radius and number concentration, and is simple and stable. It is suitable for inversion of cloud base cloud droplet and aerosol with uniform mixing and relatively stable composition. The method proposed is innovative and of practical value. However, this algorithm also has certain limitations, namely the scale of particles that can be inverted is limited.
Specific Comments:
- How to determine whether the detected object meets the scope of application of the algorithm?
- The aerosol size distribution and cloud droplet size distribution used in section 2.2 were obtained in 2005 to 2006, and if new statistical data can be used, the conclusion would be more convincing.
- What is the impact of b-value changes in the Gamma distribution on the results? Quantitative data needs to be provided in the manuscript.
- In Figure 10, there is a sharp increase in the echo signal above the cloud layer. Is this caused by ice crystals?
- Table1， Resolvable time? Minimum resolvable distance?
- There are grammatical errors in the manuscript, which need to be carefully revised.
Citation: https://doi.org/10.5194/egusphere-2024-192-RC2 -
CC1: 'Reply on RC2', Huige Di, 07 Apr 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. Please view the point-to-point replies in the attachment.
-
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. The following is our point-to-point replies.
-
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
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RC3: 'Comment on egusphere-2024-192', Anonymous Referee #2, 11 Apr 2024
The manuscript presents a method to derive microphysical observations from lidar observations at different wavelengths. Such lidar-microphysical retrieval schemes are of great importance in studying aerosol and clouds. However, I have major concerns regarding the feasibility of the technique given the systematic effects on the retrieval of the particle backscattering coefficient (needed for your approach), which is known to be a difficult retrievable for cloudy situations, due to the lack of a reference (aerosol-free) height to calibrate the lidar. It is also difficult to see a practical use of the method, considering the limited retrievable range of sizes of the droplets (or aerosols). Furthermore, to my opinion, the different sections of the study were not developed and discussed deeply enough. Major revisions need to be made.
Please see my specific comments below:
Page 1 (Introduction): You introduced the problem of getting microphysical information about aerosol and clouds, but as aerosol particles and cloud hydrometeors have been historically approached in different ways, you need to introduce the approaches separately. On the one hand, spectrally resolved information has shown a potential to retrieve microphysical information in the case of aerosol particles, in the case of water clouds, there are quite some limitations because of the larger sizes compared to aerosols.
For this reason, in the case of clouds, there have been several studies that have tried to get information using lidar/radar synergy or lidar-only approaches based on multiple scattering that can be evaluated using dual- or multiple-FOV lidar. A thoughtful literature review of current cloud-retrieval techniques is missing in the manuscript.
Eq. 2: It is not quite clear how one gets this equality using the gamma function. Can you add some more steps and explanations to this derivation? And, how valid is it to use the gamma function definition in an integration that does not go up to infinity?
Line 82: Is there a reference to cite for the cloud probe? FSSP-100-ER. You only include a reference for the aerosol probe.
Line 79: These are quite interesting results. Could you deepen the meaning of those parameters? In principle, the b parameter is related to the width and c is related to the size. Is Figure 1 saying, there is always a linear relationship between the size and the width of the distribution?
Line 110-11: How exactly can you derive the effective radius from Eq. 14?
Fig 3, Fig 4: It is not stated how the size range limits were defined (the blue lines). How can one assume one is on this range only using, e.g., the backscattering ratio 355/1064? Smaller particle sizes ( left side of the range) might also produce similar ratio values. There is no uniqueness in the parameter you propose to use.
Line 164: How do you exactly verify the algorithms? How were the backscatter and lidar ratios calculated from the size distributions? Please provide more accurate information on what is obtained from the measurements.
Line 179: Only theoretical errors are considered, what about systematic errors, such as in the retrieval of the backscattering coefficient, first needed to initialize the retrieval of microphysical properties?
Line 222: how is the black curve defined/determined?
Line 227: The backscattering coefficients at the different wavelengths are key for the retrieval scheme. So how do you exactly calculate the backscattering coefficient? In cloudy situations, it is well known, that is quite difficult to retrieve the backscattering coefficient (either using Klett/Fernald or even the Raman method) because of the strong attenuation in the cloud layer, which does not allow the usage of a reference (aerosol-free) height to calibrate. The strong attenuation also makes the retrieval of the extinction a major issue. On the other hand, multiple scattering will take place in the clouds as soon as it gets densely enough. So how does your approach avoid the multiple-scattering effect?
Finally, how do these underlying uncertainties affect the retrieval of the microphysical parameters, such as the effective radius and cloud droplet number concentration?
Line 240: How is the maximum number concentration 130 cm-3? In the exemplary profile (Fig. 9f) there are no values larger than 100 cm-3. There was also no explanation of what was done below the cloud base. Was the retrieval version for aerosols applied here, or the cloud version was used for the whole profile?
Line 250 (Fig 10f): I do not see, how can it be possible, that the concentration of droplets in the cloud layer (~2000 cm-3), is two orders of magnitude than the aerosol concentration below the cloud (~10 cm-3). Where is all that CCN coming from?
Line 258 (Fig. 12): Same issue as Fig 10. And why even bother defining regions 1 and 2, where there was no cloud at all yet?
Citation: https://doi.org/10.5194/egusphere-2024-192-RC3 -
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. The following is our point-to-point replies.
-
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-192', Anonymous Referee #3, 29 Mar 2024
This study proposed an inversion method of atmosphere aerosol or cloud microphysical parameters based on dual wavelength lidar data. However, several comments below should be settled.
- Line 31: an5bd?
- Part 2.2: This section needs to be improved by expanding to include more supportive figures and detailed descriptions.
- Figure 2: Not clear, Add more details on the description of the algorithm. For example, the look-up-table, etc.
- line 136: How to determine the boundary of the blue box?
- line 143: you claimed that the larger the value of b, themore pronounced the Gamma function describes the characteristics of large particles. Why did you choose b=6 for cloud droplets, and b=3 for aerosols?
- Figure 5: these figures do not match the description. The 3.2.2 part should be modified.
- Line 159-160: It is claimed that “According to Fig. 5, when the complex refractive index of particles changes, the color ratio curves will fluctuate, but they always monotonically decrease at 0.3 μm to 1.7 μm.”. However, the content displayed in some figures does not align with the aforementioned description. Furthermore, why is there a need to emphasize “0.3 μm to 1.7 μm”? How do the authors determine these two boundary values?
- Figure 6: The text of the legend of Figure6(a) and 6(b) such as Inversion value and True value should be modified.
- Part 3.3, line 182-183: It is claimed that the first three factors have been discussed earlier and this section focuses on the inversion error introduced by optical parameters. As the error analysis of the algorithm, the all factors which affected the inversion algorithm should be discussed.
- Part 4.2: the experimental observation of a cloud generation process was provided in this part. How can the experiment be confirmed as a test of the cloud generation process, rather than clouds drifting in from other locations?
- Figure 8: The description of the Figure 8 should be modified. Figure 8(b) is not the lidar observations.
- Figure 9 and Figure 10: What the error bars stand for?
- Only one experimental observation was provided. Could you provide more experiments?
Citation: https://doi.org/10.5194/egusphere-2024-192-RC1 -
CC2: 'Reply on RC1', Huige Di, 13 Apr 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. Please view the point-to-point replies in the attachment.
-
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. The following is our point-to-point replies.
-
RC2: 'Comment on egusphere-2024-192', Anonymous Referee #1, 29 Mar 2024
A method for retrieval of aerosol and small cloud droplet microphysical parameters using the backscatter coefficient of two wavelengths (355nm and 1064nm) of lidar is proposed in this manuscript. The algorithm is derived in detail, and the sources of error and applicable conditions of the algorithm are discussed. This algorithm only requires two wavelengths to achieve effective radius and number concentration, and is simple and stable. It is suitable for inversion of cloud base cloud droplet and aerosol with uniform mixing and relatively stable composition. The method proposed is innovative and of practical value. However, this algorithm also has certain limitations, namely the scale of particles that can be inverted is limited.
Specific Comments:
- How to determine whether the detected object meets the scope of application of the algorithm?
- The aerosol size distribution and cloud droplet size distribution used in section 2.2 were obtained in 2005 to 2006, and if new statistical data can be used, the conclusion would be more convincing.
- What is the impact of b-value changes in the Gamma distribution on the results? Quantitative data needs to be provided in the manuscript.
- In Figure 10, there is a sharp increase in the echo signal above the cloud layer. Is this caused by ice crystals?
- Table1， Resolvable time? Minimum resolvable distance?
- There are grammatical errors in the manuscript, which need to be carefully revised.
Citation: https://doi.org/10.5194/egusphere-2024-192-RC2 -
CC1: 'Reply on RC2', Huige Di, 07 Apr 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. Please view the point-to-point replies in the attachment.
-
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. The following is our point-to-point replies.
-
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
-
RC3: 'Comment on egusphere-2024-192', Anonymous Referee #2, 11 Apr 2024
The manuscript presents a method to derive microphysical observations from lidar observations at different wavelengths. Such lidar-microphysical retrieval schemes are of great importance in studying aerosol and clouds. However, I have major concerns regarding the feasibility of the technique given the systematic effects on the retrieval of the particle backscattering coefficient (needed for your approach), which is known to be a difficult retrievable for cloudy situations, due to the lack of a reference (aerosol-free) height to calibrate the lidar. It is also difficult to see a practical use of the method, considering the limited retrievable range of sizes of the droplets (or aerosols). Furthermore, to my opinion, the different sections of the study were not developed and discussed deeply enough. Major revisions need to be made.
Please see my specific comments below:
Page 1 (Introduction): You introduced the problem of getting microphysical information about aerosol and clouds, but as aerosol particles and cloud hydrometeors have been historically approached in different ways, you need to introduce the approaches separately. On the one hand, spectrally resolved information has shown a potential to retrieve microphysical information in the case of aerosol particles, in the case of water clouds, there are quite some limitations because of the larger sizes compared to aerosols.
For this reason, in the case of clouds, there have been several studies that have tried to get information using lidar/radar synergy or lidar-only approaches based on multiple scattering that can be evaluated using dual- or multiple-FOV lidar. A thoughtful literature review of current cloud-retrieval techniques is missing in the manuscript.
Eq. 2: It is not quite clear how one gets this equality using the gamma function. Can you add some more steps and explanations to this derivation? And, how valid is it to use the gamma function definition in an integration that does not go up to infinity?
Line 82: Is there a reference to cite for the cloud probe? FSSP-100-ER. You only include a reference for the aerosol probe.
Line 79: These are quite interesting results. Could you deepen the meaning of those parameters? In principle, the b parameter is related to the width and c is related to the size. Is Figure 1 saying, there is always a linear relationship between the size and the width of the distribution?
Line 110-11: How exactly can you derive the effective radius from Eq. 14?
Fig 3, Fig 4: It is not stated how the size range limits were defined (the blue lines). How can one assume one is on this range only using, e.g., the backscattering ratio 355/1064? Smaller particle sizes ( left side of the range) might also produce similar ratio values. There is no uniqueness in the parameter you propose to use.
Line 164: How do you exactly verify the algorithms? How were the backscatter and lidar ratios calculated from the size distributions? Please provide more accurate information on what is obtained from the measurements.
Line 179: Only theoretical errors are considered, what about systematic errors, such as in the retrieval of the backscattering coefficient, first needed to initialize the retrieval of microphysical properties?
Line 222: how is the black curve defined/determined?
Line 227: The backscattering coefficients at the different wavelengths are key for the retrieval scheme. So how do you exactly calculate the backscattering coefficient? In cloudy situations, it is well known, that is quite difficult to retrieve the backscattering coefficient (either using Klett/Fernald or even the Raman method) because of the strong attenuation in the cloud layer, which does not allow the usage of a reference (aerosol-free) height to calibrate. The strong attenuation also makes the retrieval of the extinction a major issue. On the other hand, multiple scattering will take place in the clouds as soon as it gets densely enough. So how does your approach avoid the multiple-scattering effect?
Finally, how do these underlying uncertainties affect the retrieval of the microphysical parameters, such as the effective radius and cloud droplet number concentration?
Line 240: How is the maximum number concentration 130 cm-3? In the exemplary profile (Fig. 9f) there are no values larger than 100 cm-3. There was also no explanation of what was done below the cloud base. Was the retrieval version for aerosols applied here, or the cloud version was used for the whole profile?
Line 250 (Fig 10f): I do not see, how can it be possible, that the concentration of droplets in the cloud layer (~2000 cm-3), is two orders of magnitude than the aerosol concentration below the cloud (~10 cm-3). Where is all that CCN coming from?
Line 258 (Fig. 12): Same issue as Fig 10. And why even bother defining regions 1 and 2, where there was no cloud at all yet?
Citation: https://doi.org/10.5194/egusphere-2024-192-RC3 -
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
Thank you very much for your nice comments. Your question and suggestion are very helpful for us to improve the quality of our paper. We appreciate the reviewer’s thoughtful review and constructive comments. The following is our point-to-point replies.
-
AC1: 'Reply on RC3', Xinhong Wang, 12 May 2024
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- 1
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Huige Di
Xinhong Wang
Ning Chen
Jing Guo
Wenhui Xin
Shichun Li
Yan Guo
Qing Yan
Yufeng Wang
Dengxin Hua
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1359 KB) - Metadata XML