Understanding Aerosol–Cloud Interactions through Lidar Techniques: A Review

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have presented a survey of diverse lidar techniques used to study ACI. Reviewing and summarizing existing progress is meaningful for identifying new study directions. However, there are some problems that need to be addressed before publication. Ground-based and spaceborne radars can capture vertical distributions including clouds and aerosols, which are crucial for ACI-related research. However, the accuracy of cloud and aerosol profile data obtained by radar is not included in this paper. It is also important to assess whether the current observational accuracy of radars meets the requirements for ACI-related research.

Author Response

Comment 1: [The authors have presented a survey of diverse lidar techniques used to study ACI. Reviewing and summarizing existing progress is meaningful for identifying new study directions. However, there are some problems that need to be addressed before publication. Ground-based and spaceborne radars can capture vertical distributions including clouds and aerosols, which are crucial for ACI-related research. However, the accuracy of cloud and aerosol profile data obtained by radar is not included in this paper. It is also important to assess whether the current observational accuracy of radars meets the requirements for ACI-related research.]

Response 1[We thank reviewer 1 for the remarks. Following the suggestion, we have addressed the capabilities of radars compared to lidars in the following text added to the Introduction:

 

“Some of these limitations are overcome by active remote sensing uses instruments that emit their own signals and measure the returned signal after interaction with aerosols and clouds. Lidar (Light Detection and Ranging) and radar (Radio Detection and Ranging) are the most common active sensors used in aerosol-cloud interaction studies. Such instruments provide vertical profiles of aerosol and cloud properties; can operate in both day and night conditions; have high spatial resolution and provide detailed characterization of aerosol and cloud layers. A drawback of these systems is that they are often complex and expensive, and often have limited spatial coverage compared to passive sensors.

 

Radars and lidars have distinct merits and drawbacks based on their operational principles and the specific information they provide. Radars operate in the microwave region of the electromagnetic spectrum, allowing them to penetrate through thick clouds and provide information on the internal structure of clouds and precipitation. So they are highly effective in detecting and quantifying precipitation, distinguishing between rain, snow and hail, quantifying their intensity and distribution. Radars have a longer detection range than lidars, allowing for the observation of large atmospheric volumes and the tracking of weather systems over considerable distances. Moreover, unlike optical sensors, radars can operate in almost all weather conditions, including during heavy precipitation and cloudy skies.

However, radars are by far less sensitive than lidars to small aerosols and cloud droplets, making it challenging to accurately measure these smaller particles. In addition, the spatial resolution of radar systems is generally lower than that of lidar systems and this can limit the ability to resolve fine-scale features within clouds and aerosol layers. So, radars are the primary choice for observing precipitation, internal cloud structures, and large-scale weather systems, due to their all-weather capability, long-range, detection efficacy for larger particles.

 

Lidars use laser light, typically in the ultraviolet, visible, or near-infrared regions, which is highly sensitive to small aerosol particles and cloud droplets. This allows for detailed characterization of aerosol properties and cloud microphysics, delivering high-resolution vertical profiles of aerosol and cloud layers, enabling detailed studies of their structure and dynamics.

Probably the most precious feature of Lidars regarding ACI investigations is the ability of differentiating between various types of aerosols based on their optical properties, such as size, shape, and composition. Polarization lidar can further distinguish between spherical and non-spherical particles.

Unfortunately, lidars struggle to penetrate through thick clouds and heavy precipitation, limiting their ability to observe the internal structure of deep cloud systems, and their range is generally shorter than that of radars, which can constrain the observation of large atmospheric volumes. Moreover, lidar performance is limited by atmospheric conditions such as fog, heavy aerosol loading, and daylight, particularly for systems operating in the visible spectrum.

Their high sensitivity to small particles, high spatial and vertical resolution, detailed aerosol characterization capability makes lidars particularly suitable for the study of aerosol while the

limited penetration in thick clouds and shorter range, affected by certain atmospheric conditions prevent their use in the study of large-scale weather systems.

 

Although radars and lidars provide complementary data, and their combined use can offer a more comprehensive understanding of aerosol-cloud interactions, in this review we will focus our interest on the contributions of lidar techniques to this field.”]

Reviewer 2 Report

Comments and Suggestions for Authors

Summary:

 

This paper provides an excellent review of the use of lidar techniques in studying the Aerosol-Cloud Interactions (ACI) in the past several decades. Specifically, it first provides an overview of ACI research for different cloud types, followed by how lidar techniques were used in characterizing aerosol and clouds. Overall, I found this review thorough, well-written, and of interest to the scientific community. I recommend its acceptance after some minor revisions.

 

General Comments:

 

- The introduction section could be improved by providing a brief overview of why lidar techniques are important and useful in studying the Aerosol-Cloud Interactions (ACI). In addition, an overview of the research approaches in studying ACI (e.g. in situ, satellite passive remote sensing) would be helpful for the reader to appreciate the usefulness of the lidar techniques in a broader context. In the current version, the introduction focuses mostly on the ACI and the lidar techniques are not introduced until Section 3. The introduction would also benefit from adding more citations in reviewing the research on ACI. 

- In Section 3.1, the limitations of each lidar should be mentioned in addition to the key strengths. Also, please also consider citing at least one or two example studies for each type of lidar. It is also worth introducing fluorescence lidar in this section.

 

Specific comments 

 

Line 109. The name for this sub-section is “Warm clouds”, but the processes discussed are not just applicable to warm clouds, but to liquid clouds in general (that also includes supercool liquid clouds below 0°C). I would suggest changing the name for this sub-section to “Liquid clouds”.

 

Line 135-138. Please explain more in the text how Figure 1 demonstrates an updraft-limited regime and how this is related to the supersaturations as color-coded in Figure 1. Please also add more text in the caption of Figure 1 to explain the color coding.   

 

Line 145. Please double-check if equation 6 contains an extra π. Also please define ρ_w.

 

Line 145. Equation 7. Please explain to the reader why the liquid water content and effective radius are written as a function of height.

Line 152-153. Please provide some typical values of fad and Γad .

Line  179. Please double-check whether reference 35 is appropriate for the rain rate dependence on the effective radius.

Line 306. Please clarify what “larger aerosol” means here. Larger aerosol particles in terms of size? Or higher aerosol concentrations?   

 

Line 754-755. Here the usage of lidar in the distinguishing cloud phase is discussed. It is worth mentioning in section 3.1 lines 361-374 that particle depolarization ratio could be also used for distinguishing the cloud phase (e.g. Yoshida et al., 2010).

 

Yoshida, R., H. Okamoto, Y. Hagihara, and H. Ishimoto (2010), Global analysis of cloud phase and ice crystal orientation from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data using attenuated backscattering and depolarization ratio, J. Geophys. Res., 115, D00H32, doi:10.1029/2009JD012334.

 

 

Line 802. Please consider adding a few sentences on why the dual-FOV lidar technique is used so widely in studying the aerosol effect on liquid clouds.

 

Line 864. Section 5. It is worth discussing how machine learning could be applied to lidar data in ACI studies. There have been some studies in the recent years(e.g. Yorks et al., 2021; Farhani et al., 2021).

 

Yorks, J. E., Selmer, P. A., Kupchock, A., Nowottnick, E. P., Christian, K. E., Rusinek, D., ... & McGill, M. J. (2021). Aerosol and cloud detection using machine learning algorithms and space-based lidar data. Atmosphere, 12(5), 606.

 

Farhani, G., Sica, R. J., & Daley, M. J. (2021). Classification of lidar measurements using supervised and unsupervised machine learning methods. Atmospheric Measurement Techniques, 14(1), 391-402.

 

Line 904. Other satellite missions worth mentioning:

 

Ke, J., Sun, Y., Dong, C., Zhang, X., Wang, Z., Lyu, L., ... & Liu, D. (2022). Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration. PhotoniX, 3(1), 17. https://link.springer.com/article/10.1186/s43074-022-00063-3

 

 

Comments on the Quality of English Language

Line 152. Please check the typo for cloud base.  

 

Line 211. Please change “medium” to “mid-”.

 

 

Line 254. “ice secondary multiplication” or “secondary ice multiplication”?

 

Line 357. Should be “large systemic errors”

 

Line 630. Should be “main sources of error”

Line 938. Please define the acronym “WV”.

Author Response

Comment 1: [This paper provides an excellent review of the use of lidar techniques in studying the Aerosol-Cloud Interactions (ACI) in the past several decades. Specifically, it first provides an overview of ACI research for different cloud types, followed by how lidar techniques were used in characterizing aerosol and clouds. Overall, I found this review thorough, well-written, and of interest to the scientific community. I recommend its acceptance after some minor revisions.

General Comments:

- The introduction section could be improved by providing a brief overview of why lidar techniques are important and useful in studying the Aerosol-Cloud Interactions (ACI). In addition, an overview of the research approaches in studying ACI (e.g. in situ, satellite passive remote sensing) would be helpful for the reader to appreciate the usefulness of the lidar techniques in a broader context. In the current version, the introduction focuses mostly on the ACI and the lidar techniques are not introduced until Section 3.]

Response 1: [We thank reviewer 2 for the sound revision of the manuscript and for the useful remarks. Following the suggestion, we have provided an overview of the research approaches in studying ACI, and specifically addressed the capabilities of lidars compared to radars in the following text added to the Introduction:

 

“Research into aerosol-cloud interactions employs a variety of approaches to understand the complex dynamics and implications for climate systems. The primary methodologies include in situ measurements, passive remote sensing, and active remote sensing. Each of these approaches provides unique insights and comes with specific advantages and limitations.

In situ measurements involve direct sampling and analysis of aerosols and cloud particles using instruments onboard aircraft, ground stations, or ships. These measurements offer highly detailed and accurate data on aerosol properties (such as size distribution, chemical composition, and optical properties) and cloud microphysics (such as droplet size, liquid water content, and cloud condensation nuclei). These high accuracy and precision data deliver detailed information on aerosol physical and chemical properties and offer the advantage to conduct controlled experiments and calibrate remote sensing instruments. However, the spatial and temporal coverage is limited to the specific locations and times of measurement campaigns, and high operational costs and logistical challenges do not allow extensive space-time coverage, therefore they are limited to studying particular processes rather than continuous monitoring.

 

Passive remote sensing involves the detection of radiation, either emitted or reflected by aerosols and clouds. Instruments onboard satellites measure this radiation across various wavelengths to infer aerosol and cloud properties. Common passive sensors include radiometers and spectrometers. This approach allows global coverage and continuous monitoring, which allows to accumulate long-term datasets useful for studying trends and variability. Nevertheless, such indirect measurements can introduce uncertainties and require complex retrieval algorithms, have limited vertical resolution and potential difficulties in distinguishing between aerosol and optically thin cloud layers. Moreover, the dependence on sunlight restricts some measurements to daytime.

 

Some of these limitations are overcome by active remote sensing using instruments that emit electromagnetic radiation and measure the returned signal after interaction with aerosols and clouds. Lidar (Light Detection and Ranging) and radar (Radio Detection and Ranging) are the most common active sensors used in aerosol-cloud interaction studies. Such instruments provide vertical profiles of aerosol and cloud properties; can operate in both day and night conditions and provide high spatial resolution and detailed characterization of aerosol and cloud layers. A drawback of these systems is that they are often  complex and expensive, and often have limited spatial coverage compared to passive sensors.

Radars and lidars have distinct merits and drawbacks based on their operational principles and the specific information they provide. Radars operate in the microwave region of the electromagnetic spectrum, allowing them to penetrate through thick clouds and provide information on the internal structure of clouds and precipitation. So they are highly effective in detecting and quantifying precipitation, distinguishing between rain, snow and hail, quantifying their intensity and distribution. Radars have a longer detection range than lidars, allowing for the observation of large atmospheric volumes and the tracking of weather systems over considerable distances. Moreover, unlike optical sensors, radars can operate in almost all weather conditions, including during heavy precipitation and cloudy skies.

However, radars are by far less sensitive than lidars to small aerosols and cloud droplets, making it challenging to accurately measure these smaller particles. In addition, the spatial resolution of radar systems is generally lower than that of lidar systems and this can limit the ability to resolve fine-scale features within clouds and aerosol layers. So radars are the primary choice for observing precipitation, internal cloud structures, and large-scale weather systems, due to their all-weather capability, long-range, detection efficacy for larger particles.

 

Lidars use laser light, typically in the ultraviolet, visible, or near-infrared regions, which is highly sensitive to small aerosol particles and cloud droplets. This allows for detailed characterization of aerosol properties and cloud microphysics, delivering high-resolution vertical profiles of aerosol and cloud layers, enabling detailed studies of their structure and dynamics.

Probably the most precious feature of Lidars regarding ACI investigations is the ability of differentiating between various types of aerosols based on their optical properties, such as size, shape, and composition. Polarization lidar can further distinguish between spherical and non-spherical particles.

Unfortunately, lidars struggle to penetrate through thick clouds and heavy precipitation, limiting their ability to observe the internal structure of deep cloud systems, and their range is generally shorter than that of radars, which can constrain the observation of large atmospheric volumes. Moreover, lidar performance is limited by atmospheric conditions such as fog, heavy aerosol loading, and daylight, particularly for systems operating in the visible spectrum.

Their high sensitivity to small particles, high spatial and vertical resolution, detailed aerosol characterization capability makes lidars particularly suitable for the study of aerosol while the

limited penetration in thick clouds and shorter range, affected by certain atmospheric conditions prevent their use in the study of large-scale weather systems.

 

Although radars and lidars provide complementary data, and their combined use can offer a more comprehensive understanding of aerosol-cloud interactions, in this review we will focus our interest on the contributions of lidar techniques to this field. ]

Comment 2: [The introduction would also benefit from adding more citations in reviewing the research on ACI.]

Response 2: [We have added the following two citations:

[12] Flossmann, A.I.; Wobrock, W. A review of our understanding of the aerosol–cloud interaction from the perspective of a bin resolved cloud scale modelling. Atmospheric Research 2010, 97, 478–497. From the Lab to Models and Global Observations: Hans R. Pruppacher and Cloud Physics, https://doi.org/https://doi.org/10.1016/j.atmosres.2010.05.008.

[11] Oreopoulos, L.; Cho, N.; Lee, D. A Global Survey of Apparent Aerosol-Cloud Interaction Signals. Journal of Geophysical Research: Atmospheres 2020, 125, e2019JD031287, [https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019JD031287]. e2019JD031287 2019JD031287, https://doi.org/https://doi.org/10.1029/2019JD031287.

 bringing the ACI reviews quotations to 13.]

Comment 3: [- In Section 3.1, the limitations of each lidar should be mentioned in addition to the key strengths. Also, please also consider citing at least one or two example studies for each type of lidar. It is also worth introducing fluorescence lidar in this section.]

Response 3: [We have extensively expanded the Section 3.1. New lines are the following:

“The primary limitation of simple elastic Rayleigh lidar in studying aerosol-cloud interactions is its inability to differentiate between aerosol particles and cloud droplets due to their similar backscattering properties, leading to challenges in accurately characterizing their respective contributions and interactions.”

“Despite its enhanced capability to differentiate between aerosols and cloud particles through polarization diversity, such setup still faces limitations in accurately quantifying aerosol-cloud interactions due to its inability to provide detailed microphysical properties and precise particle size distributions.”

“Raman and HSRL setups significantly enhance lidar potential by providing detailed measurements of aerosol optical properties with the ability to distinguish between different aerosols with high precision and accuracy  [80, 81]”

“Fluorescence lidars utilize the property of certain aerosols and biological particles to emit fluorescence when excited by specific wavelengths of light. This technique allows for the detection and characterization of biological aerosols, organic compounds, and specific chemical species in the atmosphere. The enhanced capability of fluorescence lidars lies in their ability to identify and quantify specific aerosol types, such as biogenic or anthropogenic particles, providing valuable information on the sources and composition of aerosols within cloud systems  [86, 87]”

And added the following references as suggested:

[80] Burton, S.; Ferrare, R.; Vaughan, M.; Omar, A.; Rogers, R.; Hostetler, C.; Hair, J. Aerosol classification from airborne HSRL and comparisons with the CALIPSO vertical feature mask. Atmospheric Measurement Techniques 2013, 6, 1397–1412.

[81]  Müller, D.; Ansmann, A.; Mattis, I.; Tesche, M.; Wandinger, U.; Althausen, D.; Pisani, G. Aerosol-type-dependent lidar ratios observed with Raman lidar. Journal of Geophysical Research: Atmospheres 2007, 112.

[83] Böckmann, C.; Mironova, I.; Müller, D.; Schneidenbach, L.; Nessler, R. Microphysical aerosol parameters from multiwavelength lidar. J. Opt. Soc. Am. A 2005, 22, 518–528. https://doi.org/10.1364/JOSAA.22.000518. 1215

[84] Browell, E.V.; Carter, A.; Shipley, S.T.; Allen, R.; Butler, C.; Mayo, M.; Siviter, J.; Hall, W. NASA multipurpose airborne DIAL system and measurements of ozone and aerosol profiles. Applied Optics 1983, 22, 522–534. 1217

[85] Hill, C. Coherent focused lidars for Doppler sensing of aerosols and wind. Remote Sensing 2018, 10, 466.

[86] Immler, F.; Engelbart, D.; Schrems, O. Fluorescence from atmospheric aerosol detected by a lidar indicates biogenic particles in the lowermost stratosphere. Atmospheric chemistry and physics 2005, 5, 345–355.

[87] Veselovskii, I.; Hu, Q.; Goloub, P.; Podvin, T.; Korenskiy, M.; Pujol, O.; Dubovik, O.; Lopatin, A. Combined use of Mie–Raman and fluorescence lidar observations for improving aerosol characterization: feasibility experiment. Atmospheric Measurement Techniques 2020, 13, 6691–6701.]

 

Comment 4: [Line 109. The name for this sub-section is “Warm clouds”, but the processes discussed are not just applicable to warm clouds, but to liquid clouds in general (that also includes supercool liquid clouds below 0°C). I would suggest changing the name for this sub-section to “Liquid clouds”.]

Response 4: [Done. Now the sub-section incipit reads:

“2.1 Liquid clouds

Liquid clouds consist  of tiny liquid water droplets that can exist at temperatures both above and slightly below freezing, whereas warm clouds specifically refer to those that form when the air temperature is above freezing. These are among the most interesting clouds…”]

Comment 5: [Line 135-138. Please explain more in the text how Figure 1 demonstrates an updraft-limited regime and how this is related to the supersaturations as color-coded in Figure 1. Please also add more text in the caption of Figure 1 to explain the color coding.   ]

Response 5: [We have added the following text:

“In fact, in warm clouds, the droplet number concentration is influenced by both the availability of CCN and the strength of updrafts within the cloud. The transition from a CCN-limited regime to an updraft-limited regime can be explained as follows: i. in the CCN-limited regime the droplet number concentration in the cloud is primarily controlled by the availability of CCN. When there are few CCN present, not many droplets can form regardless of the updraft strength. Thus, in a CCN-limited regime, increasing the number of CCN will lead to a corresponding increase in the droplet number;  ii. However, as the number of CCN increases, more droplets form and there comes a point where the updraft velocity starts to play a more significant role. Updrafts causes adiabatic cooling and, together with the rate of droplet condensation, controls supersaturation. When sufficient CCN are available, the ability of updrafts to continue lifting air parcels and supporting the formation of additional droplets becomes critical; iii. In the updraft-limited regime the droplets concentration is primarily controlled by the strength of the updrafts since, even if the CCN concentration is high, the droplet concentration can not increase significantly unless the updrafts are strong enough to support the continued formation and growth of cloud droplets and keep the supersaturation at high levels. Hence, when large concentrations of CCN are available, stronger updrafts are needed to provide enough cooling to maintain supersaturation and support further condensation.

 

Figure 1 shows these processes at work, where an increase in the concentration of CCN (on the horizontal axis) initially causes an increase in the concentration of cloud droplets (on the vertical axis) supported by high levels of supersaturation (color coded in the figure). This CCN-limited regime ends up in a plateau where high CCN concentrations have no further effects on the concentration of cloud droplets, due to relatively low supersaturation, in this case not supported by a sufficient vertical speed (updraft-limited regime). The data were collected in airborne measurement campaigns in 2013  [30].”

We have a new caption for Figure 1. :

“ Cloud droplet number vs. total aerosol number. Data are colored by maximum supersaturation.”]

Comment 6: [Line 145. Please double-check if equation 6 contains an extra π. Also please define ρw.]

Response 6: [Thank you for pointing this out. The eq. has been corrected.]

Comment 7: [Line 145. Equation 7. Please explain to the reader why the liquid water content and effective radius are written as a function of height.]

Response 7: [We have everywhere conformed in the notation the dependence on z.]

Comment 8[ Line 152-153. Please provide some typical values of fad and Γad .]

Response 8 [We have added the following lines :

“G_ad is characterized by a large variability “G_ad =0.45 plus-minus 0.21 according to [32]) driven by entrainment processes, but at the cloud base and close to its center we can assume that…”

“G_ad the rate of increase of LWC(z) with height in fully adiabatic conditions, a function of temperature and pressure ranging from 0.5 to 3 g m^-3 km^-1, that…”

Referenced with:

[32] Barlakas, V., Deneke, H., and Macke, A.: The sub-adiabatic model as a concept for evaluating the representation and radiative effects of low-level clouds in a high-resolution atmospheric model, Atmos. Chem. Phys., 20, 303–322, https://doi.org/10.5194/acp-20-303-2020, 2020.]

Comment 9: [Line  179. Please double-check whether reference 35 is appropriate for the rain rate dependence on the effective radius.]

Response 9: [The exact reference should have been Pruppacher, Hans R., James D. Klett, and Pao K. Wang. "Microphysics of clouds and precipitation." (1998): 381-382. Corrected in the revised manuscript.]

Comment 10: [Line 306. Please clarify what “larger aerosol” means here. Larger aerosol particles in terms of size? Or higher aerosol concentrations?  ]

Response 10: [“…higher aerosol concentration…”  ]

Comment 11: [Line 754-755. Here the usage of lidar in the distinguishing cloud phase is discussed. It is worth mentioning in section 3.1 lines 361-374 that particle depolarization ratio could be also used for distinguishing the cloud phase (e.g. Yoshida et al., 2010).]

Response 11: [We have added the following lines:

“Particle depolarization measurements are also useful for distinguishing the thermodynamical phase of cloud particles, as they can differentiate between spherical liquid droplets and non-spherical ice crystals based on the polarization characteristics of scattered light” and we have quoted the suggested reference there.

[76] Yoshida, R., H. Okamoto, Y. Hagihara, and H. Ishimoto (2010), Global analysis of cloud phase and ice crystal orientation from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data using attenuated backscattering and depolarization ratio, J. Geophys. Res., 115, D00H32, doi:10.1029/2009JD012334.]

Comment 12: [Line 802. Please consider adding a few sentences on why the dual-FOV lidar technique is used so widely in studying the aerosol effect on liquid clouds.]

Response 12: [We have rewritten the beginning of subparagraph 4.3 as:

“Studies investigating the influence of aerosols on liquid clouds predominantly employ dual-FOV lidar techniques. These techniques allow for simultaneous measurement of cloud droplet size and number concentration. By capturing backscattered light at two different fields of view, this approach provides detailed vertical profiles of cloud microphysical properties, generally for several tens of metres up from the cloud base. The cloud base is a crucial region for the development of the cloud because it marks the altitude where rising air parcels reach top saturation levels and condensation begins. This initiation of condensation leads to the formation of cloud droplets, whose concentration remains essentially constant until collision-coalescence process begins higher up in the cloud. So the processes occurring at the cloud base, including the activation of cloud condensation nuclei (CCN) and the subsequent formation of droplets, set the stage for the cloud's microphysical properties. These initial conditions influence the cloud's optical properties, precipitation potential, and overall dynamics.”]

Comment 13: [Line 864. Section 5. It is worth discussing how machine learning could be applied to lidar data in ACI studies. There have been some studies in the recent years (e.g. Yorks et al., 2021; Farhani et al., 2021).

Yorks, J. E., Selmer, P. A., Kupchock, A., Nowottnick, E. P., Christian, K. E., Rusinek, D., ... & McGill, M. J. (2021). Aerosol and cloud detection using machine learning algorithms and space-based lidar data. Atmosphere, 12(5), 606.

Farhani, G., Sica, R. J., & Daley, M. J. (2021). Classification of lidar measurements using supervised and unsupervised machine learning methods. Atmospheric Measurement Techniques, 14(1), 391-402.]

Response 13 :[We thank the Reviewer for this very useful suggestion. We have added the following text:

 

“Innovative unconventional algorithms are starting to be applied to large datasets.

Machine learning (ML), as instance, can significantly enhance our capability to extract useful information from data. ML algorithms can automate the extraction of complex features such as cloud boundaries, aerosol layers, and cloud optical properties. Classification algorithms, such as support vector machines (SVM) and convolutional neural networks (CNN), can distinguish between different types of aerosols and cloud particles, improving the understanding of their interactions [216,217]. Supervised learning models can be trained on historical lidar data to predict the impact of aerosols on cloud properties like droplet size distribution and liquid water content [218], and regression models can estimate key parameters such as aerosol optical depth (AOD) and cloud droplet number concentration (CDNC) from lidar backscatter profiles. Unsupervised learning methods, such as clustering and anomaly detection algorithms, can identify unusual patterns or events in lidar data, such as unexpected changes in aerosol concentrations or cloud formation processes [219]. These anomalies can provide insights into rare or extreme ACI events, improving the understanding of their mechanisms. Techniques like principal component analysis (PCA) [220,221] and t-distributed stochas- tic neighbor embedding (t-SNE) [219] can reduce the complexity of high-dimensional lidar data, making it easier to visualize and interpret aerosol and cloud interaction patterns. Improved visualization aids in hypothesis generation and data-driven discovery of ACI phenomena. By applying these innovative methods of analysis to lidar data, researchers can enhance the precision, efficiency, and scope of ACI studies, leading to a deeper understanding of how aerosols affect cloud formation, development, and climate impacts.”

With the following references:

216. Di Noia, A.; Hasekamp, O., Neural Networks and Support Vector Machines and Their Application to Aerosol and Cloud Remote Sensing: A Review; 2018; pp. 279–329. https://doi.org/10.1007/978-3-319-70796-9_4.
217. Yorks, J.E.; Selmer, P.A.; Kupchock, A.; Nowottnick, E.P.; Christian, K.E.; Rusinek, D.; Dacic, N.; McGill, M.J. Aerosol and cloud detection using machine learning algorithms and space-based lidar data. Atmosphere 2021, 12, 606.
218. Chen, Y.; Haywood, J.;Wang, Y.; Malavelle, F.; Jordan, G.; Partridge, D.; Fieldsend, J.; De Leeuw, J.; Schmidt, A.; Cho, N.; et al. Machine learning reveals climate forcing from aerosols is dominated by increased cloud cover. Nature Geoscience 2022, 15, 609–614.
219. Farhani, G.; Sica, R.J.; Daley, M.J. Classification of lidar measurements using supervised and unsupervised machine learning methods. Atmospheric Measurement Techniques 2021, 14, 391–402. 1674
220. Donovan, D.P.; Carswell, A.I. Principal component analysis applied to multiwavelength lidar aerosol backscatter and extinction measurements. Applied optics 1997, 36, 9406–9424. 1676
221. De Graaf, M.; Apituley, A.; Donovan, D.P. Feasibility study of integral property retrieval for tropospheric aerosol from Raman lidar data using principal component analysis. Applied Optics 2013, 52, 2173–2186.]

Comment 14: [Line 904. Other satellite missions worth mentioning:

Ke, J., Sun, Y., Dong, C., Zhang, X., Wang, Z., Lyu, L., ... & Liu, D. (2022). Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration. PhotoniX, 3(1), 17. https://link.springer.com/article/10.1186/s43074-022-00063-3]

Response 14: [We have added the following text:

 

In  2022 China launched the Atmospheric Environment Monitoring Satellite (AEMS), equipped with its first space-borne atmospheric lidar, known as the Aerosol and Carbon Detection Lidar (ACDL), which operates in a 705-km solar synchronous orbit, and includes two lidar instruments on a single platform. One of these instruments is the aerosol-cloud high-spectral-resolution lidar (ACHSRL) [215,216].

With these references:

215. Liu, D.; Zheng, Z.; Chen, W.; Wang, Z.; Li, W.; Ke, J.; Zhang, Y.; Chen, S.; Cheng, C.; Wang, S. Performance estimation of space-borne high-spectral-resolution lidar for cloud and aerosol optical properties at 532 nm. Opt. Express 2019, 27, A481–A494. https://doi.org/10.1364/OE.27.00A481. 1670
216. Liu, Q.; Huang, Z.; Liu, J.; Chen, W.; Dong, Q.; Wu, S.; Dai, G.; Li, M.; Li, W.; Li, Z.; et al. Validation of initial observation from the first spaceborne high-spectral-resolution lidar with a ground-based lidar network. Atmospheric Measurement Techniques 2024, 17, 1403–1417. https://doi.org/10.5194/amt-17-1403-2024.]

Comment 15: [Line 152. Please check the typo for cloud base.]

Response 15: [  Done]

Comment 16: [Line 211. Please change “medium” to “mid-”.]

Response 16 [ Done]

Comment 17 :[Line 254. “ice secondary multiplication” or “secondary ice multiplication”?]

Response 17: [ Done]

Comment 18: [Line 357. Should be “large systemic errors” ]

Response 18: [We prefer to keep “Systematic errors” there. According to Webster : “systematic error, noun : an error that is not determined by chance but is introduced by an inaccuracy (as of observation or measurement) inherent in the system” . that is what we aimed to mean.]

Comment 19: [Line 630. Should be “main sources of error”. ]

Response 19: [Done. ]

Comment 20: [Line 938. Please define the acronym “WV”. ]

Response 20 [Done.]

 

Reviewer 3 Report

Comments and Suggestions for Authors

ACI and its effects on the climate system have been the top subjects all the time. This paper presents a comprehensive overview of the current research in ACI, and focused on the capabilities and advancements in multiwavelength, Raman, and HSRL lidar techniques to this field. Both ground-based and satellite lidar platforms are introduced to highlight the respective contributions to the understanding of aerosol properties and their role in cloud formation and dynamics. In addition to detection techniques and inversion method, several cases are also involved in the review. Overall, it is a comprehensive review paper.

Author Response

Comment 1: [ACI and its effects on the climate system have been the top subjects all the time. This paper presents a comprehensive overview of the current research in ACI, and focused on the capabilities and advancements in multiwavelength, Raman, and HSRL lidar techniques to this field. Both ground-based and satellite lidar platforms are introduced to highlight the respective contributions to the understanding of aerosol properties and their role in cloud formation and dynamics. In addition to detection techniques and inversion method, several cases are also involved in the review. Overall, it is a comprehensive review paper.]

Response 1: [We thank reviewer 3 for the appreciation of the manuscript.]

Reviewer 4 Report

Comments and Suggestions for Authors

I have reviewed the manuscript titled " Understanding Aerosol-Cloud Interactions Through Lidar Techniques: A Review" by Cairo et al. This manuscript provides a thorough overview of the recent advancements in the study of aerosol-cloud interactions using lidar remote sensing techniques. The topic is highly relevant, as aerosol-cloud interactions play a crucial role in Earth's climate and hydrological cycle, and accurate observation of these interactions is essential for improving climate models and predictions.

It begins by describing the impact of aerosols on cloud microphysical processes and the capabilities of lidar remote sensing in characterizing aerosols and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol-cloud interactions, including the role of aerosols in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research directions.

In conclusion, the well written manuscript presents an excellent introduction, results, and conclusion. Although there are a few things to improve before accepting this manuscript for publication, I agree to accept it with minor revisions as outlined below.

Comments for author File: Comments.pdf

Author Response

Comment 1: [I have reviewed the manuscript titled " Understanding Aerosol-Cloud Interactions Through Lidar Techniques: A Review" by Cairo et al. This manuscript provides a thorough overview of the recent advancements in the study of aerosol-cloud interactions using lidar remote sensing techniques. The topic is highly relevant, as aerosol-cloud interactions play a crucial role in Earth's climate and hydrological cycle, and accurate observation of these interactions is essential for improving climate models and predictions.

It begins by describing the impact of aerosols on cloud microphysical processes and the capabilities of lidar remote sensing in characterizing aerosols and clouds. The subsequent sections delve into the key findings and insights gained from lidar-based studies of aerosol-cloud interactions, including the role of aerosols in cloud formation, evolution, and microphysical properties. Finally, the review concludes with an outlook on future research directions.

In conclusion, the well written manuscript presents an excellent introduction, results, and conclusion. Although there are a few things to improve before accepting this manuscript for publication, I agree to accept it with minor revisions as outlined below. ]

Response 1: [We thank reviewer 4 for the appreciation of the manuscript and for the suggested improvements.]

 

 

Comment 2:[1.Lines 23-27: It is recommended that relevant references be added to facilitate reading and understanding by readers in different fields.]

Response 2: [These relevant references have been added:

1. Kaufman, Y.J.; Tanre, D.; Boucher, O. A satellite view of aerosols in the climate system. Nature 2002, 419, 215–223.
2. Seinfeld, J.H.; Bretherton, C.; Carslaw, K.S.; Coe, H.; DeMott, P.J.; Dunlea, E.J.; Feingold, G.; Ghan, S.; Guenther, A.B.; Kahn, R.; et al. Improving our fundamental understanding of the role of aerosol- cloud interactions in the climate system. Proceedings of the 1119

National Academy of Sciences 2016, 113, 5781–5790.

3. Li, J.; Carlson, B.E.; Yung, Y.L.; Lv, D.; Hansen, J.; Penner, J.E.; Liao, H.; Ramaswamy, V.; Kahn, R.A.; Zhang, P.; et al. Scattering and absorbing aerosols in the climate system. Nature Reviews Earth & Environment 2022, 3, 363–379.
4. Carslaw, K.S. Chapter 2 - Aerosol in the climate system. In Aerosols and Climate; Carslaw, K.S., Ed.; Elsevier, 2022; pp. 9–52. https://doi.org/https://doi.org/10.1016/B978-0-12-819766-0.00008-0.]

 

Comment 3: [2.Lines 165:

“So, eqs. (11) and (10) tells us”

Perhaps “So, eqs. (10) and (11) tells us” is better ? ]

Response 3: [Done.]

Comment 4: [3. Perhaps Figure 6 could be more aesthetically pleasing, e.g., the size of the axis label "ACIN" could be reduced a bit.]

Response 4: [ Done.]

Comme t 5: [4.Lines 955-957: Perhaps could consider rephrasing it to emphasize the broader significance and impact of the continued progress in this field, rather than just "more accurate climate predictions".

For example, "Continued innovation and collaboration in this field will undoubtedly lead to deeper insights and advancements that enhance our ability to understand, model, and predict the complex interactions between aerosols, clouds, and climate." ]

Response 5: [Thanks for the rewording. The suggestion has been implemented in the text.]