the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Nov 2023
Observational study of factors influencing the dispersion of warm fog droplet spectrum in Xishuangbanna, China
Abstract. The microphysical characteristics of fog and stratiform clouds are somewhat similar. The study of the microphysical characteristics of warm fog, fog droplet spectral relative dispersion, and their influencing factors can deepen our understanding of the variability and influencing factors of cloud droplet spectral relative dispersion, while also investigating the formation and maintenance mechanisms of fog. This is currently a scientific issue that still remains controversial in cloud physics research and climate prediction. In this paper, we analyzed three months of Xishuangbanna radiation fog observations to explore the microphysical characteristics of fog. The results show followings: (1) When the autoconversion threshold (T) increased to greater than 0.4, the positive correlation between the relative dispersion of fog droplet spectrum and the volume mean diameter or water content of fog droplet weakened, also the positive correlation between relative dispersion and number concentration increased where the main mechanism needed to be integrated considering the interaction of collision-coalescence, condensation, and nucleation processes. It is found that the strength of the collision-coalescence process has a certain influence on the variation rule of dispersion. (2) The number concentration of 2–12 µm droplets in the fog constrained the relationship between the T and relative dispersion, with the number of large droplets reflects the strength of the collision-coalescence process. (3) Supersaturation changed microphysical quantities by increasing the number concentration of small droplets in the fog, which affected the variations of relative dispersion. For supersaturation greater than 0.12 %, the number concentration of droplets larger than 30 µm may be decreased due to gravitational settling. In addition, there is no significant relationship between supersaturation and relative dispersion if the initial nucleated fog droplet spectrum is narrow.
- Preprint
(2099 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2023-2516', Marie Mazoyer, 07 Dec 2023
First review of the manuscript. Observational study of factors influencing the dispersion of warm fog droplet spectrum in Xishuangbanna, China by Zhenya An and Xiaoli Liu Marie Mazoyer, Météo-France The paper titled “Observational study of factors influencing the dispersion of warm fog droplet spectrum in Xishuangbanna, China” by An et al., aims to study the characteristics of fog microphysical properties by considering 19 fog cases observed in a tropical rainforest . Their study mainly focuses on the relationship between self-conversion and the relative dispersion of fog droplet spectra. Additional emphasis is placed on the relationship of supersaturation with the concentration of fog droplets. I think this study is of great interest given the current challenges of forecasting and modeling fog and associated microphysics. It gives a relevant description of microphysical processes occurring during fog evolution.
However, I feel that clarification of the paper's objectives needs to be made. In this way, the novelty of this work must be emphasized. The presentation of the measurement campaign and the cases studied is completely necessary before considering publication of this work. The introduction should also focus on fog microphysical processes, their impact on the fog life cycle and their consideration in the numerical model. A summary effort is really necessary on your results section for better reading. The concentration of aerosols is sometimes commented on but no plot presents it. The captions need to be rewritten much more explicitly for the description of the figures. Finally, a conceptual diagram (“handmade” graphic) could be very interesting to conclude the article. Given these points, I'm really hesitant between rejection and major revision. As your article could be significantly improved with additional work, I would suggest a major revision.
Introduction:
-A review of fog processes affecting fog during its evolution could be nice.
-Explain why we need a better description of fog microphysical processes. -A few words about numerical modeling might be interesting. -I really appreciate the presentation of the measurement campaign but it lacks international references. On the campaigns and results C-FOG (Gultepe, 2021), LANFEX (Price, 2019), SOFOG-3D (Burnet, 2020) or WIFEX (Ghude, 2023), among others. -A deeper focus on the relevance of T and ε for fog description could provide a better understanding of the article and promote their use for future studies in the community. A “handmade” spectra plot could be useful for this task and help the reader understand how variations in T and ε are related to fog microphysical processes. -Then, new questions in relation to previous studies Zhao, 2013 for example and among others must be pointed out.
Methods:
- Present the campaign, the instruments and the cases studied. Or make references to any article that has already featured it. - Supersaturation section: Petters and Kredenweis (2007) (the reference is missing in your reference session) indicates that the formula you used for A cannot be used for kappa < 0.2. As you use it for a kappa=0.15, a discussion is in order. A diagram would be welcome for a better understanding of the method used. See Mazoyer (2009) for example.
Results and analysis:
-Present the figures and the objectives of the figures before commenting on them. -The captions are not complete enough. -A comparison of your results with previous studies is sometimes missing. -Figure 3, you comment on T, but where is T? The color legend is missing. -Figure 8 and 11, your comments on the concentration of aerosols are very interesting but must be documented with an aerosol concentration plot for example.
Conclusions:
-Please re-introduce the objectives
-Please comment your findings on the processes rather than re-presenting your findings on direct MVD, LWC, T, epsilon,… relationships.
-Please draw the most important conclusions and implications for fog forecasting and modeling.
-A conceptual diagram ('handmade' graphic) could be very nice to conclude the article
References :
-Burnet, F., Lac, C., Martinet, P., Fourrié, N., Haeffelin, M., Delanoë, J., ... & Vié, B. (2020, May). The SOuth west FOGs 3D experiment for processes study (SOFOG3D) project. In EGU General Assembly Conference Abstracts (p. 17836).
-Ghude, S. D., Jenamani, R. K., Kulkarni, R., Wagh, S., Dhangar, N. G., Parde, A. N., ... & Rajeevan, M. (2023). WiFEX: Walk into the Warm Fog over Indo-Gangetic Plain Region. Bulletin of the American Meteorological Society, 104(5), E980-E1005.
- Gultepe, I., Heymsfield, A. J., Fernando, H. J. S., Pardyjak, E., Dorman, C. E., Wang, Q., ... & Wang, S. (2021). A review of coastal fog microphysics during C-FOG. Boundary-Layer Meteorology, 181, 227-265.
-Mazoyer, M., Burnet, F., Denjean, C., Roberts, G. C., Haeffelin, M., Dupont, J. C., & Elias, T. (2019). Experimental study of the aerosol impact on fog microphysics. Atmospheric Chemistry and Physics, 19(7), 4323-4344.
-Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleusactivity, Atmos. Chem. Phys., 7, 1961–1971, doi:10.5194/acp-7-1961-2007, 2007.
-Price, J., Lane, S., Boutle, I., Smith, D., Bergot, T., Lac, C., Duconge, L., McGregor, J., Kerr-Munslow, A., Pickering, M., and Clark, R.: LANFEX: a field and modelling study to improve our understanding and forecasting of radiation fog, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-16-0299.1, 2018. a, b, c, d
-Zhao, L., Niu, S., Zhang, Y., & Xu, F. (2013). Microphysical characteristics of sea fog over the east coast of Leizhou Peninsula, China. Advances in Atmospheric Sciences, 30, 1154-1172.
-
RC2: 'Comment on egusphere-2023-2516', Anonymous Referee #2, 18 Dec 2023
The manuscript " Observational study of factors influencing the dispersion of warm fog droplet spectrum in Xishuangbanna, China " offers some observational information on fog microphysics at a remote site in China. It was my hope to find interesting new datasets of fog measurements or new finding on fog microphysics characteristics, the paper is a little bit disappointing. It provides few new related to fog event that can be learned. The presentation of the theoretical background is marginal and the analysis and conclusions appear trivial. The manuscript is outlined straightforward, however, the contents are not convincing enough. I find the study to be limited in scope (both methodology and analysis) for publication in this journal. The authors should consider more serious about the robustness of and limitations both for the data and methodology, before jumping into any statements. This study is well suited for a local publication or measurement report paper. I do not think that this manuscript can be considered for publication in this version. More specific comments are as following:
- The methodology section needs more detail. The authors should add more in-depth discussions on the calculation of microphysical quantities of fog, supersaturation, visibility and auto-conversion threshold function. Addressing potential limitations and uncertainties associated with the study's methodology and conclusions would enhance the credibility and reliability of the research. The calculation of supersaturation is highly parameterized and it seems much high than previous studies. A detailed explanation of the algorithms and techniques employed in the data analysis would also enhance the transparency and reproducibility of the study.
- I am confused by the variations between auto-conversion threshold function and LWC (or number concentration) of fog in Fig.1 and Fig.7. It seems from different datasets.
- Some of the conclusions drawn from the analysis appear speculative and not fully supported by the data. For example, the statement that "the strength of the collision-coalescence process has a certain influence on the variation rule of dispersion" could be substantiated further through additional experiments or simulations. Similarly, the observation that "supersaturation changed microphysical quantities by increasing the number concentration of small droplets in the fog" could benefit from more thorough investigation and interpretation.
Citation: https://doi.org/10.5194/egusphere-2023-2516-RC2 -
RC3: 'Comment on egusphere-2023-2516', Anonymous Referee #3, 19 Dec 2023
Review of “Observational study of factors influencing the dispersion of warm fog droplet spectrum in Xishuangbanna, China” by An and Liu for ACP
This is an interesting data set with a lot of potential, but I am recommending major revision because I think there are gaps in the presentation and interpretation of the data that must be addressed.
The authors present measurements of microphysical data from fog events in a rainforest environment. The primary measurements were made with a DMT Fog Monitor, which covers the diameter range from 2 to 50 micrometers and a 1000XP Wide range particle spectrometer, which reports aerosol size distributions from 5 nm to 10 microns. From these primary measurements, several derived quantities are calculated, including liquid water content, relative dispersion, supersaturation, and the autoconversion threshold, T. Relationships between various quantities are explored. Surprisingly, at least to me, collision-coalescence was found to play a key role in these radiation fogs.
As noted just above, the data is presented in some ways that I am not following. Are the axes labeled correctly for all these plots? For example, the caption to Figure 6 indicates that it is a plot of the probability density function for the droplet data for different values of T, but this can’t be correct, as the units don’t match. (The abscissa is in microns, and the ordinate is given in cm^(-3).)
I would recommend plotting these as dN/dDp. Plots of the pdf might also carry some useful information, but perhaps plotting the average pdf for each of the T values on the same plot would make it easier to compare the cases.
I think that the authors are using T as a proxy for collision-coalescence; I have significant reservations about using it in this way. Autoconversion is a way to parameterize the warm rain process, and the conversion threshold that the authors are using is a refinement of that parameterization. Originally T was a step function. This version smooths that transition, while still preserving the sharp turnon character of autoconversion. (This is noted in the references that the authors cite.) Given that the title refers to an observational study and T is a derived quantity, which is primarily used in a parameterization, I don’t think it fits here. As I read the manuscript, T is used to draw some conclusions about the way that collision-coalescence may be affecting the relative dispersion.
I don’t typically think of collision-coalescence as playing a significant role in the evolution of fogs. I’d like to see that claim backed up with some measurement. The measurements presented here do extend out to droplet diameters of 50 microns, which is getting into a range where appreciable fall velocities may be encountered. The highest values of T here would indicate that the fogs should have been drizzling. Were they?
Other comments:
Line 105: t is defined as the sampling frequency, but from equation 1, it looks like this should be the integration time of the sample. Please clarify.
Finding supersaturation: I know that there are very few measurements of supersaturation, though one of the only ones I know WAS a measurement in a fog. See Gerber 1991. For the data presented here… A sensitivity study of the dependence of the supersaturation on the value of kappa would be a good addition.
Line 150: I think there’s an “r” here that should be “r_c”.
Figure 3 and others: The points are color coded, but I don’t think the color coding is ever explained.
Line 245: The statement is made the condensational growth leads to broadening of the fog droplet spectrum. Condensational growth typically leads to a narrowing of the droplet spectrum. dr/dt is proportional to 1/r. Please clarify what you mean here.
Line 257: The “-3” needs to be superscripted.
Figure 9: The top panel shows that the droplet number concentration increases with increasing supersaturation. If you look at this from the perspective of exposing a collection of aerosol particles to a supersaturation, then increasing s will increase the number of particles that activate. But that isn’t what happens. The supersaturation evolves, in a feedback between the source (presumably radiation cooling here), and the sink, which is growth of the droplets. Because of this feedback, high droplet numbers are usually associated with low supersaturations. See Reutter et al. for further discussion.
Gerber, H., 1991. Supersaturation and droplet spectral evolution in fog. Journal of Atmospheric Sciences, 48(24), pp.2569-2588.
Reutter, P., Su, H., Trentmann, J., Simmel, M., Rose, D., Gunthe, S.S., Wernli, H., Andreae, M.O. and Pöschl, U., 2009. Aerosol-and updraft-limited regimes of cloud droplet formation: influence of particle number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN). Atmospheric Chemistry and Physics, 9(18), pp.7067-7080.
Citation: https://doi.org/10.5194/egusphere-2023-2516-RC3 -
AC1: 'Reply on RC1', Zhenya An, 19 Feb 2024
Dear Mazoyer,
We feel great thanks for you professional review work on our articles on our article. As you are concerned, there are several problems that need to be addressed.According to your nice suggestions, we have made extensive corrections to our previous draft, the detailed corrections are listed below.
1.introduction:
Q1:-A review of fog processes affecting fog during its evolution could be nice.
answer:Thank you for your feedback.We have revised this sentence in the article. the details are follows:
"Given the above, the microphysical characteristics investigation occupies an important position in the study of fog. The evolution process of fog involves multiple complex physical and meteorological processes, for example, radiation cooling, terrain, turbulent mixing, aerosol composition, meteorological conditions, and some microphysical processes (condensation, evaporation, collision, etc.). Therefore, the droplet size distribution (DSD) of fog (Gultepe et al, 2007) exhibits significant variability. WILLWTT (1928) emphasized the importance of all meteorological parameters that affect fog formation and proposed a classification of fog and haze based on their causes and favorable weather conditions. In general, fog can be divided into two categories: 1) airmass fogs and 2) frontal fogs.The radiation fog studied in this paper is a type of airmass fog. Wang (2021) has explored the formation mechanism of radiation fog and the impact of topography on fog, pointing out that the influence of forest understory in tropical rainforest areas cannot be ignored. “
Q2:-Explain why we need a better description of fog microphysical processes.-A few words about numerical modeling might be interesting.
Thank you for your feedback, We have added the following content:
“Therefore, the evolution process of fog may vary significantly in different regions, which increases the difficulty in understanding fog mechanisms and the challenge in numerical simulations. In current single-parameter and double-moment microphysical parameterization schemes, number concentration is often assumed to be a fixed value, and several assumptions are made regarding the physical factors affecting number concentration, which reduces the forecasting capability of models for fog, especially for visibility. Therefore, Gultepe et al. (2020) improve the fog predictability of Numerical Weather Prediction (NWP) models, threfore ˙further improvements in microphysical observations and parameterization schemes are needed. This highlights the urgent need for a better description of the microphysical processes of fog. Many academics(Okita,1962; Niu et al., 2010; Gultepe et al., 2020; Yang et al., 2021; Wang et al., 2021) essentially elucidated in their articles the impact of droplet spectrum on the microphysical characteristics of fog.The relative dispersion is a parameter that describes the degree of relative dispersion of the fog droplets size distribution, which has been the focus of research in cloud physics for the last two decades (Desai et al., 2019).”
Q3-I really appreciate the presentation of the measurement campaign but it lacks international references. On the campaigns and results C-FOG (Gultepe, 2021), LANFEX (Price, 2019), SOFOG-3D (Burnet, 2020) or WIFEX (Ghude, 2023), among others..
answer：Thank you for your feedback, this was our oversight. I have included introductions to the measurement activities by Gultepe (2021) and Burnet (2020) in the introduction section, as follows:
"In the autumn of 1959, Okita (1962) used balloons to measure the distribution of fog droplet number concentration and liquid water content with height in mountain fog in Hokkaido, Japan. In 1970, NASA supported the Cornell Laboratory (CAL) to conduct field observation experiments in New York for the purpose of artificial fog elimination, obtaining the vertical structure of fog microphysical characteristics (Pilié et al.,1975). In 2018, In order to advance understanding of liquid fog formation,development and dissipation in coastal environments, improving fog predictability and monitoring, Gultepe et al.(2020) designed a campaign named C-FOG field project. During the winter of 2019/2020, Burnet et al. (2020) synergistically combined 3D high-resolution Large Eddy Simulation with unprecedented 3D detailed observations in the South-West of France to deepen our understanding of the smallest scales in fog. This project (SOFOG3D) aimed to improve the prediction of fog events in numerical models. The earliest fog observation experiment in China took place in Shanghai (Li, 2001). In 1968 and 1969, a fog census was also conducted in the southern provinces of China, and preliminary observations of fog microstructure were made in Yunnan, Guizhou, and Sichuan (Niu et al., 1989). Niu et al. (2010) investigated the microphysical characteristics of persistent fog, using experimental data from fog observations in Pancheng town, a northern suburb of Nanjing, in winter 2006. Yang et al. (2021) used the microphysical observation data of the Tianjin radiation fog in the winter of 2016/2017 combined with meteorological tower data to reveal the observation facts of the microphysical and size distribution characteristics of fog droplets and discuss fog formation and dissipation mechanisms. Wang et al. (2021) elaborated the physical and chemical characteristics of radiation fog using observation data from December 2019 to February 2020 in the tropical rainforest area of Xishuangbanna."
Q4:-A deeper focus on the relevance of T and ε for fog description could provide a better understanding of the article and promote their use for future studies in the community. A “handmade” spectra plot could be useful for this task and help the reader understand how variations in T and ε are related to fog microphysical processes.
answer: Thank you for your feedback. Figure (a) shows the correlation between T and ε, plotted by me. As depicted in the figure, aerosol nucleation leads to the formation of small water droplets, which grow through condensation and collision coalescence, resulting in differences in their scales and thus causing dispersion. Moreover, ε is correlated with four microphysical quantities of fog: SSc, LWC, MVD, and Nf. These four fog microphysical quantities also interact with each other. This paper aims to investigate these relationships through the collision-coalescence process of fog droplets, with the intensity of the collision-coalescence process described using T.
Q5:-Then, new questions in relation to previous studies Zhao, 2013 for example and among others must be pointed out.
answer: In Niu et al., (2008), the results show that key microphysical characteristics (liquid water content, droplet concentration, mean radius, and standard deviation) overall exhibit a positive correlation. The examination of microphysical relationships constrained by the threshold functions of the respective autoconversion schemes suggests that the coalescence process may sometimes occur, thereby diminishing the positive correlation induced by droplet activation and condensational growth. Based on this, the paper further explores the influence of SSc and background aerosols on these relationships.
2. Method
Q1- Present the campaign, the instruments and the cases studied. Or make references to any article that has already featured it.
answer :Thank you for your feedback. In the methodology section, I have added an introduction to the climate background of the Xishuangbanna region. Figure (b) below illustrates the geographical location of Xishuangbanna. Additionally, I have provided a more detailed description of the instruments used in this measurement campaign and have included a table for better illustration.
Instrument Name
Manufacturer
Measured particle size range
sampling flow
Sampling Interval
Instrument Location
FM-120
DMT,America
2-50
1
1s
iron tower
WPS-1000XP
MSP,America
10-10000
1
360s
botanic garden
Q2- Supersaturation section: Petters and Kredenweis (2007) (the reference is missing in your reference session) indicates that the formula you used for A cannot be used for kappa < 0.2. As you use it for a kappa=0.15, a discussion is in order. A diagram would be welcome for a better understanding of the method used. See Mazoyer (2009) for example.
answer:Thank you for correcting the errors in this paper. I have revised the algorithm for this section accordingly. Since there are no measured data for hygroscopicity parameters, the paper still refers to k=0.15. However, when calculating supersaturation, I have changed to a formula that applies to all values of k. Below is Figure (c), illustrating the updated process for calculating SSc.
Citation: https://doi.org/10.5194/egusphere-2023-2516-AC1 -
AC2: 'Reply on RC1', Zhenya An, 19 Feb 2024
Dear Mazoyer,
We feel great thanks for you professional review work on our articles on our article. As you are concerned, there are several problems that need to be addressed.According to your nice suggestions, we have made extensive corrections to our previous draft, the detailed corrections are listed below.
1.introduction:
Q1:-A review of fog processes affecting fog during its evolution could be nice.
answer:Thank you for your feedback.We have revised this sentence in the article. the details are follows:
"Given the above, the microphysical characteristics investigation occupies an important position in the study of fog. The evolution process of fog involves multiple complex physical and meteorological processes, for example, radiation cooling, terrain, turbulent mixing, aerosol composition, meteorological conditions, and some microphysical processes (condensation, evaporation, collision, etc.). Therefore, the droplet size distribution (DSD) of fog (Gultepe et al, 2007) exhibits significant variability. WILLWTT (1928) emphasized the importance of all meteorological parameters that affect fog formation and proposed a classification of fog and haze based on their causes and favorable weather conditions. In general, fog can be divided into two categories: 1) airmass fogs and 2) frontal fogs.The radiation fog studied in this paper is a type of airmass fog. Wang (2021) has explored the formation mechanism of radiation fog and the impact of topography on fog, pointing out that the influence of forest understory in tropical rainforest areas cannot be ignored. “
Q2:-Explain why we need a better description of fog microphysical processes.-A few words about numerical modeling might be interesting.
Thank you for your feedback, We have added the following content:
“Therefore, the evolution process of fog may vary significantly in different regions, which increases the difficulty in understanding fog mechanisms and the challenge in numerical simulations. In current single-parameter and double-moment microphysical parameterization schemes, number concentration is often assumed to be a fixed value, and several assumptions are made regarding the physical factors affecting number concentration, which reduces the forecasting capability of models for fog, especially for visibility. Therefore, Gultepe et al. (2020) improve the fog predictability of Numerical Weather Prediction (NWP) models, threfore ˙further improvements in microphysical observations and parameterization schemes are needed. This highlights the urgent need for a better description of the microphysical processes of fog. Many academics(Okita,1962; Niu et al., 2010; Gultepe et al., 2020; Yang et al., 2021; Wang et al., 2021) essentially elucidated in their articles the impact of droplet spectrum on the microphysical characteristics of fog.The relative dispersion is a parameter that describes the degree of relative dispersion of the fog droplets size distribution, which has been the focus of research in cloud physics for the last two decades (Desai et al., 2019).”
Q3-I really appreciate the presentation of the measurement campaign but it lacks international references. On the campaigns and results C-FOG (Gultepe, 2021), LANFEX (Price, 2019), SOFOG-3D (Burnet, 2020) or WIFEX (Ghude, 2023), among others..
answer：Thank you for your feedback, this was our oversight. I have included introductions to the measurement activities by Gultepe (2021) and Burnet (2020) in the introduction section, as follows:
"In the autumn of 1959, Okita (1962) used balloons to measure the distribution of fog droplet number concentration and liquid water content with height in mountain fog in Hokkaido, Japan. In 1970, NASA supported the Cornell Laboratory (CAL) to conduct field observation experiments in New York for the purpose of artificial fog elimination, obtaining the vertical structure of fog microphysical characteristics (Pilié et al.,1975). In 2018, In order to advance understanding of liquid fog formation,development and dissipation in coastal environments, improving fog predictability and monitoring, Gultepe et al.(2020) designed a campaign named C-FOG field project. During the winter of 2019/2020, Burnet et al. (2020) synergistically combined 3D high-resolution Large Eddy Simulation with unprecedented 3D detailed observations in the South-West of France to deepen our understanding of the smallest scales in fog. This project (SOFOG3D) aimed to improve the prediction of fog events in numerical models. The earliest fog observation experiment in China took place in Shanghai (Li, 2001). In 1968 and 1969, a fog census was also conducted in the southern provinces of China, and preliminary observations of fog microstructure were made in Yunnan, Guizhou, and Sichuan (Niu et al., 1989). Niu et al. (2010) investigated the microphysical characteristics of persistent fog, using experimental data from fog observations in Pancheng town, a northern suburb of Nanjing, in winter 2006. Yang et al. (2021) used the microphysical observation data of the Tianjin radiation fog in the winter of 2016/2017 combined with meteorological tower data to reveal the observation facts of the microphysical and size distribution characteristics of fog droplets and discuss fog formation and dissipation mechanisms. Wang et al. (2021) elaborated the physical and chemical characteristics of radiation fog using observation data from December 2019 to February 2020 in the tropical rainforest area of Xishuangbanna."
Q4:-A deeper focus on the relevance of T and ε for fog description could provide a better understanding of the article and promote their use for future studies in the community. A “handmade” spectra plot could be useful for this task and help the reader understand how variations in T and ε are related to fog microphysical processes.
answer: Thank you for your feedback. Figure (a) shows the correlation between T and ε, plotted by me. As depicted in the figure, aerosol nucleation leads to the formation of small water droplets, which grow through condensation and collision coalescence, resulting in differences in their scales and thus causing dispersion. Moreover, ε is correlated with four microphysical quantities of fog: SSc, LWC, MVD, and Nf. These four fog microphysical quantities also interact with each other. This paper aims to investigate these relationships through the collision-coalescence process of fog droplets, with the intensity of the collision-coalescence process described using T.
Citation: https://doi.org/10.5194/egusphere-2023-2516-AC2 -
AC3: 'Reply on RC1', Zhenya An, 19 Feb 2024
Dear Mazoyer,
We feel great thanks for you professional review work on our articles on our article. As you are concerned, there are several problems that need to be addressed.According to your nice suggestions, we have made extensive corrections to our previous draft, the detailed corrections are listed below.
1.introduction:
Q1:-A review of fog processes affecting fog during its evolution could be nice.
answer:Thank you for your feedback.We have revised this sentence in the article. the details are follows:
"Given the above, the microphysical characteristics investigation occupies an important position in the study of fog. The evolution process of fog involves multiple complex physical and meteorological processes, for example, radiation cooling, terrain, turbulent mixing, aerosol composition, meteorological conditions, and some microphysical processes (condensation, evaporation, collision, etc.). Therefore, the droplet size distribution (DSD) of fog (Gultepe et al, 2007) exhibits significant variability. WILLWTT (1928) emphasized the importance of all meteorological parameters that affect fog formation and proposed a classification of fog and haze based on their causes and favorable weather conditions. In general, fog can be divided into two categories: 1) airmass fogs and 2) frontal fogs.The radiation fog studied in this paper is a type of airmass fog. Wang (2021) has explored the formation mechanism of radiation fog and the impact of topography on fog, pointing out that the influence of forest understory in tropical rainforest areas cannot be ignored. “
Q2:-Explain why we need a better description of fog microphysical processes.-A few words about numerical modeling might be interesting.
Thank you for your feedback, We have added the following content:
“Therefore, the evolution process of fog may vary significantly in different regions, which increases the difficulty in understanding fog mechanisms and the challenge in numerical simulations. In current single-parameter and double-moment microphysical parameterization schemes, number concentration is often assumed to be a fixed value, and several assumptions are made regarding the physical factors affecting number concentration, which reduces the forecasting capability of models for fog, especially for visibility. Therefore, Gultepe et al. (2020) improve the fog predictability of Numerical Weather Prediction (NWP) models, threfore ˙further improvements in microphysical observations and parameterization schemes are needed. This highlights the urgent need for a better description of the microphysical processes of fog. Many academics(Okita,1962; Niu et al., 2010; Gultepe et al., 2020; Yang et al., 2021; Wang et al., 2021) essentially elucidated in their articles the impact of droplet spectrum on the microphysical characteristics of fog.The relative dispersion is a parameter that describes the degree of relative dispersion of the fog droplets size distribution, which has been the focus of research in cloud physics for the last two decades (Desai et al., 2019).”
Q3-I really appreciate the presentation of the measurement campaign but it lacks international references. On the campaigns and results C-FOG (Gultepe, 2021), LANFEX (Price, 2019), SOFOG-3D (Burnet, 2020) or WIFEX (Ghude, 2023), among others..
answer：Thank you for your feedback, this was our oversight. I have included introductions to the measurement activities by Gultepe (2021) and Burnet (2020) in the introduction section, as follows:
"In the autumn of 1959, Okita (1962) used balloons to measure the distribution of fog droplet number concentration and liquid water content with height in mountain fog in Hokkaido, Japan. In 1970, NASA supported the Cornell Laboratory (CAL) to conduct field observation experiments in New York for the purpose of artificial fog elimination, obtaining the vertical structure of fog microphysical characteristics (Pilié et al.,1975). In 2018, In order to advance understanding of liquid fog formation,development and dissipation in coastal environments, improving fog predictability and monitoring, Gultepe et al.(2020) designed a campaign named C-FOG field project. During the winter of 2019/2020, Burnet et al. (2020) synergistically combined 3D high-resolution Large Eddy Simulation with unprecedented 3D detailed observations in the South-West of France to deepen our understanding of the smallest scales in fog. This project (SOFOG3D) aimed to improve the prediction of fog events in numerical models. The earliest fog observation experiment in China took place in Shanghai (Li, 2001). In 1968 and 1969, a fog census was also conducted in the southern provinces of China, and preliminary observations of fog microstructure were made in Yunnan, Guizhou, and Sichuan (Niu et al., 1989). Niu et al. (2010) investigated the microphysical characteristics of persistent fog, using experimental data from fog observations in Pancheng town, a northern suburb of Nanjing, in winter 2006. Yang et al. (2021) used the microphysical observation data of the Tianjin radiation fog in the winter of 2016/2017 combined with meteorological tower data to reveal the observation facts of the microphysical and size distribution characteristics of fog droplets and discuss fog formation and dissipation mechanisms. Wang et al. (2021) elaborated the physical and chemical characteristics of radiation fog using observation data from December 2019 to February 2020 in the tropical rainforest area of Xishuangbanna."
Q4:-A deeper focus on the relevance of T and ε for fog description could provide a better understanding of the article and promote their use for future studies in the community. A “handmade” spectra plot could be useful for this task and help the reader understand how variations in T and ε are related to fog microphysical processes.
answer: Thank you for your feedback. Figure (a) shows the correlation between T and ε, plotted by me. As depicted in the figure, aerosol nucleation leads to the formation of small water droplets, which grow through condensation and collision coalescence, resulting in differences in their scales and thus causing dispersion. Moreover, ε is correlated with four microphysical quantities of fog: SSc, LWC, MVD, and Nf. These four fog microphysical quantities also interact with each other. This paper aims to investigate these relationships through the collision-coalescence process of fog droplets, with the intensity of the collision-coalescence process described using T.
Citation: https://doi.org/10.5194/egusphere-2023-2516-AC3 -
AC4: 'Reply on RC1', Zhenya An, 19 Feb 2024
Dear Mazoyer,
We feel great thanks for you professional review work on our articles on our article. As you are concerned, there are several problems that need to be addressed.According to your nice suggestions, we have made extensive corrections to our previous draft, the detailed corrections are listed below.
1.introduction:
Q1:-A review of fog processes affecting fog during its evolution could be nice.
answer:Thank you for your feedback.We have revised this sentence in the article. the details are follows:
"Given the above, the microphysical characteristics investigation occupies an important position in the study of fog. The evolution process of fog involves multiple complex physical and meteorological processes, for example, radiation cooling, terrain, turbulent mixing, aerosol composition, meteorological conditions, and some microphysical processes (condensation, evaporation, collision, etc.). Therefore, the droplet size distribution (DSD) of fog (Gultepe et al, 2007) exhibits significant variability. WILLWTT (1928) emphasized the importance of all meteorological parameters that affect fog formation and proposed a classification of fog and haze based on their causes and favorable weather conditions. In general, fog can be divided into two categories: 1) airmass fogs and 2) frontal fogs.The radiation fog studied in this paper is a type of airmass fog. Wang (2021) has explored the formation mechanism of radiation fog and the impact of topography on fog, pointing out that the influence of forest understory in tropical rainforest areas cannot be ignored. “
Q2:-Explain why we need a better description of fog microphysical processes.-A few words about numerical modeling might be interesting.
Thank you for your feedback, We have added the following content:
“Therefore, the evolution process of fog may vary significantly in different regions, which increases the difficulty in understanding fog mechanisms and the challenge in numerical simulations. In current single-parameter and double-moment microphysical parameterization schemes, number concentration is often assumed to be a fixed value, and several assumptions are made regarding the physical factors affecting number concentration, which reduces the forecasting capability of models for fog, especially for visibility. Therefore, Gultepe et al. (2020) improve the fog predictability of Numerical Weather Prediction (NWP) models, threfore ˙further improvements in microphysical observations and parameterization schemes are needed. This highlights the urgent need for a better description of the microphysical processes of fog. Many academics(Okita,1962; Niu et al., 2010; Gultepe et al., 2020; Yang et al., 2021; Wang et al., 2021) essentially elucidated in their articles the impact of droplet spectrum on the microphysical characteristics of fog.The relative dispersion is a parameter that describes the degree of relative dispersion of the fog droplets size distribution, which has been the focus of research in cloud physics for the last two decades (Desai et al., 2019).”
Q3-I really appreciate the presentation of the measurement campaign but it lacks international references. On the campaigns and results C-FOG (Gultepe, 2021), LANFEX (Price, 2019), SOFOG-3D (Burnet, 2020) or WIFEX (Ghude, 2023), among others..
answer：Thank you for your feedback, this was our oversight. I have included introductions to the measurement activities by Gultepe (2021) and Burnet (2020) in the introduction section, as follows:
"In the autumn of 1959, Okita (1962) used balloons to measure the distribution of fog droplet number concentration and liquid water content with height in mountain fog in Hokkaido, Japan. In 1970, NASA supported the Cornell Laboratory (CAL) to conduct field observation experiments in New York for the purpose of artificial fog elimination, obtaining the vertical structure of fog microphysical characteristics (Pilié et al.,1975). In 2018, In order to advance understanding of liquid fog formation,development and dissipation in coastal environments, improving fog predictability and monitoring, Gultepe et al.(2020) designed a campaign named C-FOG field project. During the winter of 2019/2020, Burnet et al. (2020) synergistically combined 3D high-resolution Large Eddy Simulation with unprecedented 3D detailed observations in the South-West of France to deepen our understanding of the smallest scales in fog. This project (SOFOG3D) aimed to improve the prediction of fog events in numerical models. The earliest fog observation experiment in China took place in Shanghai (Li, 2001). In 1968 and 1969, a fog census was also conducted in the southern provinces of China, and preliminary observations of fog microstructure were made in Yunnan, Guizhou, and Sichuan (Niu et al., 1989). Niu et al. (2010) investigated the microphysical characteristics of persistent fog, using experimental data from fog observations in Pancheng town, a northern suburb of Nanjing, in winter 2006. Yang et al. (2021) used the microphysical observation data of the Tianjin radiation fog in the winter of 2016/2017 combined with meteorological tower data to reveal the observation facts of the microphysical and size distribution characteristics of fog droplets and discuss fog formation and dissipation mechanisms. Wang et al. (2021) elaborated the physical and chemical characteristics of radiation fog using observation data from December 2019 to February 2020 in the tropical rainforest area of Xishuangbanna."
Q4:-A deeper focus on the relevance of T and ε for fog description could provide a better understanding of the article and promote their use for future studies in the community. A “handmade” spectra plot could be useful for this task and help the reader understand how variations in T and ε are related to fog microphysical processes.
answer: Thank you for your feedback. Figure (a) shows the correlation between T and ε, plotted by me. As depicted in the figure, aerosol nucleation leads to the formation of small water droplets, which grow through condensation and collision coalescence, resulting in differences in their scales and thus causing dispersion. Moreover, ε is correlated with four microphysical quantities of fog: SSc, LWC, MVD, and Nf. These four fog microphysical quantities also interact with each other. This paper aims to investigate these relationships through the collision-coalescence process of fog droplets, with the intensity of the collision-coalescence process described using T.
Citation: https://doi.org/10.5194/egusphere-2023-2516-AC4 -
AC5: 'Reply on RC1', Zhenya An, 19 Feb 2024
Dear Mazoyer,
We feel great thanks for you professional review work on our articles on our article. As you are concerned, there are several problems that need to be addressed.According to your nice suggestions, we have made extensive corrections to our previous draft, the detailed corrections are listed below.
1.introduction:
Q1:-A review of fog processes affecting fog during its evolution could be nice.
answer:Thank you for your feedback.We have revised this sentence in the article. the details are follows:
"Given the above, the microphysical characteristics investigation occupies an important position in the study of fog. The evolution process of fog involves multiple complex physical and meteorological processes, for example, radiation cooling, terrain, turbulent mixing, aerosol composition, meteorological conditions, and some microphysical processes (condensation, evaporation, collision, etc.). Therefore, the droplet size distribution (DSD) of fog (Gultepe et al, 2007) exhibits significant variability. WILLWTT (1928) emphasized the importance of all meteorological parameters that affect fog formation and proposed a classification of fog and haze based on their causes and favorable weather conditions. In general, fog can be divided into two categories: 1) airmass fogs and 2) frontal fogs.The radiation fog studied in this paper is a type of airmass fog. Wang (2021) has explored the formation mechanism of radiation fog and the impact of topography on fog, pointing out that the influence of forest understory in tropical rainforest areas cannot be ignored. “
Q2:-Explain why we need a better description of fog microphysical processes.-A few words about numerical modeling might be interesting.
Thank you for your feedback, We have added the following content:
“Therefore, the evolution process of fog may vary significantly in different regions, which increases the difficulty in understanding fog mechanisms and the challenge in numerical simulations. In current single-parameter and double-moment microphysical parameterization schemes, number concentration is often assumed to be a fixed value, and several assumptions are made regarding the physical factors affecting number concentration, which reduces the forecasting capability of models for fog, especially for visibility. Therefore, Gultepe et al. (2020) improve the fog predictability of Numerical Weather Prediction (NWP) models, threfore ˙further improvements in microphysical observations and parameterization schemes are needed. This highlights the urgent need for a better description of the microphysical processes of fog. Many academics(Okita,1962; Niu et al., 2010; Gultepe et al., 2020; Yang et al., 2021; Wang et al., 2021) essentially elucidated in their articles the impact of droplet spectrum on the microphysical characteristics of fog.The relative dispersion is a parameter that describes the degree of relative dispersion of the fog droplets size distribution, which has been the focus of research in cloud physics for the last two decades (Desai et al., 2019).”
Q3-I really appreciate the presentation of the measurement campaign but it lacks international references. On the campaigns and results C-FOG (Gultepe, 2021), LANFEX (Price, 2019), SOFOG-3D (Burnet, 2020) or WIFEX (Ghude, 2023), among others..
answer：Thank you for your feedback, this was our oversight. I have included introductions to the measurement activities by Gultepe (2021) and Burnet (2020) in the introduction section, as follows:
"In the autumn of 1959, Okita (1962) used balloons to measure the distribution of fog droplet number concentration and liquid water content with height in mountain fog in Hokkaido, Japan. In 1970, NASA supported the Cornell Laboratory (CAL) to conduct field observation experiments in New York for the purpose of artificial fog elimination, obtaining the vertical structure of fog microphysical characteristics (Pilié et al.,1975). In 2018, In order to advance understanding of liquid fog formation,development and dissipation in coastal environments, improving fog predictability and monitoring, Gultepe et al.(2020) designed a campaign named C-FOG field project. During the winter of 2019/2020, Burnet et al. (2020) synergistically combined 3D high-resolution Large Eddy Simulation with unprecedented 3D detailed observations in the South-West of France to deepen our understanding of the smallest scales in fog. This project (SOFOG3D) aimed to improve the prediction of fog events in numerical models. The earliest fog observation experiment in China took place in Shanghai (Li, 2001). In 1968 and 1969, a fog census was also conducted in the southern provinces of China, and preliminary observations of fog microstructure were made in Yunnan, Guizhou, and Sichuan (Niu et al., 1989). Niu et al. (2010) investigated the microphysical characteristics of persistent fog, using experimental data from fog observations in Pancheng town, a northern suburb of Nanjing, in winter 2006. Yang et al. (2021) used the microphysical observation data of the Tianjin radiation fog in the winter of 2016/2017 combined with meteorological tower data to reveal the observation facts of the microphysical and size distribution characteristics of fog droplets and discuss fog formation and dissipation mechanisms. Wang et al. (2021) elaborated the physical and chemical characteristics of radiation fog using observation data from December 2019 to February 2020 in the tropical rainforest area of Xishuangbanna."
Q4:-A deeper focus on the relevance of T and ε for fog description could provide a better understanding of the article and promote their use for future studies in the community. A “handmade” spectra plot could be useful for this task and help the reader understand how variations in T and ε are related to fog microphysical processes.
answer: Thank you for your feedback. Figure (a) shows the correlation between T and ε, plotted by me. As depicted in the figure, aerosol nucleation leads to the formation of small water droplets, which grow through condensation and collision coalescence, resulting in differences in their scales and thus causing dispersion. Moreover, ε is correlated with four microphysical quantities of fog: SSc, LWC, MVD, and Nf. These four fog microphysical quantities also interact with each other. This paper aims to investigate these relationships through the collision-coalescence process of fog droplets, with the intensity of the collision-coalescence process described using T.
Q5:-Then, new questions in relation to previous studies Zhao, 2013 for example and among others must be pointed out.
answer: In Niu et al., (2008), the results show that key microphysical characteristics (liquid water content, droplet concentration, mean radius, and standard deviation) overall exhibit a positive correlation. The examination of microphysical relationships constrained by the threshold functions of the respective autoconversion schemes suggests that the coalescence process may sometimes occur, thereby diminishing the positive correlation induced by droplet activation and condensational growth. Based on this, the paper further explores the influence of SSc and background aerosols on these relationships.
2. Method
Q1- Present the campaign, the instruments and the cases studied. Or make references to any article that has already featured it.
answer :Thank you for your feedback. In the methodology section, I have added an introduction to the climate background of the Xishuangbanna region. Figure (b) below illustrates the geographical location of Xishuangbanna. Additionally, I have provided a more detailed description of the instruments used in this measurement campaign and have included a table for better illustration.
Citation: https://doi.org/10.5194/egusphere-2023-2516-AC5 - AC6: 'Reply on RC2', Zhenya An, 19 Feb 2024
- AC7: 'Reply on RC2', Zhenya An, 19 Feb 2024
-
AC8: '在RC2上回复', Zhenya An, 19 Feb 2024
Dear reviewer:
Thank you for your decision and constructive comments on my manuscript. We have carefully considered the suggestion of Reviewer and make some changes. We have tried our best to improve and made some changes in the manuscript.
Q1: The methodology section needs more detail. The authors should add more in-depth discussions on the calculation of microphysical quantities of fog, supersaturation, visibility and auto-conversion threshold function.
answer: After reviewing the relevant literature, the diameter of activated liquid droplets calculated by Shen et al. (2018) and according to the method of Petter& Kreidenweis (2007) is mostly around 2 micrometers. It is considered to be part of the aerosol hygroscopic growth. Therefore, concerning the microphysical parameters of fog, the first two bins were excluded, and droplets with diameters greater than 4 micrometers were considered as fog droplets.
Regarding the supersaturation part, based on the original calculation of the activation diameter (Da), the original supersaturation formula was abandoned. Thanks to Mazoyer's correction, the hygroscopicity parameter k=0.15 cannot be used with the original formula. Therefore, the supersaturation formula suitable for all hygroscopicity parameters from Petter & Kreidenweis (2007) was used for calculation.
Concerning visibility, Gultepe's(2006) method was referenced. In the original manuscript, for the extinction coefficient, the Mie scattering coefficient Qext term for droplets with diameters less than 4 micrometers was not excluded. However, according to Gultepe (2006), only fog droplets with diameters greater than 4 micrometers have Qext approximately equal to the constant value of 2.
Regarding the autoconversion threshold, there has been limited research on the T value in fog droplet spectrum evolution. However, in some studies, it was found that the T value in fog (Niu et al., 2010) can alter the relationship between microphysical parameters of fog, indicating the possibility of relatively active collision-coalescence processes during fog droplet spectrum evolution, which will inevitably affect fog droplet spectrum evolution and fog development.
Q2:Addressing potential limitations and uncertainties associated with the study's methodology and conclusions would enhance the credibility and reliability of the research. The calculation of supersaturation is highly parameterized and it seems much high than previous studies.
answer:Regarding the calculation of supersaturation, due to the lack of specific measurements of the hygroscopic parameter k in this observation, and considering that Xishuangbanna and the Amazon both belong to tropical rainforest regions, this study cited the empirical parameter values for the Amazon tropical rainforest from Pöschl et al. (2010). As for the conclusions regarding T in the manuscript, they will be further validated in the future by integrating numerical models.
Q3: A detailed explanation of the algorithms and techniques employed in the data analysis would also enhance the transparency and reproducibility of the study.
answer:Please refer to Q1. To enhance the reproducibility of the paper and clarify the algorithms used, I have also created a flowchart for the calculation of SSc (Fig. c)
The process of calculating SSc is as follows:
“To determine the critical supersaturation, the variable Dd in the formula is equivalent to Da. Fig.1c illustrates that the calculation method is based on the central diameter of aerosol spectra in 120 bins. In this paper, the upper limit of integration is fixed at the central diameter of the 120th bin of aerosols, and the lower limit of integration starts from m = 119th bin. In each iteration, it is necessary to determine whether the total fog droplet spectrum concentration for each time step is between the integration concentration of the aerosol spectrum in the previous bin and the integration concentration of the aerosol spectrum in the next bin. If not, m = m-1; if yes, interpolation is used to determine the integration when these two values are equal. Thus, through this process, the critical activation diameter Da for dry aerosols is obtained. Subsequently, the initial value of D is set as Da, and iterations continue (increasing the value of D) until the maximum supersaturation is found. The termination condition for iteration is that the supersaturation in the previous step is greater than the supersaturation in the next step.
The determination of hygroscopicity parameter k involves a relatively complex process. In the paper by Petters and Kreidenweis (2007), it is noted that atmospheric particles generally exhibit a characteristic value of k>0.2. However, lower values may be observed for specific locations and periods. Pöschl et al. (2010) discuss the hygroscopicity parameters of aerosols in the Amazon rainforest. Aerosols during the Amazon wet season are primarily composed of secondary organic compounds and primary biogenic substances from rainforest organisms. The environment is close to pre-industrial conditions and is not influenced by anthropogenic pollution. In contrast, aerosol exposure at the global continental average level is influenced by more anthropogenic pollution sources, such as urban and industrial areas. These sources release hygroscopic substances, leading to an increase in the hygroscopicity of aerosols. Therefore, the effective hygroscopicity of aerosols in the Amazon wet season, with k=0.3, is approximately half that of the global continental level. This study, focusing on the tropical rainforest, adopts a hygroscopicity parameter of k≈0.15 based on these findings."
Citation: https://doi.org/10.5194/egusphere-2023-2516-AC8 -
AC9: 'Reply on RC3', Zhenya An, 19 Feb 2024
Dear Reviewer：
Thank you for your decision and constructive comments on my manuscript. We have carefully considered the suggestion of Reviewer and make some changes. We have tried our best to improve and made some changes in the manuscript.
Q1:As noted just above, the data is presented in some ways that I am not following. Are the axes labeled correctly for all these plots? For example, the caption to Figure 6 indicates that it is a plot of the probability density function for the droplet data for different values of T, but this can’t be correct, as the units don’t match. (The abscissa is in microns, and the ordinate is given in cm-3.). I would recommend plotting these as dN/dDp. Plots of the pdf might also carry some useful information, but perhaps plotting the average pdf for each of the T values on the same plot would make it easier to compare the cases.
answer:Thank you for your suggestion. I have redrawn the probability density plot, with the y-axis now representing number density. Additionally, I have plotted the average probability density function for each T value on the same graph to compare different scenarios. For example:
Figure 8: Probability density function of fog droplet size and concentration for (a)T≤0.4,0.4≤ε≤0.7;(b)T≤0.4,ε>0.7; (c)difference of T≤0.4,ε>0.7 and T≤0.4,0.4≤ε≤0.7; (d)T>0.4,0.4≤ε≤0.7;(e)T>0.4,ε>0.7;(f) difference of T>0.4,ε>0.7 and T>0.4,0.4≤ε≤0.7.Divide the number density of fog droplets into 100 intervals and take the logarithm. Calculate the probability of each logarithmic number density interval occurring in each fog event and plot a probability density plot with filled colors as shown in the figure. The darker the color, the higher the probability of the corresponding logarithmic number density occurring in that interval.The solid red line represents the average spectrum of fog droplets in this situation.
Q2:I think that the authors are using T as a proxy for collision-coalescence; I have significant reservations about using it in this way. Autoconversion is a way to parameterize the warm rain process, and the conversion threshold that the authors are using is a refinement of that parameterization. Originally T was a step function. This version smooths that transition, while still preserving the sharp turnon character of autoconversion. (This is noted in the references that the authors cite.) Given that the title refers to an observational study and T is a derived quantity, which is primarily used in a parameterization, I don’t think it fits here. As I read the manuscript, T is used to draw some conclusions about the way that collision-coalescence may be affecting the relative dispersion.I don’t typically think of collision-coalescence as playing a significant role in the evolution of fogs. I’d like to see that claim backed up with some measurement. The measurements presented here do extend out to droplet diameters of 50 microns, which is getting into a range where appreciable fall velocities may be encountered. The highest values of T here would indicate that the fogs should have been drizzling. Were they?
answer: Although T is primarily used for parameterization, it is still of some reference value as it is calculated from measured data. Additionally, Xishuangbanna has a dense canopy, resulting in generally low wind speeds, and radiation fog mostly forms at night. According to the average diurnal variation intensity of turbulence in Xishuangbanna (Wang et al., 2021), it is believed that the growth of fog droplets in Xishuangbanna is mainly through condensation and collision-coalescence. In this paper, T is used to represent the intensity of collision-coalescence following the method of Niu et al. (2010), and subsequently, the relationship between collision-coalescence and various microphysical parameters is studied.
Drizzle may occur, but due to the lack of measurement data for this part, and the noticeable decrease in large droplets under conditions of high supersaturation, it is speculated that this is also due to the settling of large droplets.
Q3: Line 105: t is defined as the sampling frequency, but from equation 1, it looks like this should be the integration time of the sample. Please clarify.
answer: Thank you for pointing out the mistake. Here, 't' should represent the reciprocal of the sampling frequency, which is the time interval between two consecutive samplings on the instrument.
Q4:Finding supersaturation: I know that there are very few measurements of supersaturation, though one of the only ones I know WAS a measurement in a fog. See Gerber 1991. For the data presented here… A sensitivity study of the dependence of the supersaturation on the value of kappa would be a good addition.
answer:We also believe that conducting sensitivity studies on the dependency of kappa values would be a valuable addition to the paper. Following the approach of Shen et al. (2018), we adjusted the hygroscopicity parameter, kappa (k), within the range of -20% to +20%, and obtained consistent results: when k is underestimated, the estimated supersaturation values tend to be higher, while overestimating k leads to lower calculated supersaturation values. Shen et al. (2018) mentioned that such adjustments were made because, in addition to measurement errors in number concentrations, hygroscopicity also contributes to supersaturation biases. Since the hygroscopicity of aerosols was not measured simultaneously, the instantaneous hygroscopicity of aerosols may deviate from the mean hygroscopicity. The complex mixed state of environmental aerosol particles was also not considered. Additionally, organic compounds such as surfactants may evaporate during the drying process prior to measurement, leading to an underestimation of particle hygroscopicity.
Q5: Line 150: I think there’s an “r” here that should be “r_c”.
answer: Thank you for pointing it out. I have made the correction.
Q6: Figure 3 and others: The points are color coded, but I don’t think the color coding is ever explained.
answer:The color bar for Figure 3 was accidentally omitted during the paper submission process. I apologize for this oversight. The image has now been redrawn, and the caption for the figure has been revised to provide more detailed information, making the results more apparent.Additionally, the original Figure 3 has now been relabeled as Figure 4, as shown in the following figure.
Citation: https://doi.org/10.5194/egusphere-2023-2516-AC9
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 397 | 89 | 34 | 520 | 11 | 13 |
- HTML: 397
- PDF: 89
- XML: 34
- Total: 520
- BibTeX: 11
- EndNote: 13
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1