Observed relationship between drop size distribution and environmental properties in eastern Japan

Unuma, Takashi

doi:https://doi.org/10.5194/egusphere-2025-210

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https://doi.org/10.5194/egusphere-2025-210

Preprints

07 Feb 2025

| 07 Feb 2025

Observed relationship between drop size distribution and environmental properties in eastern Japan

Takashi Unuma

Abstract. The drop size distribution (DSD) is an important property for characterising precipitation processes that sometimes lead to more intense rainfall in different climate regions. Previous studies have shown that a stationary distribution with a breakup signature can be obtained not only with ground-based disdrometers but also with remote sensing instruments such as vertically pointing radars and/or wind profilers. However, these observations do not explain how the underlying microphysical processes within convective clouds that generate more rain occur and how the environmental conditions affect these processes. This study aims to investigate the environmental conditions for the development of convective clouds that induced more intense rainfall in eastern Japan. In situ observations and operational C-band polarimetric weather radar data are used to extract the convective clouds by applying a cell-tracking algorithm, and upper-air sounding data are used to diagnose their environmental conditions. The larger diameter of the DSD is likely to be associated with higher instability, whilst the higher number concentration is likely to be archived with the higher precipitable water under the weaker vertical shear condition. Convective clouds that generate more rain should have a similar three-dimensional structure within them when the DSD has a breakup signature at ground level under a humid environment. These characteristics can be diagnosed as the microphysical processes of converting from cloud drops to raindrops and/or coalescing cloud drops and raindrops.

Received: 20 Jan 2025 – Discussion started: 07 Feb 2025

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Journal article(s) based on this preprint

24 Sep 2025

Observed relationship between drop size distribution including a breakup signature and environmental properties near Kumagaya in eastern Japan

Takashi Unuma

Atmos. Chem. Phys., 25, 11109–11128, https://doi.org/10.5194/acp-25-11109-2025,https://doi.org/10.5194/acp-25-11109-2025, 2025

Short summary

Takashi Unuma

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-210', Anonymous Referee #1, 13 Mar 2025
General Comment
This study investigated raindrop size distribution (DSD) characteristics of precipitation cells observed by a polarimetric radar and ground-based disdrometer data using three-year datasets and environmental factor that can impact the DSD characteristics. The analysis method is adequate, and the figures are clean and easy to see. However, I am concerned with the sample size used in this study. I think that the data period (3 years) is good and enough to collect samples. However, the sampled cases shown in Figs. 7-9 were very small, and the correlation coefficients were very small. Therefore, I cannot be convinced with the impacts of the environmental factors discussed using Figs. 7-9. Moreover, the title says “… eastern Japan,” however, the radar data used in this study are from one radar at one location, and the disdrometer data are also from one location. I am not sure if this dataset is enough to represent the characteristics in “eastern Japan.” It is also unclear in the manuscript what types of precipitation cases were focused and why this study focused on equilibrium shape of DSD. Definitions of cells are not clear. Details are listed below. The author should address those or perhaps needs additional analyses before publication.

Specific comments

1. Title: As I mentioned above, the title says “… eastern Japan,” so I expected this study used large datasets from multiple locations in the eastern Japan. However, the radar data used in this study are from one radar at one location, and the disdrometer data are also from one location. I am not sure which area in Japan is represented by “eastern Japan” and if this dataset is enough to represent the characteristics in “eastern Japan.”

2. Introduction: It is unclear in the manuscript what types of precipitation cases were focused in this study. I supposed that the author was interested in heavy rainfall, but I am not sure why this study focused on equilibrium shape of DSD. The instruction mentioned multimodality in DSD shapes, but the mainstream of this study did not account for the multimodality (I think).

3. Definitions: Definitions of cells and target cases are unclear. Methods of tracking cells are also unclear. I have the following specific questions.
1) What type of precipitation cells targeted is not clear. The sampled period included all seasons in 3 years. Did the study target any types of precipitation, including monsoon, MCSs, isolated, embedded, Baiu, snow, and etc.? If so, what type did have most cells? It would be great if the author provided the numbers sampled for each precipitation type.

2) Lines 142-143: I am not sure what cell life stages were targeted by using the lambda values. Could the lambda values capture from the cell initiation through the cell decay? What radar reflectivity or rainfall rate values corresponded to those lambda values? Were the lambda cells in Fig. 1 consistent with reflectivity cells?

3) How did the radar data cover the 3D volume? What is the maximum height? The radar PPI volume scans cannot see higher altitudes near by the radar. What is the height limitation? Were vertical structures retrieved from all cells without the impact of this limitation?

4) Section 2.2: Please provide the domain.

5) It was unclear for me that it was tracked in 2D or 3D. If 3D, I have the similar question to. 3). Was the tracking impacted by the radar data limitation at higher angles?

6) Line 153: “the maximum of each slope is defined as highest”: I cannot follow this. Does each slope have multiple slope values? I cannot understand.

7) Lines 156-157: This sentence does not make sense to me. CAPPI data is available every 5 min. What does “before and after 1 hour of CAPPI” mean?

8) Table 1: What is the difference between feature and cell? What is the difference between event and cell?

9) Lifetime: Similar question to #3-2). What life stages were captured? Can it capture cell initiation, beginning of precipitation, beginning of cloud formation, just life stages of heavy rainfall, or time with raindrops >1 mm?

10) How did you calculate the volume and area? How did you define the volume/area? Did you use the lambda values?

11) Line 212: Similar question to #3-2). What life stage corresponds to the first detection?

12) Line 217: Why 2 km?

13) How did you decide the inside and outside features of the convective clouds. How did you define the convective cell boundary?

14) Line 228: How did you define the volume?

4. The sample size (tracked cells) seems to be enough to me in Table 1 and Figures 2 and 3. However, the samples shown in Figures 7-9 are very small (30-40 points only). Why? Moreover, the correlations are small. All discussions about the environmental factors were based on those plots with very small correlations. Therefore, I cannot be convinced with the impacts of the environmental factors discussed using Figs. 7-9. I have the following specific comments:
1) Lines 243-244: I cannot see this in the figure. It looks to me that D0 and lambda are scattered widely, too. Perhaps, did you mention the correlation coefficient values?

2) Lines 250-251: It is difficult to see this… Figure 7a and 7b both showed positive correlation coefficient values. Could you revise the sentence?

3) Line 251 “D_0 is negatively…”: This does not make sense to me. Fig. 6 shows that LWC increases when D0 increase. I suppose that LWC can increase when PW increase, so D0 increases when PW increases. Why D0 has negative correlation with PW?

4) Lines 254-255: This cannot make sense to me, too. Why does the large amount of water vapor contribute to the high concentration of raindrops? What is the mechanism? Is there an aerosol effect?

5) Line 263 “These results…”: I cannot see this in Fig. 9.

6) Figs. 7-9: Correlation coefficients are very low for all relations in those figures. Absolute values for all correlation coefficients are less than 0.4 (weak correlation), except Fig.8b. The discussions about the relationships between DSD parameters and environmental factors are based on such low correlations. Moreover, the number of samples for each plot is small (<50). I do not think that the small numbers of samples can represent general characteristics of convective cells in eastern Japan.

5. Environmental factors
The environmental factors discussed are limited. CAPR, KI, PW, wind shear, and TLR could be major factors, but other factors should also be discussed, such as humidity (low level/mid levels), aerosols, seasons, etc. As I mentioned above, the correlations shown in Figs. 7-9 are very low. I would suspect that there could be other factors that can better correlate with the DSD parameters.
6. Others:
1) Lines 238-239: Was this also associated with cell’s shape and where is the “center” of the cell?

2) Lines 293-295: Was there a size sorting effect for the horizontal distributions?

3) Lines 309-310: This also does not make sense to me. Please explain the mechanisms why weaker vertical wind shear contributed to a high number concentration.

4) Lines 313-314: Why was the change in Nw larger with increasing rainfall amount under the humid environment? “change” is an unclear word. Do you mean increase or decrease in Nw? please clarify.

5) Line 315-316: This sentence is also unclear. Do you mean "a broader shape .... non-zero mu gamma DSD? If so, need a few more sentences to explain why large D0 and small lambda can represent non-zero mu.

6) Line 344 “even though breakup signal is obtained.”: I do not know why you need this phrase here. Break up or coalescence does not affect LWC. Breakup (increase of small raindrops and decrease of large raindrops) can increase LWC at a given Rain fall rate (because fall speed decreases).

Technical comments
Lines 21-22: Unclear sentence. Do you want to say "frequently observed in heavy rain events?"

Line 55 “recent studies”: The following study is not a recent study (in 2013, more than 10 years ago)

L first appeared here. Please define.

What type of disdrometer was used? Please provide the model or specifications.

Where is the radar location in Fig. 1?

Line 229: Does not need “is”

Lines 296: Did you remove large ZDR? Unclear.

Lines 311-312: This sentence is unclear. Please revise it.
Citation: https://doi.org/10.5194/egusphere-2025-210-RC1
- AC1: 'Reply on RC1', Takashi Unuma, 20 May 2025
  
  Dear referee,
  Comments, questions and suggestions provided in this review are truly appreciated. I have carefully reviewed all your comments and made the revisions in the revised manuscript. Please find our detailed response in the attached file.
  Best regards,
  
  Takashi Unuma
  
  Citation: https://doi.org/10.5194/egusphere-2025-210-AC1
RC2:
'Comment on egusphere-2025-210', Anonymous Referee #2, 16 Apr 2025

See the attached file.

Citation: https://doi.org/10.5194/egusphere-2025-210-RC2
- AC2: 'Reply on RC2', Takashi Unuma, 20 May 2025
  
  Dear referee,
  Comments and suggestions provided in this review are truly appreciated. I have carefully reviewed all your suggestions and made the revisions in the revised manuscript. Please find our detailed response in the attached file.
  Best regards,
  
  Takashi Unuma
  
  Citation: https://doi.org/10.5194/egusphere-2025-210-AC2

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-210', Anonymous Referee #1, 13 Mar 2025
General Comment
This study investigated raindrop size distribution (DSD) characteristics of precipitation cells observed by a polarimetric radar and ground-based disdrometer data using three-year datasets and environmental factor that can impact the DSD characteristics. The analysis method is adequate, and the figures are clean and easy to see. However, I am concerned with the sample size used in this study. I think that the data period (3 years) is good and enough to collect samples. However, the sampled cases shown in Figs. 7-9 were very small, and the correlation coefficients were very small. Therefore, I cannot be convinced with the impacts of the environmental factors discussed using Figs. 7-9. Moreover, the title says “… eastern Japan,” however, the radar data used in this study are from one radar at one location, and the disdrometer data are also from one location. I am not sure if this dataset is enough to represent the characteristics in “eastern Japan.” It is also unclear in the manuscript what types of precipitation cases were focused and why this study focused on equilibrium shape of DSD. Definitions of cells are not clear. Details are listed below. The author should address those or perhaps needs additional analyses before publication.

Specific comments

1. Title: As I mentioned above, the title says “… eastern Japan,” so I expected this study used large datasets from multiple locations in the eastern Japan. However, the radar data used in this study are from one radar at one location, and the disdrometer data are also from one location. I am not sure which area in Japan is represented by “eastern Japan” and if this dataset is enough to represent the characteristics in “eastern Japan.”

2. Introduction: It is unclear in the manuscript what types of precipitation cases were focused in this study. I supposed that the author was interested in heavy rainfall, but I am not sure why this study focused on equilibrium shape of DSD. The instruction mentioned multimodality in DSD shapes, but the mainstream of this study did not account for the multimodality (I think).

3. Definitions: Definitions of cells and target cases are unclear. Methods of tracking cells are also unclear. I have the following specific questions.
1) What type of precipitation cells targeted is not clear. The sampled period included all seasons in 3 years. Did the study target any types of precipitation, including monsoon, MCSs, isolated, embedded, Baiu, snow, and etc.? If so, what type did have most cells? It would be great if the author provided the numbers sampled for each precipitation type.

2) Lines 142-143: I am not sure what cell life stages were targeted by using the lambda values. Could the lambda values capture from the cell initiation through the cell decay? What radar reflectivity or rainfall rate values corresponded to those lambda values? Were the lambda cells in Fig. 1 consistent with reflectivity cells?

3) How did the radar data cover the 3D volume? What is the maximum height? The radar PPI volume scans cannot see higher altitudes near by the radar. What is the height limitation? Were vertical structures retrieved from all cells without the impact of this limitation?

4) Section 2.2: Please provide the domain.

5) It was unclear for me that it was tracked in 2D or 3D. If 3D, I have the similar question to. 3). Was the tracking impacted by the radar data limitation at higher angles?

6) Line 153: “the maximum of each slope is defined as highest”: I cannot follow this. Does each slope have multiple slope values? I cannot understand.

7) Lines 156-157: This sentence does not make sense to me. CAPPI data is available every 5 min. What does “before and after 1 hour of CAPPI” mean?

8) Table 1: What is the difference between feature and cell? What is the difference between event and cell?

9) Lifetime: Similar question to #3-2). What life stages were captured? Can it capture cell initiation, beginning of precipitation, beginning of cloud formation, just life stages of heavy rainfall, or time with raindrops >1 mm?

10) How did you calculate the volume and area? How did you define the volume/area? Did you use the lambda values?

11) Line 212: Similar question to #3-2). What life stage corresponds to the first detection?

12) Line 217: Why 2 km?

13) How did you decide the inside and outside features of the convective clouds. How did you define the convective cell boundary?

14) Line 228: How did you define the volume?

4. The sample size (tracked cells) seems to be enough to me in Table 1 and Figures 2 and 3. However, the samples shown in Figures 7-9 are very small (30-40 points only). Why? Moreover, the correlations are small. All discussions about the environmental factors were based on those plots with very small correlations. Therefore, I cannot be convinced with the impacts of the environmental factors discussed using Figs. 7-9. I have the following specific comments:
1) Lines 243-244: I cannot see this in the figure. It looks to me that D0 and lambda are scattered widely, too. Perhaps, did you mention the correlation coefficient values?

2) Lines 250-251: It is difficult to see this… Figure 7a and 7b both showed positive correlation coefficient values. Could you revise the sentence?

3) Line 251 “D_0 is negatively…”: This does not make sense to me. Fig. 6 shows that LWC increases when D0 increase. I suppose that LWC can increase when PW increase, so D0 increases when PW increases. Why D0 has negative correlation with PW?

4) Lines 254-255: This cannot make sense to me, too. Why does the large amount of water vapor contribute to the high concentration of raindrops? What is the mechanism? Is there an aerosol effect?

5) Line 263 “These results…”: I cannot see this in Fig. 9.

6) Figs. 7-9: Correlation coefficients are very low for all relations in those figures. Absolute values for all correlation coefficients are less than 0.4 (weak correlation), except Fig.8b. The discussions about the relationships between DSD parameters and environmental factors are based on such low correlations. Moreover, the number of samples for each plot is small (<50). I do not think that the small numbers of samples can represent general characteristics of convective cells in eastern Japan.

5. Environmental factors
The environmental factors discussed are limited. CAPR, KI, PW, wind shear, and TLR could be major factors, but other factors should also be discussed, such as humidity (low level/mid levels), aerosols, seasons, etc. As I mentioned above, the correlations shown in Figs. 7-9 are very low. I would suspect that there could be other factors that can better correlate with the DSD parameters.
6. Others:
1) Lines 238-239: Was this also associated with cell’s shape and where is the “center” of the cell?

2) Lines 293-295: Was there a size sorting effect for the horizontal distributions?

3) Lines 309-310: This also does not make sense to me. Please explain the mechanisms why weaker vertical wind shear contributed to a high number concentration.

4) Lines 313-314: Why was the change in Nw larger with increasing rainfall amount under the humid environment? “change” is an unclear word. Do you mean increase or decrease in Nw? please clarify.

5) Line 315-316: This sentence is also unclear. Do you mean "a broader shape .... non-zero mu gamma DSD? If so, need a few more sentences to explain why large D0 and small lambda can represent non-zero mu.

6) Line 344 “even though breakup signal is obtained.”: I do not know why you need this phrase here. Break up or coalescence does not affect LWC. Breakup (increase of small raindrops and decrease of large raindrops) can increase LWC at a given Rain fall rate (because fall speed decreases).

Technical comments
Lines 21-22: Unclear sentence. Do you want to say "frequently observed in heavy rain events?"

Line 55 “recent studies”: The following study is not a recent study (in 2013, more than 10 years ago)

L first appeared here. Please define.

What type of disdrometer was used? Please provide the model or specifications.

Where is the radar location in Fig. 1?

Line 229: Does not need “is”

Lines 296: Did you remove large ZDR? Unclear.

Lines 311-312: This sentence is unclear. Please revise it.
Citation: https://doi.org/10.5194/egusphere-2025-210-RC1
- AC1: 'Reply on RC1', Takashi Unuma, 20 May 2025
  
  Dear referee,
  Comments, questions and suggestions provided in this review are truly appreciated. I have carefully reviewed all your comments and made the revisions in the revised manuscript. Please find our detailed response in the attached file.
  Best regards,
  
  Takashi Unuma
  
  Citation: https://doi.org/10.5194/egusphere-2025-210-AC1
RC2:
'Comment on egusphere-2025-210', Anonymous Referee #2, 16 Apr 2025

See the attached file.

Citation: https://doi.org/10.5194/egusphere-2025-210-RC2
- AC2: 'Reply on RC2', Takashi Unuma, 20 May 2025
  
  Dear referee,
  Comments and suggestions provided in this review are truly appreciated. I have carefully reviewed all your suggestions and made the revisions in the revised manuscript. Please find our detailed response in the attached file.
  Best regards,
  
  Takashi Unuma
  
  Citation: https://doi.org/10.5194/egusphere-2025-210-AC2

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Takashi Unuma on behalf of the Authors (20 May 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (26 May 2025) by Thijs Heus

RR by Anonymous Referee #2 (27 May 2025)

RR by Anonymous Referee #1 (22 Jun 2025)

Suggestions for revision or reasons for rejection

General Comment
The revised manuscript improved well. I felt that there were still a few shortages to complete the discussion.

Specific comments

1. The title and abstract seem to mention characteristics of general DSD, but going through the revised manuscript, the study focused on equilibrium drop size distribution (break up) in heavy rainfall. Those key phrases should be accounted for in the title and abstract to defile the purpose and uniqueness of this study.
2. Why eastern Japan was selected in this study? Was this area meteorologically important?
3. I understand that some of the relationships showed low p-vales in the relationship analysis (e.g. Figs. 12-14), but it is still true that the correlation coefficients for some relationships are not high. Please discuss why those relationships are week (e.g. complexity of microphysics, possible factors, microphysical processes, etc.).
4. Section 2.2 and Figure 3: From this figure, it seems to me that there was a large uncertainty in the retrievals. For example, D_0 had ~0.5 – 1 mm uncertainty, and Lambda had also 0.5 – 1 mm-1 uncertainty. The uncertainty in the retrievals could impact the result. Please discuss how the uncertainty in the retrievals could influence the relationships. The uncertainties might be large and influence the relationships that had week correlation coefficients.
5. Figure 3: At what height were the radar data compared with the surface data?
6. Lines 209-211: Do you mean that Lambda captured DSD with larger raindrops and small number concentration that produced weak Z but stronger R? R is generally proportional to D^3-4, while Z is proportional to D^6. So Z could represent more larger raindrops. I might miss understand, so please clarify more.
7. No discussion about vertical velocity in the manuscript. Vertical velocity can tend to be strong in strong wind shear and can result in producing large hydrometeor particles. Were there no impacts of vertical velocity?
Technical comments
1. Line 69: Convective systems produce heavy rain, not heavy rain produces convection.
2. Lines 70: What do you compare with to say “fewer?”
3. Figure 4: Use appropriate color range for Z
4. Line 339: “than” is better, not “among”

Hide

ED: Publish subject to minor revisions (review by editor) (30 Jul 2025) by Thijs Heus

AR by Takashi Unuma on behalf of the Authors (07 Aug 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (11 Aug 2025) by Thijs Heus

AR by Takashi Unuma on behalf of the Authors (12 Aug 2025)

Journal article(s) based on this preprint

24 Sep 2025

Observed relationship between drop size distribution including a breakup signature and environmental properties near Kumagaya in eastern Japan

Takashi Unuma

Atmos. Chem. Phys., 25, 11109–11128, https://doi.org/10.5194/acp-25-11109-2025,https://doi.org/10.5194/acp-25-11109-2025, 2025

Short summary

Takashi Unuma

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The relationship between the cloud microphysical processes within convective clouds and their environmental conditions is not fully understood. The conversion process of cloud droplets to raindrops is dominant near the ground, whilst the collisional coalescence of cloud droplets and raindrops dominates above the layer within convective clouds. These processes depend strongly on static stability and are more likely to occur in humid environments.


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