Preprints
https://doi.org/10.5194/egusphere-2023-2188
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2023-2188
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Nov 2023
| 03 Nov 2023
Impact of ice multiplication on the cloud electrification of a cold-season thunderstorm: a numerical case study
Abstract. Ice microphysics controls cloud electrification in thunderstorms, and the various secondary ice production (SIP) processes are vital in generating high ice concentration. However, the role of SIP in cold-season thunderstorms is not well understood. In this study, the impacts of SIP on the electrification in a thunderstorm occurred in late November is investigated using model simulations. The parameterizations of three SIP processes are implemented in the model, including the rime-splintering, ice-ice collisional breakup, and shattering of freezing drops. In addition, a noninductive and an inductive charging parametrization, as well as a bulk discharging model are coupled with the spectral bin microphysics scheme. The results show the simulated storm intensity and temporal variation of flash rate are improved after SIP parametrizations are implemented in the model. Among the three SIP processes, the rime-splintering and shattering of freezing drops have stronger impacts on the storm than the ice-ice collisional breakup. The graupel and snow concentration are enhanced while their sizes are suppressed due to the SIP. The changes in the ice microphysics result in substantial changes in the charge structure. The total charge density changes from an inverted tripole structure to a dipole structure (tripole structure at some locations) after SIP is considered in the model, mainly due to the enhanced collision between graupel and ice, and riming at temperatures warmer than -20 °C. These changes lead to an enhancement of vertical electric field, especially in the mature stage, which explains the improved modelling of flash rate. The results highlight that the cold-season cloud electrification is very sensitive to the SIP.
How to cite. Yang, J., Huang, S., Zhang, Q., Jing, X., Deng, Y., and Liu, Y.: Impact of ice multiplication on the cloud electrification of a cold-season thunderstorm: a numerical case study, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2188, 2023.
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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on egusphere-2023-2188', Anonymous Referee #1, 22 Nov 2023
Review of the paper “Impact of Ice Multiplication on the Cloud Electrification of a cold-season thunderstorm: a numerical case study” by Jing Yang et al.
General Comments:
Yang et al studied the role of three secondary ice production (SIP) mechanisms on cloud electrification in a simulated thunderstorm that was developed during the cold season. They implemented three major SIP mechanisms in the WRF model with fast SBM microphysics along with inductive and non-inductive charging mechanisms. Overall, the effect of SIP mechanisms on electrification is an important topic for the scientific community. However, in the current format, the paper needs major revision. Authors need to improve most of the sections including model validation, Analysis, implementation of SIPs, etc. I have enlisted specific and minor comments below.
Specific comments:
- In the present study model validation is only based on spatial distribution radar reflectivity and temporal evolution flash rates. Since the study considers 3 major SIP processes, to what extent does the model agree with the observed number of concentrations of ice particles? How well does the model simulate the liquid water mass/content and vertical velocities? All these microphysical properties are of great importance for lightning. Comparison of some of these microphysical properties with the observation will be helpful for readers to understand the accuracy of the model. It will be good to compare the vertical distribution of radar reflectivity from the model with observations. If available, surface precipitation can be compared to show the robustness of model simulations.
- Even with radar reflectivity plots, contour levels are different in observations and model, which makes it difficult to compare. To what extent does the simulated radar reflectivity is in agreement with observations when all SIP processes are active? It will be good to present some statistical analysis.
- Based on my knowledge, in most of the previous studies, ice-ice collision is a major SIP mechanism in deep convective clouds when compared with rime-splintering and drop shattering (e.g. Phillips and Patade 2022). This is because rime-splintering and drop shattering are active over a limited range of temperatures. The authors need to mention the reasons behind less active ice ice collision in the simulated case. What are the factors that resulted in high secondary production by rime-splintering and drop shattering when compared with ice-ice collisions? What are the major differences in the microphysical processes of wintertime thunderstorms and summertime thunderstorms? There should be some discussion on the relative role of SIP in modulating ice number concentration and hence cloud electrification.
- It is important to show the rates of three SIP processes implemented in the model. Or at least the concentration of ice resulting from each SIP mechanism in 3SIP simulations can be shown. Time height evolution of ice particle number concentration from each of the SIP mechanisms will help to understand their relative importance in altering total ice number concentration. Authors have shown time height evolution of mass mixing ratios, however, changes in ice number concentration are very important as far as the role of SIP is concerned.
- In Figure 8, temporal variation of ice/snow showed that there is not much effect of individual SIP process on ice/snow concentration, however when all SIPs were considered the concentration was boosted. What are the physical mechanisms behind it? I expect a significant increase in ice/snow concentration as a result of SIP in the simulations where a single SIP is considered if that mechanism is important.
- A few details of the implementation of SIP in the model are needed. What was the diameter of the tiny fragments that resulted from mode 1 in drop shattering? What kind of collisions were considered for collisional breakup mechanisms? In which category the resulting fragments were added?
- There is no information about the radar data e.g. which radar was used, what are the data corrections etc. Similarly, there is not much information available about lightning data.
- Line 378: if ice ice collision was less active what are the reasons behind the enhancement in the flash rate?
- What are the mechanisms behind the improvement in the temporal distribution of flash rate in 3SIP simulations?
- Authors should check the manuscript carefully for grammar and language corrections. In many places, articles are missing or not used properly.
Technical corrections/Minor comments:
- What was the cloud base height and temperature of simulated clouds?
- Figure 1 captions: The time mentioned in the caption does not match that mentioned in the plots. Also, plots 1a and 1b are supposed to be 500 mb geopotential height, isotherms and wind barbs, but on plot b the mentioned height is 850 hpa. The same mistake is with plots c and d.
- Figure 7: What are the averaging conditions for incloud points shown in time height plots?
- Figure 7 captions: The names of sensitivity studies mentioned in the captions “SBM-0SIP Simulation; SBM-1SIP SimulationSBM-2SIP Simulation; SBM-3SIP” do not match the names on the plot. Please correct it according to the sensitivity tests mentioned in the text earlier.
- Line 11: in a thunderstorm that occurred ;
- Line 11: are investigated …
- Line 40: correct lighting to lightning
- Line 55: Phillips et al. 2020
- Line 67: studies that highlighted ….
- Line 95: warm moist ..
- Line 111: Fig.3a not 2a
- Line 113: Fig 3c not 2c
- Line 124: Figure 4 not 3
- Line 124: a two-way nested
- Line 127: spin-up
- Line 140: Incomplete sentence
- Line 140: at temperatures colder than
- Line 144: it can also be active …
- Line 204: change “With all the three SIP processes implement” to “With all implemented”
- Line 217: units should be g kg-1 and not g ks-1
- Line 232: there are …
- Line 235: graupel mixing ratio …
- Line 245: correct quicky to quickly
- Line 281: results in changes in the
- Line 318: delete the before that
- Line 329: cross-section
- Line 380: implemented
- Line 407: replace continued by continue
- Line 408: change falling to it falls
- Line 414: change on to in
- Line 469: Define RAR and RARc
- AC2: 'Reply on RC1', Jing Yang, 03 Feb 2024
-
RC2: 'Comment on egusphere-2023-2188', Anonymous Referee #2, 06 Dec 2023
The impacts of three secondary ice production (SIP) processes on the electrification were evaluated by the mesoscale simulation. A new electrical model was constructed based on the fast spectral bin microphysics (SBM) scheme, which constitutes a significant contribution of this paper. This electrical model will serve as an effective tool for studying electrification and discharge processes. However, there are several key issues in the paper that require further clarification. Substantial revisions may be necessary to strengthen the supporting evidence. Specifically, the following matters should be considered:
- In the model validation section, it is recommended that the author can display a two-dimensional distribution of observed and simulated lightning activities. The entire inner domain is too large to effectively reflect the distribution of simulated lightning activity.
- How was Figure 7 (as well as all time-height diagrams) created? Does Figure 7 present single-point data or regional average data? If it is regional average data, is it an average of the entire inner domain? Time-height diagrams for single-point or regional average data may better display the changing trends of variables, while cross sections can provide a more intuitive understanding. Cross sections for charging rates could also be shown.
- The correlation between charge structure and electrification rate does not align. Based on the charging rate distribution presented in Figure 9, it is observed that although the inductive charging rate varies significantly across different experiments, the non-inductive charging rate is one order of magnitude higher than the inductive charging rate. Therefore, we continue to lean towards the notion that electrification in the cloud is primarily attributed to the non-inductive collision process. However, it is noteworthy that even when there is a minimal difference in the non-inductive electrification rate (Fig. 9 a, c, e, g, i), it leads to completely distinct charge distributions (Fig. 10), which is indeed perplexing. If our understanding is correct, the non-inductive charging rate depicted in Figure 9 should be targeted at graupel particles. Given this distribution, it should not cause such a considerable difference in electrification as observed in Figure 10. However, the substantial difference is difficult to explain solely by the sedimentation of graupel particles. Is it possible that the regional average has concealed some crucial information? Or we suspect that the inductive charging process may also play a vital role in the formation of charge structure. Therefore, it is suggested that the effects of inductive and non-inductive electrification should be separated.
- The rationale for the charge structure differences in various experiments is not clear. Although the author demonstrated the differences in charge structure caused by different SIP processes, we believe that the underlying reason has not been fully disclosed. When the SIP process changes, what is the fundamental alteration? Which leads to the change in electrification rate and charge structure? Has the rime accretion rate (RAR) changed significantly? What causes the change in RAR?
- The author should illustrate the specific location of the cross-sections in Figure 12 within Figure 5.
Citation: https://doi.org/10.5194/egusphere-2023-2188-RC2 - AC3: 'Reply on RC2', Jing Yang, 03 Feb 2024
-
RC3: 'Comment on egusphere-2023-2188', Anonymous Referee #3, 09 Dec 2023
Please find my comments in the attached PDF.
- AC1: 'Reply on RC3', Jing Yang, 03 Feb 2024
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
-
RC1: 'Comment on egusphere-2023-2188', Anonymous Referee #1, 22 Nov 2023
Review of the paper “Impact of Ice Multiplication on the Cloud Electrification of a cold-season thunderstorm: a numerical case study” by Jing Yang et al.
General Comments:
Yang et al studied the role of three secondary ice production (SIP) mechanisms on cloud electrification in a simulated thunderstorm that was developed during the cold season. They implemented three major SIP mechanisms in the WRF model with fast SBM microphysics along with inductive and non-inductive charging mechanisms. Overall, the effect of SIP mechanisms on electrification is an important topic for the scientific community. However, in the current format, the paper needs major revision. Authors need to improve most of the sections including model validation, Analysis, implementation of SIPs, etc. I have enlisted specific and minor comments below.
Specific comments:
- In the present study model validation is only based on spatial distribution radar reflectivity and temporal evolution flash rates. Since the study considers 3 major SIP processes, to what extent does the model agree with the observed number of concentrations of ice particles? How well does the model simulate the liquid water mass/content and vertical velocities? All these microphysical properties are of great importance for lightning. Comparison of some of these microphysical properties with the observation will be helpful for readers to understand the accuracy of the model. It will be good to compare the vertical distribution of radar reflectivity from the model with observations. If available, surface precipitation can be compared to show the robustness of model simulations.
- Even with radar reflectivity plots, contour levels are different in observations and model, which makes it difficult to compare. To what extent does the simulated radar reflectivity is in agreement with observations when all SIP processes are active? It will be good to present some statistical analysis.
- Based on my knowledge, in most of the previous studies, ice-ice collision is a major SIP mechanism in deep convective clouds when compared with rime-splintering and drop shattering (e.g. Phillips and Patade 2022). This is because rime-splintering and drop shattering are active over a limited range of temperatures. The authors need to mention the reasons behind less active ice ice collision in the simulated case. What are the factors that resulted in high secondary production by rime-splintering and drop shattering when compared with ice-ice collisions? What are the major differences in the microphysical processes of wintertime thunderstorms and summertime thunderstorms? There should be some discussion on the relative role of SIP in modulating ice number concentration and hence cloud electrification.
- It is important to show the rates of three SIP processes implemented in the model. Or at least the concentration of ice resulting from each SIP mechanism in 3SIP simulations can be shown. Time height evolution of ice particle number concentration from each of the SIP mechanisms will help to understand their relative importance in altering total ice number concentration. Authors have shown time height evolution of mass mixing ratios, however, changes in ice number concentration are very important as far as the role of SIP is concerned.
- In Figure 8, temporal variation of ice/snow showed that there is not much effect of individual SIP process on ice/snow concentration, however when all SIPs were considered the concentration was boosted. What are the physical mechanisms behind it? I expect a significant increase in ice/snow concentration as a result of SIP in the simulations where a single SIP is considered if that mechanism is important.
- A few details of the implementation of SIP in the model are needed. What was the diameter of the tiny fragments that resulted from mode 1 in drop shattering? What kind of collisions were considered for collisional breakup mechanisms? In which category the resulting fragments were added?
- There is no information about the radar data e.g. which radar was used, what are the data corrections etc. Similarly, there is not much information available about lightning data.
- Line 378: if ice ice collision was less active what are the reasons behind the enhancement in the flash rate?
- What are the mechanisms behind the improvement in the temporal distribution of flash rate in 3SIP simulations?
- Authors should check the manuscript carefully for grammar and language corrections. In many places, articles are missing or not used properly.
Technical corrections/Minor comments:
- What was the cloud base height and temperature of simulated clouds?
- Figure 1 captions: The time mentioned in the caption does not match that mentioned in the plots. Also, plots 1a and 1b are supposed to be 500 mb geopotential height, isotherms and wind barbs, but on plot b the mentioned height is 850 hpa. The same mistake is with plots c and d.
- Figure 7: What are the averaging conditions for incloud points shown in time height plots?
- Figure 7 captions: The names of sensitivity studies mentioned in the captions “SBM-0SIP Simulation; SBM-1SIP SimulationSBM-2SIP Simulation; SBM-3SIP” do not match the names on the plot. Please correct it according to the sensitivity tests mentioned in the text earlier.
- Line 11: in a thunderstorm that occurred ;
- Line 11: are investigated …
- Line 40: correct lighting to lightning
- Line 55: Phillips et al. 2020
- Line 67: studies that highlighted ….
- Line 95: warm moist ..
- Line 111: Fig.3a not 2a
- Line 113: Fig 3c not 2c
- Line 124: Figure 4 not 3
- Line 124: a two-way nested
- Line 127: spin-up
- Line 140: Incomplete sentence
- Line 140: at temperatures colder than
- Line 144: it can also be active …
- Line 204: change “With all the three SIP processes implement” to “With all implemented”
- Line 217: units should be g kg-1 and not g ks-1
- Line 232: there are …
- Line 235: graupel mixing ratio …
- Line 245: correct quicky to quickly
- Line 281: results in changes in the
- Line 318: delete the before that
- Line 329: cross-section
- Line 380: implemented
- Line 407: replace continued by continue
- Line 408: change falling to it falls
- Line 414: change on to in
- Line 469: Define RAR and RARc
- AC2: 'Reply on RC1', Jing Yang, 03 Feb 2024
-
RC2: 'Comment on egusphere-2023-2188', Anonymous Referee #2, 06 Dec 2023
The impacts of three secondary ice production (SIP) processes on the electrification were evaluated by the mesoscale simulation. A new electrical model was constructed based on the fast spectral bin microphysics (SBM) scheme, which constitutes a significant contribution of this paper. This electrical model will serve as an effective tool for studying electrification and discharge processes. However, there are several key issues in the paper that require further clarification. Substantial revisions may be necessary to strengthen the supporting evidence. Specifically, the following matters should be considered:
- In the model validation section, it is recommended that the author can display a two-dimensional distribution of observed and simulated lightning activities. The entire inner domain is too large to effectively reflect the distribution of simulated lightning activity.
- How was Figure 7 (as well as all time-height diagrams) created? Does Figure 7 present single-point data or regional average data? If it is regional average data, is it an average of the entire inner domain? Time-height diagrams for single-point or regional average data may better display the changing trends of variables, while cross sections can provide a more intuitive understanding. Cross sections for charging rates could also be shown.
- The correlation between charge structure and electrification rate does not align. Based on the charging rate distribution presented in Figure 9, it is observed that although the inductive charging rate varies significantly across different experiments, the non-inductive charging rate is one order of magnitude higher than the inductive charging rate. Therefore, we continue to lean towards the notion that electrification in the cloud is primarily attributed to the non-inductive collision process. However, it is noteworthy that even when there is a minimal difference in the non-inductive electrification rate (Fig. 9 a, c, e, g, i), it leads to completely distinct charge distributions (Fig. 10), which is indeed perplexing. If our understanding is correct, the non-inductive charging rate depicted in Figure 9 should be targeted at graupel particles. Given this distribution, it should not cause such a considerable difference in electrification as observed in Figure 10. However, the substantial difference is difficult to explain solely by the sedimentation of graupel particles. Is it possible that the regional average has concealed some crucial information? Or we suspect that the inductive charging process may also play a vital role in the formation of charge structure. Therefore, it is suggested that the effects of inductive and non-inductive electrification should be separated.
- The rationale for the charge structure differences in various experiments is not clear. Although the author demonstrated the differences in charge structure caused by different SIP processes, we believe that the underlying reason has not been fully disclosed. When the SIP process changes, what is the fundamental alteration? Which leads to the change in electrification rate and charge structure? Has the rime accretion rate (RAR) changed significantly? What causes the change in RAR?
- The author should illustrate the specific location of the cross-sections in Figure 12 within Figure 5.
Citation: https://doi.org/10.5194/egusphere-2023-2188-RC2 - AC3: 'Reply on RC2', Jing Yang, 03 Feb 2024
-
RC3: 'Comment on egusphere-2023-2188', Anonymous Referee #3, 09 Dec 2023
Please find my comments in the attached PDF.
- AC1: 'Reply on RC3', Jing Yang, 03 Feb 2024
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Jing Yang
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Precision Regional Earth Modeling and Information Center (PRMIC), Nanjing University of Information Science & Technology, Nanjing, 210044, China
CMA Cloud-Precipitation Physics and Weather Modification Key Laboratory (CPML), Beijing, 100081, China
Shiye Huang
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Precision Regional Earth Modeling and Information Center (PRMIC), Nanjing University of Information Science & Technology, Nanjing, 210044, China
Qilin Zhang
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Precision Regional Earth Modeling and Information Center (PRMIC), Nanjing University of Information Science & Technology, Nanjing, 210044, China
Xiaoqin Jing
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Precision Regional Earth Modeling and Information Center (PRMIC), Nanjing University of Information Science & Technology, Nanjing, 210044, China
Yuting Deng
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Precision Regional Earth Modeling and Information Center (PRMIC), Nanjing University of Information Science & Technology, Nanjing, 210044, China
Yubao Liu
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory/Precision Regional Earth Modeling and Information Center (PRMIC), Nanjing University of Information Science & Technology, Nanjing, 210044, China
Short summary
This study contributes to fill the dearth of understanding the impacts of different secondary ice production (SIP) processes on the cloud electrification in cold-season thunderstorm. The results suggest the SIP, especially the rime-splintering process and the shattering of freezing drops, have significant impacts on the charge structure of the storm. In addition, the modelled radar composite reflectivity and flash rate are improved after implementing the three SIP processes in the model.
This study contributes to fill the dearth of understanding the impacts of different secondary...