Estimating the concentration of silver iodide needed to detect unambiguous signatures of glaciogenic cloud seeding

Yang, Jing; Li, Jiaojiao; Chen, Meilian; Jing, Xiaoqin; Yin, Yan; Geerts, Bart; Wang, Zhien; Liu, Yubao; Chen, Baojun; Hua, Shaofeng; Hu, Hao; Dong, Xiaobo; Tian, Ping; Chen, Qian; Gao, Yang

doi:https://doi.org/10.5194/egusphere-2024-2301

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https://doi.org/10.5194/egusphere-2024-2301

Preprints

05 Aug 2024

| 05 Aug 2024

Estimating the concentration of silver iodide needed to detect unambiguous signatures of glaciogenic cloud seeding

Jing Yang, Jiaojiao Li, Meilian Chen, Xiaoqin Jing, Yan Yin, Bart Geerts, Zhien Wang, Yubao Liu, Baojun Chen, Shaofeng Hua, Hao Hu, Xiaobo Dong, Ping Tian, Qian Chen, and Yang Gao

Abstract. Detecting an unambiguous radar reflectivity signature is vital to investigate cloud seeding impacts. Radar reflectivity change attributed to seeding depends on both the cloud conditions and on the concentration of silver iodide (AgI) particles. In this study, the reflectivity change induced by glaciogenic seeding using different AgI particle concentrations is investigated under various cloud conditions, using a 1D ice growth model coupled with an AgI nucleation parameterization. In addition, an algorithm is developed to estimate the minimum AgI particle concentration needed for a measurable glaciogenic cloud seeding signature. The results show that the 1D model captures the ice growth habit compared to available observations, and yields an unambiguous reflectivity change that is consistent with 3D model simulations and previous observational studies. Simulations indicate that seeding at a temperature of about -15 °C has the highest probability of detecting the radar seeding signature. This finding is consistent with the fact that the seeding temperature was about -15 °C or slightly warmer in most documented unambiguous seeding signature cases. Using the 1D model, 1000 numerical experiments are conducted, and the outputs are used to develop a parameterization to estimate the AgI particle concentration that is needed to detect an unambiguous seeding signature. Application of this parameterization to a real case suggests that seeding between -21 °C and -11 °C can possibly produce unambiguous seeding signatures, and seeding at about -15 °C requires the least AgI particle concentration. Seeding at warmer temperatures in precipitating clouds requires an extremely high AgI amount and supercooled liquid water content. The results shown in this study deepen our understanding of the relationship between the AgI particle concentration and radar seeding signature under different cloud conditions. The parameterization can be used in operational seeding decision making of the optimal amount of AgI dispersed.

Received: 22 Jul 2024 – Discussion started: 05 Aug 2024

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Jing Yang, Jiaojiao Li, Meilian Chen, Xiaoqin Jing, Yan Yin, Bart Geerts, Zhien Wang, Yubao Liu, Baojun Chen, Shaofeng Hua, Hao Hu, Xiaobo Dong, Ping Tian, Qian Chen, and Yang Gao

Status: closed

RC1:
'Comment on egusphere-2024-2301', Jan Henneberger, 08 Sep 2024

The authors present a study that develops a 1D model aimed at predicting radar reflectivity changes resulting from glaciogenic cloud seeding using silver iodide (AgI). The primary goal of the study is to determine the AgI concentrations required to generate an unambiguous seeding signature, allowing for the detection of the effects of cloud seeding on radar. To achieve this, the authors conducted 1000 simulations under varying AgI concentrations and temperature conditions. The findings indicate that the lowest AgI concentration required to produce a detectable seeding signal occurs at -15°C, while at temperatures above -11°C, a signal is only observed in precipitating clouds when both high AgI concentrations and supercooled liquid water are present. The study’s results were evaluated using 3D model simulations and observational data, providing insights into the behavior of seeded clouds.
The manuscript is generally well-written and falls within the scope of the journal. The model presented is a valuable contribution and has the potential to aid in the planning of seeding missions. The parameterizations and methods are, for the most part, well described. However, several areas require clarification, particularly concerning the choice of parameterizations and some missing information crucial for fully understanding the model's behavior. While the structure of the paper is solid, a more in-depth discussion of specific modeling assumptions and a stronger comparison with existing literature would enhance the clarity and scientific impact of the work.
Jan Henneberger
Major comments:
A critical issue that needs addressing is the distance to the seeding when discussing AgI particle concentrations. Given that a single seeding flare can release approximately 10²⁰ AgI particles, one would expect much higher concentrations near the seeding source. It would be helpful to clarify at what distance the concentrations mention in the manuscript are assumed.
The manuscript does not provide sufficient detail on the assumed temperature profile used in the 1D model. The authors should clarify whether the results are sensitive to the temperature profile and how different profiles might impact the outcome of the simulations.
The current comparison of the 1D model with the 3D models and with observational data is limited. A single comparison is insufficient to draw robust conclusions, and further comparisons would strengthen the validation of the 1D model.
One notable omission is the lack of citation to recent relevant studies, including our own work (Henneberger and Ramelli et al., 2023). I find myself in the somewhat awkward position of promoting my own research, which I assume was not cited because it was published only recently. However, I believe it is highly relevant to the current manuscript. In our study, we observed a clear seeding signal in over 50 missions, with ice crystal concentrations reaching up to 1000 L⁻¹. This demonstrated the potential to detect unambiguous seeding signatures even at temperatures as high as -5°C, given favorable background conditions and sensitive instrumentation. Additionally, our Cloudlab dataset would provide a strong test case for the model presented here, though this might be outside the immediate scope of the current paper.
Have you considered including the linear depolarization ratio to detect the seeding signal? It may offer higher sensitivity, especially for ice crystals with large aspect ratios, and with polarization radars becoming more common, this could be a valuable addition.
Minor comments:
Line 104: Have you thought about using the Marcolli et al., 2016 parameterization for ice nucleation after Omanovic et al., 2024 showed that the DeMott (1995) parameterization was not active enough at warm temperatures.
Line 133: The WBF process is also not active in stronger downdrafts, as both ice crystals and droplets shrink. I would rather argue that weak turbulence cancels out the effects of updrafts and downdrafts.
Line 139: Clarify how you calculate ice supersaturation (S_i) during the simulation. Does S_i depend on the liquid phase or turbulence?
Line 143: A X is missing in equation 6.
Line 165: Ice crystal growth ratios are currently fit to one laboratory study. Consider using Harrington et al. (2019), which take multiple laboratory measurements into account.
Line 226: How does dispersion in the 1D model compare to the 3D model? I would expect greater dispersion in the 3D model due to the extra dimensions.
Line 257: What altitude was seeding performed on, and how was the vertical temperature profile set? What is the cause of the variation in IWC with altitude? I would have expected a linear increase as altitude decreases.
Line 261: Ice crystal concentrations seem to depend only on temperature and seeding concentration and are calculated at the start of the simulation. Please clarify if these values change over time.
Line 287: What was the seeding height?
Line 296: Provide details on the spatial dimension and temporal duration of the seeding?
Figures 4, 5, 6: Indicate the seeding height and clarify whether heights are measured above ground or sea level. In Figure 4, explain why the seeding signal is closer after 30 minutes than after 20 minutes.
Line: 355: Specify the temperature profile used. Why are T_seed and P_seed needed at independent input?
Line 358: At what distance from the seeding source are AgI concentrations assumed? Higher concentrations should be expected near the flare.
Line 366: Can radar reflectivity (Ze) decrease with increasing depth if ice crystals cannot shrink in the 1D model?
Line 385: Data must be shown if it is discussed. Also, varying the number of experiments (e.g., 500 and 2000) would provide more informative results.
Line 437: Change to “was mostly larger”
Line 438: Are you certain there is sufficient water available for ice growth below -13°C? Based on the radar reflectivity, most of the liquid water content (LWC) appears to be concentrated below the melting layer.
Line 456: State that the evaluation was conducted using only one case study.
Line 465: Replace "ceteris paribus" with "all other parameters being equal" for better readability.
References:
Harrington, J. Y., A. Moyle, L. E. Hanson, and H. Morrison, 2019: On Calculating Deposition Coefficients and Aspect-Ratio Evolution in Approximate Models of Ice Crystal Vapor Growth. J. Atmos. Sci., 76, 1609–1625, https://doi.org/10.1175/JAS-D-18-0319.1.
Henneberger, J., Ramelli, F., and Coauthors, 2023: Seeding of Supercooled Low Stratus Clouds with a UAV to Study Microphysical Ice Processes: An Introduction to the CLOUDLAB Project. Bull. Amer. Meteor. Soc., 104, E1962–E1979, https://doi.org/10.1175/BAMS-D-22-0178.1.
Marcolli, C., Nagare, B., Welti, A., and Lohmann, U.: Ice nucleation efficiency of AgI: review and new insights, Atmos. Chem. Phys., 16, 8915–8937, https://doi.org/10.5194/acp-16-8915-2016, 2016
Omanovic, N., Ferrachat, S., Fuchs, C., Henneberger, J., Miller, A. J., Ohneiser, K., Ramelli, F., Seifert, P., Spirig, R., Zhang, H., and Lohmann, U.: Evaluating the Wegener–Bergeron–Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project, Atmos. Chem. Phys., 24, 6825–6844, https://doi.org/10.5194/acp-24-6825-2024, 2024

Citation: https://doi.org/10.5194/egusphere-2024-2301-RC1
- AC1: 'Reply on RC1', Jing Yang, 22 Oct 2024
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2301/egusphere-2024-2301-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-2301-AC1
RC2:
'Comment on egusphere-2024-2301', Anonymous Referee #2, 16 Sep 2024

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2301/egusphere-2024-2301-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2024-2301-RC2
- AC2: 'Reply on RC2', Jing Yang, 22 Oct 2024
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2301/egusphere-2024-2301-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-2301-AC2

Status: closed

RC1:
'Comment on egusphere-2024-2301', Jan Henneberger, 08 Sep 2024

The authors present a study that develops a 1D model aimed at predicting radar reflectivity changes resulting from glaciogenic cloud seeding using silver iodide (AgI). The primary goal of the study is to determine the AgI concentrations required to generate an unambiguous seeding signature, allowing for the detection of the effects of cloud seeding on radar. To achieve this, the authors conducted 1000 simulations under varying AgI concentrations and temperature conditions. The findings indicate that the lowest AgI concentration required to produce a detectable seeding signal occurs at -15°C, while at temperatures above -11°C, a signal is only observed in precipitating clouds when both high AgI concentrations and supercooled liquid water are present. The study’s results were evaluated using 3D model simulations and observational data, providing insights into the behavior of seeded clouds.
The manuscript is generally well-written and falls within the scope of the journal. The model presented is a valuable contribution and has the potential to aid in the planning of seeding missions. The parameterizations and methods are, for the most part, well described. However, several areas require clarification, particularly concerning the choice of parameterizations and some missing information crucial for fully understanding the model's behavior. While the structure of the paper is solid, a more in-depth discussion of specific modeling assumptions and a stronger comparison with existing literature would enhance the clarity and scientific impact of the work.
Jan Henneberger
Major comments:
A critical issue that needs addressing is the distance to the seeding when discussing AgI particle concentrations. Given that a single seeding flare can release approximately 10²⁰ AgI particles, one would expect much higher concentrations near the seeding source. It would be helpful to clarify at what distance the concentrations mention in the manuscript are assumed.
The manuscript does not provide sufficient detail on the assumed temperature profile used in the 1D model. The authors should clarify whether the results are sensitive to the temperature profile and how different profiles might impact the outcome of the simulations.
The current comparison of the 1D model with the 3D models and with observational data is limited. A single comparison is insufficient to draw robust conclusions, and further comparisons would strengthen the validation of the 1D model.
One notable omission is the lack of citation to recent relevant studies, including our own work (Henneberger and Ramelli et al., 2023). I find myself in the somewhat awkward position of promoting my own research, which I assume was not cited because it was published only recently. However, I believe it is highly relevant to the current manuscript. In our study, we observed a clear seeding signal in over 50 missions, with ice crystal concentrations reaching up to 1000 L⁻¹. This demonstrated the potential to detect unambiguous seeding signatures even at temperatures as high as -5°C, given favorable background conditions and sensitive instrumentation. Additionally, our Cloudlab dataset would provide a strong test case for the model presented here, though this might be outside the immediate scope of the current paper.
Have you considered including the linear depolarization ratio to detect the seeding signal? It may offer higher sensitivity, especially for ice crystals with large aspect ratios, and with polarization radars becoming more common, this could be a valuable addition.
Minor comments:
Line 104: Have you thought about using the Marcolli et al., 2016 parameterization for ice nucleation after Omanovic et al., 2024 showed that the DeMott (1995) parameterization was not active enough at warm temperatures.
Line 133: The WBF process is also not active in stronger downdrafts, as both ice crystals and droplets shrink. I would rather argue that weak turbulence cancels out the effects of updrafts and downdrafts.
Line 139: Clarify how you calculate ice supersaturation (S_i) during the simulation. Does S_i depend on the liquid phase or turbulence?
Line 143: A X is missing in equation 6.
Line 165: Ice crystal growth ratios are currently fit to one laboratory study. Consider using Harrington et al. (2019), which take multiple laboratory measurements into account.
Line 226: How does dispersion in the 1D model compare to the 3D model? I would expect greater dispersion in the 3D model due to the extra dimensions.
Line 257: What altitude was seeding performed on, and how was the vertical temperature profile set? What is the cause of the variation in IWC with altitude? I would have expected a linear increase as altitude decreases.
Line 261: Ice crystal concentrations seem to depend only on temperature and seeding concentration and are calculated at the start of the simulation. Please clarify if these values change over time.
Line 287: What was the seeding height?
Line 296: Provide details on the spatial dimension and temporal duration of the seeding?
Figures 4, 5, 6: Indicate the seeding height and clarify whether heights are measured above ground or sea level. In Figure 4, explain why the seeding signal is closer after 30 minutes than after 20 minutes.
Line: 355: Specify the temperature profile used. Why are T_seed and P_seed needed at independent input?
Line 358: At what distance from the seeding source are AgI concentrations assumed? Higher concentrations should be expected near the flare.
Line 366: Can radar reflectivity (Ze) decrease with increasing depth if ice crystals cannot shrink in the 1D model?
Line 385: Data must be shown if it is discussed. Also, varying the number of experiments (e.g., 500 and 2000) would provide more informative results.
Line 437: Change to “was mostly larger”
Line 438: Are you certain there is sufficient water available for ice growth below -13°C? Based on the radar reflectivity, most of the liquid water content (LWC) appears to be concentrated below the melting layer.
Line 456: State that the evaluation was conducted using only one case study.
Line 465: Replace "ceteris paribus" with "all other parameters being equal" for better readability.
References:
Harrington, J. Y., A. Moyle, L. E. Hanson, and H. Morrison, 2019: On Calculating Deposition Coefficients and Aspect-Ratio Evolution in Approximate Models of Ice Crystal Vapor Growth. J. Atmos. Sci., 76, 1609–1625, https://doi.org/10.1175/JAS-D-18-0319.1.
Henneberger, J., Ramelli, F., and Coauthors, 2023: Seeding of Supercooled Low Stratus Clouds with a UAV to Study Microphysical Ice Processes: An Introduction to the CLOUDLAB Project. Bull. Amer. Meteor. Soc., 104, E1962–E1979, https://doi.org/10.1175/BAMS-D-22-0178.1.
Marcolli, C., Nagare, B., Welti, A., and Lohmann, U.: Ice nucleation efficiency of AgI: review and new insights, Atmos. Chem. Phys., 16, 8915–8937, https://doi.org/10.5194/acp-16-8915-2016, 2016
Omanovic, N., Ferrachat, S., Fuchs, C., Henneberger, J., Miller, A. J., Ohneiser, K., Ramelli, F., Seifert, P., Spirig, R., Zhang, H., and Lohmann, U.: Evaluating the Wegener–Bergeron–Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project, Atmos. Chem. Phys., 24, 6825–6844, https://doi.org/10.5194/acp-24-6825-2024, 2024

Citation: https://doi.org/10.5194/egusphere-2024-2301-RC1
- AC1: 'Reply on RC1', Jing Yang, 22 Oct 2024
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2301/egusphere-2024-2301-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-2301-AC1
RC2:
'Comment on egusphere-2024-2301', Anonymous Referee #2, 16 Sep 2024

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2301/egusphere-2024-2301-RC2-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2024-2301-RC2
- AC2: 'Reply on RC2', Jing Yang, 22 Oct 2024
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2301/egusphere-2024-2301-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2024-2301-AC2

Jing Yang, Jiaojiao Li, Meilian Chen, Xiaoqin Jing, Yan Yin, Bart Geerts, Zhien Wang, Yubao Liu, Baojun Chen, Shaofeng Hua, Hao Hu, Xiaobo Dong, Ping Tian, Qian Chen, and Yang Gao

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Short summary

Detecting unambiguous signatures is vital to investigate cloud seeding impacts, but in many cases seeding signature is immersed in natural variability. In this study, the reflectivity change induced by glaciogenic seeding using different AgI concentrations is investigated under various conditions, and a method is developed to estimate the AgI concentration needed to detect unambiguous seeding signatures. The results are helpful in operational seeding decision making of the AgI amount dispersed.


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