the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Oct 2025
A Dimensionless-Entropy Weight Method for Determining Cloud Physical Parameter Responses Induced by Aircraft Cloud Seeding
Abstract. To address the challenges in evaluation of aircraft cloud seeding effect, this study proposes a physical evaluation method that integrates multiple key techniques to achieve an integrated quantitative evaluation based on multiple indicators. This evaluation approach was applied to six aircraft cloud seeding operations conducted in Henan Province of China during 2023–2024. Results show that the six operations exhibited nonlinear growth patterns for dispersion in both the target and control areas, with rapid expansion at the initial stage followed by a slower rate. The dispersion rate, distance, extent and concentration of the seeding agent vary significantly depending on the meteorological conditions, such as atmospheric turbulence, wind speed, stability and humidity. For the six operations, the vertically integrated liquid water content (VIL) showed notably high entropy weights (0.06–0.43) in multiple operations (No. 2, 3 and 6), making it a relatively stable indicator for evaluating the cloud seeding effects. Due to complex cloud microphysical processes such as latent heat release from deposition, downdrafts, cloud dissipation and cloud development, the responses of cloud-top temperature to seeding varied considerably (−44.56 °C ~ −6 °C). The effects of cloud seeding on cloud effective radius and optical thickness are complex and vary substantially depending on specific seeding conditions. The responses of liquid water path were time-dependent, the seeding-induced responses of radar reflectivity exhibited distinct patterns, including delayed manifestation, strong enhancement, and ineffectiveness or being masked. The strong-echo area and the VIL in the target areas fluctuated and generally decreased over time, respectively. The integrated physical inspection dimensionless index (PIDI) values for the seven indicators ranged from 15.0 % to 69.7 %, showing a smaller variation magnitude compared with the change rates of individual indicators, which reflects the synergistic effects of multiple indicators. This study provides a quantifiable and robust framework that mitigates the interference of natural variability, thereby advancing cloud seeding techniques and improving effect evaluation capabilities for artificial precipitation enhancement.
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Status: open (until 18 Dec 2025)
- CC1: 'Comment on egusphere-2025-4482', Tianliang Zhao, 16 Nov 2025 reply
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RC1: 'Comment on egusphere-2025-4482', Tianliang Zhao, 21 Nov 2025
reply
This article focused on conducting scientific research on a series of issues existing in the assessment of aircraft cloud seeding effects, such as the interference of natural precipitation variability, the difficulty in signal separation, and the lack of comprehensive quantitative evaluation of multi-source parameters. Regarding the issues above, this manuscript established a comprehensive framework, integrating a series of key technologies, including dimensionless entropy weight processing, high-precision simulation of seedling agent diffusion, selection of control areas based on dynamic similarity, and multi-parameter comprehensive response indicators, to achieve an integrated quantitative evaluation based on multiple indicators. Results of this research could reduce attribution uncertainty and more effectively eliminate the influence of natural variability in physical evaluations, providing a quantifiable and interference-resistant basis for cloud seeding physical evaluations. Overall, the conception of this article is quite good and well-structured. The dimensionless entropy weight method for determining the response of cloud physical parameters caused by aircraft cloud seeding is technically innovative to a certain extent. This is a valuable contribution to the improvement of the assessment capability of artificial precipitation enhancement effects. So, I recommend the article be published after the following minor comments are addressed.
The main purpose is to enhance the clarity of certain sections. The technical details within can be further simplified or clarified to be more accessible to a wider range of readers.
1. The explanation of the dimensionless method is insufficient. Section 2.3.3 only mentions the "min-max normalization method" without explaining why this method was chosen over others like standardization or mean normalization. It is recommended to supplement the explanation with the characteristics or advantages of this method, as well as its applicability to the subsequent entropy weight calculation. Furthermore, the method description does not specify whether global extrema or case-specific extrema are used for normalization. It is advised to clearly state whether a unified set of extrema across all cases or independent extrema for each individual case is applied.
2. Some conclusions in the section on seeding agent transport and diffusion are verbose or repetitive. In Section 3.3, the statement "The dispersion rate of seeding agents is also strongly influenced by meteorological factors such as wind speed, atmospheric stability, turbulence intensity and atmospheric humidity……" is repeated in two places.
3. The definition of "strong echo area" is unclear. It is not explained how the metric "≥30 dBZ echo area" is calculated. Please briefly supplement the description with the calculation method for the ≥30 dBZ echo area.
4. The configuration of the target and control areas for the first and second hours of the last case shown in Figures 7-9 and Figure 12 is problematic. Please readjust these figures.
5. Regarding the conclusions of the post-seeding precipitation analysis, some statements need to be more specific and explicit. In the "8. Analysis of hourly precipitation" part of Section 3.5.1, the conclusions are largely derived from data in Table 12. However, Table 12 does not specify whether the "Change rates of hourly precipitation" have the natural precipitation background removed. It is recommended to revise and improve this section.
Citation: https://doi.org/10.5194/egusphere-2025-4482-RC1
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This article focused on conducting scientific research on a series of issues existing in the assessment of aircraft cloud seeding effects, such as the interference of natural precipitation variability, the difficulty in signal separation, and the lack of comprehensive quantitative evaluation of multi-source parameters. Regarding the issues above, this manuscript established a comprehensive framework, integrating a series of key technologies, including dimensionless entropy weight processing, high-precision simulation of seedling agent diffusion, selection of control areas based on dynamic similarity, and multi-parameter comprehensive response indicators, to achieve an integrated quantitative evaluation based on multiple indicators. Results of this research could reduce attribution uncertainty and more effectively eliminate the influence of natural variability in physical evaluations, providing a quantifiable and interference-resistant basis for cloud seeding physical evaluations. Overall, the conception of this article is quite good and well-structured. The dimensionless entropy weight method for determining the response of cloud physical parameters caused by aircraft cloud seeding is technically innovative to a certain extent. This is a valuable contribution to the improvement of the assessment capability of artificial precipitation enhancement effects. So, I recommend the article be published after the following minor comments are addressed. The main purpose is to enhance the clarity of certain sections. The technical details within can be further simplified or clarified to be more accessible to a wider range of readers. 1. The explanation of the dimensionless method is insufficient. Section 2.3.3 only mentions the "min-max normalization method" without explaining why this method was chosen over others like standardization or mean normalization. It is recommended to supplement the explanation with the characteristics or advantages of this method, as well as its applicability to the subsequent entropy weight calculation. Furthermore, the method description does not specify whether global extrema or case-specific extrema are used for normalization. It is advised to clearly state whether a unified set of extrema across all cases or independent extrema for each individual case is applied. 2. Some conclusions in the section on seeding agent transport and diffusion are verbose or repetitive. In Section 3.3, the statement "The dispersion rate of seeding agents is also strongly influenced by meteorological factors such as wind speed, atmospheric stability, turbulence intensity and atmospheric humidity……" is repeated in two places. 3. The definition of "strong echo area" is unclear. It is not explained how the metric "≥30 dBZ echo area" is calculated. Please briefly supplement the description with the calculation method for the ≥30 dBZ echo area. 4. The configuration of the target and control areas for the first and second hours of the last case shown in Figures 7-9 and Figure 12 is problematic. Please readjust these figures. 5. Regarding the conclusions of the post-seeding precipitation analysis, some statements need to be more specific and explicit. In the "8. Analysis of hourly precipitation" part of Section 3.5.1, the conclusions are largely derived from data in Table 12. However, Table 12 does not specify whether the "Change rates of hourly precipitation" have the natural precipitation background removed. It is recommended to revise and improve this section.