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: final response (author comments only)
- CC1: 'Comment on egusphere-2025-4482', Tianliang Zhao, 16 Nov 2025
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RC1: 'Comment on egusphere-2025-4482', Tianliang Zhao, 21 Nov 2025
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 -
RC2: 'Comment on egusphere-2025-4482', Anonymous Referee #2, 11 Jan 2026
Review of “A Dimensionless-Entropy Weight Method for Determining Cloud Physical Parameter Responses Induced by Aircraft Cloud Seeding”
Summary
The manuscript proposes a physical evaluation framework for aircraft cloud seeding based on a "dimensionless-entropy weight method." The authors attempt to integrate multiple data sources (ERA5, radar, and satellite) with HYSPLIT dispersion modelling to quantify the physical response of clouds to seeding. While the objective of reducing attribution uncertainty in weather modification is commendable, the manuscript suffers from significant methodological gaps. Key parts of the methods are insufficiently described, making it impossible to distinguish between the artificial seeding signal and inherent natural variability. Furthermore, the claims that the method reduces natural variability interference are not supported by the vague and often contradictory results. The language frequently employs overly polished but substantively hollow phrasing, suggesting an over-reliance on AI-assisted writing that lacks scientific precision.
I recommend rejection of the paper in its current form.
Major Comments:
Seeding Signal versus Natural Variability: The authors cite Silverman (2001) regarding the necessity of ensuring natural variability is not misinterpreted as a seeding effect. However, the study fails to prove that the observed changes exceed the "noise" of natural cloud evolution. There is no validation of the approach, for example, by applying the same PIDI metrics to similar but unseeded cloud systems to establish a baseline for natural fluctuations. Without a "null case" analysis, the attribution of any change to seeding remains speculative.
Target and Control Area Selection: The selection of these areas is central to the entire approach, yet the criteria for defining their spatial boundaries are not clearly described.
In Table 3, the authors state that the optimal control area is the one with the minimum APC value. However, for Operation 3, the minimum APC value is listed for Control Area 2 (7.67), yet the authors selected Control Area 3 (16.79) as the "optimal" choice.
Entropy Weights of Evaluation Indicators: The manuscript lacks a clear explanation of what a high or low entropy value signifies in this context. Does it represent the magnitude of variation in the variable? The weights appear to be calculated based only on the target area, rather than on the difference between the target and control areas.Furthermore, several indicators (CER, COT, LWP) are listed as "unavailable" for multiple operations (No. 2, 3, 5, 6) due to nighttime satellite limitations. Since entropy weights must sum to 1, weights from operations with seven indicators cannot be scientifically compared to operations with only four indicators. This makes the "re-calculation" of weights statistically inconsistent across cases.
Cloud-Top Temperature (CTT): In Table 5, the CTT difference between target and control areas is as high as 26.72°C (Operation 1, Hour 1). Such a massive temperature disparity suggests that the "dynamically similar" control areas are, in fact, entirely different cloud systems. This invalidates the comparison.
Cloud Effective Radius (CER): The variation and changes of CER are rather small. What is the known uncertainty of the Fengyun-4A CER product? Without error bars or a discussion of natural variability in droplet growth, it is impossible to determine if a 9% change is a seeding signal or sensor noise.
Optical Thickness (COT): In Operation 1, COT decreases in both the target and control areas over time. How do the authors rule out that this is simply the natural dissipation of the cloud system? Furthermore, in Operation 4, the control area's average COT is more than double that of the target area at Hour 1, again suggesting the clouds are not comparable.
Liquid Water Path, Radar Reflectivity, and VIL: The responses are described as "time-dependent" or having "distinct patterns" such as being "masked". These descriptions are qualitative and hollow. If a signal is "masked," the method has failed to achieve its stated goal of separating the artificial signal from natural variability.
Hourly Precipitation: It is unclear how the "change rates" in Table 12 are calculated. For Operation 1, the target area consistently has lower precipitation than the control area (e.g., 1.608 mm vs 2.187 mm at Hour 2), yet the change rate is listed as a positive 71.7%.
Physical Inspection Dimensionless Index (PIDI) Values: The PIDI is presented as a central innovation, but its physical meaning is opaque. Why can individual indicators like COT show a change rate of 121.7%? The authors claim it is "surprising" that the PIDI (an average of multiple indicators) shows smaller variation than individual indicators. This is not a scientific discovery, but basic property of averaging. The PIDI appears to be a mathematical construct that smoothes over the inconsistencies in the data rather than revealing a true physical response.
The conclusion states that VIL is a "relatively stable indicator" because it showed "notably high" entropy weights (0.06–0.43). What is the objective threshold for a weight to be "notably high"? Why does a high entropy weight, which merely indicates high information variability in the sample, translate to it being a "stable indicator" for seeding effects? The authors provide no physical or statistical reasoning for this conclusion.
Citation: https://doi.org/10.5194/egusphere-2025-4482-RC2
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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.