A method for evaluating the effect of artificial rain and snow enhancement based on layer-by-layer iteration

Abstract

The invention belongs to the technical field of artificial weather influencing, and discloses an artificial rain and snow enhancement effect evaluation method based on a layer-by-layer iteration method, comprising: using a high-resolution numerical model to determine a catalyst diffusion trajectory and an affected area, aircraft observation data including background ice crystal number concentration in the affected area and ice crystal number concentration under the condition of using a catalyst; reading the ice crystal number concentration of the spreading layer from the observation data, calculating the correlation between the ice crystal number concentration of the spreading layer L0 and the radar reflectivity factor of the first layer L1; calculating the correlation coefficient of the radar reflectivity factor between each adjacent layer from the spreading layer to the ground layer by layer; using a high-resolution numerical model to simulate the diffusion of the catalyst, reading the ice crystal number concentration of the affected area in the aircraft observation data, substituting it into a relational expression, iteratively calculating the radar reflectivity factor of each layer layer by layer, and calculating the precipitation rate according to the radar reflectivity factor of each layer; calculating the precipitation rate under the background ice crystal number concentration, and comparing them, so as to quantitatively evaluate the artificial rain and snow enhancement effect.

Description

Technical Field

[0001] The invention relates to the technical field of artificial weather modification, and in particular to a method for evaluating the effect of artificial rain and snow enhancement based on a layer-by-layer iteration method. Background Art

[0002] Using aircraft or rockets to increase precipitation and snow in cold clouds is one of the main operations of artificial weather modification, but the evaluation of its operation effect is still very challenging. At present, the evaluation of the effect of cold cloud enhancement is mainly based on individual cases with visible catalytic effects. However, in actual operations, it is difficult to intuitively see the visible catalytic effect in most individual cases, or the effect is relatively vague. This is because the effect of artificial operations is often masked by the natural variability of precipitation, so it is impossible to know what effect the catalyst of the seeding layer has on the cloud system.

[0003] The basic assumption of cold cloud enhancement is that artificially spread catalysts can increase the number and concentration of ice crystals in the seeding layer, and the ice crystals grow and fall through the Bergeron process. Therefore, to evaluate the effect of cold cloud enhancement, it is necessary to first increase the number and concentration of ice crystals in the cloud after the operation, and then test whether its influence can be reflected in the radar reflectivity of each layer below the seeding layer. Generally speaking, there are five possibilities: (1) The catalyst does not increase the number of ice crystals, indicating that the cloud conditions are not conducive to catalyst nucleation; (2) Artificial operations increase the number of ice crystals, but there is no response to the radar reflectivity below the seeding layer, indicating that the ice crystals have not grown or the growth rate is too slow, usually limited by too little supercooled water or inappropriate temperature; (3) The ice crystals increased by artificial seeding only affect the radar reflectivity of the seeding layer or part of the area below, and there is no visible signal in most areas above the ground, indicating that the microphysical characteristics of these areas are not greatly affected by the ice crystals in the seeding layer and the natural variability is large; (4) The ice crystals increased by the seeding layer can continue to affect the ground, but the signal is masked by the natural variability; (5) The ice crystals increased by the seeding layer can not only continue to affect the ground, but also produce visible signals. At present, there are more studies on the fifth case in the evaluation of the effectiveness of aircraft rain and snow enhancement operations, but most cases are more in line with (1)-(4).

[0004] In the above cases (3)-(4), artificial operations may have an impact on some areas within the cloud or on ground precipitation, but the concentration of ice crystals in the seeding layer is usually not directly correlated with the radar reflectivity below it, but has an indirect impact layer by layer. When the effect of artificial operations weakens layer by layer, it will eventually be masked by the natural variability of precipitation. Therefore, the relationship between the concentration of ice crystals in the seeding layer and ground precipitation cannot be directly constructed. The traditional method of dividing ground precipitation into a comparison area and an operation area does not take into account the change of operation effect with height, and also has great limitations. Therefore, considering the correlation between the layers below the seeding layer, estimating the impact of artificial operations from the seeding layer downward, it is expected to objectively evaluate the effect of cold cloud rain and snow enhancement. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide an artificial rain and snow effect evaluation method based on a layer-by-layer iteration method. The method has the characteristics of efficient and stable calculation. The evaluation of a process can be completed within 1 minute, and is suitable for large-scale business applications. In addition, the method is not only applicable to cases where there are visible cloud seeding effects, but also to cases where there are no visible cloud seeding effects. It solves the technical problems that the evaluation of artificial rain and snow effects is limited, easily covered by the natural variability of precipitation, and cannot be evaluated efficiently, stably, quantitatively, and on a large scale.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for evaluating the effect of artificial rain and snow enhancement based on a layer-by-layer iteration method, comprising:

[0007] Step 1: using a high-resolution numerical model to determine the catalyst diffusion trajectory and the affected area, the aircraft observation data covers the affected area, and the aircraft observation data includes the background ice crystal number concentration in the affected area and the ice crystal number concentration under catalyst conditions;

[0008] Step 2: Read the ice crystal number concentration of the sowing layer from the observation data, calculate the correlation between the ice crystal number concentration of the sowing layer L0 and the radar reflectivity factor of the first layer L1 located below, and perform a significance test. If the test passes, establish the relationship between the ice crystal number concentration of the sowing layer L0 and the radar reflectivity factor of the first layer L1 based on the polynomial fitting method;

[0009] Step 3: Calculate the correlation coefficient of the radar reflectivity factor between the sowing layer and each adjacent layer on the ground layer by layer, and perform a significance test. If the test is passed, establish the relationship between the radar reflectivity factors of the two adjacent layers based on the polynomial fitting method;

[0010] Step 4: Use a high-resolution numerical model to simulate the diffusion of the catalyst, read the ice crystal number concentration in the affected area from the aircraft observation data, substitute it into the relationship established in steps 2 and 3, iteratively calculate the radar reflectivity factor of each layer layer by layer, and calculate the precipitation rate based on the radar reflectivity factor of each layer;

[0011] Step 5: Substitute the observed background ice crystal number concentration into the relationship established in steps 2 and 3, iteratively calculate the reflectivity factor of each layer layer by layer, and calculate the precipitation rate through the ZI equation. By comparing the precipitation rates obtained in steps 4 and 5, the effect of artificial rain and snow enhancement can be quantitatively evaluated.

[0012] Furthermore, the process of calculating the precipitation rate in step 4 includes reading the ice crystal number concentration of the spreading layer L0 in the observation data, substituting it into the relationship obtained in step 2, and obtaining the radar reflectivity factor r1 of the first layer L1. Based on the radar reflectivity factor r1 and the mutual coefficient of step 3, the radar reflectivity factor of each layer is iteratively calculated layer by layer.

[0013] By introducing the mutual coefficient, the radar reflectivity factor can be adjusted more accurately, thereby obtaining more accurate radar reflectivity factors of each layer and further improving the accuracy of the assessment.

[0014] Furthermore, the correlation coefficient r between the ice crystal number concentration of the spreading layer L0 and the radar reflectivity factor of the first layer L1 is:

[0015] (1);

[0016] Among them, x i represents the i-th sample in the ice crystal number concentration data of the spreading layer L0, y i represents the i-th sample of the first layer L1 radar reflectivity factor, , Represents the average of n samples.

[0017] Furthermore, the t-test method was used to perform a significance test on the correlation coefficient r, and the formula is:

[0018] (2);

[0019] Here, n represents the number of samples.

[0020] Furthermore, the precipitation rate is obtained from the calculated near-ground reflectivity according to the ZI relationship and a reflectivity profile is drawn.

[0021] Through the mapping relationship between bottom surface reflectivity and precipitation rate, the precipitation rate can be calculated quickly, which is efficient and stable.

[0022] Furthermore, the process of calculating the precipitation rate in step five includes reading the average ice crystal number concentration in the area not affected by the catalyst, repeating the process of calculating the radar reflectivity factor in step four, and subtracting or visually comparing the precipitation rate without spreading the catalyst from the precipitation rate affected by the catalyst.

[0023] Through numerical subtraction or visual comparison, the effect of rain and snow enhancement can be quickly determined, which has the characteristics of short processing time, intuitiveness and high efficiency.

[0024] Furthermore, the ice crystal number concentration near the bottom of the seeding layer needs to be obtained by in-situ observation by aircraft. The aircraft detects along the diffusion direction of the catalyst, and uses 2D-C or CIP, 2D-P or PIP to observe the ice crystal number concentration, including the background ice crystal number concentration and the ice crystal number concentration in the area affected by the catalyst. In addition, FSSP or CDP and hot wire probes are used to assist in determining whether there is sufficient supercooled water to support ice crystal growth. The basic functions of each probe are as follows:

[0025] Cloud and Precipitation Particle Probe 2D-C or CIP: observes two-dimensional images of clouds and precipitation particles with a diameter of 50-2000 microns;

[0026] Precipitation Particle Probe 2D-P or PIP: observes two-dimensional images of precipitation particles with a diameter greater than 2000 microns;

[0027] Cloud Particle Spectrum Probe FSSP or CDP: observes the cloud droplet particle spectrum with a diameter of 1-50 microns;

[0028] Hot-wire liquid water content meter: observes the liquid water content of cloud droplets in clouds.

[0029] These probes can detect cloud droplets and ice crystals at the micron level. They are suitable for use within clouds and have a temperature range of -80℃-50℃. They have a wide range of applications and can be used at any detection angle. They can meet various working conditions and have excellent detection performance and adaptability.

[0030] Furthermore, the radar reflectivity from the seeding layer to each altitude layer on the ground requires radar volume scanning, or RHI scanning, or airborne radar observation data.

[0031] By adopting the above technical solution, the radar reflectivity from the seeding layer to each altitude layer on the ground requires radar volume scanning, RHI scanning, or airborne radar observation data. The radar should observe the cloud precipitation system in the seeding area and its downwind direction, and finally obtain the profile data along the flight direction of the observation aircraft. Through the radar reflectivity of the profile, the correlation of adjacent altitude layers can be calculated, which can be used to study the characteristics of the evolution of precipitation particles from the seeding layer downward layer by layer.

[0032] Furthermore, the detection radius of each radar is about 230 km, the resolution is 250 m, the beam width is 1 degree, and the radar completes a volume scan every 6 minutes, including 9 elevation angles of 0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees.

[0033] Usually, the radars used in my country's meteorological services can meet the above requirements. The detection radius of each radar is about 230km, the resolution is 250m, and the beam width is 1 degree. The radar completes a volume scan every 6 minutes, including 9 elevation angles (0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees), thereby obtaining a three-dimensional radar reflectivity product. If there are radars in other bands, such as X-band, Ka-band, and W-band radars, more detailed vertical structures of cloud precipitation can be studied.

[0034] In summary, the present invention mainly has the following beneficial effects:

[0035] 1) Based on objective observation data and strict reliability analysis, the present invention comprehensively considers the correlation between the layers below the seeding layer, estimates the impact of artificial operations from the seeding layer downward layer by layer, comprehensively considers the difference in ground precipitation rate with and without the influence of catalysts, and combines the statistical relationship between ice crystal concentration and radar reflectivity between two adjacent layers. An algorithm for evaluating the effect of artificial weather modification operations on rain and snow is proposed. The algorithm has a solid theoretical basis, high computational efficiency, strong stability, and has the prospect of large-scale application.

[0036] 2) The present invention uses significance tests multiple times, which makes the results more reliable and improves the accuracy of the data;

[0037] 3) The present invention iteratively calculates the radar reflectivity factor layer by layer and uses the ZI relationship to calculate the precipitation rate from the near-ground reflectivity, making the evaluation of the operation effect more objective;

[0038] 4) Currently, only the fifth case has been studied for the effectiveness evaluation of aircraft rain and snow enhancement operations, but most cases are more consistent with (1)-(4). This algorithm supplements the study of cases (3)-(4), enriches the theoretical system of the effectiveness evaluation of aircraft rain and snow enhancement operations, and breaks through the limitations of the effectiveness evaluation of aircraft rain and snow enhancement operations;

[0039] 5) The present invention quantitatively analyzes the effect of artificial rain and snow enhancement, making the effect of artificial rain and snow enhancement visual, and increasing the practicality and readability of the results;

[0040] 6) The evaluation algorithm according to the present invention is easy to operate, which can improve economic benefits and make artificial weather modification operations play a more positive role. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flowchart of the method is shown in FIG.

[0042] Figure 2It is a relationship diagram of parameters such as the number of catalysts, ice crystal number concentration, radar reflectivity profile, and radar reflectivity factor correlation coefficient in the affected area during a cold cloud rain and snow enhancement operation;

[0043] Figure 3 This is a relationship diagram of parameters such as ice crystal number concentration, radar reflectivity profile, and radar reflectivity factor correlation coefficient when no obvious cloud seeding signal was observed from the radar reflectivity. DETAILED DESCRIPTION

[0044] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0045] The following describes an embodiment of the present invention based on its overall structure.

[0046] Embodiment 1

[0047] Figure 2 This is a cold cloud rain and snow enhancement operation in Shaanxi Province on March 19, 2017. In this process, the radar reflectivity was observed to show the cloud seeding signal ( Figure 2 (c) in the figure shows the radar reflectivity profile changing with distance and altitude), which is an example of a successful operation. Figure 2 (a) in the figure (the change of catalyst number concentration in the affected area with distance) is the silver iodide catalyst number concentration obtained by the high-resolution numerical model. Figure 2 (b) (distribution diagram of ice crystal number concentration as a function of distance) is the corresponding ice crystal number concentration. In this area, the radar reflectivity is significantly enhanced, and a visible cloud seeding effect is obtained.

[0048] This method uses a probe attached to the aircraft for data observation. The ice crystal number concentration near the bottom of the seeding layer needs to be obtained by in-situ observation by the aircraft. The aircraft detects along the diffusion direction of the catalyst, and uses 2D-C or CIP, 2D-P or PIP to observe the ice crystal number concentration, including the background ice crystal number concentration and the ice crystal number concentration in the area affected by the catalyst. FSSP or CDP and hot wire probes are used to assist in determining whether there is sufficient supercooled water to support ice crystal growth. The basic functions of each probe are as follows:

[0049] Cloud and Precipitation Particle Probe 2D-C or CIP: observes two-dimensional images of clouds and precipitation particles with a diameter of 50-2000 microns;

[0050] Precipitation Particle Probe 2D-P or PIP: observes two-dimensional images of precipitation particles with a diameter greater than 2000 microns;

[0051] Cloud Particle Spectrum Probe FSSP or CDP: observes the cloud droplet particle spectrum with a diameter of 1-50 microns;

[0052] Hot-wire liquid water content meter: observes the liquid water content of cloud droplets in clouds.

[0053] The radar reflectivity from the seeding layer to each altitude layer on the ground requires radar volume scanning, RHI scanning, or airborne radar observation data. The detection radius of each radar is about 230 km, the resolution is 250 m, and the beam width is 1 degree. The radar completes a volume scan every 6 minutes, including 9 elevation angles (0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees and 19.5 degrees), thereby obtaining a three-dimensional radar reflectivity product. If there are radars in other bands, such as X-band, Ka-band, and W-band radars, more detailed vertical structures of cloud precipitation can be studied.

[0054] The specific calculation process is as follows: First, the spreading layer L0 ( Figure 2 (c) The red line is a display of the radar reflectivity profile as a function of distance and height) The correlation coefficient between the ice crystal number concentration and the radar reflectivity of the first layer L1 below ( Figure 2 The highest point in (d) is the correlation coefficient between the ice crystal number concentration and the radar reflectivity factor observed by the aircraft in-situ observation layer (usually below the seeding layer) and the correlation coefficient of the radar reflectivity factor of each layer), and then the correlation coefficient of the radar reflectivity of each adjacent layer is calculated ( Figure 2 (d) blue point), Figure 2 (d) in the figure is the correlation coefficient between the ice crystal number concentration and the radar reflectivity factor observed by the aircraft in-situ observation layer (usually below the seeding layer) and the correlation coefficient diagram of the radar reflectivity factor of each layer. The points of the highest layer are the correlation coefficients between the ice crystal number concentration and the radar reflectivity factor of the first layer below. The red points at various heights below the highest layer are the correlation coefficients between the radar reflectivity factor of each layer and the ice crystal number concentration of the in-situ observation layer, and the blue points are the correlation coefficients between the two adjacent layers. The correlation of all altitude layers passed the significance test, so the relationship between each adjacent layer was established, and different ice crystal number concentrations (including background ice crystal number concentration <0.1 L -1 , ice crystal number concentration affected by operation 1L -1 -10 L -1 ), iterative calculation to obtain the corresponding radar reflectivity profile, such as Figure 2 (e) in the figure (is the radar reflectivity profile corresponding to different ice crystal number concentrations obtained by iterative calculation of this method), the maximum rainfall enhancement effect is about 5dBZ-10dBZ, which is similar to the magnitude of Figure 2 The result in (c) is consistent with that in (c).

[0055] The method comprises the following specific steps:

[0056] (1) Obtain ice crystal number concentration observation data at the flight altitude layer (seeding layer L0);

[0057] (2) Obtain radar reflectivity factor profile observation data for each layer (L2, L3, etc.) between 100 meters below the flight altitude layer (the first layer L1) and the ground;

[0058] (3) The correlation coefficient between the ice crystal number concentration in the spreading layer L0 and the radar reflectivity factor of the first layer L1 is calculated as:

[0059] ;

[0060] Among them, x i represents the i-th sample in the ice crystal number concentration data of the spreading layer L0, y i represents the i-th sample of the first layer L1 radar reflectivity factor, , Represents the average of n samples.

[0061] The t-test method was used to test the significance of the correlation coefficient r, and the formula is:

[0062] ;

[0063] Compare the t value with t0.001 in the t-distribution table. If the significance level of the correlation coefficient r exceeds the required threshold, a strong correlation is satisfied, and the regional historical regression analysis method can be used. If the t test fails, it is considered that there is no obvious relationship between the ice crystal number concentration and the radar reflectivity. For example, if the supercooled water content is too low, even if the catalyst can increase the ice crystal number concentration, it cannot increase the radar reflectivity.

[0064] (4) Use polynomial fitting to establish the statistical relationship between the ice crystal number concentration in the seeding layer L0 and the radar reflectivity factor of the first layer L1;

[0065] (5) Calculate the correlation coefficient of the radar reflectivity factor between the first layer L1 and each adjacent layer on the ground (such as between L1 and L2, between L2 and L3, etc.), and perform statistical tests; if the correlation coefficient between any two layers does not pass the significance test, it means that the main microphysical characteristics below this height are less affected by those above this height; if the calculations of all height layers pass the significance test, it means that there is a certain correlation between the seeding layer and the ground precipitation;

[0066] (6) Use polynomials to construct the statistical relationship between the first layer L1 and each adjacent layer on the ground (such as between L1 and L2, between L2 and L3, etc.);

[0067] (7) Using the catalyst diffusion area obtained by high-resolution numerical simulation, read the ice crystal number concentration of the seeding layer L0 observed by the aircraft in this area, substitute it into the polynomial formula obtained by (4), and obtain the radar reflectivity factor of the first layer L1;

[0068] (8) Based on the calculated radar reflectivity factor of the first layer L1, the statistical relationship between each adjacent layer obtained by (6) is used to iterate layer by layer to calculate the radar reflectivity factor of each layer;

[0069] (9) If all altitude layers in (5) pass the significance test, the near-surface reflectivity calculated in (8) is converted into precipitation rate using the ZI relationship;

[0070] (10) Read the average ice crystal number concentration in the area not affected by the catalyst, and repeat (7)-(9) to calculate the precipitation rate without spreading the catalyst;

[0071] (11) Subtract the precipitation rates obtained from (9) and (10) to obtain the quantitative results of the impact of artificial operations on precipitation.

[0072] Embodiment 2

[0073] This is an example where no obvious cloud seeding signal was observed from the radar reflectivity. The aircraft observation data method and radar detection method were the same as above, but the number concentration of ice crystals observed by the aircraft had increased to a certain extent. The specific calculation steps were the same as before. Figure 3 The shaded area in (a) (distribution of ice crystal number concentration with distance) is the catalyst impact area estimated based on wind speed and direction. The maximum ice crystal number concentration is about 7 L -1 . Figure 3 (c) in the figure is the correlation coefficient between the ice crystal number concentration and the radar reflectivity factor observed by the aircraft in-situ observation layer (usually below the seeding layer) and the correlation coefficient diagram of the radar reflectivity factor of each layer. The point of the highest layer is the correlation coefficient between the ice crystal number concentration and the radar reflectivity factor of the first layer below. The red points at each height below the highest layer are the correlation coefficients between the radar reflectivity factor of each layer and the ice crystal number concentration of the in-situ observation layer, and the blue points are the correlation coefficients between the two adjacent layers. According to the above layer-by-layer iterative algorithm, when the ice crystal number concentration is 7 L -1 When the radar reflectivity is about 6 dBZ at an altitude of about 2.5 km ( Figure 3 The red line in (d) is the radar reflectivity profile corresponding to different ice crystal number concentrations obtained by iterative calculation using this method. Figure 3 (b) (a display of the radar reflectivity profile changing with distance and altitude) is close to the actual observation result. -1 or 1 L -1At 2.5 km, the radar reflectivity is less than 2 dBZ. At the same time, when the ice crystal number concentration is small, the radar reflectivity increases significantly with decreasing altitude. This feature can also be captured by the above algorithm. The effect of rainfall enhancement is 2-4 dBZ, and the precipitation rate increases by 0.04-0.07 mm hr -1 .

[0074] It should be emphasized that this method is different from directly comparing the radar reflectivity of the area affected by the catalyst with the radar reflectivity of the area not affected by the catalyst. Instead, it first verifies the correlation between the ice crystal number concentration and the radar reflectivity, providing a complete chain of evidence. Only when there is a significant correlation between the ice crystal number concentration and the radar reflectivity of the seeding layer, and a significant correlation between the radar reflectivity of adjacent layers, can it be proved that the catalyst can affect the ground.

[0075] Although an embodiment of the present invention has been shown and described, this specific embodiment is merely an explanation of the present invention and is not a limitation of the invention. The specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions and variations to the embodiments without creative contributions as needed without departing from the principles and purpose of the present invention. However, as long as they are within the scope of the claims of the present invention, they are protected by patent law.

Claims

1. A method for evaluating the effect of artificial rain and snow enhancement based on a layer-by-layer iteration method, characterized in that: include: Step 1: using a high-resolution numerical model to determine the catalyst diffusion trajectory and the affected area, the aircraft observation data covers the affected area, and the aircraft observation data includes the background ice crystal number concentration in the affected area and the ice crystal number concentration under the condition of using the catalyst; Step 2: Read the ice crystal number concentration of the sowing layer from the observation data, calculate the correlation between the ice crystal number concentration of the sowing layer L0 and the radar reflectivity factor of the first layer L1 located below, and perform a significance test. If the test passes, establish the relationship between the ice crystal number concentration of the sowing layer L0 and the radar reflectivity factor of the first layer L1 based on the polynomial fitting method; Step 3: Calculate the correlation coefficient of the radar reflectivity factor between the sowing layer and each adjacent layer on the ground layer by layer, and perform a significance test. If the test is passed, establish the relationship between the radar reflectivity factors of the two adjacent layers based on the polynomial fitting method; Step 4: Use a high-resolution numerical model to simulate the diffusion of the catalyst, read the ice crystal number concentration in the affected area from the aircraft observation data, substitute it into the relationship established in steps 2 and 3, iteratively calculate the radar reflectivity factor of each layer layer by layer, and calculate the precipitation rate based on the radar reflectivity factor of each layer; Step 5: Substitute the observed background ice crystal number concentration into the relationship established in steps 2 and 3, iteratively calculate the reflectivity factor of each layer layer by layer, and calculate the precipitation rate through the ZI equation. By comparing the precipitation rates obtained in steps 4 and 5, quantitatively evaluate the effect of artificial rain and snow enhancement; The process of calculating the precipitation rate in step 4 includes reading the ice crystal number concentration of the spreading layer L0 in the observation data, substituting it into the relationship obtained in step 2, and obtaining the radar reflectivity factor r1 of the first layer L1. Based on the radar reflectivity factor r1 and the correlation coefficient of step 3, the radar reflectivity factor of each layer is iteratively calculated layer by layer.

2. The method according to claim 1, characterized in that: The correlation coefficient r between the ice crystal number concentration in the spreading layer L0 and the radar reflectivity factor of the first layer L1 is: （1）； Among them, x i represents the i-th sample in the ice crystal number concentration data of the spreading layer L0, y i represents the i-th sample of the first layer L1 radar reflectivity factor, , Represents the average of n samples.

3. The method according to claim 2, characterized in that: The t-test method was used to test the significance of the correlation coefficient r, and the formula is: （2） ； Here, n represents the number of samples.

4. The method according to claim 1, characterized in that: According to the ZI relationship, the precipitation rate is obtained from the calculated near-ground reflectivity and a reflectivity profile is drawn.

5. The method according to claim 1, characterized in that: The process of calculating the precipitation rate in step 5 includes reading the average ice crystal number concentration in the area not affected by the catalyst, repeating the process of calculating the radar reflectivity factor in step 4, and subtracting or visually comparing the precipitation rate without spreading the catalyst with the precipitation rate affected by the catalyst.

6. The method according to claim 1, characterized in that: The ice crystal number concentration near the bottom of the seeding layer needs to be obtained by in-situ observation by aircraft. The aircraft detects along the diffusion direction of the catalyst, and uses 2D-C or CIP, 2D-P or PIP to observe the ice crystal number concentration, including the background ice crystal number concentration and the ice crystal number concentration in the area affected by the catalyst. FSSP or CDP and hot wire probes are used to assist in determining whether there is sufficient supercooled water to support ice crystal growth. The basic functions of each probe are as follows: Cloud and Precipitation Particle Probe 2D-C or CIP: observes two-dimensional images of clouds and precipitation particles with a diameter of 50-2000 microns; Precipitation Particle Probe 2D-P or PIP: observes two-dimensional images of precipitation particles with a diameter greater than 2000 microns; Cloud Particle Spectrum Probe FSSP or CDP: observes the cloud droplet particle spectrum with a diameter of 1-50 microns; Hot-wire liquid water content meter: observes the liquid water content of cloud droplets in clouds.

7. The method according to claim 4, characterized in that: The radar reflectivity from the seeding layer to each altitude layer on the ground requires radar volume scanning, RHI scanning, or airborne radar observation data.

8. The method according to claim 5, characterized in that: Each radar has a detection radius of 230 km, a resolution of 250 m, and a beam width of 1 degree. The radar completes a volume scan every 6 minutes, including 9 elevation angles of 0.5 degrees, 1.5 degrees, 2.4 degrees, 3.4 degrees, 4.3 degrees, 6.0 degrees, 9.9 degrees, 14.6 degrees, and 19.5 degrees.

9. The method according to claim 5, characterized in that: The radar band is X-band, Ka-band or W-band.

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