Quantitative analysis method for artificial precipitation enhancement effect based on refined calculation of influence area

Abstract

The application discloses a quantitative analysis method for artificial rain enhancement effect based on influence area fine calculation, which comprises the following steps: obtaining an influence area according to artificial ice crystal concentration or silver iodide concentration in a catalyst diffusion area of artificial seeding; extracting and qualitatively analyzing radar products in the influence area; performing space-time matching on the influence area and the radar products, and calculating the mean value and distribution of radar parameters in the influence area; performing spatial distribution and time sequence change analysis on the radar parameters, and calculating the parameter change and variability of the radar products in the influence area; and quantitatively analyzing the parameter change and the variability, and giving the rain enhancement operation effect. The method can not only improve the precision of quantitative analysis of artificial rain enhancement effect based on influence area fine calculation, but also has good interpretability, and can be directly applied to a quantitative analysis system for artificial rain enhancement effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of atmospheric coupling, in particular to a quantitative analysis method for artificial rain enhancement effect based on refined calculation of an influence area. BACKGROUND

[0002] Statistical test, physical test and numerical model test are the three main technical means for evaluating the effect of artificial weather modification. Among them: numerical model test refers to adding artificial cloud seeding parameterization scheme in the numerical model describing cloud precipitation process, comparing the macro and micro parameters of cloud and the ground precipitation under the conditions of quantitative prediction and non-catalysis with the actual observation results, and judging the operation effect, but the current numerical simulation still has uncertainty, and the simulation results cannot completely represent the complex cloud precipitation physical information.

[0003] Physical test is to obtain the evidence of various physical changes that should occur after artificial catalysis through series observation of cloud precipitation process physical parameters. Among them, radar observation is widely used in physical test of artificial weather modification. Compared with statistical test and model test, physical test based on radar and other remote sensing data is difficult to realize quantitative evaluation of artificial cloud seeding effect. The previous researches are more from the qualitative analysis of the changes of cloud precipitation macro and micro parameters, and it has been a major problem in physical test that which of the changed physical quantities are caused by natural changes and which are caused by artificial cloud seeding. Therefore, accurate and refined selection of operation influence area is an important prerequisite for quantitative and accurate physical test evaluation.

[0004] At present, the quantitative evaluation of artificial rain enhancement operation effect at home and abroad generally adopts single statistical test, numerical test or physical test method, and lacks quantitative physical effect test means of objective and quantitative and refined operation influence area in combination with numerical model simulation and radar network observation.

[0005] Therefore, the quantitative analysis of radar products after rain enhancement operation based on refined calculation of numerical model influence area provides a new feasible method for quantitative analysis of physical effect of rain enhancement operation, fully utilizes the advantages of numerical model simulation and remote sensing observation, and solves the existing problems of uncertainty of cloud physical process in numerical simulation and difficulty in judging the influence area in radar observation. The refined operation influence area is obtained by using numerical model simulation, the radar parameter products of artificial rain enhancement operation influence area are calculated by combining the physical parameters measured by radar, and the physical change characteristics of target operation cloud after catalytic operation are analyzed. The refined quantitative physical test provides support, and has reference value for evaluation of artificial rain enhancement effect. SUMMARY

[0006] The purpose of the present application is to provide a quantitative analysis method for artificial rain enhancement effect based on refined calculation of an influence area.

[0007] To achieve the above object, the present application is implemented according to the following technical solutions:

[0008] The present application comprises the following steps:

[0009] In the artificial seeding catalyst diffusion area, the influence area is obtained according to the artificial ice crystal concentration or silver iodide concentration;

[0010] The radar products in the influence area are extracted and qualitatively analyzed; the radar products refer to radar observation data and derived parameters for quantitatively analyzing the effect of artificial precipitation snow operation;

[0011] The influence area and the radar products are spatio-temporally matched, and the mean value and distribution of the radar parameters in the influence area are calculated; the radar parameters include reflectivity factor, differential reflectivity factor and differential propagation phase shift rate;

[0012] The spatial distribution and time series variation of the radar parameters are analyzed, and the parameter variation and variability of the radar products in the influence area are calculated;

[0013] The parameter variation and the variability are quantitatively analyzed, and the effect of precipitation operation is given.

[0014] Further, the method for obtaining the influence area comprises:

[0015] The artificial seeding catalyst data is format-processed; based on a numerical model or a diffusion transmission model, the artificial seeding catalyst information and the meteorological reanalysis data field are input into the meteorological bureau cloud precipitation explicit prediction system to simulate and calculate the artificial seeding catalyst concentration and the spatio-temporal distribution of artificial ice crystals; the area with artificial ice crystal concentration ≥ 0.1 / L or silver iodide concentration ≥ 0.1 / L is taken as the influence area.

[0016] Further, the method for extracting and qualitatively analyzing the radar products in the influence area comprises:

[0017] According to the spatio-temporal information of the three-dimensional influence area, the radar mosaic product data within 3 minutes in time interval and within 500 meters in spatial distance from the influence area is extracted;

[0018] Through visual interpretation, the spatio-temporal evolution characteristics of the radar echo in the influence area are qualitatively analyzed.

[0019] Further, the method for spatio-temporally matching the influence area and the radar products comprises:

[0020] According to the latitude and longitude range, the influence area and the radar product monitoring area are spatially divided into different levels of grid tiles to obtain a set of spatial encodings, and the time is divided according to the time granularity to obtain a subset of time encodings; wherein the higher the level, the finer the grid;

[0021] A hierarchy is established, when the level is 0, it represents the coarsest level, and the spatial granularity becomes finer as the level increases. At the coarsest level, a preliminary wide-range matching is performed on the impact area and the radar product monitoring area; at the fine level, accurate matching is performed.

[0022] For the cth level, the time coding intersection of the impact area and the radar product is calculated:

[0023]

[0024] The time coding set of the ith impact area of the cth level is The time coding set of the zth radar product of the cth level is The time coding intersection of the impact area and the radar product of the cth level is

[0025] If the time coding intersection is empty, the time dimension has no intersection at this level, and the matching point number is 0; if the time coding intersection is not empty, the spatial similarity is calculated:

[0026]

[0027] The spatial level weight of the cth level in the a-level tile coding is The position point number of the impact area and the radar product with the same time coding w in the a-level tile coding is E a (w), the sub-region point number of the impact area on the time coding w is Z(w), the sub-region point number of the radar product on the time coding w is R(w), and the spatial similarity of the impact area and the radar product at the cth level is H c .

[0028] The time level weight is introduced to calculate the matching degree of the impact area and the radar product at different levels, and the overall similarity is calculated:

[0029]

[0030] The matching degree of the impact area and the radar product at the cth level is B c , the time level weight of the cth level is The number of levels is M1;

[0031] The spatio-temporal matching result with a similarity higher than the similarity threshold is output.

[0032] Further, the method for calculating the mean value and distribution of the radar parameters in the impact area comprises:

[0033] In the influence area after spatiotemporal matching, the zero-layer height is obtained according to the sounding information on the day, and the average and intensity distribution of reflectivity factor above the zero layer in the influence area are calculated; the zero-layer height is obtained according to the sounding information on the day, and the average and intensity distribution of reflectivity factor below the zero layer in the influence area are calculated; the average and intensity distribution of radar parameters in the three-dimensional catalytic influence area are calculated; the average and intensity distribution of radar parameters of the seeding height layer are calculated; the average and intensity distribution of the combined reflectivity factor in the two-dimensional influence area are calculated; the average and intensity distribution of the vertically integrated liquid water content in the two-dimensional influence area are calculated; the average and intensity distribution of the hourly rain intensity in the two-dimensional influence area are calculated; wherein, the "average" is the average value of the radar parameters in the operation influence area; and the "distribution" is the spatial volume and the spatial volume ratio occupied by the radar parameters in different intensity intervals in the operation influence area.

[0034] Further, the method for analyzing the spatial distribution and time sequence change of the radar parameters comprises:

[0035] A multi-dimensional feature vector is constructed according to the spatial coding layer for the plurality of radar parameters, the feature distance of different spatial coding units is calculated, and the radar parameters are spatially labeled according to the feature distance;

[0036] The time sequence data, liquid water content and catalytic operation time of the radar parameters are obtained, the time sequence data is scanned by using cumulative and control charts, and a significant mutation point is identified, and the expression is:

[0037] U(t) = max(0, U(t-1) + (V(t) - θ o ))

[0038] wherein the radar parameter at the t time is V(t), the average before the catalyst is θ o , the radar parameter cumulative statistical quantity at the t time is U(t), and the radar parameter cumulative statistical quantity at the t-1 time is U(t-1);

[0039] Three types of time-varying stages are divided according to the catalysis, and the three types of time-varying stages are the natural development stage of the cloud system self-evolution before the catalysis, the catalytic response period dominated by the catalyst, and the precipitation attenuation period of the natural precipitation dissipation;

[0040] The three types of time-varying stages are analyzed by dimension analysis, and the stage characteristics are obtained. The natural development stage characteristics are cloud system expansion speed and parameter baseline trend. The stage characteristics of the catalytic response period are parameter jump amplitude and spatial response synchronization. The stage characteristics of the precipitation attenuation period are attenuation speed and secondary development possibility. The cloud system expansion speed is obtained by the reflectivity area growth rate per unit time. The parameter baseline trend is obtained by the linear fitting slope of the liquid water content. The parameter jump amplitude is obtained by the area jump of the region with reflectivity greater than 30 dBZ. The spatial response synchronization is obtained by the correlation of the parameter change outside the influence area. The secondary development possibility is the probability of new echo core appearing. The attenuation speed is obtained by calculating the rain intensity attenuation index.

[0041] The catalytic gain factor is calculated by the parameter change of the natural development period and the catalytic response period.

[0042]

[0043] The xth catalytic gain factor is ρ x The xth parameter change of the natural development period is The xth parameter change of the catalytic response period is The minimum control value is ε.

[0044] In the natural development period, linear regression is used to predict the parameter trend, and the deviation between the predicted value and the actual value is analyzed. In the catalytic response period, a spatial clustering algorithm is used to identify the response core of the catalyst. In the precipitation attenuation period, an exponential fitting is used to evaluate the precipitation duration.

[0045] Further, the method for calculating the parameter change and variability of the radar product in the influence area includes:

[0046] The mean and intensity distribution of the reflectivity factor below the zero layer in the influence area are calculated, and the variable and variability of the mean and intensity distribution with time are calculated.

[0047] The mean and intensity distribution of the reflectivity factor above the zero layer in the influence area are calculated, and the variable and variability of the mean and intensity distribution with time are calculated.

[0048] The mean and intensity distribution of the radar parameter in the three-dimensional catalytic influence area are calculated, and the variable and variability of the mean and intensity distribution with time are calculated. The mean and intensity distribution of the radar parameter in the seeding height layer are calculated, and the variable and variability of the mean and intensity distribution with time are calculated. The mean and intensity distribution of the combined reflectivity factor in the two-dimensional influence area are calculated, and the variable and variability of the mean and intensity distribution with time are calculated. The mean and intensity distribution of the hourly rain intensity in the two-dimensional influence area are calculated, and the variable and variability of the mean and intensity distribution with time are calculated.

[0049] Further, the method for calculating the quantitative analysis results and summarizing the effect of rain enhancement operation includes:

[0050] According to the change information of the combined reflectivity factor in the influence area, the development trend of the overall cloud precipitation process after the catalytic operation is analyzed; according to the change information of the radar physical parameters at different height layers in the influence area, the cloud water conversion and the precipitation particle growth trend after the catalytic operation are analyzed; according to the change information of the vertically integrated liquid water in the influence area, the liquid water content change characteristics in the influence area after the catalytic operation are analyzed; according to the rain intensity change information in the influence area, the precipitation change characteristics in the influence area after the catalytic operation are analyzed; based on the analysis of the overall cloud precipitation process development trend, the cloud water conversion and the precipitation particle growth trend, the liquid water content change characteristics in the influence area and the precipitation change characteristics in the influence area, the quantitative physical test results of the operation effect are obtained.

[0051] The beneficial effects of the present application are:

[0052] The present application is based on the quantitative analysis method of artificial rain effect based on fine calculation of influence area, compared with the prior art, the present application has the following technical effects:

[0053] The present application can improve the accuracy of quantitative analysis of artificial rain effect, thereby improving the accuracy of quantitative analysis of artificial rain effect, optimizing the quantitative analysis of artificial rain effect, which can greatly save resources and improve work efficiency, can realize quantitative analysis of artificial rain effect, real-time quantitative analysis of artificial rain effect based on fine calculation of influence area, has important significance for quantitative analysis of artificial rain effect, can adapt to different standards of quantitative analysis of artificial rain effect, different quantitative analysis of artificial rain effect, has certain universality. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The step flow chart of the quantitative analysis method of artificial rain effect based on fine calculation of influence area of the present application is shown in the figure;

[0055] Figure 2 The three-dimensional influence area of the spread catalyst in the embodiment of the present application is shown in the figure;

[0056] Figure 3 The spatial distribution evolution process of the radar mosaic product in the influence area in the embodiment of the present application is shown in the figure;

[0057] Figure 4 The time sequence change graph of the radar product in the influence area in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0058] The present application will be further described below through specific embodiments, the illustrative embodiments of the present application and the description are used to explain the present application, but not as a limitation of the present application.

[0059] The present invention provides a quantitative analysis method for the effects of artificial rain enhancement based on refined calculations of the affected area, comprising the following steps:

[0060] like Figure 1 As shown, this embodiment includes the following steps:

[0061] In the artificially acquired diffusion area of ​​the affected zone, the affected area is determined based on the concentration of artificial ice crystals or silver iodide.

[0062] In actual assessment, the overall technical approach mainly includes four parts: data input, affected area calculation, radar parameter extraction, and quantitative analysis of radar products in the affected area.

[0063] For a specific aircraft-based rain enhancement operation, the attached figure shows an operation conducted on November 16, 2022, during winter drought relief efforts in a certain province, as a case study. Aircraft operation information and reanalysis data were input, and model simulations were used to obtain... Figure 2 The spatial distribution of catalyst concentration at different times and the two-dimensional distribution of artificial ice crystal concentration are shown. Based on the spatial distribution of catalyst or artificial ice crystal concentration, the range of the operation impact zone is determined by the concentration of artificial ice crystal ≥ 0.1 crystals / L or the concentration of silver iodide particles ≥ 0.1 crystals / L.

[0064] The radar products within the affected area are extracted and qualitatively analyzed; the radar products refer to radar observation data and derived parameters used for quantitative analysis of the effects of artificial rain and snow enhancement operations.

[0065] In actual assessment, such as Figure 3 The spatial distribution evolution characteristics of radar parameters after the operation were analyzed. The solid black line represents the aircraft seeding operation trajectory, the black dashed box represents the operation-affected area, and the non-operation-affected area was whitened and masked. The three rows from top to bottom are the combined reflectivity (CR), vertical integrated liquid water content (VIL), and hourly rainfall intensity estimation product (QPR). The three columns from left to right are 1 hour after the operation (14:00), 2 hours after the operation (15:00), and 3 hours after the operation (16:00). It can be seen that the echo shifted eastward as a whole, and the echo intensity generally tended to increase during the eastward movement of the target cloud. It is preliminarily judged that the operation promoted cloud water conversion and produced a rain enhancement effect.

[0066] Spatiotemporal matching is performed on the affected area and the radar product to calculate the mean and distribution of radar parameters within the affected area; the radar parameters include reflectivity factor, differential reflectivity factor, and differential propagation phase shift rate.

[0067] Spatial distribution and temporal variation analysis of the radar parameters are performed to calculate the parameter changes and variability of the radar products within the affected area;

[0068] In actual assessment,Figure 4 The temporal variation information of the main two-dimensional radar parameters characterizing the physical properties of cloud precipitation was analyzed, including the combined two-dimensional radar parameters: reflectivity factor (CR), vertical integrated liquid water content (VIL), and hourly rainfall intensity (QPR). It can be seen that at 12:05, when the operation ended, the echo intensity in the affected area was relatively low, approximately 27 dBZ, with a rainfall intensity of 1.6 mm / h. Two hours later, at 14:00, the average echo intensity in the affected area increased from 27 dBZ to 38 dBZ. The proportion of weak precipitation echoes (20-30 dBZ) decreased from 50% to about 10%, while the proportion of echoes above 40 dBZ increased from almost non-existent to about 40%. The vertical integrated liquid water content increased from 1 kg / m³ at the end of the operation. 2 The horizontal increase reached 2.5 kg / m. 2 1kg / m 2 The following percentage decreased from 70% to 10%, 3kg / m 2 The above percentage increased from none to about 30%; based on the rainfall intensity, at the beginning of the operation, the average rainfall intensity in the affected area was about 2 mm / h, and 2 hours after the operation ended, the average rainfall intensity in the affected area reached a maximum of 13 mm / h. From the perspective of the rainfall intensity percentage in the affected area, precipitation of 10-20 mm / h and 20-50 mm / h began to appear one hour and two hours after the operation ended, respectively, indicating that the rainfall intensity in the affected area gradually increased;

[0069] Natural growth period: 14:00-14:30, average CR value 25dBZ, average VIL value 1.0kg / m³ 2 Catalytic response period: 14:30-15:30, average CR value increased to 38 dBZ, VIL increased to 2.5 kg / m³. 2 During the precipitation decay period from 15:30 to 16:30, the CR dropped to 30 dBZ, and the VIL decreased to 1.8 kg / m³. 2 During the natural growth period, the CR change was +2 dBZ, and during the catalytic response period, it was +11 dBZ, with a CR gain factor of 4.29.

[0070] Quantitative analysis of the changes in the parameters and the variability is used to provide the effect of rain enhancement operations;

[0071] In the actual assessment, after this rain enhancement operation, compared with the non-operational affected area, the reflectivity factor, vertical integrated liquid water content, and radar-retrieved rainfall intensity all showed a significant increase. Two hours after the operation ended, the overall combined reflectivity factor increased from 27 dBZ to a maximum of 38 dBZ, with the proportion of echoes above 40 dBZ increasing from non-existent to about 40%. The overall precipitation intensity increased from 2 mm / h to a maximum of 13 mm / h. One hour and two hours after the operation ended, precipitation of 10-20 mm / h and 20-50 mm / h began to appear, respectively, indicating that the rainfall intensity in the affected area gradually increased.

[0072] In the embodiment, the method for obtaining the influence area comprises:

[0073] The artificial seeding catalyst data is formatted, the artificial seeding catalyst information and the meteorological reanalysis data field are input into the meteorological bureau cloud precipitation explicit prediction system based on a numerical model or a diffusion transmission model, the artificial seeding catalyst concentration and the artificial ice crystal space-time distribution are simulated and calculated, and the area with the artificial ice crystal concentration greater than or equal to 0.1 / L or the silver iodide concentration greater than or equal to 0.1 / L is taken as the influence area.

[0074] In the embodiment, the method for extracting and qualitatively analyzing the radar product in the influence area comprises:

[0075] According to the space-time information of the three-dimensional influence area, the radar mosaic product data within a time interval of 3 min and a spatial distance of 500 m from the influence area is extracted;

[0076] The space-time evolution characteristics of the radar echo in the influence area are qualitatively analyzed through visual interpretation.

[0077] In the embodiment, the method for matching the influence area and the radar product in space-time comprises:

[0078] According to the latitude and longitude range, the influence area and the radar product monitoring area are spatially divided into different levels of grid tiles to obtain a spatial coding set, and the time is divided according to the time granularity to obtain a time coding subset; wherein the higher the level, the finer the grid;

[0079] A hierarchical structure is established, when the level is 0, it represents the coarsest granularity level, and as the level increases, the space-time granularity becomes finer, at the coarsest granularity level, the influence area and the radar product monitoring area are preliminarily matched in a wide range, and at the fine granularity level, the accurate matching is performed;

[0080] For the cth level, the time coding intersection of the influence area and the radar product is calculated:

[0081]

[0082] The time coding set of the ith influence area of the cth level is The time coding set of the zth radar product of the cth level is The time coding intersection of the influence area and the radar product of the cth level is

[0083] If the time coding intersection is empty, the time dimension has no intersection at this level, and the number of matching points is 0; if the time coding intersection is not empty, the spatial similarity is calculated:

[0084]

[0085] Wherein the spatial level weight of the cth layer in the a-level tile encoding is The number of location points of the impact area and the radar product with the same time encoding w in the a-level tile encoding is E a (w), the number of sub-region points of the impact area at the time encoding w is Z(w), the number of sub-region points of the radar product at the time encoding w is R(w), and the spatial similarity of the impact area and the radar product at the cth layer is H c ;

[0086] The time level weight is introduced to calculate the matching degree of the impact area and the radar product at different levels, and the overall similarity is calculated:

[0087]

[0088] Wherein the matching degree of the impact area and the radar product at the cth layer is B c , the cth layer time level weight is The number of levels is M1;

[0089] The spatio-temporal matching result with a similarity higher than the similarity threshold is output.

[0090] In this embodiment, the method for calculating the mean value and distribution of the radar parameters in the impact area comprises:

[0091] In the impact area after spatio-temporal matching, the zero-degree layer height is obtained according to the sounding information of the day, the mean value and intensity distribution of the reflectivity factor above the zero-degree layer in the impact area are calculated, the mean value and intensity distribution of the reflectivity factor below the zero-degree layer in the impact area are calculated according to the sounding information of the day, the mean value and intensity distribution of the radar parameters in the three-dimensional catalytic impact area are calculated, the mean value and intensity distribution of the radar parameters at the seeding height layer are calculated, the mean value and intensity distribution of the combined reflectivity factor in the two-dimensional impact area are calculated, the mean value and intensity distribution of the vertically integrated liquid water content in the two-dimensional impact area are calculated, and the mean value and intensity distribution of the hourly rain intensity in the two-dimensional impact area are calculated. The "mean value" is the average value of the radar parameters in the operation impact area; and the "distribution" is the spatial volume and spatial volume ratio occupied by the radar parameters in different intensity intervals in the operation impact area.

[0092] In actual evaluation, the different intensity intervals of the parameters are, for example, 10-20 dBZ, 20-30 dBZ, and 30-40 dBZ.

[0093] In this embodiment, the method for performing spatial distribution and time series change analysis on the radar parameters comprises:

[0094] A multi-dimensional feature vector is constructed according to the spatial encoding layer for a plurality of radar parameters, the feature distance of different spatial encoding units is calculated, and the radar parameters are spatially labeled according to the feature distance;

[0095] The time series data of radar parameters, liquid water content and catalytic operation time are obtained, the cumulative and control chart scanning time series data is used to identify significant mutation points, and the expression is:

[0096] U(t) = max(0, U(t-1) + (V(t) - θ o ))

[0097] Wherein the radar parameter at the t time is V(t), the catalyst front average is θ o , the radar parameter cumulative statistical quantity at the t time is U(t), and the radar parameter cumulative statistical quantity at the t-1 time is U(t-1);

[0098] According to the catalysis, three kinds of time-varying stages are divided, and the three kinds of time-varying stages are the natural development stage of cloud system self-evolution before catalysis, the catalytic response period dominated by catalyst effect, and the precipitation attenuation period of natural precipitation dissipation;

[0099] The exclusive dimension analysis is performed on the three kinds of time-varying stages to obtain the stage characteristics. The natural development stage characteristics are cloud system expansion speed and parameter baseline trend, the stage characteristics of catalytic response period are parameter jump amplitude and spatial response synchronization, and the stage characteristics of precipitation attenuation period are attenuation speed and secondary development possibility. Wherein the cloud system expansion speed is obtained by the reflectivity area growth rate per unit time, the parameter baseline trend is obtained by the linear fitting slope of liquid water content, the parameter jump amplitude is obtained by the area jump of reflectivity greater than 30 dBZ region, the spatial response synchronization is obtained by the correlation of parameter change outside the influence area, the secondary development possibility is the probability of new echo core appearing, and the attenuation speed is obtained by calculating the rain intensity attenuation index;

[0100] The catalytic gain factor is calculated through the parameter change of natural development period and catalytic response period:

[0101]

[0102] Wherein the xth catalytic gain factor is ρ x , the xth parameter change of natural development period is The xth parameter change of catalytic response period is The minimum control value is ε;

[0103] In the natural development period, the linear regression is used to predict the parameter trend, and the deviation between the predicted value and the actual value is analyzed; in the catalytic response period, the spatial clustering algorithm is used to identify the response core of the catalyst; in the precipitation attenuation period, the exponential fitting is used to evaluate the precipitation duration.

[0104] In this embodiment, the method for calculating the parameter change and variability of the radar product in the influence area comprises:

[0105] The mean value and intensity distribution of the radar parameter in the three-dimensional catalytic influence area are calculated, and the mean value and intensity distribution of the radar parameter in the seeding height layer are calculated, and the mean value and intensity distribution of the combined reflectivity factor in the two-dimensional influence area are calculated, and the mean value and intensity distribution of the vertically integrated liquid water content in the two-dimensional influence area are calculated, and the mean value and intensity distribution of the hourly rain intensity in the two-dimensional influence area are calculated.

[0106] The mean value and intensity distribution of the radar parameter in the three-dimensional catalytic influence area are calculated, and the mean value and intensity distribution of the radar parameter in the seeding height layer are calculated, and the mean value and intensity distribution of the combined reflectivity factor in the two-dimensional influence area are calculated, and the mean value and intensity distribution of the vertically integrated liquid water content in the two-dimensional influence area are calculated, and the mean value and intensity distribution of the hourly rain intensity in the two-dimensional influence area are calculated.

[0107] The mean value and intensity distribution of the radar parameter in the three-dimensional catalytic influence area are calculated, and the mean value and intensity distribution of the radar parameter in the seeding height layer are calculated, and the mean value and intensity distribution of the combined reflectivity factor in the two-dimensional influence area are calculated, and the mean value and intensity distribution of the vertically integrated liquid water content in the two-dimensional influence area are calculated, and the mean value and intensity distribution of the hourly rain intensity in the two-dimensional influence area are calculated.

[0108] In the embodiment, the method for summarizing the effect of the rain enhancement operation comprises:

[0109] According to the change information of the combined reflectivity factor in the influence area, the development trend of the overall cloud precipitation process after the catalytic operation is analyzed, according to the change information of the radar physical parameter in different height layers in the influence area, the cloud water conversion and precipitation particle growth trend after the catalytic operation are analyzed, according to the change information of the vertically integrated liquid water in the influence area, the liquid water content change characteristics in the influence area after the catalytic operation are analyzed, according to the change information of the rain intensity in the influence area, the precipitation change characteristics in the influence area after the catalytic operation are analyzed, and based on the analysis of the overall cloud precipitation process development trend, the cloud water conversion and precipitation particle growth trend, the liquid water content change characteristics in the influence area and the precipitation change characteristics in the influence area, the quantitative physical test result of the operation effect is obtained.

[0110] The above only describes the preferred embodiments of the present application and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A quantitative analysis method for artificial rain enhancement effect based on influence area refinement calculation, characterized in that, The method comprises the following steps: An influence area is obtained according to the concentration of artificial ice crystals or the concentration of silver iodide in the artificial catalyst diffusion area; Radar products in the influence area are extracted and qualitatively analyzed; the radar products refer to radar observation data and derived parameters used for quantitative analysis of the effect of artificial precipitation snow operation; The influence area and the radar products are spatiotemporally matched, and the mean value and distribution of the radar parameters in the influence area are calculated; the radar parameters include reflectivity factor, differential reflectivity factor and differential propagation phase shift rate; The spatial distribution and time sequence variation of the radar parameters are analyzed, and the parameter variation and rate of change of the radar products in the influence area are calculated; The parameter variation and the rate of change are quantitatively analyzed, and the effect of the artificial precipitation operation is given.

2. The method according to claim 1, wherein, The method for obtaining the influence area comprises: The artificial catalyst data is format-processed; based on a numerical model or a diffusion transmission model, the artificial catalyst information and the meteorological reanalysis data field are input into a meteorological cloud precipitation explicit prediction system to simulate and calculate the concentration of artificial catalyst and the spatiotemporal distribution of artificial ice crystals; the area with the artificial ice crystal concentration greater than or equal to 0.1 / L or the silver iodide concentration greater than or equal to 0.1 / L is taken as the influence area.

3. The method according to claim 1, wherein the method is characterized by, The method for extracting and qualitatively analyzing the radar products in the influence area comprises: According to the spatiotemporal information of the three-dimensional influence area, radar mosaic product data within a time interval of 3 minutes and a spatial distance of 500 meters from the influence area are extracted; The spatiotemporal evolution characteristics of the radar echo in the influence area are qualitatively analyzed by visual interpretation.

4. The method according to claim 1, wherein the method is characterized by, The method for spatiotemporally matching the influence area and the radar products comprises: According to the latitude and longitude range, the influence area and the radar product monitoring area are spatially divided into different levels of grid tiles to obtain a spatial coding set, and the time is divided according to the time granularity to obtain a time coding subset, wherein the higher the level, the finer the grid; A hierarchical structure is established, when the level is 0, it represents the coarsest granularity level, and as the level increases, the spatiotemporal granularity becomes finer, at the coarsest granularity level, the influence area and the radar product monitoring area are preliminarily matched in a large range, and at the fine granularity level, accurate matching is performed; For the cth level, the time coding intersection of the influence area and the radar products is calculated: wherein the time code set of the zth radar product of the cth layer is wherein the time code set of the zth radar product of the cth layer is wherein the time code set of the zth radar product of the cth layer is If the time coding intersection is empty, there is no intersection in the time dimension of this level, and the number of matching points is 0; if the time coding intersection is not empty, the spatial similarity is calculated: wherein the spatial level weight of the cth level in the a-tile encoding is The number of location points of the impact zone and the radar product having the same time encoding w in the a-tile encoding is E a (w), the number of sub-region points of the impact zone on the time encoding w is Z(w), the number of sub-region points of the product radar on the time encoding w is R(w), and the spatial similarity of the impact zone and the radar product on the cth level is H c ; The time level weight is introduced to calculate the matching degree of the influence area and the radar products at different levels, and the overall similarity is calculated: wherein the matching degree of the impact area and the radar product at the cth layer is B c , the time layer weight of the cth layer is The number of layers is M1; The spatiotemporal matching results with a similarity higher than a similarity threshold are output.

5. The method according to claim 1, wherein the method is characterized by, The method for calculating the mean value and distribution of the radar parameters in the influence area comprises: In the influence area after spatiotemporal matching, the mean and intensity distribution of reflectivity factor above the zero-degree layer in the influence area are calculated according to the zero-degree layer height obtained from the sounding information on the day; the mean and intensity distribution of reflectivity factor below the zero-degree layer in the influence area are calculated according to the zero-degree layer height obtained from the sounding information on the day; the mean and intensity distribution of radar parameters in the three-dimensional catalytic influence area are calculated; the mean and intensity distribution of radar parameters at the seeding height layer are calculated; the mean and intensity distribution of combined reflectivity factor in the two-dimensional influence area are calculated; the mean and intensity distribution of vertically integrated liquid water content in the two-dimensional influence area are calculated; and the mean and intensity distribution of hourly rain intensity in the two-dimensional influence area are calculated.

6. The method according to claim 1, wherein the method is characterized by, The method for analyzing the spatial distribution and time sequence change of the radar parameters comprises: A multi-dimensional feature vector is constructed for the radar parameters according to spatial coding layers, the feature distance of different spatial coding units is calculated, and the radar parameters are spatially labeled according to the feature distance; The time sequence data, liquid water content and catalytic operation time of the radar parameters are obtained, the time sequence data is scanned by using cumulative and control charts, and a significant mutation point is identified, with the expression being: U(t) = max(0, U(t-1) + (V(t) - θ o )) wherein the radar parameter at the tth moment is V(t), the pre-catalyst average value is θ o , the radar parameter cumulative statistics at the tth moment is U(t), and the radar parameter cumulative statistics at the t-1th moment is U(t-1); Three types of time-varying stages are divided according to the catalysis, the three types of time-varying stages being a natural development stage of cloud system autonomous evolution before catalysis, a catalytic response period dominated by catalyst action, and a precipitation attenuation period of natural precipitation dissipation; The stage characteristics are obtained by performing exclusive dimension analysis on the three types of time-varying stages, the stage characteristics of the natural development stage being cloud system expansion speed and parameter baseline trend, the stage characteristics of the catalytic response period being parameter jump amplitude and spatial response synchronism, and the stage characteristics of the precipitation attenuation period being attenuation speed and secondary development possibility, wherein the cloud system expansion speed is obtained by the reflectivity area growth rate per unit time, the parameter baseline trend is obtained by the linear fitting slope of liquid water content, the parameter jump amplitude is obtained by the area jump of the region with reflectivity greater than 30 dBZ, the spatial response synchronism is obtained by the correlation of parameter change outside the influence area, and the secondary development possibility is the probability of new echo core appearing, and the attenuation speed is obtained by calculating the rain intensity attenuation index; The catalytic gain factor is calculated according to the parameter change of the natural development period and the catalytic response period: wherein the xth catalytic gain factor is p x the xth parameter change during the natural development period is the xth parameter change during the catalytic response period is the minimum control value is ε; In the natural development period, the linear regression is used to predict the parameter trend, and the deviation between the predicted value and the actual value is analyzed; in the catalytic response period, the spatial clustering algorithm is used to identify the response core of the catalyst; and in the precipitation attenuation period, the exponential fitting is used to evaluate the precipitation duration.

7. The method according to claim 1, wherein the method is characterized by, The method for calculating the parameter change and variability of the radar products in the influence area comprises: The mean and intensity distribution of reflectivity factor below the zero-degree layer in the influence area are calculated; The mean and intensity distribution of reflectivity factor above the zero-degree layer in the influence area are calculated; The mean and intensity distribution of radar parameters in the three-dimensional catalytic influence area are calculated; the mean and intensity distribution of radar parameters at the seeding height layer are calculated; the mean and intensity distribution of combined reflectivity factor in the two-dimensional influence area are calculated; the mean and intensity distribution of vertically integrated liquid water content in the two-dimensional influence area are calculated; and the mean and intensity distribution of hourly rain intensity in the two-dimensional influence area are calculated.

8. The method according to claim 1, wherein the method is characterized by, The method for obtaining the effect of the rain enhancement operation comprises: According to the change information of the combined reflectivity factor in the influence area, the development trend of the overall cloud precipitation process after the catalytic operation is analyzed; according to the change information of the radar physical parameters at different height layers in the influence area, the cloud water conversion and precipitation particle growth trend after the catalytic operation is analyzed; according to the change information of the vertically integrated liquid water in the influence area, the liquid water content change characteristics in the influence area after the catalytic operation are analyzed; according to the rain intensity change information in the influence area, the precipitation change characteristics in the influence area after the catalytic operation are analyzed; based on the analysis of the overall cloud precipitation process development trend, the cloud water conversion and precipitation particle growth trend, the liquid water content change characteristics in the influence area and the precipitation change characteristics in the influence area, the quantitative physical inspection result of the operation effect is obtained.

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