Quantitative evaluation method of human shadow operation effect based on simulation and actual precipitation

Abstract

The present invention discloses a quantitative evaluation method for the effect of human shadow operation based on simulation and actual precipitation, including collecting relevant data and preprocessing the relevant data; the relevant data includes reanalysis field or analysis field data, gridded actual precipitation field information, and artificial weather modification operation information; starting numerical simulation and calculating the rainfall increase rate and the scope of the operation impact area, and evaluating the accuracy of precipitation in the impact area of ​​the model simulation; calculating the average rainfall increase rate on the hit grid points of the precipitation simulation, and compensating the average rainfall increase rate on the missed grid points of the precipitation simulation; calculating the rainfall increase amount based on actual precipitation, optimizing the rainfall increase amount according to the bias to obtain the corrected rainfall increase amount, and outputting the corrected rainfall increase amount as the evaluation result. This method can not only improve the accuracy of the quantitative evaluation of the effect of human shadow operation, but also has good interpretability, and can be directly applied to the quantitative evaluation system of the effect of human shadow operation.

Description

Technical Field

[0001] The invention relates to the field of assessment, and in particular to a quantitative assessment method for human shadow operation effects based on simulation and actual precipitation. Background Art

[0002] Numerical simulation verification is one of the main technical methods for evaluating the effects of weather modification operations. Establishing a numerical simulation system that can simulate the actual human shadow operation process is an important basis for the practical application of such methods. After years of research and development, the Weather Modification Center of the China Meteorological Administration has completed the construction of the CMA-CPEFS v2.0 system, a numerical forecast model for weather modification, and achieved the technical capability of simulating the catalytic process of human shadow operations. It has also carried out numerical simulation evaluation of the catalytic effects of actual operations in some typical cases. However, in these applications, it is also found that the current numerical simulation effect evaluation method has great limitations. Its results are based on the precipitation field obtained by numerical model simulation, and the precipitation field simulated by the model is different from the actual precipitation. Therefore, the evaluated rainfall increase results are more of a relative reference significance, and it is impossible to give quantitative results on the actual rainfall background field, which limits its wider application in actual business. In view of the above situation, we combined the simulation of CMA-CPEFS v2.0 model with the actual rainfall data, and established a quantitative numerical simulation evaluation method for the effect of human shadow operations based on the actual precipitation background field. This method can be applied to the evaluation of artificial rainfall operation effects in different scenarios and give quantitative evaluation results.

[0003] The resolution of the numerical simulation of this method is in the range of 1-3km, and a more refined assessment can also reach a resolution of several hundred meters. The resolution of the actual precipitation data used as the assessment background field is currently up to 1km. Taking the above factors into consideration, the horizontal resolution of the assessment results of this method can normally reach 1km. Moreover, based on the improvement or downscaling technology of the resolution of the actual data, this method has the potential to further improve the resolution.

[0004] This method has good applicability and can carry out quantitative evaluation of the effects of rainfall enhancement operations in different scenarios such as a single operation process in a short period of time and multiple operation processes, and give quantitative effect evaluation results. Compared with the current method that relies solely on numerical simulation for evaluation, this method has more advantages in practical application. Summary of the invention

[0005] The purpose of the present invention is to provide a quantitative evaluation method for the effect of human shadow operation based on simulation and actual precipitation.

[0006] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0007] The present invention comprises the following steps:

[0008] Collect relevant data and pre-process the relevant data; the relevant data include reanalysis field or analysis field data, gridded real-time precipitation field information, and artificial weather modification operation information;

[0009] Start the numerical simulation and calculate the rainfall increase rate and the scope of the operation impact area, and evaluate the accuracy of the precipitation in the impact area simulated by the model;

[0010] Calculate the average rainfall increase rate at the grid points hit by the precipitation simulation, and compensate for the average rainfall increase rate at the grid points missed by the precipitation simulation;

[0011] Calculating the rainfall increase based on the actual precipitation, optimizing the rainfall increase according to the bias to obtain a revised rainfall increase, and outputting the revised rainfall increase as an evaluation result;

[0012] The method for compensating the average rainfall increase rate at the missed grid points of precipitation simulation includes:

[0013] Get the average rainfall increase rate of the hit grid point, and search and identify the adjacent grid points according to the search radius:

[0014] When the number of searched grid points is greater than or equal to 3, the average rainfall increase rate of the missed grid points is calculated based on the average rainfall increase rate data of the rainfall increase rate grid points:

[0015]

[0016]

[0017]

[0018] The average rainfall increase rate of the missed grid points is The average rainfall increase rate at the i-th hit grid point is , the actual rainfall at the i-th hit grid point is , the actual rainfall at the missed grid point is , the inverse of the actual rainfall difference between the i-th hit grid point and the missed grid point is , the weight coefficient of the rainfall increase rate of the i-th hit grid point is , the total number of grid points is n;

[0019] The smaller the error between the actual rainfall and the missed rainfall, the greater the weight of the grid point in calculating the average rainfall increase rate.

[0020] Iterate successively to obtain the rainfall increase rate data of all missed grid points;

[0021] When the number of searched grid points is less than 3, the average rainfall increase rate of the entire affected area of ​​the model is used for assignment;

[0022] The average rainfall increase rate of the grid points in the impact area is taken as the rainfall increase rate on the grid points of the actual precipitation data field;

[0023] The method for calculating the amount of rainfall increase based on actual precipitation comprises:

[0024] The precipitation increase of the grid points in the operation impact area is calculated by combining the precipitation increase rate data of the grid points with the actual rainfall data:

[0025]

[0026] The rainfall increase at the i-th grid point is , in 10,000 tons, the actual rainfall at the i-th grid point is , in millimeters, the grid spacing of the actual rainfall grid points is , , in meters, the density of water is , unit: kg / m3;

[0027] The total amount of rainfall increase in the area affected by the human shadow operation during the assessment period is the sum of the rainfall increases at the grid points in the target area.

[0028] Furthermore, the method of starting numerical simulation and calculating the rainfall increase rate and the scope of the operation impact area includes:

[0029] According to the input actual human shadow operation data, the mode simulation period is determined, the initial field and boundary data field are obtained through the driving field data, and the numerical mode is started to perform numerical simulation;

[0030] The numerical simulation includes two parts: natural cloud simulation and catalytic cloud simulation. The catalytic cloud simulation is a realistic simulation of the actual precipitation process affected by catalytic operations.

[0031] For natural cloud simulation, the output precipitation field data is used as the background field data for catalytic simulation evaluation, which means that the simulation results are not affected by the actual catalytic operation;

[0032] For catalytic cloud simulation, actual human shadow operation data and the simulated catalytic simulation function of the CMA-CPEFS model will be used to evaluate the precipitation field and catalytic impact area data output during the spreading process of all human shadow operations within the evaluation period and the evaluation area simulated by the simulation model;

[0033] The precipitation output by the natural cloud simulation and the precipitation output by the catalytic cloud simulation are compared and calculated, and the rainfall increase rate data at all precipitation grid points in the evaluation area simulated by the model are calculated. The rainfall increase rate of each precipitation grid point simulated by the model is calculated as follows:

[0034]

[0035] The simulated rainfall increase rate of the i-th precipitation grid point simulated by the model is , the precipitation of the catalytic cloud at the i-th grid point is , the precipitation simulated by natural clouds at the i-th grid point is , the total number of precipitation grid points is n.

[0036] Furthermore, the method for evaluating the accuracy of precipitation in the impact area simulated by the model includes:

[0037] Based on the impact area product output by the catalytic cloud simulation, determine the impact area of ​​the human shadow operation during the evaluation period simulated by the model;

[0038] The grid field data of the gridded multi-source fusion real-time analysis precipitation product of a meteorological bureau is taken as the true value of actual precipitation, and the TS scoring method is used to evaluate the accuracy of the accumulated precipitation results within the evaluation period of catalytic cloud simulation and the operation influence area.

[0039] The numerical model simulation adopts a horizontal resolution of 1 to 3 km. Before the TS score calculation, when the resolutions of the actual and model products are different, the model products are processed into grid point data with the same grid as the actual precipitation field through interpolation method;

[0040] The TS scoring results and hit rate indicators will be used as reference indicators for the reliability of the operation effect evaluation;

[0041] When the precipitation forecast hit rate in an affected area is greater than 0.5, the simulation results are used as input data for subsequent evaluation, and the sunny and rainy TS score data are used as a reference for the reliability of the evaluation results;

[0042] While TS scoring, the impact area data, precipitation data, and actual precipitation data output by the simulation are combined to determine and save the information on the hit grid points and missed grid points that catalyze the simulated precipitation and the actual precipitation.

[0043] Furthermore, the method for calculating the average rainfall increase rate on the precipitation simulation hit grid point includes:

[0044] Determine the search radius range, and take the hit grid points and the grid points within the search radius range as the neighborhood grid point set;

[0045] The average rainfall increase rate of the neighboring grid points is used to replace the rainfall increase rate of the hit grid point, and the average rainfall increase rate of the grid point is used as the actual rainfall increase rate of the grid point.

[0046] According to the rainfall increase rate data on the neighborhood grid point set, the weighted average of the actual rainfall increase rate hitting the grid point is calculated:

[0047]

[0048]

[0049]

[0050] The weighted average rainfall increase rate of the hit grid point is The rainfall increase rate of the i-th grid point in the search range is , the weight coefficient of the rainfall increase rate of the i-th hit grid point is , the total number of grid points within the search range is , the simulated precipitation data at the i-th grid point is , the actual fused precipitation data at the i-th grid point is , the inverse of the difference between the simulated and actual precipitation at the ith grid point is ;

[0051] The weight coefficient assumes that the effects of catalytic operations in a small area are similar. When the simulated precipitation on the searched grid points is closer to the actual precipitation value, the precipitation generation process is similar.

[0052] Furthermore, the method for optimizing the rainfall increase amount according to the bias to obtain a corrected rainfall increase amount includes:

[0053] Introduce the particle swarm algorithm search bias to calculate the fitness of particles:

[0054]

[0055] The fitness function of the cth particle is , the penalty term for the cth particle at the sth moment is , the upper limit of the observation time is M, and the simulated rainfall increase of the cth particle at the sth time is , the actual rainfall increase of the cth particle at the sth moment is , the bias is b;

[0056] The particle position with the highest comfort level is taken as the optimal position, and the particle position is updated according to the search space. The expression is:

[0057]

[0058] The updated position of the cth particle in the tth iteration is , the initial position of the cth particle is , the optimal position is , the upper bound of the search space is , the lower bound of the search space is , the current iteration number is t, and the random number between 0 and 1 is ;

[0059] Calculate the distance and offset between the particle and the searcher:

[0060]

[0061]

[0062] The chaotic variation is , a random number from 0 to 1 is , , , the linear attenuation factor is , the distance between the particle and the searcher is , the optimal position of the tth iteration is , the position vector of the particle at the tth iteration is , the bias of the tth iteration is , the maximum number of iterations is ;

[0063] The adaptive position is obtained by updating the particle position by step size and distance. The expression is:

[0064]

[0065] The adaptive position of the cth particle in the tth iteration is , a random number from 0 to 1 is ;

[0066] Until the maximum number of iterations is reached and the fitness is lower than 0.183, the bias is output, otherwise the step size and distance are updated and the particle fitness is recalculated;

[0067] The corrected rainfall increase is given according to the bias, and the expression is:

[0068]

[0069] The corrected rainfall increase at the i-th grid point is , the rainfall increase at the i-th grid point is , with a bias of b.

[0070] The beneficial effects of the present invention are:

[0071] The present invention is a quantitative evaluation method for the effect of human shadow operation based on simulation and actual precipitation. Compared with the prior art, the present invention has the following technical effects:

[0072] The present invention can improve the accuracy of quantitative evaluation of human shadow operation effect through preprocessing, numerical simulation, accuracy evaluation, calculation of average rainfall increase rate, supplementary calculation of average rainfall increase rate, calculation of rainfall increase amount and bias optimization steps, thereby improving the precision of quantitative evaluation of human shadow operation effect, and optimizing the quantitative evaluation of human shadow operation effect, which can greatly save resources and improve work efficiency, and can realize automatic quantitative evaluation of human shadow operation effect, supplementary calculation of average rainfall increase rate and bias optimization for the quantitative evaluation of human shadow operation effect in real time, which is of great significance to the quantitative evaluation of human shadow operation effect, can adapt to quantitative evaluation of human shadow operation effect of different standards and different quantitative evaluation requirements of human shadow operation effect, and has certain universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 The present invention is a flowchart of the steps of the method for quantitatively evaluating the effect of human shadow operations based on simulation and actual precipitation. DETAILED DESCRIPTION

[0074] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0075] The method for quantitatively evaluating the effect of human shadow operation based on simulation and actual precipitation of the present invention comprises the following steps:

[0076] like Figure 1 As shown, in this embodiment, the following steps are included:

[0077] Collect relevant data and pre-process the relevant data; the relevant data include reanalysis field or analysis field data, gridded real-time precipitation field information, and artificial weather modification operation information;

[0078] In the actual evaluation, the artificial rainfall enhancement operation process by aircraft in Region A on November 1, 2020 was taken as the research object; the evaluation period was included in the simulation period;

[0079] Case overview: On November 1, 2020, affected by the low-level shear system, there was a light rain precipitation process in the southern part of area A, the southern part of area B and area C; an aircraft cold cloud catalysis operation was carried out in the southern part of area A. The target cloud system was layered cloud. The operation altitude was about 5300m. The operation time was 15:06-16:07 on the 1st. A total of 40 flame strips were burned and a total of 1440g of silver iodide was spread;

[0080] The evaluation period is from 15:00 to 22:00 on November 1, 2020, and the simulation period is from 08:00 to 22:00 on November 1. The data used for the evaluation include: ECWMC global model reanalysis field data, 1km resolution hourly precipitation near-real-time products of regional multi-source fusion real-time analysis of a certain country, and operation data of rainmaking aircraft; the operation data include the trajectory of aircraft catalytic operations and catalyst spreading rate data;

[0081] Start the numerical simulation and calculate the rainfall increase rate and the scope of the operation impact area, and evaluate the accuracy of the precipitation in the impact area simulated by the model;

[0082] In the actual evaluation, the TS score of the simulated precipitation in the impact area was 0.639, with a hit rate of 0.668;

[0083] Calculate the average rainfall increase rate at the grid points hit by the precipitation simulation, and compensate for the average rainfall increase rate at the grid points missed by the precipitation simulation;

[0084] Calculating the rainfall increase based on the actual precipitation, optimizing the rainfall increase according to the bias to obtain a revised rainfall increase, and outputting the revised rainfall increase as an evaluation result;

[0085] In the actual assessment, the total rainfall increase of this operation was 6.36869 million tons, and the revised rainfall increase was 6.369 million tons;

[0086] The method for compensating the average rainfall increase rate at the missed grid points of precipitation simulation includes:

[0087] Get the average rainfall increase rate of the hit grid point, and search and identify the adjacent grid points according to the search radius:

[0088] When the number of searched grid points is greater than or equal to 3, the average rainfall increase rate of the missed grid points is calculated based on the average rainfall increase rate data of the rainfall increase rate grid points:

[0089]

[0090]

[0091]

[0092] The average rainfall increase rate of the missed grid points is The average rainfall increase rate at the i-th hit grid point is , the actual rainfall at the i-th hit grid point is , the actual rainfall at the missed grid point is , the inverse of the actual rainfall difference between the i-th hit grid point and the missed grid point is , the weight coefficient of the rainfall increase rate of the i-th hit grid point is , the total number of grid points is n;

[0093] The smaller the error between the actual rainfall and the missed rainfall, the greater the weight of the grid point in calculating the average rainfall increase rate.

[0094] Iterate successively to obtain the rainfall increase rate data of all missed grid points;

[0095] When the number of searched grid points is less than 3, the average rainfall increase rate of the entire affected area of ​​the model is used for assignment;

[0096] The average rainfall increase rate of the grid points in the impact area is taken as the rainfall increase rate on the grid points of the actual precipitation data field;

[0097] The method for calculating the amount of rainfall increase based on actual precipitation comprises:

[0098] The precipitation increase of the grid points in the operation impact area is calculated by combining the precipitation increase rate data of the grid points with the actual rainfall data:

[0099]

[0100] The rainfall increase at the i-th grid point is , in 10,000 tons, the actual rainfall at the ith grid point is , in millimeters, the grid spacing of the actual rainfall grid points is , , in meters, the density of water is , unit: kg / m3;

[0101] The total amount of rainfall increase in the area affected by the human shadow operation during the assessment period is the sum of the rainfall increase at the grid points in the target area;

[0102] In the actual evaluation, the search radius ranges from 5 to 20 km.

[0103] In this embodiment, the method of starting numerical simulation and calculating the rainfall increase rate and the scope of the operation impact area includes:

[0104] According to the input actual human shadow operation data, the mode simulation period is determined, the initial field and boundary data field are obtained through the driving field data, and the numerical mode is started to perform numerical simulation;

[0105] The numerical simulation includes two parts: natural cloud simulation and catalytic cloud simulation. The catalytic cloud simulation is a realistic simulation of the actual precipitation process affected by catalytic operations.

[0106] For natural cloud simulation, the output precipitation field data is used as the background field data for catalytic simulation evaluation, which means that the simulation results are not affected by the actual catalytic operation;

[0107] For catalytic cloud simulation, actual human shadow operation data and the simulated catalytic simulation function of the CMA-CPEFS model will be used to evaluate the precipitation field and catalytic impact area data output during the spreading process of all human shadow operations within the evaluation period and the evaluation area simulated by the simulation model;

[0108] The precipitation output by the natural cloud simulation and the precipitation output by the catalytic cloud simulation are compared and calculated, and the rainfall increase rate data at all precipitation grid points in the evaluation area simulated by the model are calculated. The rainfall increase rate of each precipitation grid point simulated by the model is calculated as follows:

[0109]

[0110] The rainfall increase rate of the i-th precipitation grid point simulated by the model is , the precipitation of the catalytic cloud at the i-th grid point is , the precipitation simulated by natural clouds at the i-th grid point is , the total number of precipitation grid points is n.

[0111] In this embodiment, the method for evaluating the accuracy of precipitation in the impact area simulated by the model includes:

[0112] Based on the impact area product output by the catalytic cloud simulation, determine the impact area of ​​the human shadow operation during the evaluation period simulated by the model;

[0113] The grid field data of the gridded multi-source fusion real-time analysis precipitation product of a meteorological bureau is taken as the true value of actual precipitation, and the TS scoring method is used to evaluate the accuracy of the accumulated precipitation results within the evaluation period of catalytic cloud simulation and the operation influence area.

[0114] Numerical model simulations generally use a horizontal resolution of 1 to 3 km. Before calculating the TS score, when the resolutions of the actual and model products are different, the model products are processed into grid point data with the same grid as the actual precipitation field through interpolation methods;

[0115] The TS scoring results and hit rate indicators will be used as reference indicators for the reliability of the operation effect evaluation;

[0116] When the precipitation forecast hit rate in an affected area is greater than 0.5, the simulation results are used as input data for subsequent evaluation, and the sunny and rainy TS score data are used as a reference for the reliability of the evaluation results;

[0117] While TS scoring, the impact area data, precipitation data, and actual precipitation data output by the simulation are combined to determine and save the information on the hit grid points and missed grid points that catalyze the simulated precipitation and the actual precipitation.

[0118] In this embodiment, the method for calculating the average rainfall increase rate on the precipitation simulation hit grid point includes:

[0119] Determine the search radius range, and take the hit grid points and the grid points within the search radius range as the neighborhood grid point set;

[0120] The average rainfall increase rate of the neighboring grid points is used to replace the rainfall increase rate of the hit grid point, and the average rainfall increase rate of the grid point is used as the actual rainfall increase rate of the grid point.

[0121] According to the rainfall increase rate data on the neighborhood grid point set, the weighted average of the actual rainfall increase rate hitting the grid point is calculated:

[0122]

[0123]

[0124]

[0125] The weighted average rainfall increase rate of the hit grid point is The rainfall increase rate of the i-th grid point in the search range is , the weight coefficient of the rainfall increase rate of the i-th hit grid point is , the total number of grid points within the search range is , the simulated precipitation data at the i-th grid point is , the actual fused precipitation data at the i-th grid point is , the inverse of the difference between the simulated and actual precipitation at the ith grid point is ;

[0126] The weight coefficient assumes that the effects of catalytic operations in a small area are similar. When the simulated precipitation on the searched grid points is closer to the actual precipitation value, the precipitation generation process is similar.

[0127] In this embodiment, the method of optimizing the rainfall increase amount according to the bias to obtain the corrected rainfall increase amount includes:

[0128] Introduce the particle swarm algorithm search bias to calculate the fitness of particles:

[0129]

[0130] The fitness function of the cth particle is , the penalty term for the cth particle at the sth moment is , the upper limit of the observation time is M, and the simulated rainfall increase of the cth particle at the sth time is , the actual rainfall increase of the cth particle at the sth moment is , the bias is b;

[0131] The particle position with the highest comfort level is taken as the optimal position, and the particle position is updated according to the search space. The expression is:

[0132]

[0133] The updated position of the cth particle in the tth iteration is , the initial position of the cth particle is , the optimal position is , the upper bound of the search space is , the lower bound of the search space is , the current iteration number is t, and the random number between 0 and 1 is ;

[0134] Calculate the distance and offset between the particle and the searcher:

[0135]

[0136]

[0137] The chaotic variation is , a random number from 0 to 1 is , , , the linear attenuation factor is , the distance between the particle and the searcher is , the optimal position of the tth iteration is , the position vector of the particle at the tth iteration is , the bias of the tth iteration is , the maximum number of iterations is ;

[0138] The adaptive position is obtained by updating the particle position by step size and distance. The expression is:

[0139]

[0140] The adaptive position of the cth particle in the tth iteration is , a random number from 0 to 1 is ;

[0141] Until the maximum number of iterations is reached and the fitness is lower than 0.183, the bias is output, otherwise the step size and distance are updated and the particle fitness is recalculated;

[0142] The corrected rainfall increase is given according to the bias, and the expression is:

[0143]

[0144] The corrected rainfall increase at the i-th grid point is , the rainfall increase at the i-th grid point is , with a bias of b.

[0145] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A quantitative evaluation method for the effect of human shadow operation based on simulation and actual precipitation, characterized in that: The following steps are involved: Collect relevant data and pre-process the relevant data; the relevant data include reanalysis field or analysis field data, gridded real-time precipitation field information, and artificial weather modification operation information; Start the numerical simulation and calculate the rainfall increase rate and the scope of the operation impact area, and evaluate the accuracy of the precipitation in the impact area simulated by the model; Calculate the average rainfall increase rate at the grid points hit by the precipitation simulation, and compensate for the average rainfall increase rate at the grid points missed by the precipitation simulation; Calculating the rainfall increase based on the actual precipitation, optimizing the rainfall increase according to the bias to obtain a revised rainfall increase, and outputting the revised rainfall increase as an evaluation result; The method for compensating the average rainfall increase rate at the missed grid points of precipitation simulation includes: Get the average rainfall increase rate of the hit grid point, and search and identify the adjacent grid points according to the search radius: When the number of searched grid points is greater than or equal to 3, the average rainfall increase rate of the missed grid points is calculated based on the average rainfall increase rate data of the rainfall increase rate grid points: The average rainfall increase rate of the missed grid points is The average rainfall increase rate at the i-th hit grid point is , the actual rainfall at the i-th hit grid point is , the actual rainfall at the missed grid point is , the inverse of the actual rainfall difference between the i-th hit grid point and the missed grid point is , the weight coefficient of the rainfall increase rate of the i-th hit grid point is , the total number of grid points is n; The smaller the error between the actual rainfall and the missed rainfall, the greater the weight of the grid point in calculating the average rainfall increase rate. Iterate successively to obtain the rainfall increase rate data of all missed grid points; When the number of searched grid points is less than 3, the average rainfall increase rate of the entire affected area of ​​the model is used for assignment; The average rainfall increase rate of the grid points in the impact area is taken as the rainfall increase rate on the grid points of the actual precipitation data field; The method for calculating the amount of rainfall increase based on actual precipitation comprises: The precipitation increase of the grid points in the operation impact area is calculated by combining the precipitation increase rate data of the grid points with the actual rainfall data: The rainfall increase at the i-th grid point is , in 10,000 tons, the actual rainfall at the ith grid point is , in millimeters, the grid spacing of the actual rainfall grid points is , , in meters, the density of water is , unit kg / m3, the rainfall increase rate of the i-th precipitation grid point is ; The total amount of rainfall increase in the area affected by the human shadow operation during the assessment period is the sum of the rainfall increases at the grid points in the target area.

2. The method for quantitatively evaluating the effect of human shadow operation based on simulation and actual precipitation according to claim 1 is characterized in that: The method of starting numerical simulation and calculating the rainfall increase rate and the scope of the operation impact area includes: According to the input actual human shadow operation data, the mode simulation period is determined, the initial field and boundary data field are obtained through the driving field data, and the numerical mode is started for numerical simulation; The numerical simulation includes two parts: natural cloud simulation and catalytic cloud simulation. The catalytic cloud simulation is a realistic simulation of the actual precipitation process affected by catalytic operations. For natural cloud simulation, the output precipitation field data is used as the background field data for catalytic simulation evaluation, which means that the simulation results are not affected by the actual catalytic operation; For catalytic cloud simulation, actual human shadow operation data and the simulated catalytic simulation function of the CMA-CPEFS model will be used to evaluate the precipitation field and catalytic impact area data output during the spreading process of all human shadow operations within the evaluation period and the evaluation area simulated by the simulation model; The precipitation output by the natural cloud simulation and the precipitation output by the catalytic cloud simulation are compared and calculated, and the rainfall increase rate data at all precipitation grid points in the evaluation area simulated by the model are calculated. The rainfall increase rate of each precipitation grid point simulated by the model is calculated as follows: The rainfall increase rate of the i-th precipitation grid point simulated by the model is , the precipitation of the catalytic cloud at the i-th grid point is , the precipitation simulated by natural clouds at the i-th grid point is , the total number of precipitation grid points is n.

3. The method for quantitatively evaluating the effect of human shadow operation based on simulation and actual precipitation according to claim 1 is characterized in that: The method for evaluating the accuracy of precipitation in the impact area simulated by the model comprises: Based on the impact area product output by the catalytic cloud simulation, determine the impact area of ​​the human shadow operation during the evaluation period simulated by the model; The grid field data of the gridded multi-source fusion real-time analysis precipitation product is used as the true value of actual precipitation. The TS scoring method is used to evaluate the accuracy of the cumulative precipitation results in the evaluation period of catalytic cloud simulation, the operation impact area, and the cumulative precipitation in the model simulation period. The numerical model simulation adopts a horizontal resolution of 1 to 3 km. Before the TS score calculation, when the resolutions of the actual and model products are different, the model products are processed into grid point data with the same grid as the actual precipitation field through interpolation method; The TS scoring results and hit rate indicators will be used as reference indicators for the reliability of the operation effect evaluation; When the precipitation forecast hit rate in an affected area is greater than 0.5, the simulation results will be used as the basic requirement for subsequent evaluation, and the TS score data of sunny and rainy weather will be used as a reference for the reliability of the evaluation results; While TS scoring, the impact area data, precipitation data, and actual precipitation data output by the simulation are combined to determine and save the information on the hit grid points and missed grid points of the catalytic simulated precipitation relative to the actual precipitation.

4. The method for quantitatively evaluating the effect of human shadow operation based on simulation and actual precipitation according to claim 1 is characterized in that: The method for calculating the average rainfall increase rate on the precipitation simulation hit grid point comprises: Determine the search radius range, and take the hit grid point and the grid points within the search radius range as neighboring grid points; The rainfall increase rate at the hit grid point is replaced by the average rainfall increase rate at the neighboring grid points, and the average rainfall increase rate at the grid point is taken as the actual rainfall increase rate at the grid point. According to the rainfall increase rate data on the neighboring grid points, the weighted average of the actual rainfall increase rate hitting the grid point is calculated: The weighted average rainfall increase rate of the hit grid point is The rainfall increase rate of the i-th grid point in the search range is , the weight coefficient of the rainfall increase rate of the i-th hit grid point is , the total number of grid points within the search range is , the simulated precipitation data at the i-th grid point is , the actual fused precipitation data at the i-th grid point is , the inverse of the difference between the simulated and actual precipitation at the ith grid point is .

5. The method for quantitatively evaluating the effect of human shadow operation based on simulation and actual precipitation according to claim 1 is characterized in that: The method for optimizing the rainfall increase amount according to the bias to obtain the corrected rainfall increase amount comprises: Introduce the particle swarm algorithm search bias to calculate the fitness of particles: The fitness function of the cth particle is , the penalty term for the cth particle at the sth moment is , the upper limit of the observation time is M, and the simulated rainfall increase of the cth particle at the sth time is , the actual rainfall increase of the cth particle at the sth moment is , the bias is b; The particle position with the highest comfort level is taken as the optimal position, and the particle position is updated according to the search space. The expression is: The updated position of the cth particle in the tth iteration is , the initial position of the cth particle is , the optimal position is , the upper bound of the search space is , the lower bound of the search space is , the current iteration number is t, and the random number between 0 and 1 is ; Calculate the distance and offset between the particle and the searcher: The chaotic variation is , a random number from 0 to 1 is , , , the linear attenuation factor is , the distance between the particle and the searcher is , the optimal position of the tth iteration is , the position vector of the particle at the tth iteration is , the bias of the tth iteration is , the maximum number of iterations is ; The adaptive position is obtained by updating the particle position by step size and distance. The expression is: The adaptive position of the cth particle in the tth iteration is , a random number from 0 to 1 is ; Until the maximum number of iterations is reached and the fitness is lower than 0.183, the bias is output, otherwise the step size and distance are updated and the particle fitness is recalculated; The corrected rainfall increase is given according to the bias, and the expression is: The corrected rainfall increase at the i-th grid point is , the rainfall increase at the i-th grid point is , with a bias of b.

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