A method for estimating the silver iodide concentration required for artificial precipitation and snow enhancement using radar observation

Abstract

The invention discloses a method for estimating the silver iodide concentration required for artificial precipitation and snow enhancement by radar observation, and belongs to the technical field of artificial precipitation and snow enhancement. The method comprises the following steps: obtaining radar observation results of any cloud precipitation case, and statistically analyzing the vertical distribution of radar reflectivity factors; setting a threshold value of a target layer reflectivity factor according to the vertical distribution of radar reflectivity factors; inputting a seeding layer temperature, a seeding layer air pressure, a height difference between a seeding layer and a target layer, and a threshold value of the target layer reflectivity factor into a parameterized equation of a pre-acquired silver iodide concentration, and calculating the required silver iodide concentration; and the invention optimizes cloud seeding operations and improves the effect of artificial precipitation enhancement by selecting a suitable silver iodide concentration.

Description

Technical Field

[0001] The invention relates to a method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation, and belongs to the technical field of artificial precipitation and snow enhancement. Background Art

[0002] Artificial snow and rain enhancement is a commonly used technical means for developing cloud water resources in the air. By selecting the best operation time, the catalyst is spread in appropriate amounts on the precipitation cloud system to fully develop the cloud water resources in the air and increase ground precipitation, thereby playing a role in agricultural drought resistance, forest fire prevention, and ecological environment protection. Among them, spreading silver iodide in cold clouds is the most commonly used method for artificial snow and rain enhancement, and its scientific basis has been confirmed by a large number of indoor experiments, numerical simulations and field tests.

[0003] Radar observation is one of the important methods to detect the effectiveness of weather modification operations. Through the radar system, researchers can capture the changes in cloud physical properties in real time, such as the increase in ice crystal concentration, the adjustment of cloud structure and the increase in precipitation intensity, which are directly related to the effectiveness of cloud seeding.

[0004] Traditional cloud seeding schemes mostly rely on experience or simple models to determine the amount of silver iodide required. However, the thermodynamic and microphysical conditions of clouds may vary significantly between different clouds and different parts of the same cloud. Traditional schemes cannot accurately estimate the amount of silver iodide required for different clouds, which is likely to lead to excessive use of silver iodide and potential negative impacts on the environment.

[0005] Therefore, how to accurately estimate the silver iodide concentration required for artificial rain and snow enhancement under different conditions is crucial to protecting the environment, optimizing cloud seeding operations, and improving the effectiveness of artificial rain and snow enhancement. Summary of the invention

[0006] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for estimating the silver iodide concentration required for artificial rainfall and snow enhancement by radar observation, so as to optimize the cloud seeding operation and improve the effect of artificial rainfall enhancement by selecting a suitable silver iodide concentration.

[0007] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0008] In a first aspect, the present invention provides a method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation, comprising:

[0009] Obtain radar observation results for any cloud precipitation case and calculate the vertical distribution of radar reflectivity factor;

[0010] The threshold of the reflectivity factor of the target layer is set according to the vertical distribution of the radar reflectivity factor;

[0011] The temperature of the spreading layer, the air pressure of the spreading layer, the height difference between the spreading layer and the target layer, and the threshold of the reflectivity factor of the target layer are input into the parameterized equation of the pre-acquired silver iodide concentration to calculate the required silver iodide concentration.

[0012] Furthermore, the method for obtaining the parameterized equation of the silver iodide concentration includes:

[0013] A one-dimensional ice crystal growth model is established, and the ice water content of the one-dimensional ice crystal growth model is calculated;

[0014] The ice water content of the one-dimensional ice crystal growth model was compared with the three-dimensional simulation results to obtain the applicable scope of the one-dimensional ice crystal growth model;

[0015] The test steps include: within the applicable range, randomly selecting a spreading layer temperature, a spreading layer height, a target layer height, and a silver iodide concentration to conduct a simulation test to obtain a radar reflectivity factor of the target layer;

[0016] Repeat the test steps to a set number of times to obtain the radar reflectivity factor of the target layer under different conditions;

[0017] Based on the radar reflectivity factor of the target layer under different conditions, polynomial regression was used to fit the data points and a parameterized equation for silver iodide concentration was established.

[0018] Furthermore, the calculation formula of the one-dimensional ice crystal growth model is as follows:

[0019] (1);

[0020] in, represents the rate of change of ice crystal mass over time, is the ratio of pi, is the specific gas constant for water vapor, It is the heat released when a unit mass of water vapor condenses into ice. is the thermal conductivity of air, represents the air temperature in the growth environment of ice crystals, is the diffusion rate of water vapor in air, is the density of saturated water vapor, is the parameter capacitance, represents the ventilation factor, is the water vapor supersaturation relative to the ice surface;

[0021] Among them, for a prolate ellipsoid, the parameter capacitance The calculation formula is as follows:

[0022] (2);

[0023] For a prolate ellipsoid, the parameter capacitance The calculation formula is as follows:

[0024] (3);

[0025] in, represents the length of the ice crystal, represents the width of the ice crystal, is the aspect ratio of the ice crystal, .

[0026] Furthermore, the ice water content calculation method of the one-dimensional ice crystal growth model is as follows:

[0027] The initial ice crystal size distribution is assumed to follow the modified gamma distribution, and the formula is as follows:

[0028] (4);

[0029] in, is the reference size of ice crystals, is the shape parameter in the gamma distribution, Indicates the reference size of ice crystals The corresponding ice crystal concentration, is the size of the ice crystal projected perpendicular to the falling direction. For plate-shaped ice crystals, ; For columnar ice crystals, ; For spherical ice crystals, ; is the size of the ice crystals The concentration of ice crystals under

[0030] Integrate both sides of formula (1) to obtain the ice crystal size The mass of ice crystals ;

[0031] According to ice crystal size The mass of ice crystals , calculate the ice water content of the one-dimensional ice crystal growth model, the formula is as follows:

[0032] (5);

[0033] in, represents the ice water content of the one-dimensional ice crystal growth model, is the differential symbol.

[0034] Furthermore, based on the radar reflectivity factor of the target layer under different conditions, polynomial regression is used to fit the data points to establish a parameterized equation for the concentration of silver iodide, including:

[0035] The data from the repeated test steps are grouped and substituted into the polynomial regression model with undetermined coefficients to obtain the polynomial regression prediction value, which is recorded as :

[0036] (6);

[0037] in, Indicates the sequence number of the data group. Indicates the sequence number of the independent variable, For the The first independent variable values, is the exponent of the independent variable, is the polynomial regression order, For the The independent variable index is The model coefficients when is the intercept term;

[0038] The least squares method is used to estimate the polynomial coefficients. When the least squares error function has a minimum value, the polynomial regression prediction value Approximate the corresponding dependent variable data When , determine the polynomial coefficients , the least squares error Error calculation formula is as follows:

[0039] (7);

[0040] in, Indicates that there is Group data, It is The dependent variable data in the group, For the Polynomial regression predictions for group data;

[0041] Compute polynomial regression predictions With dependent variable data The root mean square error RMSE and correlation coefficient between them are used to determine the optimal polynomial regression order, so that the optimal polynomial regression order should minimize the RMSE and maximize the correlation coefficient.

[0042] Substituting the polynomial coefficients and the optimal polynomial regression order into equation (6), we can obtain the silver iodide concentration that can be used to predict the silver iodide concentration under given input conditions. The parameterized equation is as follows:

[0043] (10).

[0044] Furthermore, the calculation of the polynomial regression prediction value With dependent variable data The root mean square error RMSE and correlation coefficient between them are as follows:

[0045] and The calculation formula of RMSE between is as follows:

[0046] (8);

[0047] and The correlation coefficient between The calculation formula is as follows:

[0048] (9);

[0049] In the above formula, Indicates the sequence number of the data group. Indicates that there is Group data.

[0050] In a second aspect, the present invention provides a device for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation, comprising:

[0051] The statistical module is used to obtain the radar observation results of any cloud precipitation case and to calculate the vertical distribution of the radar reflectivity factor;

[0052] A setting module, used for setting a threshold value of a target layer reflectivity factor according to a vertical distribution of the radar reflectivity factor;

[0053] The calculation module is used to input the temperature of the spreading layer, the air pressure of the spreading layer, the height difference between the spreading layer and the target layer, and the threshold value of the reflectivity factor of the target layer into the parameterized equation of the pre-acquired silver iodide concentration to calculate the required silver iodide concentration.

[0054] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the aforementioned methods.

[0055] In a fourth aspect, the present invention provides a computer device, comprising:

[0056] Memory, for storing computer programs / instructions;

[0057] A processor is used to execute the computer program / instructions to implement the steps of any of the aforementioned methods.

[0058] In a fifth aspect, the present invention provides a computer program product, comprising a computer program / instruction, which implements the steps of any one of the aforementioned methods when executed by a processor.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] The present invention provides a method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation. The method uses a polynomial regression model to fit data points and establish a parameterized equation for the concentration of silver iodide, thereby realizing parameterization of the estimation of the concentration of silver iodide under radar observation conditions. The method can obtain the concentration of silver iodide required under actual conditions by inputting the temperature and air pressure of the seeding layer, the height difference between the seeding layer and the target layer, and the reflectivity factor threshold expected to be achieved, thereby realizing rapid estimation of the concentration of silver iodide required for artificial precipitation and snow enhancement. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flow chart of a parameterized method for estimating silver iodide concentration for radar observation provided by an embodiment of the present invention;

[0062] Figure 2 It is a flow chart of a method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation provided by an embodiment of the present invention;

[0063] Figure 3 It is a simulation schematic diagram of the reflectivity factor (Ze) of the target layer under different seeding temperatures, silver iodide concentrations and seeding layer depths provided by an embodiment of the present invention;

[0064] Figure 4 It is a schematic diagram of the root mean square error and correlation coefficient between the parameterized silver iodide concentration and the silver iodide concentration simulated by the model under different order conditions provided by an embodiment of the present invention;

[0065] Figure 5 is a schematic diagram of the fitting effect of parameterized silver iodide concentration provided by an embodiment of the present invention;

[0066] Figure 6 is a schematic diagram of cloud observation in a certain area on August 3, 2023 provided by an embodiment of the present invention;

[0067] Figure 7 is a schematic diagram of echo frequencies observed at a certain height and reflectivity factor provided by an embodiment of the present invention;

[0068] Figure 8 It is a schematic diagram of the silver iodide concentration required for cloud seeding at different heights provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0070] Embodiment 1: This embodiment introduces a method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation, comprising:

[0071] Obtain radar observation results for any cloud precipitation case and calculate the vertical distribution of radar reflectivity factor;

[0072] The threshold of the reflectivity factor of the target layer is set according to the vertical distribution of the radar reflectivity factor;

[0073] The temperature of the spreading layer, the air pressure of the spreading layer, the height difference between the spreading layer and the target layer, and the threshold of the reflectivity factor of the target layer are input into the parameterized equation of the pre-acquired silver iodide concentration to calculate the required silver iodide concentration.

[0074] like Figure 1 , Figure 2 As shown, the method for estimating the silver iodide concentration required for artificial precipitation and snow enhancement by radar observation provided in this embodiment specifically involves the following steps:

[0075] Step 1: Establish a one-dimensional ice crystal growth model. Calculation method of (ice water content);

[0076] The method for establishing a one-dimensional ice crystal growth model in step 1 is as follows:

[0077] In mixed-phase stratiform clouds, the ice-water phase transition process (i.e., the process in which ice crystals grow at the expense of liquid water) is the most important mechanism for ice crystal growth. The present invention mainly considers the effect of diffusion growth on the mass and size of ice crystals. According to the Maxwell-Mason equation, the effect of diffusion growth of ice crystals on the mass of ice crystals is:

[0078] (1);

[0079] in, represents the rate of change of ice crystal mass over time, is the ratio of pi, is the specific gas constant for water vapor, It is the heat released when a unit mass of water vapor condenses into ice. is the thermal conductivity of air, represents the air temperature in the growth environment of ice crystals, is the diffusion rate of water vapor in air, is the density of saturated water vapor, is the parameter capacitance, represents the ventilation factor, is the water vapor supersaturation relative to the ice surface; , , , , , All can be determined experimentally, parameter capacitance Depends on the shape of the ice crystals.

[0080] For a prolate ellipsoid, the parameter capacitance The calculation formula is as follows:

[0081] (2);

[0082] For a prolate ellipsoid, the parameter capacitance The calculation formula is as follows:

[0083] (3);

[0084] in, represents the length of the ice crystal, represents the width of the ice crystal, is the aspect ratio of the ice crystal, .

[0085] The one-dimensional ice crystal growth model is established, and the parameter capacitance is determined according to the ice crystal shape Substituting it into equation (1), the growth rate of ice crystals can be quantified.

[0086] In step 1, we obtain the one-dimensional ice crystal growth model (Ice water content) is calculated as follows:

[0087] Assume that the initial ice crystal size distribution follows a modified gamma distribution:

[0088] (4);

[0089] in, is the reference size of ice crystals, ; is the shape parameter in the gamma distribution, ; represents the ice crystal concentration at the maximum function point, is the size of the ice crystal projected perpendicular to the falling direction. For plate-shaped ice crystals, ; For columnar ice crystals, ; For spherical ice crystals, ; is the size of the ice crystals The concentration of ice crystals under.

[0090] Integrate both sides of formula (1) to obtain the ice crystal size The mass of ice crystals ;

[0091] According to ice crystal size The mass of ice crystals , calculate the ice water content of the one-dimensional ice crystal growth model The formula is as follows:

[0092] (5);

[0093] In the above formula, according to the length of the ice crystal and the width of the ice crystal Calculate the size of ice crystals projected perpendicular to the falling direction , and calculate Aspect ratio of ice crystals The algebraic relationship between Expressed as The function of about The function of is substituted into equation (2) or (3) according to the shape of the ice crystal, and the parameter capacitance is obtained. about The parameter capacitance Substituting into equation (1), we can obtain the corresponding ice crystal mass change rate about Integrating both sides of equation (1), we can get the corresponding ice crystal mass with respect to is a function of the size of a certain ice crystal The mass of ice crystals under The mass of ice crystals under and ice crystal concentration Substituting into (5), we can get (ice water content) quantitative expression.

[0094] Step 2: Compare the one-dimensional ice crystal growth model under the same weather conditions. (ice water content) and three-dimensional simulation results to obtain the applicable scope of the one-dimensional ice crystal growth model;

[0095] Step 3: Randomly select the sowing layer temperature between -7 °C and -30 °C, randomly select the sowing layer height between 500 m and 5000 m, and calculate the air pressure based on the height and temperature; randomly select the target layer height between 300 m and 4500 m below the sowing layer; and randomly select the target layer height between 1 cm and 1 cm. -3 ~ 1000000 cm -3 The silver iodide concentration was randomly selected between;

[0096] Step 4: Use the random results in step 3 as initial conditions to conduct simulation tests and obtain the target layer radar reflectivity factor ( );

[0097] Step 5, repeat step 3 to step 4 1000 times to obtain the radar reflectivity factor of the target layer under different conditions;

[0098] Step 6: Based on the results of step 5 (radar reflectivity factor of target layer under different conditions), polynomial regression is used to fit the data points to establish a parameterized equation for silver iodide concentration.

[0099] like Figure 1 As shown, the specific method of using polynomial regression to establish the parameterized equation in step 6 is as follows:

[0100] The data from the 1000 repeated experiments in step 5 are divided into groups, each group contains 5 data points from one experiment. Group as an example ( is the serial number of the data group):

[0101] The independent variables are: seed layer temperature ( )、The air pressure of the spreading layer( ), the height difference between the target layer and the spreading layer ( ), target layer radar reflectivity factor ( ); dependent variable is: silver iodide concentration ( );

[0102] The independent variable data ( , , , ) is substituted into the polynomial regression model with undetermined coefficients to obtain the polynomial regression prediction value, which is recorded as :

[0103] (6);

[0104] in, Indicates the sequence number of the data group. Indicates the sequence number of the independent variable, For the The first independent variable values, is the exponent of the independent variable, is the polynomial regression order (assuming n=1), For the The independent variable index is The model coefficients when is the intercept term.

[0105] The least squares method is used to estimate the polynomial coefficients. When the least squares error function has a minimum value, the polynomial regression prediction value Approximate the corresponding dependent variable data When , determine the polynomial coefficients , the least squares error Error calculation formula is as follows:

[0106] (7);

[0107] in, Indicates that there is Group data, It is The dependent variable data in the group, For the Polynomial regression predictions for group data.

[0108] After determining the polynomial coefficients, adjust the polynomial regression order and repeat the above steps. Calculate the polynomial regression prediction value With dependent variable data The root mean square error RMSE and correlation coefficient between them are used to determine the optimal polynomial regression order, so that the optimal polynomial regression order should minimize the RMSE and maximize the correlation coefficient.

[0109] and The calculation formula of RMSE between is as follows:

[0110] (8);

[0111] and The correlation coefficient between The calculation formula is as follows:

[0112] (9);

[0113] In the above formula, Indicates the sequence number of the data group. Indicates that there is Group data.

[0114] Substituting the polynomial coefficients and the optimal polynomial regression order into equation (6), we can obtain the silver iodide concentration that can be used to predict the silver iodide concentration under given input conditions. The parameterized equation is as follows:

[0115] (10).

[0116] like Figure 2 As shown, the present invention also provides a method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement suitable for radar observation, comprising:

[0117] Step 1: For any radar observation result of cloud precipitation, the vertical distribution of its radar reflectivity factor is calculated;

[0118] Step 2: setting a threshold of the target layer reflectivity factor according to the radar reflectivity factor;

[0119] The method used to determine the target layer reflectivity factor threshold in step 2 is:

[0120] The radar reflectivity factor of all altitudes in the observation time and observation range ( ) plus one or more standard deviations as the threshold of the target layer reflectivity factor.

[0121] Step 3, in the parameterized equation for estimating the silver iodide concentration, input the temperature of the spreading layer, the air pressure of the spreading layer, the height difference between the spreading layer and the target layer, and the desired reflectivity factor threshold; and obtain the required silver iodide concentration after calculation;

[0122] The present invention uses a polynomial regression model to fit data points and establish a parameterized equation for silver iodide concentration. The parameterization of silver iodide concentration estimation under radar observation conditions is achieved. By inputting the temperature and air pressure of the seeding layer, the height difference between the seeding layer and the target layer, and the reflectivity factor threshold expected to be achieved, the silver iodide concentration required in the actual situation can be obtained, and the silver iodide concentration required for artificial rain and snow enhancement can be quickly estimated.

[0123] The contents involved in the above embodiment are described below in conjunction with a preferred embodiment.

[0124] According to a parameterized method for estimating silver iodide concentration suitable for radar observation provided by the present invention, a parameterized model is established.

[0125] Step 1, establishing a one-dimensional ice crystal growth model, comparing the ice crystal mass and size predicted by the model with the actual observed values, the error is less than 20%, which also proves the effectiveness of the one-dimensional ice crystal growth model mentioned in the present invention;

[0126] Step 2: Compare the established one-dimensional ice crystal growth model with the three-dimensional simulation results of shallow stratiform clouds observed in northern China on January 1, 2022, and obtain that the one-dimensional ice crystal growth model is applicable to the part above 300m below the seeding layer;

[0127] Step 3: Randomly select the sowing layer temperature between -7 °C and -30 °C, randomly select the sowing layer height between 500 m and 5000 m, and calculate the air pressure based on the height and temperature; randomly select the target layer height between 300 m and 4500 m below the sowing layer; and randomly select the target layer height between 1 cm and 1 cm. -3 ~ 1000000 cm -3 The silver iodide concentration was randomly selected between .

[0128] Step 4: Use the random results in step 3 as initial conditions to conduct simulation tests and obtain the target layer radar reflectivity factor ( ).

[0129] Step 5: Repeat steps 3 to 4 for 1000 times to obtain the radar reflectivity factor of the target layer under different conditions. The results are as follows: Figure 3 As shown: Figure 3 The reflectivity factor of the target layer ( ), Figure 3 The middle three axes represent temperature, silver iodide concentration, and seeding layer depth, respectively.

[0130] Step 6: Based on the results of step 5, the data points are fitted using a polynomial regression model to establish a parameterized equation for estimating the concentration of silver iodide. In the process of selecting the polynomial regression order, the root mean square error is minimized and the correlation coefficient is maximized. After several rounds of experiments, the optimal polynomial regression order is obtained, such as Figure 4 As shown in the figure: The figure shows the root mean square error and correlation coefficient between the parameterized silver iodide concentration and the silver iodide concentration simulated by the one-dimensional model under different order conditions. The horizontal axis represents the number of polynomial regressions, and the vertical axis represents the root mean square error and correlation coefficient. It can be seen from the figure that when the polynomial regression order is 6, the fitting effect is the best. The fitting effect is shown in Figure 5 As shown in the figure, the horizontal axis represents the silver iodide concentration in the one-dimensional model, and the vertical axis represents the parameterized silver iodide concentration. The data points are distributed along the 1:1 line, indicating that the parameterization can reasonably reveal the relationship between the silver iodide concentration and the radar reflectivity factor under different cloud conditions.

[0131] According to a method for estimating the silver iodide concentration required for artificial precipitation and snow enhancement based on radar observation provided by the present invention, the cloud observation results in a certain area on August 3, 2023 are brought into play to calculate the silver iodide concentration required for artificial precipitation and snow enhancement.

[0132] Step 1: According to the cloud observation results of a certain area on August 3, 2023, the radar reflectivity factor ( ), the observed results are as follows Figure 6 As shown in the figure, the left figure shows the temperature profile of the cloud layer, with the horizontal axis representing temperature and the vertical axis representing cloud height. The right figure shows the radar reflectivity factor ( ), the horizontal axis represents time, and the vertical axis represents the radar reflectivity factor ( ).

[0133] Step 2: According to the radar reflectivity factor, set the target layer reflectivity factor threshold that is expected to be achieved, such as Figure 7 , where the horizontal axis represents the radar reflectivity factor ( ), the vertical axis represents the height, and the color depth represents the frequency or probability of the echo observed at that height and reflectivity factor. If the radar reflectivity factor ( ) is relatively high (for example, at 16:40), the threshold is set as: the radar reflectivity factor of all altitudes at the observation time and within the observation range ( ) plus three standard deviations; if the radar reflectivity factor ( ) is relatively low (for example, at 16:10), the threshold is set as: the radar reflectivity factor of all altitudes at the observation time and within the observation range ( ) plus one standard deviation;

[0134] Step 3, input the seeding layer temperature, seeding layer pressure, height difference between the seeding layer and the target layer, and the desired reflectivity factor threshold into the parameterized equation to calculate the required silver iodide concentration.

[0135] The final result is Figure 8 The figure shows the silver iodide concentration required for cloud seeding at different altitudes (-22°C to -7°C). The horizontal axis represents the silver iodide concentration and the vertical axis represents the altitude. At an altitude of about 7.3 km (-15°C), the required silver iodide concentration is the lowest: 20 cm^-3 of silver iodide is sufficient to induce a comprehensive Over 17dBZ. Seeding at cooler or warmer temperatures may also exceed 17dBZ if the silver iodide concentration is high enough. Threshold.

[0136] Embodiment 2: This embodiment provides a device for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation, comprising:

[0137] The statistical module is used to obtain the radar observation results of any cloud precipitation case and to calculate the vertical distribution of the radar reflectivity factor;

[0138] A setting module, used for setting a threshold value of a target layer reflectivity factor according to a vertical distribution of the radar reflectivity factor;

[0139] The calculation module is used to input the temperature of the spreading layer, the air pressure of the spreading layer, the height difference between the spreading layer and the target layer, and the threshold value of the reflectivity factor of the target layer into the parameterized equation of the pre-acquired silver iodide concentration to calculate the required silver iodide concentration.

[0140] The specific functional implementation of each of the above modules can be found in the relevant contents of the method in Example 1 and will not be elaborated here.

[0141] Embodiment 3, this embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in Embodiment 1 are implemented.

[0142] Embodiment 4: This embodiment provides a computer device, including:

[0143] Memory, for storing computer programs / instructions;

[0144] A processor, configured to execute the computer program / instructions to implement the steps of any one of the methods described in Example 1.

[0145] Embodiment 5: This embodiment provides a computer program product, including a computer program / instruction, which implements the steps of any method described in Embodiment 1 when executed by a processor.

[0146] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0147] It should be understood by those skilled in the art that the embodiments of the present disclosure may be provided as methods, systems or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure rather than to limit its protection scope. Although the present disclosure has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present disclosure, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the disclosed claims to be approved.

Claims

1. A method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation, characterized in that: include: Obtain radar observation results for any cloud precipitation case and calculate the vertical distribution of radar reflectivity factor; The threshold of the reflectivity factor of the target layer is set according to the vertical distribution of the radar reflectivity factor; wherein the arithmetic mean value of the radar reflectivity factors of all heights within the observation time and observation range plus one or more standard deviations is used as the threshold of the reflectivity factor of the target layer; The temperature of the sowing layer, the air pressure of the sowing layer, the height difference between the sowing layer and the target layer, and the threshold value of the reflectivity factor of the target layer are input into the parameterized equation of the pre-acquired silver iodide concentration to calculate the required silver iodide concentration; The method for obtaining the parameterized equation of the silver iodide concentration includes: A one-dimensional ice crystal growth model is established, and the ice water content of the one-dimensional ice crystal growth model is calculated; The ice water content of the one-dimensional ice crystal growth model was compared with the three-dimensional simulation results to obtain the applicable scope of the one-dimensional ice crystal growth model; The test steps include: within the applicable range, randomly selecting a spreading layer temperature, a spreading layer height, a target layer height, and a silver iodide concentration to conduct a simulation test to obtain a radar reflectivity factor of the target layer; Repeat the test steps to a set number of times to obtain the radar reflectivity factor of the target layer under different conditions; Based on the radar reflectivity factor of the target layer under different conditions, polynomial regression is used to fit the data points and a parameterized equation for silver iodide concentration is established; The calculation formula of the one-dimensional ice crystal growth model is as follows: (1)； in, represents the rate of change of ice crystal mass over time, is the ratio of pi, is the specific gas constant for water vapor, It is the heat released when a unit mass of water vapor condenses into ice. is the thermal conductivity of air, represents the air temperature in the growth environment of ice crystals, is the diffusion rate of water vapor in air, is the density of saturated water vapor, is the parameter capacitance, represents the ventilation factor, is the water vapor supersaturation relative to the ice surface; Where, for a prolate ellipsoid, the parameter capacitance The calculation formula is as follows: （2）； For a prolate ellipsoid, the parameter capacitance The calculation formula is as follows: （3）； in, represents the length of the ice crystal, represents the width of the ice crystal, is the aspect ratio of the ice crystal, .

2. The method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation according to claim 1, characterized in that: The ice water content calculation method of the one-dimensional ice crystal growth model is as follows: The initial ice crystal size distribution is assumed to follow the modified gamma distribution, and the formula is as follows: （4）； in, is the reference size of ice crystals, is the shape parameter in the gamma distribution, Indicates the reference size of ice crystals The corresponding ice crystal concentration, is the size of the ice crystal projected perpendicular to the falling direction. For plate-shaped ice crystals, ; For columnar ice crystals, ; For spherical ice crystals, ; is the size of the ice crystals The concentration of ice crystals under Integrate both sides of formula (1) to obtain the ice crystal size The mass of ice crystals ; According to ice crystal size The mass of ice crystals , calculate the ice water content of the one-dimensional ice crystal growth model, the formula is as follows: （5）； in, represents the ice water content of the one-dimensional ice crystal growth model, is the differential symbol.

3. The method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation according to claim 2, characterized in that: The method uses polynomial regression to fit the data points based on the radar reflectivity factor of the target layer under different conditions to establish a parameterized equation for the concentration of silver iodide, including: The data from the repeated test steps are grouped and substituted into the polynomial regression model with undetermined coefficients to obtain the polynomial regression prediction value, which is recorded as : （6）； in, Indicates the sequence number of the data group. Indicates the sequence number of the independent variable, For the The first independent variable values, is the exponent of the independent variable, is the polynomial regression order, For the The index of the independent variable is The model coefficients when is the intercept term; The least squares method is used to estimate the polynomial coefficients. When the least squares error function has a minimum value, the polynomial regression prediction value Approximate the corresponding dependent variable data When , determine the polynomial coefficients , the least squares error Error calculation formula is as follows: （7）； in, Indicates that there is Group data, It is The dependent variable data in the group, For the Polynomial regression predictions for group data; Compute polynomial regression predictions With dependent variable data The root mean square error RMSE and correlation coefficient between them are used to determine the optimal polynomial regression order, so that the optimal polynomial regression order should minimize the RMSE and maximize the correlation coefficient. Substituting the polynomial coefficients and the optimal polynomial regression order into equation (6), we can obtain the silver iodide concentration that can be used to predict the silver iodide concentration under given input conditions. The parameterized equation is as follows: （10）。 4. The method for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation according to claim 3, characterized in that: The calculation of polynomial regression prediction value With dependent variable data The root mean square error RMSE and correlation coefficient between them are as follows: and The calculation formula of RMSE between is as follows: （8）； and The correlation coefficient between The calculation formula is as follows: （9）； In the above formula, Indicates the sequence number of the data group. Indicates that there is Group data.

5. A device for estimating the concentration of silver iodide required for artificial precipitation and snow enhancement by radar observation, characterized in that: include: The statistical module is used to obtain the radar observation results of any cloud precipitation case and to calculate the vertical distribution of the radar reflectivity factor; A setting module is used to set the threshold of the reflectivity factor of the target layer according to the vertical distribution of the radar reflectivity factor; wherein the arithmetic mean of the radar reflectivity factors of all heights within the observation time and observation range plus one or more standard deviations is used as the threshold of the reflectivity factor of the target layer; A calculation module, for inputting the temperature of the sowing layer, the air pressure of the sowing layer, the height difference between the sowing layer and the target layer, and the threshold value of the reflectivity factor of the target layer into a parameterized equation of the pre-acquired silver iodide concentration, and calculating the required silver iodide concentration; The method for obtaining the parameterized equation of the silver iodide concentration includes: A one-dimensional ice crystal growth model is established, and the ice water content of the one-dimensional ice crystal growth model is calculated; The ice water content of the one-dimensional ice crystal growth model was compared with the three-dimensional simulation results to obtain the applicable scope of the one-dimensional ice crystal growth model; The test steps include: within the applicable range, randomly selecting a spreading layer temperature, a spreading layer height, a target layer height, and a silver iodide concentration to conduct a simulation test to obtain a radar reflectivity factor of the target layer; Repeat the test steps to a set number of times to obtain the radar reflectivity factor of the target layer under different conditions; Based on the radar reflectivity factor of the target layer under different conditions, polynomial regression is used to fit the data points and a parameterized equation for silver iodide concentration is established; The calculation formula of the one-dimensional ice crystal growth model is as follows: (1)； in, represents the rate of change of ice crystal mass over time, is the ratio of pi, is the specific gas constant for water vapor, It is the heat released when a unit mass of water vapor condenses into ice. is the thermal conductivity of air, represents the air temperature in the growth environment of ice crystals, is the diffusion rate of water vapor in air, is the density of saturated water vapor, is the parameter capacitance, represents the ventilation factor, is the water vapor supersaturation relative to the ice surface; Where, for a prolate ellipsoid, the parameter capacitance The calculation formula is as follows: （2）； For a prolate ellipsoid, the parameter capacitance The calculation formula is as follows: （3）； in, represents the length of the ice crystal, represents the width of the ice crystal, is the aspect ratio of the ice crystal, .

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 4 are implemented.

7. A computer device, characterized in that: include: Memory, for storing computer programs / instructions; A processor, configured to execute the computer program / instructions to implement the steps of the method according to any one of claims 1 to 4.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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