Artificial hail suppression operation area identification method, system, equipment and medium

Abstract

The invention provides an artificial hail suppression operation area identification method. The artificial hail suppression operation area identification method comprises the following steps: determining an initial artificial hail suppression operation area based on related information; dividing the initial artificial hail suppression operation area into grid points under a preset resolution ratio, and according to meteorological sounding second-level data, respectively calculating altitudes corresponding to 0 DEG C,-10 DEG C and-20 DEG C above each grid point by using a difference algorithm, and constructing a 0 DEG C layer, a-10 DEG C layer and a-20 DEG C layer; performing artificial hail suppression operation area identification by combining the dual-polarization radar parameters under different radar scanning elevation angles; the artificial hail suppression operation area comprises a strong upflow area, a hail embryo formation area and a hail wet growth area; wherein the hail embryo formation area comprises a frozen drop type hail embryo area and an aragonia type hail embryo area. The method not only can improve the accuracy of operation area identification, but also can effectively cope with constantly changing weather conditions, thereby providing more scientific and reliable guidance for artificial hail suppression operation.

Description

Technical Field

[0001] The invention relates to the field of artificial hail prevention operations, and more particularly to an artificial hail prevention operation area identification method, system, equipment and medium. Background Art

[0002] In the field of artificial hail prevention operations, traditional methods mainly rely on indicators such as radar reflectivity and vertical integrated liquid water content provided by the new generation of weather radars to identify hail. Although these indicators can provide information about the liquid water content and reflection intensity in the cloud body, they are insufficient in describing the microphysical characteristics related to hail in the cloud, especially in the early stages of hail formation.

[0003] For example, the VIL threshold and its jump phenomenon can be used to indicate areas where hail may form, but these methods can often only provide effective signals when hail has formed or is about to form, missing the best time for human intervention.

[0004] In recent years, with the development of dual-polarization radar technology, researchers have begun to explore the use of dual-polarization radar parameters to improve hail identification methods. These studies mainly focus on the identification of hail after it is formed, usually using fuzzy logic-based methods or other algorithms to analyze multiple parameters in radar echoes, such as horizontal reflectivity factor, differential reflectivity, correlation coefficient, etc., to identify the presence of hail. Although these methods have made significant progress in hail identification, they mainly focus on the already formed hail, rather than the embryonic stage or wet growth stage of hail before it forms.

[0005] Traditional hail identification indicators can only identify areas where hail has already formed, but cannot effectively identify hail embryo formation areas, that is, areas in the cloud that have not yet formed hail but have the potential to form hail. At the same time, in the process of hail growing from smaller ice nuclei to larger hail, there is a critical wet growth stage, which is crucial to the final size of the hail, but existing technologies fail to clearly identify this area, resulting in the inability to conduct effective human intervention at this stage.

[0006] In addition, traditional hail prevention methods usually rely on a single data source, such as radar data or weather forecast data, and lack the ability to conduct comprehensive analysis of multiple sources of data, such as weather forecast data, artificial hail prevention protection area information, and artificial hail prevention operation point information. This leads to inaccurate identification of the operation area and inability to fully consider the actual feasibility and pertinence of the operation.

[0007] Moreover, when determining the altitude corresponding to key temperature layers (such as 0℃, -10℃ and -20℃ layers), a relatively rough method is usually adopted, lacking the ability to accurately locate the temperature layers. This makes the understanding of hail formation conditions inaccurate and reduces the effectiveness of the operation.

[0008] Therefore, how to design a method for identifying artificial hail prevention operation areas to make up for the shortcomings of existing technologies in comprehensive information analysis, temperature layer positioning, radar parameter analysis capabilities and three-dimensional spatial analysis, and improve the accuracy and effectiveness of operations is an urgent problem that technical personnel in this field need to solve. Summary of the invention

[0009] In view of this, the present invention provides a method for identifying an artificial hail prevention operation area, which combines weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information, uses a difference algorithm to calculate the altitude corresponding to a specific temperature layer, and combines dual-polarization radar parameters to identify the artificial hail prevention operation area.

[0010] In order to achieve the above object, the present invention adopts the following technical solution:

[0011] In a first aspect, the present invention provides a method for identifying an artificial hail prevention operation area, comprising the following steps:

[0012] S1. Determine an initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information;

[0013] S2. Divide the initial artificial hail prevention operation area into grid points at a preset resolution, and use the difference algorithm to calculate the altitudes corresponding to 0℃, -10℃ and -20℃ above each grid point based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer;

[0014] S3. Identify the artificial hail prevention operation area in combination with the dual-polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: a strong updraft area, a hail embryo formation area and a hail wet growth area; wherein the hail embryo formation area includes: a frozen drop type hail embryo area and a graupel type hail embryo area.

[0015] Preferably, in S1, determining the initial artificial hail prevention operation area based on relevant information includes:

[0016] The probability of hail occurrence at time t and location (x, y) is defined as P(t, x, y) based on weather forecast data; the number of days or times of historical hail disasters at location (x, y) is defined as H(x, y) based on the artificial hail protection protection area information; the demand level of economic crops protected by artificial hail protection at location (x, y) is defined as S(x, y) based on the artificial hail protection protection area information; the traffic accessibility T(x, y) at location (x, y) is defined based on the artificial hail protection operation point information, where T(x, y) = e -αd , d represents the distance to the nearest transportation infrastructure, and α represents the rate of decrease of transportation accessibility when the distance increases;

[0017] The initial artificial hail prevention operation area is determined by the risk assessment model R(t,x,y); where R(t,x,y) = β 1 P(t,x,y)+β 2 H(x,y)+β 3 S(x,y)+β 4 T(x,y), β 1 , β 2 , β 3 and β 4 is the weight coefficient.

[0018] Preferably, in S2, using a difference algorithm to calculate the altitudes corresponding to 0°C, -10°C and -20°C above each grid point, respectively, includes:

[0019] For each grid point, the two height points Z closest to the target temperature T in the second-level meteorological sounding data are used to calculate the 1 and Z 2 and its corresponding temperature T 1 and T 2 , use the interpolation algorithm to calculate the height Z corresponding to the target temperature T;

[0020]

[0021] The interpolation algorithm is used to calculate the altitude Z corresponding to 0℃, -10℃ and -20℃ above each grid point. 0 , Z -10 and Z -20 .

[0022] Preferably, in S3, the identification of the strong updraft area includes:

[0023] According to the dual polarization radar parameters at different radar scanning elevation angles, the regional layers in each layer are identified layer by layer from the lowest layer upwards; the regional layers are Z DR Areas with values ​​greater than 2dB and an area greater than 1 square kilometer;

[0024] Combine the altitude and longitude and latitude coordinates to merge all the area layers and construct a three-dimensional image;

[0025] Based on the three-dimensional image, identifying an area extending continuously upward from the lowest elevation layer to an altitude exceeding 1 kilometer above the 0°C layer;

[0026] Determine whether the identified area forms a continuous columnar area; if so, the columnar area is a strong updraft area.

[0027] Preferably, in said S3, the identification of the frozen drop type hail embryo area comprises:

[0028] According to the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than 0℃ layer. H Greater than 45dBz and differential reflectivity Z DR Areas less than or equal to 0dB;

[0029] Determine whether the identified area is less than 5 kilometers from the top edge of the strong updraft area; if so, proceed to the next step;

[0030] Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a frozen drop type hail embryo area.

[0031] Preferably, in S3, the identification of hail embryo areas of graupel type includes:

[0032] Based on the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than -10℃. H Greater than 35dBz, differential reflectivity Z DR The area with a correlation coefficient CC greater than 0.99 is less than 1dB;

[0033] Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a sleet type hail embryo area.

[0034] Preferably, in S3, identifying the hail wet growth area includes:

[0035] According to the dual polarization radar parameters at different radar scanning elevation angles, in the area with an altitude greater than -10℃ layer and less than -20℃ layer, the identification correlation coefficient CC is greater than 0.85 and less than 0.95, the horizontal reflectivity factor Z H Greater than 50dBz, differential reflectivity Z DR Less than 0dB and better than differential phase K DP Areas that are missing;

[0036] Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a hail wet growth area.

[0037] In a second aspect, the present invention provides an artificial hail prevention operation area identification system, comprising:

[0038] An initial artificial hail prevention operation area determination module is used to determine an initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information;

[0039] Temperature layer construction module: It is used to divide the initial artificial hail prevention operation area into grid points at a preset resolution, and calculate the altitude corresponding to 0℃, -10℃ and -20℃ above each grid point using the difference algorithm based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer;

[0040] Artificial hail prevention operation area identification module: used to identify the artificial hail prevention operation area in combination with dual-polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: strong updraft area, hail embryo formation area and hail wet growth area; among which, the hail embryo formation area includes: frozen drop type hail embryo area and graupel type hail embryo area.

[0041] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for identifying an artificial hail prevention operation area when executing the computer program.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for identifying an artificial hail prevention operation area is implemented.

[0043] The descriptions of the second to fourth aspects of the present invention can refer to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second to fourth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0044] It can be seen from the above technical solution that compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. This method integrates multiple data sources such as weather forecast data, artificial hail protection protection area information, and artificial hail protection operation point information. By establishing a risk assessment model, the initial operation area is determined, which not only improves the accuracy of identification, but also ensures that the selection of the operation area is more in line with actual needs and can better guide the implementation of hail prevention operations.

[0046] 2. By using the difference algorithm to calculate the altitude corresponding to specific temperature layers (0℃, -10℃ and -20℃), this method can accurately locate the key layer for hail formation, providing an accurate vertical position reference for subsequent work area identification, thereby enhancing the ability to identify hail formation conditions.

[0047] 3. By constructing a three-dimensional image and combining data at different radar scanning elevation angles, this method can realize a three-dimensional analysis of the operating area, especially for the identification of strong updraft areas. It can ensure that the identified areas are not only reasonably distributed in the horizontal plane, but also meet specific conditions in the vertical direction, providing more comprehensive information support for hail prevention operations.

[0048] 4. By using different combinations of dual-polarization radar parameters, this method can efficiently identify key areas such as strong updraft areas, frozen droplet type hail embryo areas, graupel type hail embryo areas and hail wet growth areas. These precise identifications are helpful for early warning and effective intervention in the formation and development process of hail. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0050] Figure 1 A flow chart of a method for identifying an artificial hail prevention operation area provided by an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a strong updraft area identification process provided by an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a frozen drop type hail embryo area identification process provided in an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of a process for identifying hail embryo areas of graupel type provided in an embodiment of the present invention;

[0054] Figure 5 A schematic diagram of a hail wet growth area identification process provided by an embodiment of the present invention;

[0055] Figure 6 A schematic diagram of the structure of an artificial hail prevention operation area identification system provided by an embodiment of the present invention;

[0056] Figure 7 A schematic diagram of the structure of an electronic device is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The identification method provided in the embodiment of the present application can be applied to an identification server. The above-mentioned identification server can be hardware or software. When the identification server is hardware, it can be implemented as a distributed server cluster that provides identification services, or it can be implemented as a single server. When the identification server is software, it can be installed in the servers listed above. It can be implemented as multiple software or software modules, or it can be implemented as a single software or software module, which is not specifically limited here.

[0059] Embodiment 1:

[0060] like Figure 1 As shown, the present invention provides a method for identifying an artificial hail prevention operation area, comprising the following steps:

[0061] S1. Determine an initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information;

[0062] S2. Divide the initial artificial hail prevention operation area into grid points at a preset resolution, and use the difference algorithm to calculate the altitudes corresponding to 0℃, -10℃ and -20℃ above each grid point based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer;

[0063] S3. Identify the artificial hail prevention operation area in combination with the dual-polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: a strong updraft area, a hail embryo formation area and a hail wet growth area; wherein the hail embryo formation area includes: a frozen drop type hail embryo area and a graupel type hail embryo area.

[0064] The above-mentioned artificial hail prevention operation area identification method realizes the accurate identification of key areas for hail formation by comprehensively using weather forecast data, artificial hail prevention protection area information and operation point information, combining risk assessment models and dual-polarization radar parameters. It not only improves the accuracy and practicality of operation area selection, but also can effectively intervene before hail formation or in the hail embryo stage, thereby significantly improving the effectiveness and pertinence of artificial hail prevention operations.

[0065] The above steps and related features are further described in detail below:

[0066] In this embodiment S1, determining the initial artificial hail prevention operation area based on relevant information includes:

[0067] The probability of hail at time t and location (x, y) is defined as P(t, x, y) based on weather forecast data. This parameter is used to predict the possibility of hail at a specific time and location.

[0068] The number of days or times of historical hail disasters at the location (x, y) defined based on the artificial hail protection zone information is recorded as H(x, y). The parameter reflects the hail disaster situation in a certain area over a period of time in the past and can be used to assess the hail risk level in the area.

[0069] The demand level for economic crops protected by artificial hail protection at location (x, y) is defined as S(x, y) based on the artificial hail protection zone information. This parameter takes into account the needs of local agricultural production, especially the demand for crops that are susceptible to hail.

[0070] Based on the artificial hail prevention operation point information, the traffic accessibility T(x,y) at the location (x,y) is defined, where T(x,y) = e -αd , d represents the distance to the nearest transportation infrastructure, and α represents the rate of decrease in transportation accessibility as the distance increases; this parameter mainly considers the convenience and efficiency of implementing artificial hail prevention operations, that is, whether it is possible to arrive quickly and carry out work.

[0071] Comprehensively consider the influence of the above four factors to determine the area most in need of artificial hail prevention operations. The initial artificial hail prevention operation area is determined by the risk assessment model R(t,x,y); where R(t,x,y)=β 1 P(t,x,y)+β 2 H(x,y)+β 3 S(x,y)+β 4 T(x,y), β 1 , β 2 , β 3 and β 4 is the weight coefficient.

[0072] In this embodiment, for the weight coefficient β 1 , β 2 , β 3 and β 4 Optimizations include:

[0073] Collect historical data sets including weather forecast data, historical hail disaster records, and traffic accessibility; preprocess the data to extract features related to the risk assessment model; use machine learning methods such as support vector machine (SVM) or random forest (RandomForest) to build the model, and train the model through cross-validation; find the optimal weight combination through grid search or random search to optimize the risk assessment model; use an independent test set to verify the performance of the optimized model, and apply the optimal weights to the risk assessment model R(t,x,y).

[0074] It uses real-time meteorological data, historical disaster records, agricultural production needs, transportation accessibility and other information, and through corresponding mathematical models and statistical analysis, generates a comprehensive and accurate initial artificial hail prevention operation area.

[0075] In this embodiment S2, the initial artificial hail prevention operation area is divided into grid points at a preset resolution, and the altitudes corresponding to 0°C, -10°C and -20°C above each grid point are calculated using a difference algorithm based on the second-level meteorological sounding data to construct a 0°C layer, a -10°C layer and a -20°C layer;

[0076] In this step, firstly, the appropriate grid resolution is selected according to the needs of hail prevention operations and the accuracy of available data, for example, each grid point covers a range of 1 km × 1 km, while taking into account the needs of computing resources and balancing the requirements of computing efficiency and accuracy. Next, the initial artificial hail prevention operation area is divided into grids to ensure that the geographical space covered by each grid point is consistent, and the grid point size is dynamically adjusted according to the complexity of the terrain and the changes in meteorological conditions. Smaller grid points are used in areas with large terrain fluctuations or drastic changes in meteorological conditions. Finally, the meteorological sounding second-level data is matched with the grid coordinates. For meteorological sounding data that spans multiple grid points, it is integrated by averaging or other statistical methods to ensure that the data of each grid point represents the situation in the area.

[0077] For each grid point, the two height points Z closest to the target temperature T in the second-level meteorological sounding data are used to calculate the 1 and Z 2 and its corresponding temperature T 1 and T 2 The second-level meteorological sounding data here contains temperature measurements at different altitudes.

[0078] Use the interpolation algorithm to calculate the height Z corresponding to the target temperature T;

[0079]

[0080] The interpolation algorithm is used to calculate the altitude Z corresponding to 0℃, -10℃ and -20℃ above each grid point. 0 , Z -10 and Z -20 .

[0081] In this embodiment S3, the artificial hail prevention operation area is identified by combining the dual polarization radar parameters at different radar scanning elevation angles;

[0082] In this step, the dual polarization parameters of the radar provide important information about the morphology and structure of precipitation particles, which is crucial for identifying the strong updraft area, hail embryo formation area and hail wet growth area in the artificial hail prevention operation area. By analyzing these parameters, the key areas for hail formation and development can be determined more accurately, thus providing scientific basis and support for artificial hail prevention operations.

[0083] Specifically include:

[0084] Horizontal reflectivity factor Z H (in dBz): An indicator used to quantify the strength of radar echoes, which represents the radar reflectivity factor in the horizontal polarization direction. The larger the value, the stronger the echo, which usually means that there are more precipitation particles.

[0085] Differential reflectivity Z DR (in dB): It is the logarithmic form of the ratio of the radar reflectivity factors in the vertical polarization direction to the horizontal polarization direction. Its positive value usually indicates that the reflectivity factor in the vertical polarization direction is higher than that in the horizontal polarization direction, which is caused by the non-spherical shape of precipitation particles.

[0086] Ratio differential phase K DP (unit: ° / km): is the rate of change of the phase difference between the vertical polarization and horizontal polarization signals. It reflects the non-spherical degree of precipitation particles and the complexity of the internal structure of precipitation particles.

[0087] Correlation coefficient CC (unit is dimensionless): It is the correlation degree between vertical polarization and horizontal polarization signals. A value close to 1 indicates that there is a strong correlation between the two polarization signals, which means that the shapes of precipitation particles are relatively consistent.

[0088] like Figure 2 As shown in Figure 1, identification of strong updraft areas includes:

[0089] According to the dual polarization radar parameters at different radar scanning elevation angles, the regional layers in each layer are identified layer by layer from the lowest layer upwards; the regional layers are Z DR Areas with values ​​greater than 2dB and an area greater than 1 square kilometer;

[0090] Combine the altitude and longitude and latitude coordinates to merge all the area layers and construct a three-dimensional image;

[0091] Based on the three-dimensional image, identifying an area extending continuously upward from the lowest elevation layer to an altitude exceeding 1 kilometer above the 0°C layer;

[0092] Determine whether the identified area forms a continuous columnar area; if so, the columnar area is a strong updraft area.

[0093] like Figure 3 As shown, the identification of frozen drop type hail embryo area includes:

[0094] According to the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than 0℃ layer. H Greater than 45dBz and differential reflectivity Z DR Areas less than or equal to 0dB;

[0095] Determine whether the identified area is less than 5 kilometers from the top edge of the strong updraft area; if so, proceed to the next step;

[0096] Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a frozen drop type hail embryo area.

[0097] like Figure 4 As shown, the identification of hail embryo area of ​​graupel type includes:

[0098] Based on the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than -10℃. H Greater than 35dBz, differential reflectivity Z DR The area with a correlation coefficient CC greater than 0.99 is less than 1dB;

[0099] Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a sleet type hail embryo area.

[0100] like Figure 5 As shown, identification of hail wet growth areas includes:

[0101] According to the dual polarization radar parameters at different radar scanning elevation angles, in the area with an altitude greater than -10℃ layer and less than -20℃ layer, the identification correlation coefficient CC is greater than 0.85 and less than 0.95, the horizontal reflectivity factor Z H Greater than 50dBz, differential reflectivity Z DR Less than 0dB and better than differential phase K DP Areas that are missing;

[0102] Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a hail wet growth area.

[0103] In this step, by analyzing the dual-polarization radar parameters at different radar scanning elevation angles, key areas such as strong updraft areas, frozen droplet type hail embryo areas, graupel type hail embryo areas and hail wet growth areas are efficiently identified.

[0104] Specifically, the strong updraft area is determined by constructing a three-dimensional image recognition columnar area extending continuously from the lowest elevation angle to the 1 km layer above 0℃. In the area above 0℃, the horizontal reflectivity factor is greater than 45dBz and the differential reflectivity is less than or equal to 0dB as the frozen droplet type hail embryo area. In the area above -10℃, the horizontal reflectivity factor is greater than 35dBz, the differential reflectivity is less than 1dB and the correlation coefficient is greater than 0.99 as the graupel type hail embryo area. In the area above -10℃ and less than -20℃, the correlation coefficient is between 0.85 and 0.95, the horizontal reflectivity factor is greater than 50dBz, the differential reflectivity is less than 0dB and the differential phase KDP is missing as the hail wet growth area. It not only improves the accuracy of identification, but also helps to give early warning and effectively intervene in the formation and development process of hail.

[0105] Furthermore, after identifying the key areas such as the strong updraft area, frozen droplet type hail embryo area, graupel type hail embryo area and hail wet growth area, the specific process of hail prevention operation includes:

[0106] According to the identified key areas, make a detailed operation plan, including the operation time, location, type of catalyst used, etc. Prepare operation tools such as rockets or anti-aircraft guns carrying cold cloud catalysts, and ensure that the equipment is in good condition.

[0107] After identifying the strong updraft area, the catalyst is sent into the area by rocket or anti-aircraft gun. The catalyst is brought higher into the cloud by the updraft, especially to ensure that the catalyst reaches the hail embryo area or wet growth area to inhibit the formation and growth of hail.

[0108] Operations in the frozen droplet type hail embryo area: Once the frozen droplet type hail embryo area is identified, the operation procedure is immediately initiated. The formation of hail is stopped or slowed down by spraying catalysts on the area, and intervention is carried out at the hail embryo stage as much as possible to prevent further development of hail.

[0109] Operations in graupel-type hail embryo areas: After identifying graupel-type hail embryo areas, take prompt action to place catalysts in the area. The catalytic effect changes the characteristics of graupel particles and prevents or slows down the formation of hail.

[0110] Operations in wet growth areas of hail: After identifying wet growth areas of hail, take operational measures quickly. Prevent or slow down the formation of large hail (more than 2 cm in diameter) by operating in this area, especially to ensure that the catalyst can effectively act on the wet growth stage of hail.

[0111] In addition, radar data and other meteorological parameters are continuously monitored during the operation to evaluate the operation effect. Based on the monitoring results, the operation strategy is adjusted in a timely manner to ensure the best effect of the operation.

[0112] Through the above operation process, this embodiment can not only accurately identify the key areas, but also take effective measures before or in the early stage of hail formation, thereby significantly improving the effect and pertinence of artificial hail prevention operations.

[0113] Embodiment 2;

[0114] like Figure 6 As shown, this embodiment provides an artificial hail prevention operation area identification system, including:

[0115] An initial artificial hail prevention operation area determination module is used to determine an initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information;

[0116] Temperature layer construction module: It is used to divide the initial artificial hail prevention operation area into grid points at a preset resolution, and calculate the altitude corresponding to 0℃, -10℃ and -20℃ above each grid point using the difference algorithm based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer;

[0117] Artificial hail prevention operation area identification module: used to identify the artificial hail prevention operation area in combination with dual-polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: strong updraft area, hail embryo formation area and hail wet growth area; among which, the hail embryo formation area includes: frozen drop type hail embryo area and graupel type hail embryo area.

[0118] The system consists of three main modules: the initial artificial hail prevention operation area determination module, which is used to determine the initial operation area based on weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information; the temperature layer construction module, which is responsible for gridding the initial operation area and using the difference algorithm to calculate the altitude corresponding to 0℃, -10℃ and -20℃ above each grid point to construct the temperature layer; the artificial hail prevention operation area identification module, which identifies key areas such as strong updraft areas, frozen droplet type hail embryo areas, graupel type hail embryo areas and hail wet growth areas by analyzing the dual-polarization radar parameters at different radar scanning elevation angles. This system can efficiently and accurately identify the key areas where hail is formed, providing important decision-making support for artificial hail prevention operations.

[0119] Embodiment 3;

[0120] like Figure 7 As shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned method for identifying an artificial hail prevention operation area when executing the computer program.

[0121] Embodiment 4;

[0122] This embodiment provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for identifying an artificial hail prevention operation area is implemented.

[0123] In the above embodiments provided in the present application, it should be understood that the disclosed methods, systems, devices and media can be implemented in other ways. The above described methods, systems, devices and media embodiments are only schematic. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. Each functional unit can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0124] The units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0126] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for identifying an artificial hail prevention operation area, characterized in that: The following steps are involved: S1. Determine an initial artificial hail prevention operation area based on relevant information; the relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information; S2. Divide the initial artificial hail prevention operation area into grid points at a preset resolution, and use the difference algorithm to calculate the altitudes corresponding to 0℃, -10℃ and -20℃ above each grid point based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer; S3. Identify the artificial hail prevention operation area in combination with the dual-polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: a strong updraft area, a hail embryo formation area and a hail wet growth area; wherein the hail embryo formation area includes: a frozen drop type hail embryo area and a graupel type hail embryo area.

2. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S1, determining the initial artificial hail prevention operation area based on relevant information includes: The probability of hail occurrence at time t and location (x, y) is defined as P(t, x, y) based on weather forecast data; the number of days or times of historical hail disasters at location (x, y) is defined as H(x, y) based on the artificial hail protection protection area information; the demand level of economic crops protected by artificial hail protection at location (x, y) is defined as S(x, y) based on the artificial hail protection protection area information; the traffic accessibility T(x, y) at location (x, y) is defined based on the artificial hail protection operation point information, where T(x, y) = e -αd , d represents the distance to the nearest transportation infrastructure, and α represents the rate of decrease of transportation accessibility when the distance increases; The initial artificial hail prevention operation area is determined by the risk assessment model R(t,x,y); where R(t,x,y)=β1P(t,x,y)+β2H(x,y)+β3S(x,y)+β4T(x,y), and β1, β2, β3 and β4 are weight coefficients.

3. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S2, the altitude corresponding to 0°C, -10°C and -20°C above each grid point is calculated using a difference algorithm, including: For each grid point, the height Z corresponding to the target temperature T is calculated using the interpolation algorithm based on the two height points Z1 and Z2 closest to the target temperature T in the second-level meteorological sounding data and their corresponding temperatures T1 and T2; The interpolation algorithm is used to calculate the altitudes Z0, Z2 corresponding to 0℃, -10℃ and -20℃ above each grid point. -10 and Z -20 .

4. The method for identifying an artificial hail prevention operation area according to claim 3, characterized in that: In S3, the identification of the strong updraft area includes: According to the dual polarization radar parameters at different radar scanning elevation angles, the regional layers in each layer are identified layer by layer from the lowest layer upwards; the regional layers are Z DR Areas with values ​​greater than 2dB and an area greater than 1 square kilometer; Combine the altitude and longitude and latitude coordinates to merge all the area layers and construct a three-dimensional image; Based on the three-dimensional image, identifying an area extending continuously upward from the lowest elevation layer to an altitude of more than 1 kilometer above the 0°C layer; Determine whether the identified area forms a continuous columnar area; if so, the columnar area is a strong updraft area.

5. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S3, the identification of the frozen drop type hail embryo area includes: According to the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than 0℃ layer. H Greater than 45dBz and differential reflectivity Z DR Areas less than or equal to 0dB; Determine whether the identified area is less than 5 kilometers from the top edge of the strong updraft area; if so, proceed to the next step; Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a frozen drop type hail embryo area.

6. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S3, identification of hail embryo areas of graupel types includes: Based on the dual polarization radar parameters at different radar scanning elevation angles, the horizontal reflectivity factor Z is identified in the area with an altitude greater than -10℃. H Greater than 35dBz, differential reflectivity Z DR The area with a correlation coefficient CC greater than 0.99 is less than 1dB; Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a sleet-type hail embryo area.

7. The method for identifying an artificial hail prevention operation area according to claim 1, characterized in that: In S3, the identification of the hail wet growth area includes: According to the dual polarization radar parameters at different radar scanning elevation angles, in the area with an altitude greater than -10℃ layer and less than -20℃ layer, the identification correlation coefficient CC is greater than 0.85 and less than 0.95, the horizontal reflectivity factor Z H Greater than 50dBz, differential reflectivity Z DR Less than 0dB and better than differential phase K DP Areas that are missing; Determine whether the length, width and height of the identified area are all greater than 500 meters; if so, the area is a hail wet growth area.

8. An artificial hail prevention operation area identification system, characterized in that: include: An initial artificial hail prevention operation area determination module is used to determine an initial artificial hail prevention operation area based on relevant information; The relevant information includes: weather forecast data, artificial hail prevention protection area information and artificial hail prevention operation point information; Temperature layer construction module: It is used to divide the initial artificial hail prevention operation area into grid points at a preset resolution, and calculate the altitude corresponding to 0℃, -10℃ and -20℃ above each grid point using the difference algorithm based on the second-level meteorological sounding data, and construct the 0℃ layer, -10℃ layer and -20℃ layer; Artificial hail prevention operation area identification module: used to identify the artificial hail prevention operation area in combination with dual-polarization radar parameters at different radar scanning elevation angles; the artificial hail prevention operation area includes: strong updraft area, hail embryo formation area and hail wet growth area; among which, the hail embryo formation area includes: frozen drop type hail embryo area and graupel type hail embryo area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for identifying the artificial hail prevention operation area according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying an artificial hail prevention operation area according to any one of claims 1 to 7 is implemented.

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