Hail suppression operation method and system based on convective monomer hazard index and hail identification

Abstract

The invention discloses a hail suppression operation method and system based on a convective monomer hazard index and hail identification, and relates to the technical field of meteorological monitoring and artificial influence weather command operation. According to the method, a severe convection monomer hazard model and a hail identification model are established based on multi-source observation such as weather radar and air sounding, the type of each severe convection monomer is identified in the hail identification model by combining two sections of score thresholds, and the development stage corresponding to the severe convection monomer is determined. A sectional type operation strategy is constructed according to the identification type and stage of the severe convection monomers, and a task distribution mechanism of multiple convection monomers and multiple operation points and an element method for calculating cannonball launching parameters are established. According to the invention, a precise operation process is provided for hail suppression command, human intervention in hail suppression operation is reduced, and the efficiency and accuracy of decision execution are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological monitoring and artificial weather influencing command operations, and in particular to a hail prevention operation method and system based on a convective cell hazard index and hail identification. Background Art

[0002] Artificial hail suppression technology is a complex and delicate task, with hail identification and prevention being key aspects. The principle behind this approach is to use tools like anti-aircraft guns and rockets to deliver silver iodide as artificial ice nuclei into appropriate locations within a hail cloud. These nuclei then form artificial hail embryos, which compete with natural hail embryos for the limited supercooled water in the cloud, thereby suppressing the formation of larger hailstones. Artificial hail suppression, primarily using anti-aircraft guns and rockets, is a crucial measure for mitigating the impact of hailstorms.

[0003] Hail identification is fundamental to hail prevention. Hail, characterized by suddenness, instantaneity, and localized occurrence, is a key and challenging aspect of short-term nowcasting and warning. The life cycle of a hail cloud consists of three stages: formation and development, mature hail, and extinction. Hail occurs in environments below 0°C or lower. Ice crystals primarily grow and form hail embryos at ambient temperatures between -7°C and -20°C. Large hailstones may form in areas where the altitude corresponding to the -20°C isotherm exceeds the altitude corresponding to the radar reflectivity factor of 45dBZ. Currently, there is considerable theoretical research on large hailstone identification, such as the presence of high-hanging strong and weak echoes on the vertical structure of single-polarization radars, a strong hail index exceeding 80%, and correlation coefficients between differential reflectivity values ​​close to zero and low values ​​corresponding to strong reflectivity factors on dual-polarization radars. However, heavy rainfall observations before strong hailstorms and the small hailstones produced by weak hail clouds can impact crop growth and fruit quality. Therefore, methods for identifying small hailstones are also crucial for hail prevention.

[0004] During field trials of hail suppression, it was discovered that due to the short time it takes for a normal convective cell to develop into a hail cloud, in the face of rapidly developing multi-cell storms, the operation team needs to make quick decisions on whether and how to conduct hail suppression operations. Currently, in terms of operational conditions, the manual hail suppression decision-making and command system primarily utilizes historical meteorological data and weather radar technology. By analyzing radar characteristic parameters, especially by statistically analyzing echo cloud area and intensity, it can automatically track and analyze hail clouds, providing a scientific basis for hail suppression operations. As an example, Chinese patent application CN202111339839.4 provides a method for commanding artificial hail prevention operations based on dual-polarization weather radar, including: S1, data entry and preprocessing, wherein the horizontal polarization reflectivity factor, differential reflectivity factor, differential propagation phase shift, zero-lag correlation coefficient of horizontal polarization and vertical polarization echo power, and ambient temperature are selected as judgment parameters, and the above parameter data detected by the polarization radar are entered in real time, and all parameters are preprocessed with attenuation correction using polarization parameters; S2, data quality control; S3, using fuzzy logic element method to identify the phase state of hydrometeors, wherein hail is identified by fuzzy logic method, and according to the particle phase characteristics before the formation of hail particles, the early echo characteristics of hail such as sleet particles formed by hail embryos are discovered before the hail is formed; S4, strategy judgment, the best time for artificial hail prevention operations is given according to the discrimination strategy, and combined with the gun point information, it is converted into accurate information for human shadow operations; S5, producing and publishing hail prevention operation information. The above scheme utilizes dual-polarization information from dual-polarization weather radar, combined with the microphysical characteristics of hail cloud and hail formation, to determine the timing and location for early artificial hail suppression operations in hail clouds. It also provides intelligent real-time audio-visual reminders and instructions to avoid missed operations and improve the efficiency of artificial hail suppression operations. However, the above scheme relies primarily on polarization radar detection data to identify hail, which cannot determine the development and change characteristics of hail clouds. Its ability to detect and identify the rapid changes in rapidly developing severe convective hail weather processes is far from sufficient, making it impossible to achieve high-precision hail suppression operations. Hail clouds develop and change rapidly, and the opportunity for hail suppression operations is fleeting. The precision of artificial hail suppression operations, such as grasping the operating conditions and timing, determines the effectiveness of hail suppression. Ensuring that hail prevention measures can be implemented in an orderly and efficient manner is an important and difficult task in artificial hail suppression operations.

[0005] On the other hand, existing technologies offer some solutions for refined observation of severe convective targets. For example, Chinese patent ZL201910641616.X discloses a collaborative adaptive control method for networked X-band weather radars, which can better achieve rapid tracking and early warning of severe convective weather. Building on this solution, Chinese patent application CN202310834165.8 also discloses a method for collaborative observation of severe convective cells by a network of X-band radars. Upon detecting the presence of a severe convective region, the method performs a segmentation operation based on the severe convective region. If multiple severe convective cells are segmented, the hazard level of each severe convective cell is determined based on its corresponding target characteristic. Furthermore, scanning tasks are assigned to each X-band radar in the radar network based on the hazard level of each severe convective cell, the distance between the centroid of each severe convective cell and the X-band radar in the radar network, and the vertical spread of each severe convective cell. The scanning tasks include volume scanning and vertical scanning. Building on the above scheme, Chinese patent ZL202410446117.6 discloses a multi-band radar adaptive collaborative observation method for convective processes. This method focuses on hail and short-term heavy rainfall, and studies an intelligent collaborative tracking algorithm for highly hazard-prone single-cell targets in severe convection. It introduces a comprehensive hazard index to evaluate the hazardness of strong convective cells. After ranking storm cells based on their strength within and outside key areas and the comprehensive hazard index, radar observation task priorities are uniformly assigned. Furthermore, storm cell pre-identification is introduced, with different scanning strategies configured for pre-identifying hail and precipitation. This scheme can accurately identify and segment each strong convective cell in the context of rapidly developing multi-cell storms. It also extracts characteristic parameters of hazardous weather based on these cells and calculates a comprehensive hazard index (or hazard index) for each strong convective cell. The weather type (whether hail will form) of the strong convective cell is then pre-identified based on hail forecast judgment conditions and the comprehensive hazard index score.

[0006] According to the requirements of precise hail prevention operations, the present invention further improves the above-mentioned convective monomer hazard index scoring and hail identification technology, and proposes a hail prevention operation scheme based on the convective monomer hazard index and hail identification. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a hail prevention method and system based on the convective cell hazard index and hail identification. The present invention is based on a strong convective cell hazard model and a hail identification model. In the hail identification model, the type of each strong convective cell is identified by combining two-segment score thresholds, and the stage corresponding to the strong convective cell is determined. A segmented operation strategy is constructed according to the identification type and stage of the strong convective cell, thereby realizing the task allocation of multiple convective cells and multiple operation points. The present invention provides a precise operation process for hail prevention command, reduces human intervention in hail prevention operations, and improves the efficiency and accuracy of decision-making execution.

[0008] To achieve the above objectives, the present invention provides the following technical solutions:

[0009] A hail prevention method based on a convective cell hazard index and hail identification comprises the following steps:

[0010] After segmenting all strong convective cells within the observation area, the hazard index (RSIK) for each strong convective cell is calculated. Furthermore, after determining the corresponding strong convective cell hazard model information based on the local hail occurrence conditions, the hazard index scores corresponding to the hail identification index and the pre-hailfall index are determined based on the strong convective cell hazard model.

[0011] Hail is identified on severe convective cells using a hail identification model. The hail identification model is configured to: determine two score thresholds for hail identification based on the hazard index scores corresponding to the two aforementioned indicators; and identify the type of each severe convective cell based on the segmentation information of the severe convective cell and the hazard index RSIK, in combination with the two score thresholds and the sounding temperature profile, and determine the stage corresponding to the severe convective cell;

[0012] According to the identification type and corresponding stage of the strong convective cell, a hail prevention operation strategy is configured for each strong convective cell to form a segmented hail prevention operation strategy.

[0013] Furthermore, the hazard index scores corresponding to the hail identification index and the pre-hailfall index are related to the ratio of the weight coefficients w1, w2, and w3 of the network reflectivity, the network vertical liquid water content VIL, and the network hail index POSH. After obtaining the local hail occurrence condition ratio data, the ratio of the aforementioned weight coefficients w1, w2, and w3 is determined according to the local hail occurrence condition ratio. Different ratios correspond to different severe convective cell hazard model information.

[0014] After determining the ratio and obtaining the severe convective single-unit hazard model information corresponding to the ratio, the hazard index score corresponding to each indicator is obtained according to the set hail identification indicator and hail pre-landing indicator, the hazard index score corresponding to the hail identification indicator is set as the first score threshold, and the hazard index score corresponding to the hail pre-landing indicator is set as the second score threshold.

[0015] Furthermore, before calculating the hazard index RSIK of a severe convective cell, the following steps are also included:

[0016] Obtain the segmentation information of the convection cell, and obtain the high threshold boundary segmentation area S of the strong convection cell according to the set reflectivity high threshold;

[0017] Determine whether the area S exceeds the preset area threshold. When the area S is less than or equal to the area threshold, it is determined that the RSIK of the strong convective cell has no score. When the area S exceeds the area threshold, it is determined that a convective cell is generated and the RSIK of the strong convective cell has a score. Calculate the RSIK value of the strong convective cell.

[0018] Furthermore, the steps to calculate the RSIK value of a strong convective cell are as follows:

[0019] Based on the maximum reflectivity of the network of strong convective cells, the vertical liquid water content of the network, and the severe hail index, a normalized model was established in combination with the statistical analysis results of the spatial distribution and parameter characteristics of the local convection storm.

[0020] Let the hazard index of the Kth severe convective cell be RSIK K ,RSIK K The normalized calculation formula is as follows:

[0021]

[0022] Wherein, K is a natural number greater than or equal to 1;

[0023] Z K represents the network reflectivity of the Kth strong convection cell, max(Z K ) represents the maximum reflectivity of the Kth strong convective cell;

[0024] VIL K represents the liquid water content of the Kth strong convection cell, max(VIL K ) represents the maximum vertical liquid water content of the Kth strong convective cell;

[0025] POSH represents the network hail index of this strong convective cell. The value range of POSH is greater than or equal to 0 and less than or equal to 1, that is, [0, 1]. After segmenting each strong convective cell, the height and maximum reflectivity of each strong convective cell are identified, and the POSH is obtained by combining it with the temperature profile of the sounding.

[0026] ref(Z K ) and ref(VIL K ) represent the local reference values ​​of network reflectivity and vertical liquid water content in the area where the K-th strong convective cell is located;

[0027] w i Represents the weight coefficient, i=1, 2, 3, w1+w2+w3=1.

[0028] Furthermore, the severe convective cell is configured to include five types, namely, no-risk type, ordinary precipitation HR type, heavy precipitation HR type, suspected hail HI type and hail HI type, which correspond to the RSIK no-score stage, convective cell development stage, possible development into hail cloud stage, hail cloud development stage and hail cloud mature development and landing stage respectively.

[0029] Furthermore, the hail identification model is configured to identify the type of each severe convective cell through the following steps:

[0030] For a strong convective cell with no score in the hazard index RSIK, the cell is judged to be of the non-risk type;

[0031] For strong convective cells with a hazard index score, a secondary screening is performed based on the two determined score thresholds S1 and S2 as follows:

[0032] Compare the hazard index RSIK of the strong convective cell with the score thresholds S1 and S2. When the hazard index RSIK of the strong convective cell does not reach the first score threshold S1, that is, RSIK < S1, it is identified as ordinary precipitation HR type; when the hazard index RSIK of the strong convective cell reaches the second score threshold S2, that is, RSIK ≥ S2, it is identified as hail HI type; when the hazard index RSIK of the strong convective cell reaches the first score threshold S1 but does not reach the second score threshold S2, that is, S1 ≤ RSIK < S2, it enters the third screening, as follows:

[0033] Determine whether the height of the high reflectivity threshold of the strong convective cell exceeds the corresponding height of -20° in the sounding. When the high reflectivity threshold height does not exceed the corresponding height of -20° in the sounding, it is identified as heavy rainfall HR type. When the high reflectivity threshold height exceeds the corresponding height of -20° in the sounding, it is identified as suspected hail HI type.

[0034] Furthermore, according to the identification type and corresponding stage of the severe convective cell, the steps of configuring a hail suppression operation strategy for each severe convective cell include:

[0035] For strong convective cells with RSIK scores, the following parameters of each strong convective cell are obtained: RSIK score, latitude and longitude of the core location of the convective cell, identification type of the convective cell, maximum height of the high reflectivity threshold of the convective cell, and range of the convective cell;

[0036] Search for hail prevention operation points within a preset radius of the core position of each severe convective cell; sort the hail prevention operation points in order of their distance from the core position of the convective cell, from near to far, to form a list of operation points;

[0037] Based on the type and stage of the strong convective cell, the hail prevention operation points and operation parameters within the strong convective cell matching range are calculated; among them, the longitude and latitude of the core point of the strong convective cell and the longitude and latitude of the matched human shadow hail prevention operation point are used to calculate the distance between the two points and the azimuth angle θ of the human shadow operation point toward the cell core; and the elevation angle of the human shadow operation point is calculated according to the distance between the two points and the maximum height of the high reflectivity threshold to form the operation parameters of the human shadow operation.

[0038] Furthermore, the steps for matching hail suppression operation points and operation parameters within the range of a severe convective cell are as follows:

[0039] For a strong convective cell identified as HR, a single-point operation is triggered;

[0040] For strong convective cells of normal precipitation type, the first operation strategy is adopted as follows: start the shadow operation, match the nearest operation point, calculate the azimuth angle of the anti-aircraft gun firing at the operation point based on the longitude and latitude of the strong convective cell core and the longitude and latitude of the operation point, calculate the elevation angle of the shadow operation point based on the height and range of the high reflectivity threshold, and then perform single-point operation; continuously monitor the development of the convective cell, and dynamically adjust the operation parameters based on the updated observation data and forecast results until the RSIK score is zero;

[0041] For strong convective cells of the heavy precipitation type, the second operation strategy is adopted as follows: start the human shadow operation, match one of the nearest operation points, calculate the azimuth angle of the anti-aircraft gun firing at the operation point based on the core longitude and latitude of the strong convective cell and the longitude and latitude of the operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of the high reflectivity threshold; after this operation is completed, recalculate the hazard index RSIK of the strong convective cell, compare the new hazard index RSIK with the score threshold S1, when RSIK>S1, determine that the number of shells needs to be increased, and configure the secondary anti-aircraft gun operation parameters, and continue to perform the single-point operation until RSIK<S1 after the operation, that is, when it is judged as normal precipitation HR, execute the operation strategy corresponding to normal precipitation until RSIK has no score;

[0042] For strong convective cells identified as HI, multi-point joint operations are triggered;

[0043] Among them, for strong convective cells suspected of hail type, the third operation strategy is adopted as follows: start the human shadow operation, match at least two operation points, calculate the azimuth angle of each operation point for launching anti-aircraft guns based on the core longitude and latitude of the strong convective cell and the longitude and latitude of each operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of the high reflectivity threshold; after the completion of this operation, re-identify the type of the strong convective cell. When it is identified as precipitation HR type, adjust to single-point operation, and execute the corresponding operation strategy according to whether it is ordinary precipitation or heavy precipitation; otherwise, it is determined that the number of shells needs to be increased, and the secondary anti-aircraft gun operation parameters are configured, and multi-point operation continues until it is identified as precipitation HR type; and so on, until RSIK has no score;

[0044] For severe convective cells of the hail type, corresponding to the mature stage of hail cloud development, the fourth operation strategy is implemented as follows: start the human shadow operation, match all operation points, calculate the azimuth of the anti-aircraft gun firing at each operation point based on the longitude and latitude of the core of the severe convective cell and the longitude and latitude of each operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of the high reflectivity threshold; after this operation is completed, recalculate the hazard index RSIK of the severe convective cell, compare the new hazard index RSIK with the score thresholds S1 and S2; when RSIK ≥ S2, determine that the number of shells needs to be increased, configure the secondary anti-aircraft gun operation parameters, and continue to execute the joint operation strategy of all points until S1 ≤ RSIK < S2 is reached after the operation, and then execute the third operation strategy; and so on until RSIK has no score.

[0045] Furthermore, the steps for calculating the elevation angle β of the shadow operation point and the azimuth angle θ of the anti-aircraft gun launched at the operation point are as follows:

[0046] According to the longitude and latitude coordinates of the core of the strong convective cell (λ1, φ1) and the longitude and latitude coordinates of the operation point (λ2, φ2), the spherical distance d between the two points is calculated using the formula d = R·c, where

[0047]

[0048] φ1, φ2 are the latitudes of the two points in radians;

[0049] λ1, λ2 are the longitudes of the two points in radians;

[0050] Δφ is the difference in latitude between the two points: Δφ = φ2 - φ1;

[0051] Δλ is the difference in longitude between the two points: Δλ = λ2 - λ1;

[0052] R is the radius of the Earth, usually taken as 6371 km;

[0053] According to the aforementioned spherical distance d, the elevation angle β of the shadow working point is calculated as β = tan -1 (H / d),

[0054] Where d is the spherical distance between the two points, H is the maximum height of the high reflectivity threshold of the convective cell;

[0055] Calculate the azimuth angle θ using the following formula:

[0056]

[0057] Furthermore, it also includes a networking data fusion step and a convection monomer segmentation step;

[0058] In the network data fusion step, data from multiple weather radars covering the observation area are collected, and after quality control and coordinate conversion, network fusion products are obtained;

[0059] In the convective cell segmentation step, based on the aforementioned network fusion product and the network reflectivity, a double threshold method is used to identify and segment all strong convective cells in the observation area to obtain one or more strong convective cells;

[0060] When calculating the hazard index of a strong convective cell, the characteristic parameters of disastrous weather are first extracted for each strong convective cell, and the hazard index of each strong convective cell is calculated based on the extracted characteristic parameters of disastrous weather. The characteristic parameters related to the comprehensive disaster index include the single / multi-layer network reflectivity, the network vertical liquid water content VIL and the network hail index POSH.

[0061] Furthermore, the method further includes a hail prevention effect evaluation step, comprising: after the hail prevention operation, using the strong convective cell hazard model and the hail identification model to perform hail identification on the strong convective cell after the operation to evaluate the effectiveness of the hail prevention operation;

[0062] And / or, after the hail prevention operation is completed, the meteorological data before and after the hail prevention operation is compared with the actual disaster information to evaluate the effectiveness of the hail prevention operation and optimize the hail prevention operation strategy based on the feedback evaluation results.

[0063] The present invention also provides a human shadow hail prevention system, comprising a hail identification module and a hail prevention module;

[0064] The hail identification module is configured to: segment all strong convective cells within the observation area and calculate the hazard index RSIK of each strong convective cell; determine the corresponding strong convective cell hazard model information based on the local hail occurrence condition ratio, and then determine the hazard index score corresponding to the hail identification index and the hail pre-fall index based on the strong convective cell hazard model; identify hail on the strong convective cells using the hail identification model, wherein the hail identification model is configured to: determine two score thresholds for hail identification based on the hazard index scores corresponding to the above two indicators; and identify the type of each strong convective cell based on the segmentation information of the strong convective cell and the hazard index RSIK, in combination with the two score thresholds and the sounding temperature profile, and determine the stage corresponding to the strong convective cell;

[0065] The hail prevention module is used to receive the recognition result of the hail recognition module, and configure a hail prevention operation strategy for each strong convective cell according to the identification type and corresponding stage of the strong convective cell to form a segmented hail prevention operation strategy.

[0066] Due to the adoption of the above technical solutions, the present invention has the following advantages and positive effects compared with the prior art, as an example: based on the strong convective cell hazard model and the hail identification model, the type of each strong convective cell is identified in combination with the two-segment score threshold in the hail identification model, and the stage corresponding to the strong convective cell is determined. A segmented operation strategy is constructed according to the identification type and stage of the strong convective cell, thereby realizing the task allocation of multiple convective cells and multiple operation points. The present invention provides a precise operation process for hail prevention command, reduces human intervention in hail prevention operations, and improves the efficiency and accuracy of decision-making execution. In this way, in the face of the rapid development of multiple-cell storms, the operation team can quickly make hail prevention operation decisions through the solution provided by the present invention.

[0067] Furthermore, the operation plan can be dynamically adjusted based on the development of convective cells, combined with the latest observation data and forecast results. This can include determining whether to increase the number of hail suppression shells fired, or whether multiple operation points require joint operations. This ensures that hail suppression operations can adapt to weather changes in a timely manner.

[0068] Furthermore, after the hail prevention operation is completed, the hail prevention effect of the operation can be evaluated, and the hail prevention operation strategy can be optimized based on the evaluation results, thereby improving the accuracy and scientific nature of future decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flowchart of a hail prevention method based on a convective cell hazard index and hail identification, provided by an embodiment of the present invention.

[0070] Figure 2Five identification types and stage diagrams formed by three screenings provided in an embodiment of the present invention.

[0071] Figure 3 A flowchart of configuring a hail suppression strategy for each severe convective cell according to the identified type and corresponding stage of the severe convective cell provided in an embodiment of the present invention.

[0072] Figure 4 A schematic diagram of calculating the elevation angle β of a shadow operation point provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The following is a further detailed description of the hail prevention method and system based on the convective cell hazard index and hail identification disclosed in the present invention, in conjunction with the accompanying drawings and specific embodiments. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects. In the drawings of the following embodiments, the same reference numerals appearing in each drawing represent the same features or components, which can be applied to different embodiments. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0074] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not intended to limit the conditions under which the invention can be implemented. Any structural modification, change in proportional relationship, or adjustment of size should fall within the scope of the technical content disclosed in the invention without affecting the efficacy and purpose of the invention. The scope of the preferred embodiments of the present invention includes alternative implementations, in which the functions can be performed in a non-described or discussed order, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the art to which the embodiments of the present invention belong.

[0075] Technologies, methods, and apparatus known to persons of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0076] Example

[0077] To meet the demands of precise hail prevention operations, this paper further improves upon the convective cell hazard index scoring and hail identification technology disclosed in Chinese Patent ZL202410446117.6, proposing a hail prevention solution. Based on models and thresholds, this paper establishes a segmented hail prevention strategy, enabling task allocation across multiple convective cells and multiple operation points.

[0078] Specifically, this embodiment provides a hail prevention method based on the convective cell hazard index and hail identification, including the following steps: first, after segmenting the strong convective cells in the observation area, the hazard index RSIK of each strong convective cell is calculated; and, after determining the corresponding strong convective cell hazard model information based on the local hail occurrence conditions, the hazard index score corresponding to the hail identification index and the hail pre-landing index is determined based on the strong convective cell hazard model. Then, hail is identified for the strong convective cells using the hail identification model, and the hail identification model is configured to: determine two-segment score thresholds for hail identification based on the hazard index scores corresponding to the above two indicators; and, based on the segmentation information and hazard index RSIK of the strong convective cells, in combination with the two-segment score thresholds and the sounding temperature profile, identify the type of each strong convective cell and determine the stage corresponding to the strong convective cell. Subsequently, a hail prevention operation strategy is configured for each strong convective cell according to the identification type and corresponding stage of the strong convective cell; among them, strong convective cells in different stages correspond to different operation strategies to form a segmented hail prevention operation strategy.

[0079] This embodiment involves two indicators when analyzing the occurrence of hail: a hail identification indicator and a hail pre-landing indicator.

[0080] The hazard index scores corresponding to the hail identification index and pre-hailfall index are related to the ratio of weight coefficients w1, w2, and w3 for three factors: network reflectivity, network vertical liquid water content (VIL), and network hail index (POSH). In specific implementation, local hail occurrence condition ratio data can be first obtained. Then, based on the local hail occurrence condition ratio, the ratio of the aforementioned weight coefficients w1, w2, and w3 can be determined. Different ratios correspond to different severe convective cell hazard model information.

[0081] After determining the ratio of the weight coefficients w1, w2, and w3 of the three factors and obtaining the strong convective single-cell hazard model information corresponding to the ratio, the hazard index scores corresponding to the hail identification index and the hail pre-landing index can be obtained in the aforementioned corresponding hazard model information based on the set hail identification index and the hail pre-landing index. Then, the hazard index score corresponding to the hail identification index is set as the first score threshold, and the hazard index score corresponding to the hail pre-landing index is set as the second score threshold. In this way, two score thresholds for hail identification are determined.

[0082] In this embodiment, the severe convective cell hazard model is used to analyze the parameter conditions for hail occurrence. It is pre-constructed based on hail growth theory and hail radar echo characteristics. The severe convective cell hazard model records the score changes of the severe convective cell hazard index under different ratios (the ratios of weight coefficients w1, w2, and w3).

[0083] Different ratios correspond to different strong convective single-cell hazard submodels. These submodels record the corresponding hazard index score changes when the reflectivity intensity and vertical liquid water content change according to the set network hail index (POSH) under the corresponding ratio. Based on the ratio of weight coefficients w1, w2, and w3, the corresponding hazard submodel is determined within the strong convective single-cell hazard model. The hazard submodel is then used to determine the hazard index scores corresponding to the hail identification index and pre-hailfall index for that ratio.

[0084] The hail identification and pre-hailfall indicators can be set by system default or customized by the user based on local conditions. It should be noted that, based on hail growth theory and hail radar echo characteristic analysis, the high-value range of vertical liquid water content (VIL) is one of the indicators for determining hail potential. When the VIL value of the front and rear body scans increases significantly, the probability of hail is higher. Therefore, the proportional threshold of the vertical liquid water content (VIL) is generally higher than that of the other two factors.

[0085] As an example of a typical method, how to determine the two-stage score threshold for hail identification is described in conjunction with a specific implementation method.

[0086] Taking a certain observation area as an example, if the weight coefficients w1, w2, and w3 of the three factors (network reflectivity, network vertical liquid water content (VIL), and network hail index (POSH)) are determined to be 40%, 50%, and 10% based on the local hail occurrence conditions, the corresponding severe convective single cell hazard model information (hazard sub-model) is shown in the table below:

[0087]

[0088] It can be seen that the network reflectivity changes by approximately 0.03 per 5dBZ, and the VIL changes by 5kg / m 2 The change is roughly 0.05 (depending on the different proportion configurations, the increase of each factor is different); assuming POSH ≥ 50% as the severe hail warning threshold. The reflectivity of the strong convective cell is greater than 50dBZ, and VIL ≥ 20kg / m 2When set as hail identification index, the corresponding strong convective monomer hazard index is 55 points; the strong convective monomer reflectivity ≥ 55dBZ, VIL ≥ 30kg / m 2 When set as the pre-fall hail indicator, the corresponding severe convective cell hazard index is 64. Then, the hazard index score of 55 corresponding to the hail identification indicator is set as the first score threshold S1, and the hazard index score of 64 corresponding to the pre-fall hail indicator is set as the second score threshold S2, that is, S1 = 55, S2 = 64. This determines the two score thresholds for hail identification when the ratios of w1, w2, and w3 are 40%, 50%, and 10%.

[0089] For another example, if the weight coefficients w1, w2, and w3 of the three elements of network reflectivity, network vertical liquid water content (VIL), and network hail index (POSH) are determined to be 30%, 50%, and 20% based on the local hail occurrence conditions in another observation area, the corresponding severe convective single cell hazard model information (hazard sub-model) is shown in the following table:

[0090]

[0091] It can be seen that the network reflectivity changes by approximately 0.02 per 5dBZ, and the VIL changes by 5kg / m 2 The change is roughly 0.04 (depending on the different proportion configurations, the increase of each factor is inconsistent); assuming POSH ≥ 50% as the severe hail warning threshold. The reflectivity of the strong convective cell is greater than 50dBZ, and VIL ≥ 20kg / m 2 When set as hail identification index, the corresponding strong convective monomer hazard index is 52 points; the strong convective monomer reflectivity ≥ 55dBZ, VIL ≥ 30kg / m 2 When set as the pre-fall hail indicator, the corresponding severe convective cell hazard index is 61. Then, the hazard index score of 52 corresponding to the hail identification indicator is set as the first score threshold S1, and the hazard index score of 61 corresponding to the pre-fall hail indicator is set as the second score threshold S2, that is, S1 = 52, S2 = 61. This determines the two score thresholds for hail identification when the ratios of w1, w2, and w3 are 40%, 50%, and 10%.

[0092] Preferably, this embodiment performs three screenings based on the two-stage score thresholds S1 and S2 for possible hail formation, forming five-stage results, and constructs a five-stage hail prevention operation strategy based on the five-stage results.

[0093] The following combination Figures 1 to 4 The specific process of establishing a segmented hail suppression operation strategy based on the model and threshold and allocating tasks to multiple convective units and multiple operation points is described in detail.

[0094] In order to accurately grasp the timing of the occurrence and development of severe convective cells and the possibility of hail development, with precipitation and hail as the focus, based on networked weather radar and sounding temperature profile data, through networked weather radar data quality control and fusion, convective cell segmentation, hazard index calculation, hail identification and classification, a five-segment decision-making basis is established, and the operation point matching and operation parameters are formed in combination with the operation point distance to support hail prevention operation decision-making and evaluation.

[0095] For details, see Figure 1 As shown, the hail prevention operation method includes the following steps:

[0096] S100, data collection, quality control and coordinate conversion for multiple weather radars.

[0097] S200, network reflectivity and strong convection monomer segmentation, calculation of network vertical liquid water content and network hail index.

[0098] S300: Calculate the hazard index of a strong convective cell and identify hail in the strong convective cell.

[0099] S400, five-stage operation parameters for hazard index zoning and precipitation and hail formation.

[0100] S500: Matching of severe convective cells and hail prevention operation points is achieved based on the identified types and stages.

[0101] S600: Configure the operation point parameters and the number of operations. The operation parameters for each operation include the azimuth and elevation of the operation point.

[0102] Among them, step S100 is mainly used for data fusion, which collects data from multiple weather radars covering the observation area, performs quality control and coordinate conversion, and obtains a network fusion product. Preferably, the temperature profile data of the S-band weather radar covering the area and the sounding radar in the area can be collected. The weather radar undergoes main quality control methods such as ground clutter interference filtering, radial interference filtering and point clutter processing, and the radar reflectivity data is projected on the three-dimensional grid field through the network S-band weather radar volume scanning mode analysis and coordinate conversion. Combined with the network radar coverage analysis, the maximum value method or weight function method is adopted, that is, the maximum value of the multiple radar estimation values ​​covering the same grid unit is assigned to the grid unit R max or the distance between a grid cell and the radar position, assigning the cell R with an exponential weight function cappi .

[0103] Step S200 is mainly used for convective cell segmentation. Based on the network fusion product obtained in the previous step and the network reflectivity, all strong convective cells in the observation area are identified and segmented to obtain one or more strong convective cells. In specific implementation, a dual threshold method is preferably used to identify and segment all strong convective cells in the observation area, as follows: according to a pre-set reflectivity high threshold value - for example, set to 45dBZ, when the 45dBZ area of ​​a strong convective cell does not exceed the preset area threshold - for example, 16km 2 If the value is 0, no segmentation is performed; otherwise, convective cell segmentation is performed. For those that meet the segmentation conditions, based on the pre-set reflectivity high threshold (e.g., 45dBZ) and low threshold (e.g., 35dBZ), the high threshold is used to detect the core of the strong convective cell, and the low threshold is used as the boundary reflectivity detection threshold to determine the boundary of the strong convective cell. The high threshold is then decreased according to the preset reflectivity step size, and the corresponding reflectivity contour is used to perform an expansion search until the cell boundary corresponding to the low threshold is reached, thereby obtaining the boundary range of each strong convective cell.

[0104] When calculating the hazard index RSIK of a strong convective cell, it is necessary to first extract the characteristic parameters of disastrous weather for each strong convective cell, and then calculate the hazard index of each strong convective cell based on the extracted characteristic parameters of disastrous weather. The characteristic parameters related to the comprehensive disaster index include single / multi-layer network reflectivity, network vertical liquid water content VIL, and network hail index POSH. Specifically, through the network fusion algorithm, parameter characteristics such as network reflectivity and height, network vertical liquid water content VIL and density, and network hail index POSH can be obtained.

[0105] Among them, the liquid water content (VIL) is based on the derived value of the liquid water mixing ratio on the vertical scale based on the networked reflectivity grid data and then vertically accumulated, which has a good warning effect on the early detection of local severe convection.

[0106] In the networked vertical liquid water content (VIL) algorithm, a reflectivity factor less than 55dBZ reflects liquid water content, while a reflectivity exceeding 55dBZ to 70dBZ, indicating a high-hanging strong reflectivity factor, could correspond to either liquid or solid water. The presence of solid water can lead to deviations in measurement accuracy. Therefore, the networked hail index (POSH) was introduced, replacing the relationship between the reflectivity factor and liquid water in the VIL algorithm with the reflectivity factor in the POSH. POSH reflects the catastrophic potential of hail kinetic energy and is closely related to the potential for falling hail. In practice, after segmenting strong convective cells based on the networked reflectivity, the height and maximum value of each strong convective cell can be identified. Combined with the sounding temperature profile, the networked hail probability, corresponding to the POSH, is calculated. The networked hail probability is rounded and output at preset intervals, such as 10%. This algorithm primarily measures the probability of hailstones larger than 20mm. According to experience, when the POSH value is relatively large, the hail index tends to significantly overestimate severe convection (i.e., there are more false alarms). However, even if hail does not occur, other severe convective weather such as thunderstorms will often occur.

[0107] The network reflectivity reflects the intensity of the storm, while the network vertical liquid water content and severe hail probability reflect the impact of liquid water and solid water, respectively, playing different roles. The specific implementation of steps S100 and S200 can be referenced to the relevant content disclosed in patent ZL202410446117.6 and will not be repeated here.

[0108] In step S300, the hazard index of the strong convective cell is first calculated, and then hail is identified on the strong convective cell.

[0109] Specifically, for each strong convective cell, the steps for calculating the RSIK value of the strong convective cell can be as follows: based on the maximum network reflectivity, network vertical liquid water content and strong hail index of the strong convective cell, combined with the statistical analysis results of the storm spatial distribution and parameter characteristics of the local local convection, a normalized model is established.

[0110] Let the hazard index of the Kth severe convective cell be RSIK K ,RSIK K The normalized calculation formula is as follows:

[0111]

[0112] Wherein, K is a natural number greater than or equal to 1.

[0113] Z K represents the network reflectivity of the Kth strong convection cell, max(Z K ) represents the maximum reflectivity of the Kth strong convective cell.

[0114] VIL K represents the liquid water content of the Kth strong convection cell, max(VIL K ) represents the maximum vertical liquid water content of the Kth strong convection cell.

[0115] POSH represents the networked hail index for this severe convective cell. The POSH value range is between 0 and 1, i.e., [0, 1]. After segmenting each severe convective cell, the altitude and maximum reflectivity of each cell are identified, and then combined with the sounding temperature profile to determine the POSH.

[0116] ref(Z K ) and ref(VIL K ) represent the local reference values ​​of network reflectivity and vertical liquid water content in the area where the K-th strong convective cell is located. The specific setting can be based on the statistical analysis results of the spatial distribution and parameter characteristics of the local convection storm. For example, the network reflectivity setting range can be 0-70dBZ, and the vertical liquid water content range can be 0-55kg / m 2 .

[0117] w i Represents the weight coefficient, i=1, 2, 3, w1+w2+w3=1.

[0118] In this embodiment, before calculating the hazard index RSIK of a strong convective cell, the following steps may be included: obtaining convective cell segmentation information, obtaining the area S of the 45dBZ boundary segmentation of the strong convective cell according to a set reflectivity high threshold, such as 45dBZ; and determining whether the area S exceeds a preset area threshold, such as 16km. 2 When the area S is less than or equal to the area threshold, the RSIK of the strong convective cell is determined to have no score (or RSIK=0). When the area S exceeds the area threshold, it is determined that a convective cell is generated and the RSIK of the strong convective cell has a score. The RSIK value of the strong convective cell is calculated.

[0119] After obtaining the hazard index RSIK value of the strong convective cell, hail can be identified for the strong convective cell through the hail identification model.

[0120] In this embodiment, the severe convective cell is configured to include five types, namely, no-risk type, ordinary precipitation HR type, heavy precipitation HR type, suspected hail HI type and hail HI type, which correspond to the RSIK no-score stage, convective cell development stage, possible development into hail cloud stage, hail cloud development stage and hail cloud mature landing stage respectively.

[0121] The hail identification model is configured to: identify the type of each strong convective cell based on the segmentation information of the strong convective cell and the hazard index RSIK, combined with two-segment score thresholds and the sounding temperature profile, and determine the stage corresponding to the strong convective cell.

[0122] Specifically, the hail identification model is configured to identify the type of each severe convective cell through the following steps.

[0123] First, the first screening is performed by judging whether the hazard index RSIK has a score, see Figure 2 shown.

[0124] For a strong convective cell with no score for the hazard index RSIK, the cell is judged to be of the risk-free type, corresponding to the RSIK no-score stage.

[0125] For strong convective cells with hazard index scores, a second screening is required based on the two determined score thresholds S1 and S2.

[0126] The steps of the second screening are as follows: compare the hazard index RSIK of the strong convective cell with the score thresholds S1 and S2. When the hazard index RSIK of the strong convective cell does not reach the first score threshold S1, that is, RSIK<S1, it is judged as the ordinary precipitation HR type, corresponding to the development stage of the convective cell; when the hazard index RSIK of the strong convective cell reaches the second score threshold S2, that is, RSIK≥S2, it is judged as the hail HI type, corresponding to the stage of hail cloud development, maturity and landing; when the hazard index RSIK of the strong convective cell reaches the first score threshold S1 but does not reach the second score threshold S2, that is, S1≤RSIK<S2, enter the third screening.

[0127] The steps of the third screening are as follows: determine the high threshold height of the reflectivity of the strong convective cell, for example, whether the 45dBZ height exceeds the corresponding height of -20° in the sounding; when the 45dBZ height does not exceed the corresponding height of -20° in the sounding, it is judged as the heavy rainfall HR type, which may develop into the hail cloud stage; when the 45dBZ height exceeds the corresponding height of -20° in the sounding, it is judged as the suspected hail HI type, which corresponds to the hail cloud development stage.

[0128] Then, according to the identification type and corresponding stage of the strong convective cell, a hail prevention operation strategy is configured for each strong convective cell. The specific steps are as follows.

[0129] First, for strong convective cells with RSIK scores, the following parameters of each strong convective cell are obtained: the convective cell hazard index RSIK score, the longitude and latitude of the maximum value of the hazard index RSIK within the cell, the identification type of the convective cell, the maximum height of the convective cell high reflectivity threshold (for example, 45dBZ), and the cell range.

[0130] Then, hail prevention operation points are searched within a preset radius (e.g. 10-15km) of the core position of each strong convective cell (the position with the maximum RSIK value within the cell); and the hail prevention operation points are sorted in order from near to far according to the distance between the hail prevention operation points and the core position of the convective cell hazard index.

[0131] Finally, based on the type and stage of the strong convective cell, hail suppression operation points and operation parameters are matched within the strong convective cell. The longitude and latitude of the strong convective cell core and the longitude and latitude of the matched human shadow hail suppression operation point are used to calculate the distance between the two points and the azimuth angle θ of the human shadow operation point toward the cell core. Furthermore, the elevation angle of the human shadow operation point is calculated based on the distance between the two points and the maximum height of the high reflectivity threshold, forming the operation parameters for the human shadow operation.

[0132] The following combination Figure 3 As shown, taking the high reflectivity threshold of 45dBZ as an example, the steps of hail prevention operation points and operation parameters within the matching range of strong convective cells of different types and stages are described in detail.

[0133] For a strong convective cell identified as HR, a single-point operation is triggered.

[0134] For severe convective cells with normal precipitation, the first operation strategy is adopted: Initiate a shadow operation, match a nearest operation point, calculate the azimuth angle for firing the AA gun from the operation point based on the core longitude and latitude of the severe convective cell and the operation point, calculate the elevation angle of the shadow operation point based on the 45dBZ altitude and range, and then conduct a single-point operation. Continuously monitor the development of the convective cell and dynamically adjust operation parameters based on updated observation data and forecast results until the RSIK score is zero. The effective range of the AA gun operation varies depending on the gun type. For example, the high-frequency range can be 10km.

[0135] For strong convective cells of the heavy rainfall type, the second operation strategy is adopted as follows: start the human shadow operation, match the nearest operation point, calculate the azimuth of the anti-aircraft gun firing at the operation point based on the longitude and latitude of the strong convective cell core and the longitude and latitude of the operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of 45dBZ; after this operation is completed, recalculate the hazard index RSIK of the strong convective cell, compare the new hazard index RSIK with the score threshold S1, when RSIK>S1, determine that the number of shells needs to be increased, configure the secondary anti-aircraft gun operation parameters, and continue to perform single-point operations until RSIK<S1 after the operation, that is, when it is judged as normal precipitation HR, execute the operation strategy corresponding to normal precipitation until RSIK has no score.

[0136] For strong convective cells identified as HI, multi-point joint operations are triggered.

[0137] Among them, for strong convective cells suspected of hail type, the third operation strategy is adopted, as follows: start the human shadow operation, match at least 2 operation points, calculate the azimuth of the anti-aircraft gun launched at each operation point according to the longitude and latitude of the core of the strong convective cell and the longitude and latitude of each operation point, and calculate the elevation angle of the human shadow operation point according to the height and range of 45dBZ; after this operation is completed, re-identify the type of the strong convective cell. When it is identified as precipitation HR type, adjust to single-point operation, and execute the corresponding operation strategy according to whether it is ordinary precipitation or heavy precipitation; otherwise, it is determined that the number of shells needs to be increased, and the secondary anti-aircraft gun operation parameters are configured, and multi-point operation continues until it is identified as precipitation HR type and adjusted to single-point operation; and so on, until RSIK has no score.

[0138] For hail-type strong convective cells, corresponding to the mature stage of hail cloud development, the fourth operation strategy is implemented as follows: start the human shadow operation, match all operation points, calculate the azimuth of each operation point for launching anti-aircraft guns based on the core longitude and latitude of the strong convective cell and the longitude and latitude of each operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of 45dBZ; after the completion of this operation, recalculate the hazard index RSIK of the strong convective cell, compare the new hazard index RSIK with the score thresholds S1 and S2, when RSIK ≥ S2, determine that the number of shells needs to be increased, and configure the secondary anti-aircraft gun operation parameters, continue to execute the joint operation strategy of all points, until S1 ≤ RSIK < S2 is reached after the operation, and execute the third operation strategy; and so on, until RSIK has no score, participate in Figure 3 The process shown.

[0139] In this embodiment, the steps for calculating the elevation angle β of the shadow working point and the azimuth angle θ of the anti-aircraft gun fired at the working point are as follows.

[0140] First, the spherical distance d between the two points is calculated based on the longitude and latitude coordinates of the core of the strong convective cell (λ1, φ1) and the longitude and latitude coordinates of the operation point (λ2, φ2).

[0141] Specifically, the Haversine formula is used to calculate the shortest distance between two points on Earth (assuming the Earth is a perfect sphere). This distance is known as the great circle distance. It is a common method in navigation and aviation to estimate the distance between two geographic coordinates (longitude and latitude). The algorithm is based on spherical trigonometry.

[0142] The Haversine formula is as follows:

[0143] d = R·c;

[0144]

[0145] Where φ1 and φ2 are the latitudes of the two points in radians.

[0146] λ1, λ2 are the longitudes of the two points in radians.

[0147] Δφ is the difference in latitude between two points: Δφ = φ2 - φ1.

[0148] Δλ is the difference in longitude between two points: Δλ = λ2 - λ1.

[0149] R is the radius of the Earth, usually taken as 6371 kilometers.

[0150] Then, based on the aforementioned spherical distance d, calculate the elevation angle β of the shadow working point. The calculation formula is:

[0151] β=tan -1 (H / d).

[0152] Where d is the spherical distance between the two points, H is the maximum height of the convection cell at 45 dBZ, see Figure 4 shown.

[0153] At the same time, calculate the azimuth angle θ, the calculation formula is as follows:

[0154]

[0155] The above scheme is based on the five-segment results formed by the severe convective hazard index and hail identification. It calculates the distance and azimuth of the shadow operation point toward the core of the severe convective single cell through the longitude and latitude of the single cell core and the matching longitude and latitude of the shadow hail prevention operation point; calculates the elevation angle according to the distance between the two points and the maximum height of 45dBZ to form the azimuth, elevation and other operation parameters of the shadow operation, providing precise parameters for rapid response and automatic command of the shadow hail prevention operation, meeting the urgency requirements of artificial weather modification operations, and providing a scientific basis for hail prevention operations.

[0156] In another implementation of this embodiment, a hail prevention effect evaluation step is further included, including: after the hail prevention operation, hail identification is performed on the strong convective cell after the operation using the strong convective cell hazard model and the hail identification model to evaluate the effectiveness of the hail prevention operation.

[0157] Also, after the hail prevention operation is completed, the meteorological data before and after the hail prevention operation will be compared with the actual disaster information to evaluate the effectiveness of the hail prevention operation, and the hail prevention operation strategy will be optimized based on the feedback evaluation results.

[0158] The solution provided by the present invention includes hail identification, hail prevention, and hail prevention assessment technologies. During hail identification, the primary task is to monitor and assess the risk of impending convective cells using multi-source observation data to determine whether to initiate hail prevention operations. This phase relies on the analysis of real-time meteorological data and the application of risk assessment models. Through the use of multi-source observations such as weather radar, soundings, and satellite remote sensing, potential severe convective weather systems can be identified. Based on established hail identification criteria, when the identification results indicate a significant need for hail prevention, the system can issue an early warning, initiate preparatory work for hail prevention operations, and promptly notify relevant departments and personnel.

[0159] During hail prevention operations, responses are based on hail identification results, with specific operational strategy adjustments and resource deployments tailored to accommodate changing weather conditions. This allows for continuous monitoring of the development of convective cells, combined with the latest observational data and forecasts, to assess the effectiveness of hail prevention operations and dynamically adjust the operational plan. For example, if the severity of a convective cell is detected to be increasing, decisions can be made regarding additional operations (i.e., increasing the number of hail suppression rounds fired) or the need for coordinated operations at multiple locations. This provides flexibility and responsiveness to hail prevention efforts, ensuring they can adapt promptly to changing weather conditions.

[0160] After the operation is completed, a hail prevention assessment can be conducted. Its main task is to comprehensively evaluate and summarize the hail prevention results, thereby optimizing future hail prevention strategies. At this time, the effectiveness of hail prevention measures is evaluated by comparing meteorological data before and after the operation with the actual damage situation. The evaluation results are fed back into various models in the system to optimize assessment and operation strategies, and improve the accuracy and scientific nature of future decisions.

[0161] Another embodiment of the present invention provides a human shadow hail prevention system, which includes a hail recognition module and a hail prevention module.

[0162] The hail identification module is used to: after segmenting all strong convective cells in the observation area, calculate the hazard index RSIK of each strong convective cell using a strong convective cell hazard model; and identify hail in the strong convective cells using a hail identification model; the hail identification model is configured to: determine two-segment score thresholds for hail identification based on the hazard index scores corresponding to the hail identification index and the pre-hail landing index; and, based on the segmentation information and hazard index RSIK of the strong convective cell, combine the two-segment score thresholds and the sounding temperature profile to identify the type of each strong convective cell and determine the stage corresponding to the strong convective cell.

[0163] The hail prevention module is used to receive the recognition results of the hail recognition module and configure a hail prevention operation strategy for each strong convective cell according to the identification type and corresponding stage of the strong convective cell; wherein, strong convective cells in different stages correspond to different operation strategies to form a segmented hail prevention operation strategy.

[0164] Furthermore, a hail prevention effectiveness evaluation module may be included. After hail prevention operations, the severe convective cell hazard model and hail identification model can be used to identify hail in the severe convective cell to assess the effectiveness of the hail prevention operation. Furthermore, after the hail prevention operation is completed, meteorological data before and after the operation can be compared with actual disaster information to evaluate the effectiveness of the hail prevention operation and optimize the hail prevention strategy based on the feedback.

[0165] For other technical features, please refer to the description of the previous embodiment and will not be repeated here.

[0166] In the above description, the disclosure of the present invention is not intended to limit itself to these aspects. Rather, within the scope of the intended protection of the present disclosure, the components can be selectively and operationally combined in any number. In addition, terms such as "including", "encompassing" and "having" should be interpreted as inclusive or open by default, rather than exclusive or closed, unless they are explicitly defined to the contrary. All technical, scientific or other terms have the meaning understood by those skilled in the art unless they are defined to the contrary. Common terms found in dictionaries should not be interpreted too idealistically or too impractically in the context of relevant technical documents, unless the present disclosure explicitly defines them as such. Any changes and modifications made by a person of ordinary skill in the field of the present invention based on the above disclosure are within the scope of protection of the claims.

Claims

1. A hail prevention method based on convective cell hazard index and hail identification, characterized in that The method includes the following steps: segmenting strong convective cells within the observation area and calculating the hazard index RSIK of each strong convective cell; determining the corresponding strong convective cell hazard model information according to the local hail occurrence condition ratio, and determining the hazard index score corresponding to the hail identification index and the pre-hail landing index based on the strong convective cell hazard model; Hail is identified on severe convective cells using a hail identification model. The hail identification model is configured to: determine two score thresholds for hail identification based on the hazard index scores corresponding to the two aforementioned indicators; and identify the type of each severe convective cell based on the segmentation information of the severe convective cell and the hazard index RSIK, in combination with the two score thresholds and the sounding temperature profile, and determine the stage corresponding to the severe convective cell; According to the identification type and corresponding stage of the strong convective cell, a hail prevention operation strategy is configured for each strong convective cell to form a segmented hail prevention operation strategy.

2. The method according to claim 1, characterized in that The hazard index scores corresponding to the hail identification index and the hail pre-landing index are related to the ratio of the weight coefficients w1, w2, and w3 of the network reflectivity, the network vertical liquid water content VIL, and the network hail index POSH. After obtaining the local hail occurrence condition ratio data, the ratio of the aforementioned weight coefficients w1, w2, and w3 is determined according to the local hail occurrence condition ratio. Different ratios correspond to different severe convective single cell hazard model information. After determining the ratio and obtaining the severe convective single-unit hazard model information corresponding to the ratio, the hazard index score corresponding to each indicator is obtained according to the set hail identification indicator and hail pre-landing indicator, the hazard index score corresponding to the hail identification indicator is set as the first score threshold, and the hazard index score corresponding to the hail pre-landing indicator is set as the second score threshold.

3. The method according to claim 1 or 2, characterized in that Before calculating the severe convective cell hazard index RSIK, the following steps are also included: Obtain the segmentation information of the convection cell, and obtain the high threshold boundary segmentation area S of the strong convection cell according to the set reflectivity high threshold; Determine whether the area S exceeds the preset area threshold. When the area S is less than or equal to the area threshold, it is determined that the RSIK of the strong convective cell has no score. When the area S exceeds the area threshold, it is determined that a convective cell is generated and the RSIK of the strong convective cell has a score. Calculate the RSIK value of the strong convective cell.

4. The method according to claim 3, characterized in that The steps to calculate the RSIK value of a strong convective cell are as follows: Based on the maximum reflectivity of the network of strong convective cells, the vertical liquid water content of the network, and the severe hail index, a normalized model was established in combination with the statistical analysis results of the spatial distribution and parameter characteristics of the local convection storm. Let the hazard index of the Kth severe convective cell be RSIK K ,RSIK K The normalized calculation formula is as follows: Wherein, K is a natural number greater than or equal to 1; Z K represents the network reflectivity of the Kth strong convection cell, max(Z K ) represents the maximum reflectivity of the Kth strong convective cell; VIL K represents the liquid water content of the Kth strong convection cell, max(VIL K ) represents the maximum vertical liquid water content of the Kth strong convective cell; POSH represents the network hail index of this strong convective cell. The value range of POSH is greater than or equal to 0 and less than or equal to 1, that is, [0, 1]. After segmenting each strong convective cell, the height and maximum reflectivity of each strong convective cell are identified, and the POSH is obtained by combining it with the temperature profile of the sounding. ref(Z K ) and ref(VIL K ) represent the local reference values ​​of network reflectivity and vertical liquid water content in the area where the K-th strong convective cell is located; w i Represents the weight coefficient, i=1, 2, 3, w1+w2+w3=1.

5. The method according to claim 3, wherein: The severe convective cell is configured to include five types, namely, no-risk type, ordinary precipitation HR type, heavy precipitation HR type, suspected hail HI type and hail HI type, which correspond to the RSIK no-score stage, convective cell development stage, possible development into hail cloud stage, hail cloud development stage and hail cloud mature development and landing stage respectively.

6. The method according to claim 5, characterized in that The hail identification model is configured to identify the type of each severe convective cell through the following steps: For a strong convective cell with no score in the hazard index RSIK, the cell is judged to be of the non-risk type; For strong convective cells with a hazard index score, a secondary screening is performed based on the two determined score thresholds S1 and S2 as follows: Compare the hazard index RSIK of the strong convective cell with the score thresholds S1 and S2. When the hazard index RSIK of the strong convective cell does not reach the first score threshold S1, that is, RSIK < S1, it is identified as ordinary precipitation HR type; when the hazard index RSIK of the strong convective cell reaches the second score threshold S2, that is, RSIK ≥ S2, it is identified as hail HI type; when the hazard index RSIK of the strong convective cell reaches the first score threshold S1 but does not reach the second score threshold S2, that is, S1 ≤ RSIK < S2, it enters the third screening, as follows: Determine whether the height of the high reflectivity threshold of the strong convective cell exceeds the corresponding height of -20° in the sounding. When the high reflectivity threshold height does not exceed the corresponding height of -20° in the sounding, it is identified as heavy rainfall HR type. When the high reflectivity threshold height exceeds the corresponding height of -20° in the sounding, it is identified as suspected hail HI type.

7. The method according to claim 6, characterized in that Based on the identification type and corresponding stage of a severe convective cell, the steps for configuring a hail suppression strategy for each severe convective cell include: For strong convective cells with RSIK scores, the following parameters of each strong convective cell are obtained: RSIK score, latitude and longitude of the core location of the convective cell, identification type of the convective cell, maximum height of the high reflectivity threshold of the convective cell, and range of the convective cell; Search for hail prevention operation points within a preset radius of the core position of each severe convective cell; sort the hail prevention operation points in order of their distance from the core position of the convective cell, from near to far, to form a list of operation points; Based on the type and stage of the strong convective cell, the hail prevention operation points and operation parameters within the strong convective cell matching range are calculated; among them, the longitude and latitude of the core point of the strong convective cell and the longitude and latitude of the matched human shadow hail prevention operation point are used to calculate the distance between the two points and the azimuth angle θ of the human shadow operation point toward the cell core; and the elevation angle of the human shadow operation point is calculated according to the distance between the two points and the maximum height of the high reflectivity threshold to form the operation parameters of the human shadow operation.

8. The method according to claim 7, characterized in that The steps for matching hail suppression operation points and operation parameters within the range of a severe convective cell are as follows: For a strong convective cell identified as HR, a single-point operation is triggered; For strong convective cells of normal precipitation type, the first operation strategy is adopted as follows: start the shadow operation, match the nearest operation point, calculate the azimuth angle of the anti-aircraft gun firing at the operation point based on the longitude and latitude of the strong convective cell core and the longitude and latitude of the operation point, calculate the elevation angle of the shadow operation point based on the height and range of the high reflectivity threshold, and then perform single-point operation; continuously monitor the development of the convective cell, and dynamically adjust the operation parameters based on the updated observation data and forecast results until the RSIK score is zero; For strong convective cells of the heavy precipitation type, the second operation strategy is adopted as follows: start the human shadow operation, match the nearest operation point, calculate the azimuth of the anti-aircraft gun firing at the operation point based on the longitude and latitude of the core of the strong convective cell and the longitude and latitude of the operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of the high reflectivity threshold; after this operation is completed, recalculate the hazard index RSIK of the strong convective cell, compare the new hazard index RSIK with the score threshold S1, when RSIK>S1, determine that the number of shells needs to be increased, and configure the secondary anti-aircraft gun operation parameters, and continue to perform single-point operations until RSIK<S1 after the operation, that is, when it is judged as normal precipitation HR, execute the operation strategy corresponding to normal precipitation until RSIK has no score; For strong convective cells identified as HI, multi-point joint operations are triggered; Among them, for strong convective cells suspected of hail type, the third operation strategy is adopted as follows: start the human shadow operation, match at least two operation points, calculate the azimuth angle of each operation point for launching anti-aircraft guns based on the core longitude and latitude of the strong convective cell and the longitude and latitude of each operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of the high reflectivity threshold; after the completion of this operation, re-identify the type of the strong convective cell. When it is identified as precipitation HR type, adjust to single-point operation, and execute the corresponding operation strategy according to whether it is ordinary precipitation or heavy precipitation; otherwise, it is determined that the number of shells needs to be increased, and the secondary anti-aircraft gun operation parameters are configured, and multi-point operation continues until it is identified as precipitation HR type; and so on, until RSIK has no score; For severe convective cells of the hail type, corresponding to the mature stage of hail cloud development, the fourth operation strategy is implemented as follows: start the human shadow operation, match all operation points, calculate the azimuth of the anti-aircraft gun firing at each operation point based on the longitude and latitude of the core of the severe convective cell and the longitude and latitude of each operation point, and calculate the elevation angle of the human shadow operation point based on the height and range of the high reflectivity threshold; after this operation is completed, recalculate the hazard index RSIK of the severe convective cell, compare the new hazard index RSIK with the score thresholds S1 and S2; when RSIK ≥ S2, determine that the number of shells needs to be increased, configure the secondary anti-aircraft gun operation parameters, and continue to execute the joint operation strategy of all points until S1 ≤ RSIK < S2 is reached after the operation, and then execute the third operation strategy; and so on until RSIK has no score.

9. The method according to claim 7, characterized in that The steps for calculating the elevation angle β of the shadow operation point and the azimuth angle θ of the anti-aircraft gun launched from the operation point are as follows: According to the longitude and latitude coordinates of the core of the strong convective cell (λ1, φ1) and the longitude and latitude coordinates of the operation point (λ2, φ2), the spherical distance d between the two points is calculated using the formula d = R·c, where φ1, φ2 are the latitudes of the two points in radians; λ1,λ2 are the longitudes of the two points in radians; Δφ is the difference in latitude between the two points: Δφ = φ2 - φ1; Δλ is the difference in longitude between the two points: Δλ = λ2 - λ1; R is the radius of the Earth, usually taken as 6371 km; According to the aforementioned spherical distance d, the elevation angle β of the shadow working point is calculated as β = tan -1 (H / d), Where d is the spherical distance between the two points, H is the maximum height of the high reflectivity threshold of the convective cell; Calculate the azimuth angle θ using the following formula:

10. The method according to claim 1, characterized in that It also includes the networking data fusion step and the convection monomer segmentation step; In the network data fusion step, data from multiple weather radars covering the observation area are collected, and after quality control and coordinate conversion, network fusion products are obtained; In the convective cell segmentation step, based on the aforementioned network fusion product and the network reflectivity, a double threshold method is used to identify and segment all strong convective cells in the observation area to obtain one or more strong convective cells; When calculating the hazard index of a strong convective cell, the characteristic parameters of disastrous weather are first extracted for each strong convective cell, and the hazard index of each strong convective cell is calculated based on the extracted characteristic parameters of disastrous weather. The characteristic parameters related to the comprehensive disaster index include the single / multi-layer network reflectivity, the network vertical liquid water content VIL and the network hail index POSH.

11. The method according to claim 1, wherein The method further includes a hail prevention effect evaluation step, comprising: after the hail prevention operation, performing hail identification on the strong convective cell after the operation by using the strong convective cell hazard model and the hail identification model to evaluate the effectiveness of the hail prevention operation; And / or, after the hail prevention operation is completed, the meteorological data before and after the hail prevention operation is compared with the actual disaster information to evaluate the effectiveness of the hail prevention operation and optimize the hail prevention operation strategy based on the feedback evaluation results.

12. A human shadow hail prevention system, characterized in that: Including hail identification module and hail prevention module; The hail identification module is configured to: segment all strong convective cells within the observation area and calculate the hazard index RSIK of each strong convective cell; determine the corresponding strong convective cell hazard model information based on the local hail occurrence condition ratio, and then determine the hazard index score corresponding to the hail identification index and the hail pre-fall index based on the strong convective cell hazard model; identify hail on the strong convective cells using the hail identification model, wherein the hail identification model is configured to: determine two score thresholds for hail identification based on the hazard index scores corresponding to the above two indicators; and identify the type of each strong convective cell based on the segmentation information of the strong convective cell and the hazard index RSIK, in combination with the two score thresholds and the sounding temperature profile, and determine the stage corresponding to the strong convective cell; The hail prevention module is used to receive the recognition result of the hail recognition module, and configure a hail prevention operation strategy for each strong convective cell according to the identification type and corresponding stage of the strong convective cell to form a segmented hail prevention operation strategy.

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