Severe storm monomer splitting identification method based on meteorological radar base data

Abstract

The invention discloses a severe storm monomer splitting identification method based on meteorological radar base data, and relates to the technical field of weather radar signal processing and artificial influence weather operation. According to the method, based on meteorological radar base data, two-dimensional storm structure identification is realized through multi-threshold connected domain dynamic cutting, and a three-dimensional storm body is constructed in combination with a spatial association relationship of adjacent height layers; in a three-dimensional structure construction process, a multi-level mark (Flag) reflecting the complexity of a vertical structure in a storm body is introduced and is used for representing a one-to-many or many-to-one association relationship of two-dimensional structures in different height layers. And by analyzing the evolution characteristics of the multiple marks in the vertical direction, the serious storm monomer splitting process is identified in advance. Furthermore, in combination with a storm body time sequence matching and trajectory prediction method, stable tracking and splitting early warning of storm monomers are realized. According to the method, the adhesion misjudgment of a plurality of adjacent severe convection monomers in the two-dimensional projection can be effectively avoided while the weak echo edge information of the primary hail cloud is reserved, and a reliable technical support is provided for manual hail suppression operation command.

Description

Technical Field

[0001] This invention relates to the fields of weather radar signal processing and weather modification operations, and particularly to a method for optimized identification and fragmentation discrimination of severe storm cells based on weather radar data. This method can be used in severe convective weather monitoring, hail cloud identification, and artificial hail suppression operation command systems. Background Technology

[0002] Hailstorms are sudden and destructive severe convective weather events that occur widely in many parts of my country, seriously impacting agricultural production, transportation, building facilities, and power system safety. In response to the need for hailstorm disaster prevention, weather modification operations have become one of the important technical means for disaster reduction and mitigation.

[0003] In artificial hail suppression operations, accurately acquiring the spatial structural characteristics of hail clouds and promptly identifying their formation, development, and evolution are crucial prerequisites for scientific decision-making and precise operational command. Weather radar, as the primary detection equipment for acquiring three-dimensional cloud structure information, can provide high temporal and spatial resolution radar echo data, offering an important information source for the identification, tracking, and early warning of severe convective storms.

[0004] Existing storm cell identification and tracking methods based on weather radar echoes mostly employ fixed reflectivity thresholds or two-dimensional projection-based structure identification to extract and correlate storm cells over time. However, under complex weather conditions, these methods generally suffer from the following shortcomings: Firstly, fixed threshold strategies struggle to simultaneously identify both weak echo-forming storms and strong convective mature storms, easily leading to the neglect or underestimation of initial hail clouds. Secondly, when multiple strong convective cells are spatially adjacent or vertically overlapping, two-dimensional projection-based identification methods are prone to cell "sticking" or erroneous merging, thus affecting the accuracy of storm number statistics and morphological evolution assessments.

[0005] Furthermore, during storm development, clouds are often accompanied by significant vertical structural changes and complex evolutionary behaviors such as splitting and merging. Existing methods have limited ability to characterize the three-dimensional structural evolution of storms, making it difficult to capture early signs of storm splitting in a timely manner. This results in insufficient advance warning for hail suppression operations, affecting the scientific validity and effectiveness of operation timing selection.

[0006] Therefore, there is an urgent need for a storm cell identification and splitting discrimination method that can make full use of meteorological radar base data, comprehensively consider the two-dimensional and three-dimensional structural characteristics of storms, and take into account the temporal evolution law of storms, so as to improve the completeness of hail cloud identification, the stability of storm tracking, and the reliability of splitting warning, thereby providing more accurate and timely technical support for artificial hail suppression operations. Summary of the Invention

[0007] The purpose of this invention is to provide an optimized identification and splitting discrimination method for severe storm cells based on meteorological radar data. Through multi-threshold connectivity identification, three-dimensional structure reconstruction, multiplicity index discrimination, and storm time series correlation, it achieves accurate identification, stable tracking, and early warning of storm cell splitting. The method of this invention includes the following steps: Step 1: Radar data preprocessing: Acquire weather radar base data, perform coordinate transformation and spatial interpolation on the radar base data to construct radar three-dimensional volume scan data; generate equal height reflectivity products (CAPPI) for multiple height layers based on the three-dimensional volume scan data, and perform noise suppression and clutter removal on the generated radar data, retaining the convective echo region for subsequent storm two-dimensional structure identification and three-dimensional structure construction.

[0008] Step 2, Two-dimensional storm structure identification: In the aforementioned equal height reflectivity product (CAPPI), multiple reflectivity thresholds are set for radar echo data at different height levels, and multi-threshold connected region identification is performed on the radar echo region to extract potential two-dimensional storm structures. By analyzing the distribution of high-threshold echoes within low-threshold reflectivity regions, the identification benchmark of two-dimensional structures is dynamically adjusted. Regions with multiple high-intensity echo cores are further segmented and separated, thereby avoiding misidentification of multiple spatially adjacent but physically independent storm cells as a single storm structure under two-dimensional projection conditions, and obtaining a set of two-dimensional storm structures for subsequent three-dimensional structure construction.

[0009] Step 3: Generation of vertical association and multiplicity markers for two-dimensional structures: Vertical correlation analysis is performed on the two-dimensional storm structures obtained at different height layers. Based on the spatial adjacency relationship between two-dimensional storm structures at adjacent height layers, it is determined whether they belong to the same three-dimensional storm body, and the inheritance relationship between two-dimensional storm structures at different height layers is established. In the vertical association process, when a single two-dimensional storm structure in a height layer corresponds to multiple two-dimensional storm structures in an adjacent height layer, or multiple two-dimensional storm structures correspond to a single two-dimensional storm structure in an adjacent height layer, a multiplicity marker is generated in the corresponding height layer to characterize the structural bifurcation or convergence state of the three-dimensional storm body in the vertical direction. The multiplicity markers are used to characterize the complex evolutionary features of the internal structure of the storm body and provide a basis for subsequent three-dimensional storm body construction and early splitting identification.

[0010] Step 4: Construction of 3D Storm Body and Extraction of Structural Features: Based on the two-dimensional storm structure and its vertical correlation obtained in steps 2 and 3, the two-dimensional structures of the same storm body at different height layers are integrated to construct a complete three-dimensional storm body. During the construction of the three-dimensional storm body, multi-dimensional structural parameters are extracted to characterize the spatial structure and development characteristics of the storm body, including reflectivity intensity characteristics, spatial scale characteristics, and height distribution characteristics. The multiplicity markers are retained during the construction process to characterize the internal structural complexity of the storm body and provide basic input for subsequent temporal correlation, state discrimination, and motion prediction of the storm body.

[0011] Step 5: Analysis of storm body multiplicity indicators and identification of breakup precursors: Based on the structural correlation of the three-dimensional storm body at different height layers, the existence of multiple two-dimensional structures coexisting inside the storm body is analyzed, and a multiplicity index reflecting the complexity of the vertical structure of the storm body is generated. By analyzing the distribution characteristics of the multiplicity index at different altitudes and its changing trend over time, when the multiplicity index appears continuously in multiple altitudes or shows an evolutionary characteristic of expanding from high to low levels, it is determined that the storm body has early signs of splitting, providing a basis for subsequent confirmation and early warning of storm body splitting.

[0012] Step 6: Determining the temporal correlation and evolution status of the storm body: Based on three-dimensional storm bodies obtained at multiple consecutive time points, a matching cost is constructed to characterize the correlation between storm bodies at adjacent time points, taking into account the changes in the storm bodies' spatial location, scale, and structural features. In the temporal correlation process of storm bodies, a virtual correlation object is introduced to represent the new and disappearance states of storm bodies. Under a unified matching framework, the real storm bodies and the virtual correlation object are jointly matched, and the optimal correlation relationship between storm bodies at adjacent time points is determined through a global optimization method. Based on the optimal correlation, the evolutionary state of the storm body in the time series, such as continuation, splitting, merging, new formation, or dissipation, is determined, and the determination result is used as the input condition for storm split confirmation and subsequent storm path prediction.

[0013] Step 7: Comprehensive judgment of storm body splitting and merging events: Based on the temporal correlation results of the storm body obtained in step 6, the correlation type of the storm body between adjacent time points is analyzed to characterize the structural evolution state of the storm body in the time dimension. By combining the multiplicity markers obtained in steps 3 and 5 and their changing trends at different altitudes, a comprehensive analysis is conducted on the vertical structural evolution characteristics inside the storm body. When the temporal correlation results of a storm body indicate that it exhibits a one-to-many correlation relationship in the time series, and the multiplicity markers appear consecutively in multiple height layers or exhibit an evolutionary feature of expanding from high to low layers, it is determined that the storm body has experienced a splitting event. When the temporal correlation results of a storm body indicate that it exhibits a many-to-one correlation relationship in the time series, it is determined that a storm body has undergone a merging event.

[0014] Step 8: Short-term prediction of the storm's movement path: Based on the determined evolution state of the storm body and its spatial location and motion characteristics at continuous time, a state model and an observation model are established to describe the motion process of the storm body. In the process of predicting the movement path of a storm, the uncertainty parameters in the model are dynamically corrected based on the deviation between the actual observation results and the prediction results. Furthermore, the historical observation information is weighted and attenuated by introducing a forgetting factor, so that the prediction model can adapt to the changes in turning, acceleration or deceleration that occur during the development and evolution of the storm. The adaptive prediction process obtains the storm's position and direction of movement in the near future, providing a basis for parameter calculation and decision-making for operational units in artificial hail suppression operations.

[0015] Step 9, Result Output and Application: The system outputs the three-dimensional structural feature parameters, spatial location, motion trajectory, and splitting and merging event identifiers of the storm body, and provides the output results to the artificial hail suppression operation system to support real-time monitoring, decision analysis, and operation parameter calculation during the hail suppression operation.

[0016] Based on the above technical solution, the present invention has the following beneficial effects: (1) The present invention adopts a two-dimensional storm structure identification strategy that combines multi-threshold connectivity identification with dynamic cutting. This strategy can effectively distinguish multiple strong convective cells that are spatially adjacent to each other while retaining weak echo edge information. It avoids the misidentification problem caused by the "adhesion" of storm cells under two-dimensional projection conditions, and improves the integrity and accuracy of storm cell identification.

[0017] (2) The present invention introduces multiplicity markers in the process of constructing a three-dimensional storm body to characterize the structural bifurcation or convergence state of the storm body at different height layers; by analyzing the distribution of the multiplicity markers in the vertical direction and their evolution trend over time, it is possible to identify early structural signs before the storm splits, thereby achieving early warning of storm split events and significantly improving the timeliness of hail prevention operations.

[0018] (3) By performing global temporal correlation on storm bodies at continuous times, this invention comprehensively considers the changes in the spatial location and structural characteristics of storm bodies, effectively reducing the problem of target number jumps and trajectory breaks caused by local matching errors during storm splitting, merging or dissipation, and improving the stability and continuity of storm life cycle tracking results.

[0019] (4) The present invention adopts an adaptive storm movement path prediction method, which dynamically corrects the prediction model based on real-time observation information, so that the prediction results can respond in a timely manner to changes in turning, acceleration or deceleration during the movement of the storm body, thereby improving the accuracy of short-term storm location prediction and providing more reliable technical support for parameter calculation and decision-making of work units in artificial hail suppression operations. Attached Figure Description

[0020] The invention will be further described below with reference to the accompanying drawings: Figure 1 Here is a diagram of the overall algorithm structure; Figure 2 Flowcharts for building two-dimensional and three-dimensional monoliths; Figure 3 This is a diagram illustrating dual-threshold segmentation. Figure 4 Two-dimensional storms at different heights of CAPPI layers Figure 5 A flag illustration for a specific hailstorm event; Figure 6 This is a schematic diagram of a single unit connection; Figure 7 This is a sequence of radar CR images depicting the evolution of a single hailstone. Figure 8 Flowchart for storm splitting detection; Figure 9 This is a diagram illustrating the evolution of the monomer splitting process. Detailed Implementation

[0021] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following embodiments are only used to illustrate the technical solutions of this invention and are not intended to limit the scope of protection of this invention.

[0022] Example 1: Storm Cell Identification and 3D Structure Construction Method like Figure 1 and Figure 2 As shown in the figure, this embodiment provides a method for storm cell identification and three-dimensional structure construction based on meteorological radar base data. The processing flow includes the following steps.

[0023] 1. Data preprocessing and rectangular coordinate mapping In this embodiment, Doppler weather radar base data is selected as input. The radar base data may include information such as reflectivity factor, radial velocity and spectral width. The radar system and operating band are not limited.

[0024] Reading: First, read the reflectivity, velocity and spectral width data obtained from radar scanning, and organize them into a multi-dimensional data structure in polar coordinates according to the scanning elevation angle, azimuth angle and radial distance.

[0025] Mapping: The polar coordinate data is mapped to a three-dimensional Cartesian coordinate system. This mapping process can be completed using a nearest-neighbor interpolation method based on a spatial search structure. During the mapping process, the beam height is corrected by considering the influence of the Earth's curvature and atmospheric refraction on the radar beam propagation path, thereby generating a three-dimensional volume scan data cube.

[0026] CAPPI Generation: Based on the volume scan data cube, vertical interpolation is performed on different height layers to generate a CAPPI (Contour Plane Position Display) data product. The CAPPI height resolution can be set according to application requirements.

[0027] Denoising: The generated CAPPI data is thresholded, and a connected component labeling method is used to identify continuous echo regions. Connected components with an area smaller than a preset threshold are identified as noise and removed, thereby retaining the main convection echo regions.

[0028] 1. Dynamic threshold two-dimensional structure recognition To avoid misidentifying multiple adjacent storm cells as a single target under severe convective weather conditions, this embodiment introduces a two-dimensional structure recognition strategy with dynamic thresholds at each CAPPI height level, such as... Figure 3 As shown. Specifically, a first reflectivity threshold and a second reflectivity threshold are set, wherein the second reflectivity threshold is higher than the first reflectivity threshold, to characterize the strong echo core region inside the storm.

[0029] Step A: Based on the first reflectivity threshold, identify connected components in the CAPPI data to obtain the initial two-dimensional connected components.

[0030] Step B: Within each of the initial connected regions, the number of strong echo cores is detected based on a second reflectivity threshold.

[0031] Step C (Structure Determination): When an initial connected region contains only zero or one strong echo core, the region is determined to be a single storm cell, and its boundary is defined by a first reflectivity threshold. When an initial connected region contains multiple strong echo cores, the region is determined to be an adhesion region of multiple storm cells on a two-dimensional projection. This region is then segmented using multiple reflectivity thresholds higher than the first reflectivity threshold, thereby separating the internal independent two-dimensional storm structures. The two-dimensional storms identified by each CAPPI layer are as follows: Figure 4 As shown.

[0032] 2. Vertical Association and Multiplicity Flags After completing the two-dimensional structure identification of each height layer, the two-dimensional structures between different height layers are vertically correlated to construct the three-dimensional shape of the storm.

[0033] Vertical association steps: Starting from the highest CAPPI height layer, traverse layer by layer downwards, calculating the centroid distance between two-dimensional structures at adjacent height layers and their projection overlap relationship on the horizontal plane. When the preset spatial association conditions are met, it is determined that the two-dimensional structures belong to the same three-dimensional storm body, and the identifier of the upper structure is inherited to the lower structure.

[0034] Multiplicity marker generation steps: During the vertical association process, if a two-dimensional structure in the upper height layer corresponds to multiple two-dimensional structures in the lower height layer, or multiple upper-level structures correspond to the same lower-level structure, then a multiplicity marker (Flag) is marked in the corresponding height layer.

[0035] The multiplicity markers are used to characterize the complexity of the storm's internal structure. Their distribution at different altitudes and their trends over time can serve as important bases for subsequent storm breakup identification and early warning. Figure 5 As shown, flags typically appear at higher altitudes first and then develop downwards, which is consistent with the physical process of hail cloud splitting.

[0036] 3. Construction of a 3D Storm Body Two-dimensional structures with the same identifier and vertically related are combined to construct a complete three-dimensional storm body, and the physical characteristic parameters of the three-dimensional storm body are calculated, including but not limited to volume, three-dimensional centroid position, vertical structural features and comprehensive intensity index, thereby realizing the digital three-dimensional reconstruction of the storm body.

[0037] Example 2: A Storm Tracing and Path Prediction Method Based on Integer Linear Programming and Adaptive Filtering This embodiment, based on Embodiment 1, further illustrates the method for tracking the correlation of storm cells in time series and predicting their future paths, corresponding to the technical content of storm matching, tracking, and short-term extrapolation in the claims of this invention.

[0038] like Figure 1 and Figure 6 As shown, the processing flow of this embodiment includes the following steps.

[0039] 1. Construction of the storm matching cost function For the three-dimensional storm cells identified at two adjacent radar volume scan times t and t+1, a comprehensive cost function is constructed to measure the correlation between the cells.

[0040] The cost function comprehensively considers the differences in spatial location and physical structure of storm cells, and its expression may include indicators such as centroid distance difference, volume difference, and horizontal projected area difference. By weighting and combining the above indicators, a comprehensive metric is obtained to characterize the matching cost of different storm cells.

[0041] The weighting coefficients can be set according to the radar scanning cycle, storm evolution speed and application requirements to adjust the degree of influence of different physical factors.

[0042] 2. Global matching based on integer linear programming To achieve stable correlation between storm cells in a continuous sweep and to properly handle the complex evolutionary processes of storm splitting, merging, formation, and dissipation, this embodiment employs an integer linear programming method for global optimal matching.

[0043] Variable definition: Define a binary matching variable in the model to indicate whether a correlation is established between the storm cell at the previous time and the storm cell at the current time.

[0044] Virtual Node Introduction: To handle the creation and dissipation of storm cells, virtual parent nodes and virtual child nodes are introduced into the matching model to represent newly created storm cells and dissipating storm cells, respectively.

[0045] Objective function construction: Construct an optimization function with the objective of minimizing the overall matching cost. The objective function includes the matching cost between real storm units and the penalty cost corresponding to association with virtual nodes.

[0046] Constraint settings: The constraints ensure that each storm unit in the previous time step must match one storm unit or virtual dead node in the subsequent time step, and each storm unit in the subsequent time step must also match one storm unit or virtual new node in the previous time step.

[0047] Matching Result Determination: Based on the matching results obtained from solving integer linear programming, when one storm cell from a previous time step corresponds to multiple storm cells from subsequent time steps, it is determined as a storm splitting event; when multiple storm cells from previous time steps correspond to the same storm cell from subsequent time steps, it is determined as a storm merging event; other cases correspond to the storm's persistence, formation, or dissipation states, respectively. Hail cloud tracking for a certain hail event is as follows: Figure 7 As shown.

[0048] 3. Storm path prediction based on adaptive filtering After completing the temporal correlation of storm cells, short-term predictions are made on the future movement paths of storm cells.

[0049] State modeling: Construct a state vector with the position of the storm cell's centroid and its velocity as state variables, and establish corresponding state transition models and observation models to describe the motion characteristics of the storm cell within a continuous time step.

[0050] Adaptive Noise Estimation: To adapt to potential non-stationary changes in speed and direction during storm movement, this embodiment introduces an adaptive noise estimation mechanism during the filtering process. By utilizing observation residual information, the process noise covariance and observation noise covariance are recursively updated, thereby dynamically adjusting the filtering model parameters. A forgetting factor can be introduced during the adaptive update process to balance the influence weights of historical information and current observation information on noise estimation, improving the filter's responsiveness to sudden changes in storm movement.

[0051] Path prediction output: Based on the updated state estimation results, the centroid position of the storm cell is predicted within a certain number of time steps in the future, providing short-term path reference for subsequent hail suppression operation command and decision-making.

[0052] 4. Technical Effects By introducing a global matching strategy based on integer linear programming, this embodiment can maintain the continuity of storm cell identification and trajectory during complex evolution stages such as storm splitting, merging, and dissipation. Combined with an adaptive filtering path prediction method, it can effectively improve the stability and reliability of short-term storm cell movement trend prediction, and is suitable for real-time monitoring and operational support under complex convective weather conditions.

[0053] Example 3: Monitoring Application Method for Severe Storm Splitting Processes This embodiment, combined with a specific severe convective weather process, illustrates the application effect of the method of the present invention in the monitoring and early warning of severe storm splitting, corresponding to the storm splitting discrimination in the claims and the appendix to the specification. Figure 1 , Figure 8 and Figure 9 The content shown.

[0054] 1. Application Scenario Description In this embodiment, a severe convective weather event was selected as the monitoring target. During the continuum scan, the radar identified a significantly developing storm cell and continuously tracked and analyzed it.

[0055] 2. Early warning stage of splitting At the initial moment The system constructs the three-dimensional structure of the storm cell based on the method described in Example 1. At this time, although the storm appears as a single integral structure in the two-dimensional radar echo image, the system detects a multiplicity flag at a higher altitude during the vertical correlation process in Example 1, indicating that the storm body has presented multiple internal structural units at higher altitudes.

[0056] The appearance of the multiplicity markers reflects the increasing complexity of the three-dimensional structure inside the storm body. Based on this, the system marks the storm cell as a target with potential breakup risk and puts it into a state of key monitoring.

[0057] 3. The stage of splitting and event determination Subsequent body scan time +1. Based on the storm matching and tracking method described in Example 2, the system performs global matching analysis on three-dimensional storm cells at adjacent time points. The matching results show that the single parent storm cell at the initial time point corresponds to multiple child storm cells at the current time point, forming a one-to-many matching relationship.

[0058] Based on the matching criteria, the system officially determines that the storm cell has split, and assigns new identifiers to the multiple sub-storm cells generated after the split, thereby achieving accurate identification and continuous tracking of the splitting process.

[0059] 4. Results Output and Application Support The system comprehensively depicts the evolution of a storm from a single storm cell to multiple storm cells, including changes in storm morphology and the evolution of its three-dimensional structural features. The system also outputs the three-dimensional structural parameters and motion status information of each storm cell after its split.

[0060] Based on this, and in conjunction with the path prediction method described in Example 2, the short-term movement trajectories of each sub-storm cell after splitting are predicted, and the prediction results are provided to the artificial hail suppression system to support the selection of operation areas and parameter settings.

[0061] 5. Explanation As can be seen from this embodiment, the method of the present invention can detect changes in the internal structure of a storm in advance through multiple characteristics before the storm splits, and when the split actually occurs, it can accurately identify and continuously track the split event by combining the global matching results, thereby improving the timeliness and reliability of severe storm monitoring and hail prevention operations.

[0062] The above embodiments are only used to illustrate the technical solutions of the present invention and do not constitute a limitation on the scope of protection of the present invention. For those skilled in the art, equivalent substitutions or improvements made to the above embodiments without departing from the concept of the present invention should all fall within the scope of protection of the present invention.

Claims

1. A method for identifying severe storm cell fragmentation based on meteorological radar data, characterized in that, Includes the following steps: Step 1, Radar Data Preprocessing: Acquire weather radar base data and perform spatial mapping and height interpolation processing on the radar base data to generate three-dimensional radar volume scan data and equal height reflectivity products (CAPPI) for multiple height layers for storm structure analysis. Step 2, Two-dimensional structure identification: Multiple reflectivity thresholds are set on each of the equal-height reflectivity products, and a connected component labeling method is used to identify two-dimensional storm structures; wherein, a first reference reflectivity threshold is used to identify connected regions, and it is determined whether the connected regions contain multiple sub-connected regions identified by a second reference reflectivity threshold; when multiple sub-connected regions exist, the second reference reflectivity threshold and a higher reflectivity threshold are used to cut and separate the connected regions to obtain multiple independent two-dimensional storm structures; Step 3, Vertical Association of Two-Dimensional Structures and Generation of Multiplicity Markers: Vertical association is performed based on the spatial overlap relationship between two-dimensional storm structures at different height levels. When there is a one-to-many or many-to-one association relationship between two-dimensional storm structures at adjacent height levels, multiplicity marks are generated at the corresponding height level to characterize the complexity of the three-dimensional structure. Step 4: Construction of 3D Storm Body: Based on the vertical correlation results, the 2D storm structures of multiple height layers are stacked and combined to construct a 3D storm body, and the multiplicity marker is retained in the 3D storm body; Step 5: Temporal correlation and splitting discrimination of storm bodies: Based on the spatial and morphological differences between three-dimensional storm bodies at consecutive time points, establish the matching relationship between storm bodies and determine the continuation, splitting, merging, new formation or extinction state of storm bodies; Step 6: Storm Path Prediction: Based on the historical location information of the storm body, make a short-term prediction of the future movement path of the storm body.

2. The method for identifying severe storm cell fragmentation based on meteorological radar data according to claim 1, characterized in that, Step 1 includes: Read the reflectivity data from the weather radar base data and organize and store it according to the radar scanning elevation and azimuth information; The radar base data is transformed into spatial coordinates. Taking into account the effects of Earth's curvature and atmospheric refraction, the radar base data in polar coordinate form is mapped to a rectangular coordinate system to generate three-dimensional radar volume scan data. Based on the three-dimensional radar volume scan data, height interpolation processing is performed on the reflectivity data to obtain equal height reflectivity products (CAPPI) for multiple preset height layers. The high-level reflectivity products are preprocessed to remove small-scale isolated echo regions, providing input data for subsequent two-dimensional storm structure identification.

3. The method for identifying severe storm cell fragmentation based on meteorological radar data according to claim 1, characterized in that, Step 2 includes the following two-dimensional storm structure identification logic: Set at least two different levels of reflectivity thresholds on products with high-level reflectivity, including a first reference reflectivity threshold for preliminary identification of storm range, and a second reference reflectivity threshold higher than the first reference reflectivity threshold. The first reference reflectivity threshold is used to identify connected regions, and it is determined whether there are multiple high reflectivity core regions identified by the second reference reflectivity threshold inside the connected regions; When there are multiple high-reflectivity core regions inside the connected region, the connected region is determined to be a multi-monomer adhesion region, and the connected region is segmented based on the second reference reflectivity threshold and a reflectivity threshold sequence above it to obtain multiple independent two-dimensional storm structures. When there are no multiple high-reflectivity core regions within the connected region, the structure of the connected region is extracted based on the first reference reflectivity threshold and a reflectivity threshold sequence above it, and retained as a single two-dimensional storm structure.

4. The method for identifying severe storm cell fragmentation based on meteorological radar data according to claim 1, characterized in that, Step 3 includes the following two-dimensional structure vertical association and multiplicity marker generation process: According to the order of height layers, the two-dimensional storm structures at different height layers are vertically correlated and judged. Based on the spatial adjacency relationship between two-dimensional storm structures at adjacent height layers, it is determined whether they belong to the same three-dimensional storm body. When the two-dimensional storm structures of adjacent height layers meet the preset spatial association conditions, the association relationship between the upper and lower two-dimensional storm structures is established, and the identification information of the upper two-dimensional storm structure is inherited to the lower two-dimensional storm structure. When, during the vertical association process, a single two-dimensional storm structure in a height layer corresponds to multiple two-dimensional storm structures in an adjacent height layer, or multiple two-dimensional storm structures correspond to a single two-dimensional storm structure in an adjacent height layer, a multiplicity flag is generated in the corresponding height layer to characterize the structural bifurcation or merging state of the three-dimensional storm body in that height layer. The multiplicity indicator is used to indicate the presence of multiple single-unit structures in the vertical direction within the three-dimensional storm body. When the multiplicity indicator appears continuously in multiple height layers or shows a trend of expansion from high to low layers, it is determined that the three-dimensional storm body has an early sign of splitting.

5. The method for identifying severe storm cell fragmentation based on meteorological radar data according to claim 1, characterized in that, Step 5 includes the following process for temporal correlation and state determination of storm bodies: Based on three-dimensional storm bodies obtained at consecutive time points, a matching cost is constructed to reflect the spatial positional changes and structural differences between different storm bodies, which is used to characterize the correlation probability between storm bodies at adjacent time points. In the storm body matching process, a virtual matching object is introduced to characterize the new and dead states of the storm body, and the real storm body and the virtual matching object are jointly matched under a unified matching framework; By performing global optimization on the matching cost, the optimal correlation between storm bodies at adjacent time points is obtained, thereby determining the continuation, splitting, merging, new formation, or extinction state of the storm body in the time series.

6. The method for identifying severe storm cell fragmentation based on meteorological radar data according to claim 1, characterized in that, Step 6 includes the following short-term prediction process for the storm body's movement path: Based on the information on the center of mass position and motion state of the storm body at continuous moments, a state model and observation model of the storm body motion are established to describe the positional changes of the storm body in space. Based on the deviation between the actual observation results and the prediction results, the uncertainty parameters in the state model are dynamically adjusted to achieve adaptive correction of the storm body's motion state. By introducing a forgetting factor, the influence of historical observation information is weighted and attenuated, enabling the prediction process to respond promptly to changes in direction, acceleration, or deceleration that occur during the movement of the storm body, thereby obtaining prediction results of the storm body's movement path in the near future.

7. The method for identifying severe storm cell fragmentation based on meteorological radar data according to claim 1, characterized in that, The method also includes a comprehensive discrimination process for storm body splitting and merging events: Based on the temporal correlation results between storm bodies at adjacent times, the one-to-many or many-to-one correlation relationships of storm bodies in the time series are identified to characterize the structural evolution state of storm bodies. By combining the multiplets generated by the three-dimensional storm body at different height layers, the changing trend of the vertical structure inside the storm body is analyzed; When the time sequence correlation results indicate that the storm body has a one-to-many correlation relationship, and the multiplicity indicator appears continuously in multiple height layers or shows a trend of expansion from high to low layers, it is determined that the storm body has experienced a splitting event. When the time sequence association result indicates that there is a many-to-one association relationship in the storm body, it is determined that the storm body has undergone a merging event.

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