Three-dimensional multi-parameter collaborative strong convective echo body identification method and computer equipment

Abstract

The invention provides a three-dimensional multi-parameter collaborative strong convective echo recognition method and computer equipment, and belongs to the technical field of radars. The method comprises the following steps: step 1, inputting and preprocessing radar data; 2, identifying a single-elevation-angle severe convection pixel; step 3, marking a single elevation angle pixel; 4, performing multi-elevation three-dimensional consistency statistics and comprehensive judgment; and step 5, outputting and storing the strong convection echo body. The method aims at solving the technical problems that an existing strong convection echo body recognition method is complex in calculation process, the number of iteration segmentation times is large, and the real-time processing efficiency is insufficient.

Description

Technical Field

[0001] This invention belongs to the field of radar technology, and in particular relates to a three-dimensional multi-parameter coordinated method and computer equipment for identifying strong convection echo bodies. Background Technology

[0002] Severe convective weather refers to a type of hazardous weather phenomenon caused by the rapid development of small- to medium-scale weather systems under unstable atmospheric conditions. It mainly includes thunderstorms, short-duration heavy rainfall, hail, thunderstorm winds, and tornadoes. These weather processes are typically characterized by small spatial scale, rapid development, short lifespan, dramatic evolution, and strong localization, often causing significant disasters within a short period. Short-duration heavy rainfall can easily induce urban flooding, flash floods, mudslides, and flooding of small and medium-sized rivers; hail and thunderstorm winds may lead to reduced crop yields and damage to buildings and facilities; while extreme phenomena such as tornadoes pose a direct threat to human safety. Therefore, severe convective weather has become one of the important types of meteorological disasters affecting people's lives and property and socio-economic activities (such as aviation operations, transportation, power supply, agricultural production, and various outdoor operations).

[0003] Due to the suddenness, small scale, and rapid evolution of severe convective weather, its monitoring, identification, and early warning have always been key and challenging issues in modern meteorological operations. Traditional monitoring methods based on ground-based observation stations or radiosonde data are insufficient in terms of temporal resolution and spatial coverage to meet the needs of real-time monitoring of severe convective weather, and are particularly inadequate in depicting the fine structure of convective systems and their rapid evolution.

[0004] Weather radar, as an active remote sensing method, can continuously and three-dimensionally observe atmospheric precipitation systems by emitting electromagnetic waves and receiving echoes scattered from cloud and rain particles. Among these, the new generation of operational weather radar systems (such as...) CINRAD / SA, CINRAD / SB (etc.) have formed a relatively complete observation network with high temporal and spatial resolution. For example, it can achieve volume scan updates every 6 minutes and radial resolution on the order of hundreds of meters, providing an important data foundation for the refined monitoring of severe convective weather.

[0005] By processing radar echo signals, multi-dimensional radar observation elements such as reflectivity factor, radial velocity, spectral width, and various dual-polarization parameters can be obtained. Among these, the reflectivity factor characterizes precipitation intensity and the spatial distribution of condensate; the radial velocity reflects wind field structure and dynamic characteristics; the spectral width is closely related to turbulence and wind shear; and the dual-polarization parameters provide information on the morphology and phase of condensate in clouds. These radar parameters collectively describe the intensity of severe convective systems and the physical structure and dynamic evolution of wind fields, forming a crucial physical basis for the identification and early warning of severe convective weather. Based on the echo body identification method used in this invention, further combining dual-polarization radar parameters can accurately distinguish between heavy precipitation and hail clouds, demonstrating significant application value in radar meteorological operations and research.

[0006] However, in actual operational use, a single radar volume scan often contains a large amount of echo information, including strong convective echoes as well as various non-target signals such as stratiform precipitation, weak echoes, clutter, and noise. How to accurately identify strong convective echo bodies with potential disaster risks from massive, multi-source, and rapidly updated radar data, and provide reliable input for subsequent individual body tracking, intensity assessment, and early warning product generation, has become one of the key technical issues in radar meteorological applications. Therefore, automatic identification technology for strong convective echo bodies is a fundamental and prerequisite component of strong convective monitoring and early warning systems, and its performance directly affects the accuracy and timeliness of early warning results.

[0007] Prior art related to this invention Compared to the radial velocity map method for storms, this technique focuses on using radial velocity field data from Doppler radar to identify the dynamic core of a storm. (See attached image) Figure 1 As shown, the implementation scheme mainly includes the following steps: First, the relative storm radial velocity map acquired by radar is analyzed to identify the adjacent velocity segments with positive (towards the radar) and negative (away from the radar) velocities; second, through radial velocity segment pairing technology, it is preliminarily determined whether there are small- to medium-scale vortices or convergence / divergence zones; then, using three-dimensional vertical correlation analysis, the relevant velocity features on different elevation angle scanning layers are correlated in the vertical direction to construct a complete three-dimensional radial convergence body (e.g., a mesocyclone); finally, key feature parameters such as intensity, thickness, and center height are extracted from this three-dimensional dynamic structure to determine the storm's intensity and development potential.

[0008] The relative storm radial velocity map method is highly dependent on the quality of radial velocity data. The reliability of the radial velocity field decreases significantly at radar detection edges, in low signal-to-noise ratio regions, or when velocity ambiguity and folding exist, thus affecting the stability of dynamic structure identification. Furthermore, this method underutilizes radar elements characterizing cloud physical processes, such as reflectivity factors. When severe convective weather primarily manifests as heavy precipitation or hail, with insignificant dynamic structures, its identification capability is limited. Simultaneously, the three-dimensional vertical correlation analysis process is computationally complex and computationally intensive, hindering the implementation of high-efficiency real-time processing in operational systems.

[0009] Prior art related to this invention A morphology-based three-dimensional storm echo identification method aims to address the "spurious merging" problem that easily occurs when using the traditional reflectivity factor threshold method to identify spatially adjacent storm cells. (See attached image.) Figure 2 As shown, its implementation scheme is based on mathematical morphology methods in digital image processing. The specific process is as follows: First, a low reflectance factor threshold (e.g., 30) is adopted. dBZ The radar echo images are binarized to initially delineate potential storm areas. Subsequently, morphological operations are performed on these binary images using pre-defined structural elements of pre-defined shapes and sizes (e.g., erosion to break the connecting bridges of cells, followed by dilation to restore the general outline of individual cells). This effectively separates visually connected but physically independent storm cells, providing a more accurate basis for subsequent cell tracking and attribute calculation.

[0010] The recognition performance of this method is highly dependent on the manual setting of morphological manipulation parameters. For example, the size and shape of structural elements are usually determined based on experience and lack adaptive adjustment capabilities. When the scale, shape, or intensity of the storm echo changes, fixed parameters can easily lead to over-segmentation or under-segmentation of the echo, thus affecting the stability and accuracy of the recognition results.

[0011] Prior art related to this invention (3) An automatic segmentation method for convective cells is proposed. This technical solution attempts to combine image segmentation techniques with traditional machine learning methods to achieve automatic identification and segmentation of convective cells. (See attached image) Figure 3 As shown, the implementation process includes: First, constructing a region tree structure based on radar reflectivity factor images to describe the hierarchical relationship between different connected echo regions; second, selecting candidate convection cell regions from the region tree according to pre-defined morphological feature indicators (such as echo area, perimeter, and compactness); further, introducing a support vector machine (SVM). SVMThe classification model uses manually labeled sample data to train the classifier, enabling it to determine whether candidate regions need further segmentation. Based on this, through multiple iterative segmentation operations, the adhered convective echo bodies are gradually separated, ultimately achieving the identification of the main convective cells.

[0012] This method relies on manually designed morphological features and traditional machine learning models, which have limited feature representation capabilities and struggle to fully characterize the complex nonlinear structural features in radar echoes. Consequently, the model's recognition accuracy and generalization ability are somewhat constrained. Furthermore, the multi-round iterative segmentation process based on region trees has low computational efficiency and slow overall processing speed, making it difficult to meet the real-time recognition requirements of large-scale, high-resolution radar data on a minute-level timescale. Summary of the Invention

[0013] This invention addresses the shortcomings of existing strong convection echo identification methods in radar operational applications. It proposes a three-dimensional, multi-parameter collaborative strong convection echo body identification method and computer equipment, aiming to solve the following technical problems directly related to existing technologies.

[0014] (1) Solving the technical problem of existing technologies' strong dependence on a single radial velocity element and insufficient stability in identification under complex observation conditions. As described in the prior art, the relative storm radial velocity map method mainly relies on the Doppler radial velocity field to identify the dynamic structure of strong convection. When the radar is at the edge of detection or in a low signal-to-noise ratio region, or when there is velocity ambiguity or folding, the quality of radial velocity data deteriorates, making it difficult to stably identify the dynamic structure. At the same time, this type of method does not make sufficient use of radar elements that characterize physical processes such as reflectivity factor. In view of the above shortcomings, this invention aims to solve the technical problem of existing strong convection echo identification methods' excessive dependence on a single radial velocity element and insufficient stability in identification under conditions of limited radial velocity data quality or insignificant dynamic characteristics.

[0015] (2) Solving the technical problem of insufficient adaptability of existing fixed threshold and empirical rule methods. As described in prior art 2, the three-dimensional storm echo body recognition method based on reflectivity threshold and mathematical morphology operations usually relies on thresholds and structural element parameters set manually. When the scale, shape or intensity of the strong convection echo body changes, the fixed parameters are difficult to adjust adaptively, which can easily lead to over-segmentation or under-segmentation of the echo body, affecting the consistency and reliability of the recognition results. In view of the above shortcomings, this invention aims to solve the technical problem that existing strong convection echo body recognition methods rely heavily on manual empirical parameters and have insufficient adaptability to strong convection systems of different shapes and scales.

[0016] (3) Solving the technical problems of complex calculation process and insufficient processing efficiency of existing region tree-based and iterative segmentation methods. As described in the prior art, the automatic segmentation method for convective bodies usually requires the construction of a region tree and the combination of traditional machine learning models to perform multiple rounds of iterative discrimination and segmentation of candidate regions. Its processing process is relatively complex, the amount of computation is large, and the overall processing efficiency is low, making it difficult to meet the real-time identification requirements of large-scale, high-resolution radar data on a minute-level time scale. In view of the above shortcomings, the present invention aims to solve the technical problems of complex calculation process, large number of iterative segmentation times, and insufficient real-time processing efficiency of existing strong convective echo body identification methods.

[0017] The present invention adopts the following technical solution: A three-dimensional, multi-parameter coordinated method for identifying strong convection echo volumes includes: Step 1. Radar data input and preprocessing: (1) Radar data input: Multi-elevation angle weather radar volume scan data is used as the input data source, and the radar data includes at least the following radar parameters: a) dBZ, b) CR c) SW d) MCV, e) Multi-angle floor k (Corresponding to different elevation angle scanning layers).

[0018] All of the above parameters can be obtained directly from operational Doppler weather radar or calculated from raw radar data.

[0019] (2) Data preprocessing: The following preprocessing steps are performed on the input multi-elevation angle weather radar volume scan base data: a) Data format standardization and spatial registration; b) Remove invalid values ​​and abnormal noise; c) Resample multi-elevation angle weather radar volume scan data to a unified grid; d) Construct a multi-band raster data structure, where each band corresponds to an elevation layer.

[0020] Step 2. Identification of strong convection pixels at a single elevation angle: Within a single elevation angle layer, a pixel-level identification method combining strength-structural conditions and wind field-dynamic conditions is adopted.

[0021] (1) Strength-structure criterion: Within a single elevation angle layer, the following judgment is performed on each radar pixel: Determine the pixel's dBZ Is it greater than the first threshold? dBZ 0 ; Determine the corresponding pixel CR Is it greater than the second threshold? CR 0 .

[0022] When both of the above conditions are met, the pixel is considered to have obvious convection intensity and vertical structure characteristics, and is used to exclude areas with stratiform precipitation or weak echoes.

[0023] (2) Wind field-dynamic criteria: Based on satisfying the strength-structural conditions, further judgment is made: SW Is it greater than the third threshold? SW 0 To characterize the turbulent features within the convection; MCV Whether the dynamic criteria are met reflects the dynamic characteristics of small- and medium-scale wind fields, such as convergence and divergence, wind shear, and rotation.

[0024] In practical implementation, the spectral width criterion and MCV The criterion adopts a logical OR relation, that is, when any condition is met, the pixel is considered to have significant corresponding dynamic characteristics in the elevation layer.

[0025] Step 3. Single elevation angle cell marking: A pixel is labeled as follows when it simultaneously satisfies either the wind field-dynamic condition or the intensity-structure condition: Strong convection pixel (label value = 1); Otherwise, mark it as: Non-strong convection pixels (label value = 0).

[0026] This process is performed independently pixel by pixel within each elevation layer, resulting in the binarized strong convection distribution results for each elevation layer.

[0027] Step 4. Multi-angle three-dimensional consistency statistics and comprehensive judgment: After completing the individual identification of each elevation angle layer, a multi-elevation angle collaborative determination mechanism is further introduced to construct a three-dimensional vertical consistency constraint, such as... Figure 7 As shown.

[0028] (1) Statistics on multiple elevation angles: For pixels at the same horizontal position, the number of elevation layers at different elevation angles that label them as strong convection pixels is counted, including: SZ The number of elevation angle layers that satisfy the reflectivity and structural conditions; SV The number of elevation layers that satisfy the wind field-dynamic conditions.

[0029] (2) Three-dimensional consistency judgment: A pixel is considered a three-dimensional strong convection pixel when it meets one of the following conditions in the vertical direction: SZ ≥Preset threshold (e.g., no less than 2 elevation angle layers); SV ≥Preset threshold (e.g., no less than 2 elevation angle layers).

[0030] The aforementioned three-dimensional consistency constraints effectively avoid misjudgment of a single elevation angle and improve the ability to characterize the vertical continuous structure of strong convection echo bodies.

[0031] Step 5. Strong Convection Echo Output and Storage: After completing the comprehensive assessment of three-dimensional consistency, the recognition results will be output in a unified manner: Generate a multi-band binary raster file, where each band corresponds to the recognition result of an elevation angle layer; Simultaneously, a three-dimensional integrated strong convection echo distribution result is generated.

[0032] The output result can be adopted TIFF Stored in common raster formats, the jagged polygonal outlines obtained directly from binary raster conversion are processed. SHP Smoothing is performed to eliminate pixelation and jagged edges while preserving the basic morphological characteristics of the echo, supporting direct loading to... ArcGIS , QGIS Spatial analysis software, such as those for subsequent strong convection analysis, single-cell tracking, and early warning applications.

[0033] A computer device includes a processor and a memory, the memory containing a computer program, which, when executed by the processor, implements the aforementioned three-dimensional multi-parameter collaborative method for identifying strong convection echo bodies.

[0034] The beneficial effects of this invention are: a. Stability and reliability of convection echo body identification Through joint utilization dBZ , CR , SW and MCV It uses multiple radar parameters and sets up a collaborative identification mechanism for strength-structure conditions and wind field-dynamic conditions within a single elevation angle, avoiding the identification instability problem caused by relying on only a single radar physical quantity (such as only applying reflectivity or radial velocity) in the existing technology.

[0035] Compared to the relative storm radial velocity map method, which is highly sensitive to radial velocity quality, this invention can still achieve effective identification by relying on parameters such as reflectivity and spectral width even when radial velocity quality is poor, velocity ambiguity exists, or signal-to-noise ratio is low, thereby significantly improving the reliability of strong convection echo body identification results.

[0036] b. Adaptability to different types of strong convective echo bodies By simultaneously introducing strength structural features during the identification process ( dBZ, CR ) and wind field dynamic characteristics ( SW, MCV It can be applied to: A strong convection system with a significant dynamic structure; A strong convective system characterized by heavy precipitation or hail, but with indistinct radial velocity characteristics.

[0037] This overcomes the limitation of existing technologies that are "only applicable to a certain type of strong convection pattern" and improves the universality of the method for different strong convection echo structure patterns.

[0038] c. Adjust complex parameters to improve the adaptability of the method; Compared to 3D morphological recognition methods that rely on human experience parameters such as the size and shape of morphological structural elements, this invention employs a judgment strategy based on radar parameter threshold combinations with clear physical meaning, eliminating the need for repeated adjustments to morphological parameters for different storm scales. Through multi-parameter collaborative constraints, the recognition process exhibits good adaptability under different weather backgrounds and echo scales, reducing human intervention and improving consistency and stability in operational applications.

[0039] d The computational complexity is optimized to meet the application requirements of real-time services. This invention employs a pixel-by-pixel and elevation-by-elevation angle identification method, and performs multi-elevation angle statistics and 3D structure construction based on this method, avoiding complex 3D correlation calculations or multi-round iterative segmentation processes. Compared with existing methods based on region tree iteration or 3D vertical correlation analysis, the calculation process of this invention is simpler, the computational complexity is significantly reduced, and it can meet the real-time identification and operational requirements of large-area and high-resolution radar data on a minute-level timescale.

[0040] e The results have a clear structure, which facilitates subsequent business applications and system integration; The recognition results output by this invention are represented in the form of multi-band binary raster data, where each band corresponds to the strong convection identification result of an elevation layer, and a comprehensive three-dimensional strong convection echo body identification result can be generated simultaneously. This data format can be directly... GIS The system or meteorological business platform can be loaded and used to facilitate subsequent applications such as convective cell tracking, severe weather warning, and disaster risk analysis, and has good engineering feasibility and business promotion value. Attached Figure Description

[0041] Figure 1 This is a flowchart of the relative storm radial velocity diagram method.

[0042] Figure 2This is a flowchart of morphology-based 3D storm recognition.

[0043] Figure 3 This is a flowchart of the automatic segmentation method for convective cells.

[0044] Figure 4 This is a diagram of the overall technical solution.

[0045] Figure 5 This is a flowchart for identifying pixels in strong convection echoes at a single elevation angle.

[0046] Figure 6 This is a flowchart for cyclone identification.

[0047] Figure 7 This is a statistical and comprehensive judgment chart for three-dimensional consistency at multiple elevation angles.

[0048] Figure 8 This is a map showing actual precipitation.

[0049] Figure 9 The image shows the original radar reflectivity factor image after preprocessing at 16:29.

[0050] Figure 10 For generated Z9973_20230803_082935_Multi01.tif Partial elevation layer ArcGIS Display the image; where (a) is... band1 (b) is band2 (c) is band3 .

[0051] Figure 11 For the generated single-channel binarization TIFF document.

[0052] Figure 12 The polygonal outline of the generated echo body (before processing).

[0053] Figure 13 The processed echo polygonal outline (after processing). Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some embodiments of this invention, not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0055] Reflectivity factor ( Decibel Relative to Z ), representing the logarithmic form of the intensity of the target-scattered echo received by the weather radar, in decibels. dBZThe larger the value, the stronger the radar echo intensity, which is usually used to characterize the intensity of precipitation and the development of convective systems.

[0056] CR Combined reflectance ( Composite Reflectivity The maximum reflectivity value obtained by comprehensively processing the reflectivity factors on multiple elevation angle scanning layers is used to characterize the overall intensity distribution of the convective system in the vertical direction.

[0057] Z DR Differential reflectivity ( Differential Reflectivity to Z The logarithmic form of the ratio of horizontal polarization reflectance factor to vertical polarization reflectance factor is used to reflect the shape, orientation, and phase characteristics of precipitation particles, and is of great significance in distinguishing types of water condensates such as rain, snow, and hail.

[0058] K DP Differential phase shift rate ( Specific Differential Phase ( ), which represents the rate of change of phase difference between horizontal and vertical polarized signals per unit distance during the propagation of radar waves, usually expressed in degrees per kilometer. K DP It exhibits good linear response characteristics to heavy precipitation and is less affected by attenuation and calibration errors, and is often used for quantitative precipitation estimation and severe convection identification.

[0059] CC The correlation coefficient represents the degree of correlation between horizontally polarized and vertically polarized echo signals, and its value usually ranges from 0 to 1. CC The value can be used to characterize the uniformity of precipitation particle composition and plays an important role in identifying hail, clutter, or mixed-phase precipitation.

[0060] SW Spectral width ( Spectral Width The doppler spectrum of the radar echo is broadened, reflecting the discreteness of the target's radial velocity. SW It is usually related to the intensity of airflow turbulence, wind shear, and dynamic instability within the convection.

[0061] MCV Small and medium-scale convergence characteristics ( small-to-mediumscale Convergence Vorticity ( ), refers to the small- to medium-scale convergence, wind shear, or rotation structure derived from the radial velocity field, used to characterize the dynamic organization features inside a strong convective system, such as a three-dimensional mesocyclone or strong convergence region.

[0062] GIS Geographic Information System (GIS) Geographic Information SystemThis invention relates to a computer system for collecting, managing, analyzing, and visualizing spatial geographic data. In this invention, it is used to spatially locate and visualize the identified strong convective echo bodies.

[0063] TIFF Tag image file format ( TaggedImageFileFormat (This is a type of image format that supports multi-channel and high-precision raster data storage and is often used to save radar echo fields, tag images, and intermediate processing results.)

[0064] SHP : Shapefile Vector file format, used to store spatial elements such as points, lines, and surfaces and their attribute information, is used in this invention to save the boundary contour and attribute information of strong convection echo bodies, facilitating... GIS The data will be displayed and analyzed on the platform.

[0065] like Figure 4 As shown, the present invention provides a three-dimensional multi-parameter coordinated method for identifying strong convection echo volumes, comprising: Step 1. Radar data input and preprocessing: (1) Radar data input: Multi-elevation angle weather radar volume scan data is used as the input data source, and the radar data includes at least the following radar parameters: a) dBZ, b) CR c) SW d) MCV, e) Multiple elevation angle layers k (Corresponding to different elevation angle scanning layers). All of the above parameters can be obtained directly from operational Doppler weather radar or calculated from raw radar data.

[0066] (2) Data preprocessing: The following preprocessing steps are performed on the input multi-elevation angle weather radar volume scan base data: a) Data format standardization and spatial registration; b) Remove invalid values ​​and abnormal noise; c) Resample multi-elevation angle weather radar volume scan data to a unified grid; d) Construct a multi-band raster data structure, where each band corresponds to an elevation layer.

[0067] Step 2. Identification of strong convection pixels at a single elevation angle: like Figure 5 As shown, within a single elevation angle layer, a pixel-level identification method combining strength-structural conditions and wind field-dynamic conditions is adopted.

[0068] (1) Strength-structure criterion: Within a single elevation angle layer, the following judgment is performed on each radar pixel: The judgment of this pixel... Is it greater than the first threshold? ( Take 30 dBZ ); Determine the corresponding pixel Is it greater than the second threshold? ( Take 35 dBZ When both of the above conditions are met, the pixel is considered to have obvious convection intensity and vertical structure characteristics, and is used to exclude areas with stratiform precipitation or weak echoes.

[0069] (2) Wind field-dynamic criteria: Based on satisfying the strength-structural conditions, further judgment is made: Is it greater than the third threshold? ( Take 2.5 ), to characterize the turbulent features within the convection; MCV Whether the dynamic criteria are met reflects the dynamic characteristics at small and medium scales. The average intensity of inflow and outflow, as well as the intensity of cyclone rotation, are core indicators in meteorology for identifying the "strong rotational characteristics" of mesocyclones. Based on three-dimensional radial velocity data from radar, feature identification first distinguishes strong convective dynamic characteristics such as cyclones, radial convergence, and cyclonic / anticyclonic convergence in the far and near fields. Then, it filters out mesocyclones with "vertical continuity + strong rotation," and finally generates a standardized classification array (labeled "no features / cyclone / convergence / strong cyclone") to provide feature input for the identification of strong convective echo bodies. The cyclone identification process is as follows: Figure 6 As shown.

[0070] To achieve a standardized expression of dynamic characteristics, this invention maps the above identification results to discrete hierarchical quantities. MCV The value of . Where, MCV The value is 1 (simplified expression) MCV =1 indicates that a cyclonic structure with rotational shear has been detected (i.e., the azimuthal velocity dipole feature is valid); MCV=2 indicates that a normal velocity convergence structure exists (including radial convergence, cyclonic convergence and anticyclonic convergence). MCV =3 indicates a strong cyclonic convergence structure (satisfying strong rotation and continuous occurrence in the vertical direction, and constrained by neighborhood consistency). The radial velocity field is discriminated using a radar polar coordinate grid. To uniformly present the dynamic characteristic types, discrimination conditions, and physical meanings, this invention organizes the radial velocity dynamic criteria as shown in Table 1. The dynamic characteristic criteria (…) flagMC , flagCC , flagAntiC , flagJC The threshold criterion is output using a hierarchical mapping. A value is assigned when a pixel meets the cyclone criterion. MCV =1; when a pixel satisfies any convergence criterion ( flagCC , flagAntiCor flagJC When assigned a value MCV =2; This value is assigned when the strong rotation threshold is met, and the rotated structure appears continuously in the vertical direction and passes the neighborhood consistency filter. MCV =3. Therefore, MCV In engineering semantics, ≥2 is equivalent to "the establishment of a convergence-type dynamic structure," and can be directly used as the critical condition for radial velocity convergence. Further combining... SW>SW 0 The internal dynamic strength constraint is determined, and the vertical continuity is comprehensively screened to finally output a standardized classification array ("no feature / cyclone / convergence / strong cyclone"), which is used as the dynamic feature input for the identification of strong convective echo bodies.

[0071] Table 1 Wind field-dynamic criteria based on radar radial velocity The following is an explanation of Table 1: ①. In the table, "Current Azimuth" refers to the target's radial direction in the azimuth direction during radar volume scanning, and "Forward Azimuth" refers to the azimuth radial direction adjacent to this target's radial direction. In the Doppler radial velocity field, the rotational shear of a cyclone / mesocyclone usually exhibits an opposite sign for the azimuth-adjacent radial velocities (a "velocity dipole" structure with one side being inflow and the other outflow). Therefore, using the sign combination between adjacent azimuths can achieve regular discrimination of the rotational shear. The selection direction of adjacent azimuths is based on the azimuth sorting of the volume scan data; consistency is sufficient in engineering implementation.

[0072] ②. In the table, r represents the r-th range cell (range library) centered on the radar station along the radar beam direction; r+1 represents a range library cell adjacent to r but farther from the radar station. Radial velocity convergence / convergence structures in radar observations often manifest as a consistent change in the orientation of radial velocities along the beam direction between near and far ranges. Therefore, a combination of r and r+1 is introduced in the discrimination process to characterize the convergence trend in the radial direction and the spatial consistency of the rotating signal.

[0073] ③. In the table, "near range" and "far range" refer to the distance relative to the radar station, centered on the radar station itself; "positive / negative" in the table represents the radar radial velocity. Vᵣ The symbol is used to indicate the directionality of radial velocity; a negative radial velocity indicates that the target is moving towards the radar, and a positive radial velocity indicates that the target is moving away from the radar.

[0074] Step 3. Single elevation angle cell marking: When a pixel simultaneously satisfies either the wind field-dynamic condition or the intensity-structure condition mentioned above, the pixel is marked as a strong convection pixel (mark value = 1); otherwise, it is marked as a non-strong convection pixel (mark value = 0). This process is performed independently pixel by pixel within each elevation layer, forming the binarized strong convection distribution results for each elevation layer.

[0075] Step 4. Multi-angle three-dimensional consistency statistics and comprehensive judgment: After completing the individual identification of each single elevation angle layer, a multi-elevation angle collaborative determination mechanism is further introduced to construct a three-dimensional vertical consistency constraint, such as... Figure 7 As shown.

[0076] (1) Statistics on multiple elevation angles: For pixels at the same horizontal position, the number of elevation layers at different elevation angles that label them as strong convection pixels is counted, including: SZ The number of elevation angle layers that satisfy the reflectivity and structural conditions; SV The number of elevation layers that satisfy the wind field-dynamic conditions.

[0077] (2) Three-dimensional consistency judgment: A pixel is considered a three-dimensional strong convection pixel when it meets one of the following conditions in the vertical direction: SZ ≥Preset threshold (e.g., no less than 2 elevation angle layers); SV ≥Preset threshold (e.g., no less than 2 elevation angle layers).

[0078] The aforementioned three-dimensional consistency constraints effectively avoid misjudgment of a single elevation angle and improve the ability to characterize the vertical continuous structure of strong convection echo bodies.

[0079] Step 5. Strong Convection Echo Output and Storage: After completing the comprehensive assessment of three-dimensional consistency, the recognition results will be output in a unified manner: Generate multi-band binary raster files, where each band corresponds to the identification result of an elevation layer; at the same time, generate three-dimensional integrated strong convection echo distribution results.

[0080] The output result can be adopted TIFF Stored in common raster formats, the jagged polygonal outlines obtained directly from the binarized raster are... SHP Smoothing is performed to eliminate pixelation and jagged edges while preserving the basic morphological characteristics of the echo, supporting direct loading to... ArcGIS , QGIS Spatial analysis software, such as those for subsequent strong convection analysis, single-cell tracking, and early warning applications.

[0081] The present invention also provides a computer device, including a processor and a memory, wherein the memory contains a computer program, and when the processor executes the computer program, it implements the above-mentioned three-dimensional multi-parameter coordinated strong convection echo body identification method.

[0082] Example The core of this invention lies in the comprehensive utilization of reflectivity factors in multi-elevation radar data. ), combined reflectance ( CR ), spectral width ( SW ), small- and medium-scale convergence characteristics ( MCV By combining multi-parameter information such as pixel-by-pixel identification with three-dimensional consistency, the invention achieves accurate identification of strong convective echo bodies. This invention provides a real strong convective weather event (e.g., radar station observation data at 16:29:35 on August 3, 2023) as an example. During this event, the regional station with the maximum hourly precipitation (16:00-17:00) recorded 50.2 mm. Figure 8 As shown.

[0083] The solution described in this embodiment runs on a general-purpose computer device, which includes a processor and a memory. The processor executes program instructions stored in the memory. Based on this method, the identification of strong convective echo bodies is achieved. The implementation steps of the method are described in detail below: (1) Data preparation and preprocessing: Before implementing this method, multi-elevation radar volume scan data needs to be acquired. The data comes from a dual-polarization radar and includes baseline data for multiple elevation angles (0.5° to 19.5°). Extracted parameters include the reflectivity factor (…). dBZ ), combined reflectance ( CR ), spectral width ( SW and small- and medium-scale convergence characteristics ( MCV Parameters. The preprocessing stage involves quality control of the data, including ground clutter suppression, distance correction, and other processing to ensure data reliability. Data is interpolated to a 1km × 1km regular network to unify the coordinate system for subsequent analysis, such as... Figure 9 As shown.

[0084] (2) Identification of strong convection pixels at a single elevation angle: This step involves identification on an elevation angle-by-elevation and pixel-by-pixel basis. The identification logic is based on two types of conditions: intensity-structure conditions and wind field-dynamic flow conditions. The intensity-structure conditions require the pixel's reflectivity factor. dBZ Reaching the threshold dBZ 0 ≥30 dBZ And combined reflectivity CR Exceeding the threshold CR 0 ≥35 dBZThis is to exclude shallow or non-convective echoes. Wind field-dynamic conditions require a wide spectral width. SW Exceeding the threshold SW 0 ≥2.5m / s or MCV The dynamic criterion is met. If any condition is met, the pixel is marked as a strong convection pixel (value 1) at the current elevation angle. The identification process iterates through all elevation angle layers, generating multi-band binarized data. TIFF Files (such as) Z9973_20230803_ 082935_Multi01.tif Each band corresponds to a recognition result for an elevation angle. For example... Figure 10 As shown, ArcGIS Display image ( band1, band2, band3 The image shows the distribution of strong convection at some elevation angles, with the black areas representing the identified strong convection pixels, clearly reflecting the differences in the vertical structure of the echoes at different elevation angles.

[0085] (3) Three-dimensional consistency assessment at multiple elevation angles: Based on single-layer identification, three-dimensional collaborative identification is performed, such as... Figure 8 (Three-dimensional multi-parameter collaborative strong convection echo body identification process) The flowchart on the right is shown. For each pixel location, the number of layers that satisfy the intensity-structure condition across all elevation angle layers is counted. ) and the number of layers that satisfy the wind field-dynamic conditions ( ).like ≥2 or If the value is ≥2, the pixel is identified as a single pixel in a three-dimensional strong convection echo; otherwise, it is considered a non-strong convection region. This step ensures that the identification results reflect the vertical continuity and dynamic characteristics of the echo body, avoiding misjudgment of isolated or shallow echoes. The output result is a single-channel three-dimensional synthesis. TIFF The file integrates multi-elevation angle information, such as... Figure 11 As shown.

[0086] (4) Vector transformation and boundary optimization: The identified raster data needs to be converted into vector format for spatial analysis. Through raster vectorization algorithms, connected cell regions are extracted as polygonal contours. SHP .like Figure 12 As shown, the initial vector boundary (before processing) exhibits a noticeable jagged effect, which is caused by the discreteness of the raster cells. To optimize the boundary, an ensemble smoothing algorithm is applied to smooth the polygons, and valid echo volumes are selected based on an area threshold. Figure 13 The processed echo body polygon outline is displayed, with smooth and natural boundaries, enhancing its appearance. GIS The visualization effect in the video.

[0087] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A three-dimensional, multi-parameter coordinated method for identifying strong convection echo volumes, characterized in that, include: Step 1. Radar data input and preprocessing: Multi-elevation angle weather radar volume scan base data is used as the input data source. Data format unification and spatial registration, removal of invalid values ​​and abnormal noise are performed on the input multi-elevation angle weather radar volume scan base data. The multi-elevation angle weather radar volume scan base data is resampled to a unified grid and a multi-band raster data structure is constructed, where each band corresponds to an elevation angle layer. Step 2. Identification of strong convection pixels at a single elevation angle: Within a single elevation angle layer, a pixel-level identification method combining strength-structural conditions and wind field-dynamic conditions is adopted; Step 3. Single elevation angle cell marking: When a pixel satisfies both the dynamic condition and the intensity-structure condition, the pixel is marked as a strong convection pixel; otherwise, it is marked as a non-strong convection pixel. The process is performed independently pixel by pixel within each elevation layer to form the binarized strong convection distribution results for each elevation layer. Step 4. Multi-angle three-dimensional consistency statistics and comprehensive judgment: After completing the individual identification of each single elevation angle layer, a multi-elevation angle collaborative determination mechanism is introduced to construct a three-dimensional vertical consistency constraint. Step 5. Strong Convection Echo Output and Storage: After completing the three-dimensional consistency comprehensive judgment, the recognition results are output in a unified manner to generate a multi-band binary raster file, where each band corresponds to the recognition result of an elevation angle layer, and at the same time, the three-dimensional comprehensive strong convection echo distribution result is generated.

2. The method according to claim 1, characterized in that, The radar data described in step 1 includes at least the following radar parameters: dBZ, CR, SW, MCV and multiple elevation angles of the floors k .

3. The method according to claim 2, characterized in that, Step 2 includes: Strength-Structural Criteria: Within a single elevation angle layer, the following judgment is performed on each radar pixel: Determine the pixel's dBZ Is it greater than the first threshold? dBZ 0 ; Determine the corresponding pixel CR Is it greater than the second threshold? CR 0 ; When both conditions are met, the pixel is considered to have obvious convection intensity and vertical structure characteristics, which is used to exclude areas of layered precipitation or weak echo. Wind field-dynamic criteria: SW Is it greater than the third threshold? SW 0 To characterize the turbulent features within the convection; MCV Whether the dynamic criteria are met reflects the dynamic characteristics of small- and medium-scale wind fields, such as convergence and divergence, wind shear, and cyclones. Spectral width criterion and MCV The criterion uses logical OR relation to determine whether the pixel has significant corresponding dynamic features within the elevation angle layer.

4. The method according to claim 1, characterized in that, Step 4 includes: Multi-angle elevation statistics: For pixels at the same horizontal position, the number of elevation layers at different elevation angles that label them as strong convection pixels is counted, including: SZ The number of elevation angle layers that satisfy the reflectivity and structural conditions; SV The number of elevation layers that satisfy the wind field-dynamic conditions; Three-dimensional consistency determination: A pixel is considered a three-dimensional strong convection pixel when it meets one of the following conditions in the vertical direction: SZ ≥Preset threshold; SV ≥Preset threshold.

5. The method according to claim 1, characterized in that, Step 5 also includes: using the output results TIFF Stored in common raster formats, the jagged polygonal outlines obtained directly from binary raster conversion are processed. SHP Smoothing is performed to eliminate pixelation and jagged edges while preserving the basic morphological characteristics of the echo, supporting direct loading to... ArcGIS and QGIS Spatial analysis software.

6. A computer device comprising a processor and a memory, characterized in that, The memory contains a computer program that, when executed by the processor, implements the three-dimensional multi-parameter coordinated strong convection echo body identification method as described in any one of claims 1-5.

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