Downburst symmetric and asymmetric structure analysis method facing meteorological space field data and based on Fourier wave number decomposition, medium and program product

Abstract

The invention discloses a downburst symmetric and asymmetric structure analysis method facing meteorological space field data and based on Fourier wave number decomposition, a medium and a program product, and belongs to the technical field of meteorological data processing and diagnosis. According to the method, for the attributes of small temporal-spatial scale and rapid evolution of downburst, a downburst disaster point is determined by using a ground observation maximum wind speed threshold criterion, an event constraint area is determined, and a target variable field Var is collected; and calculating the horizontal center of the weather system based on Var amplitude weighting. A radius and azimuth angle sampling frame is constructed by taking a horizontal center as an original point, interpolation and polar coordinate resampling are realized by adopting natural adjacent point interpolation, and a regularized polar coordinate variable field Var (r, theta) is obtained. Fourier series expansion is executed along an azimuth angle, a zero wave number symmetric component and a multi-order non-zero wave number asymmetric mode are separated, an amplitude-phase parameter set is output, structural feature extraction and quantitative characterization are achieved, and support is provided for disaster falling area analysis and business evaluation.

Description

Technical Field

[0001] This invention belongs to the field of meteorological data processing and numerical diagnostics technology, and relates to the extraction of spatial field features and structural decoupling technology of small and medium-scale weather systems. Specifically, it is a method, medium and program product for analyzing the symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition for meteorological spatial field data. Background Technology

[0002] Downbursts are an important type of sudden, strong wind disaster in near-surface convective weather. They are characterized by strong descending airflows within convective clouds rapidly reaching the ground and diverging outwards, creating short-duration strong gusts and significant low-level wind shear, posing a direct threat to airport safety, urban public facilities, and power lines. Due to their sudden onset, short duration, strong non-stationarity, and strong locality, conventional meteorological observation networks are objectively limited in terms of station density, temporal resolution, and vertical detection capabilities, making it difficult to capture complete and accurate process data of downbursts. This has resulted in relatively insufficient simulation and research on downbursts, and a relative scarcity of related research findings.

[0003] The spatial structure of downbursts exhibits complex and non-uniform characteristics. Ideally, a downburst should form an axisymmetric divergent flow field upon ground impact, meaning that physical quantities vary only with radial distance and are independent of azimuth. However, actual observations and high-resolution numerical simulations show that downbursts are influenced by various factors during their development and evolution, including environmental wind fields, topographic conditions, convective system organization, and multi-cell interactions. Their horizontal structure generally deviates from the ideal axisymmetric morphology, exhibiting asymmetric characteristics such as single-wave, double-wave, and even multi-wave superposition. This asymmetry not only affects the prediction of the spatial distribution of disaster impact areas but is also closely related to the intensity evolution, movement path, and attenuation mechanism of downbursts. Therefore, quantitatively separating and analyzing the symmetric and asymmetric structural components of downbursts is of significant scientific importance and practical value for a deeper understanding of their dynamic nature and improving the accuracy of disaster early warning.

[0004] Currently, the analysis methods for the spatial structure of downbursts mainly rely on empirical statistical analysis or simplified decomposition methods based on physical assumptions. Commonly used methods include radial averaging, synthetic analysis, and empirical orthogonal function decomposition. Radial averaging characterizes symmetric components by calculating the annular average value of physical quantities at different azimuth angles and considers the deviation between the original field and the mean field as asymmetric components. However, this method fails to establish a correspondence between asymmetric components and wave modes, and the determination of the center point relies on empirical judgment; center deviation can lead to the aliasing of symmetric and asymmetric components. Synthetic analysis extracts common features by superimposing the spatial fields of multiple cases, but it lacks specificity when dealing with the structural analysis of a single downburst event. While empirical orthogonal function decomposition can extract the main modes of the spatial field, its spatial basis functions are determined by the statistical characteristics of the data and lack clear physical meaning. It is difficult to directly correspond to symmetric and asymmetric structures, and it cannot distinguish asymmetric modes of different wave numbers. Furthermore, existing methods lack a systematic analytical framework for the evolution of vertical structures when processing high-resolution three-dimensional meteorological spatial field data output from numerical models, making it difficult to generate verifiable and comparable quantitative outputs from the analytical results.

[0005] In existing technologies, various technical approaches exist for modeling and characterizing downburst wind fields. However, their focus is mostly on wind field generation, profile parameterization, or axisymmetric simplification simulation. A framework for analyzing wavenumber modes of symmetric and asymmetric structures based on meteorological spatial field data has not yet been established. For example, Chinese patent CN117829025A discloses a numerical simulation method for downburst wind fields, which uses a 2D axisymmetric impingement jet model to calculate wind speed time histories and synthesize fluctuating wind speeds. However, it relies on axisymmetric simplification and does not support the quantitative separation of asymmetric structural components. CN103473386B discloses a method for determining the wind profile of horizontally moving downbursts. It derives wind speed formulas based on jet models and CFD results for design, but emphasizes profile parameterization and does not establish a mechanism for distinguishing wavenumber modes of symmetric and asymmetric structures. CN105488256B discloses a method for modeling tilted micro-downbursts based on burst intensity. It generates a synthetic wind field by using a multi-vortex ring model and introducing tilt angle and orientation angle, and provides parameter selection. However, it relies on prior assumptions and lacks a verifiable analytical output for meteorological spatial fields.

[0006] In summary, existing technologies for the quantitative analysis of symmetric and asymmetric structures of downbursts suffer from several drawbacks, including unclear physical meaning, insufficient wave mode identification capabilities, susceptibility to biased central references, and a lack of a systematic analytical framework for three-dimensional meteorological spatial fields. Therefore, constructing a quantitative analysis method for symmetric and asymmetric structures with a unified reference, clear physical orientation, and the ability to distinguish wavenumber modes under multi-source meteorological spatial field data conditions, in order to achieve stable extraction and verifiable characterization of downburst structural features, is a pressing technical problem to be solved in this field. Summary of the Invention

[0007] (a) Purpose of the invention To address the aforementioned deficiencies and shortcomings of existing technologies, this invention aims to provide a method, medium, and program product for analyzing the symmetric and asymmetric structures of downbursts based on meteorological spatial field data and Fourier wavenumber decomposition. This method establishes event references with disaster points as constraints and organizes multi-source meteorological spatial field data. It calculates the horizontal center of the weather system leading to the downburst and constructs a centrally consistent structural coordinate system. Based on this, high-precision interpolation is performed to obtain a continuously usable spatial field. The target variable field is then converted into radial and azimuthal expressions and subjected to Fourier wavenumber decomposition. This achieves quantitative separation and consistent characterization of symmetric structural components and various orders of asymmetric wave modes, thereby supporting the stable extraction of downburst structural characteristics, verifiable output, and quantitative analysis of the evolution of disaster hazard azimuth and intensity.

[0008] (II) Technical Solution To achieve the objective of this invention and solve its technical problems, the present invention adopts the following technical solution: The first objective of this invention is to provide an analytical method for the symmetric and asymmetric structure of downbursts based on Fourier wavenumber decomposition and oriented towards meteorological spatial field data. This method is used to perform structured decomposition of the meteorological spatial field associated with downbursts and output symmetric and asymmetric components, and includes at least the following steps: SS1. Disaster Location Determination: Obtain ground observation data at the moment of downburst occurrence, and identify the geographical location of downburst disaster locations based on the condition that the maximum ground wind speed exceeds a preset threshold; SS2. Data Collection: Based on the occurrence time of the downburst, a preset time range is determined, and the event constraint area is determined centered on the downburst disaster point. Meteorological spatial field data covering the event constraint area are collected, and the target variable field Var to be analyzed is determined from it. SS3. Horizontal Center Calculation: Horizontal center calculation is performed based on the target variable field Var within the event constraint region to obtain the horizontal center of the weather system that caused the downburst. The amplitude of Var is used as the weight to perform a weighted summation and normalization of the latitude and longitude plane coordinates of each sampling point to obtain the coordinates of the horizontal center of the weather system. SS4. Construction of Polar Coordinate Sampling Framework: Using the horizontal center coordinates of the weather system as the origin of polar coordinates, construct a polar coordinate sampling framework for structural analysis within the event constraint region. This framework includes at least a preset radius sequence and azimuth sequence. Based on the polar coordinate sampling framework, determine the set of central neighborhood sampling points for analysis. SS5. Interpolation and Polar Resampling: At the central neighborhood sampling point set, extract the sampling point data corresponding to the target variable field Var from the meteorological spatial field data and perform two-dimensional interpolation and resampling on it to generate a regularized polar coordinate variable field Var(r,θ) with radius r and azimuth angle θ as independent variables; SS6. Fourier Wavenumber Decomposition: For each radius r, perform a Fourier series expansion along the azimuth angle θ on the Var(r,θ) azimuth discrete sequence, decomposing it into a zero wavenumber component WN0 with a wavenumber of 0 and multiple non-zero wavenumber components WN. j Where WN0 represents the symmetric radial structure, WN j Characterizes the asymmetric wave mode structure, where j is the non-zero wavenumber order; SS7. Analytical Results Output: Output structured analytical results, including at least the symmetric component field, the asymmetric component field, and the set of asymmetric modal parameters organized by wavenumber, to characterize the spatial distribution of the symmetric and asymmetric structures of downbursts and their wave modes.

[0009] The second objective of this invention is to provide a computer program product, including computer instructions, which are used to execute the above-mentioned analytical method for the symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition and oriented towards meteorological spatial field data.

[0010] The third objective of this invention is to provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned analytical method for the symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition and oriented towards meteorological spatial field data.

[0011] (III) Technical Effects Compared with the prior art, the present invention has the following beneficial and significant technical effects: (1) The present invention uses ground observation threshold criteria to determine disaster points and uses the target variable amplitude weighted calculation of the horizontal center of the weather system as a unified reference benchmark. Combined with spatial connectivity screening to suppress the interference of discrete noise points, the risk of symmetric and asymmetric components mixed due to center deviation is reduced from the source, so that the structural analysis results of the same event under different data sources and different resolution conditions have a consistent alignment benchmark and verifiability.

[0012] (2) The present invention constructs a regularized polar coordinate sampling framework around the horizontal center and achieves two-dimensional high-precision resampling through natural neighbor interpolation of triangulation topological constraints, taking into account the influence of complex observation point distribution and model grid differences on interpolation stability; at the same time, it applies neighborhood sample number and extrapolation distance constraints to the sparse boundary region to suppress high wavenumber pseudo signals caused by extrapolation, thereby obtaining a Var(r,θ) input field with stronger physical consistency and improving engineering applicability.

[0013] (3) The present invention performs Fourier series expansion on Var(r,θ) along the azimuth direction to explicitly separate the zero-wavenumber symmetric radial structure and the multi-order non-zero-wavenumber asymmetric modes, and outputs a set of modal parameters such as amplitude and phase organized by wavenumber, so as to realize the quantitative classification of asymmetric structure and identification of dominant modes. This structured output can directly support the identification of dangerous azimuth, the fine characterization of the landing area, and the comparative analysis between different events and different times, thereby improving the support capability for the study of the spatial structure mechanism of downbursts and operational assessment. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 The diagram shows the implementation flowchart of the analytical method for symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition for meteorological spatial field data according to the present invention. Figure 2 The following is a flowchart illustrating the implementation of two-dimensional interpolation and resampling in step SS5; Figure 3 The following is a flowchart of the implementation of the Fourier series expansion in step SS6; Figure 4 The diagram shows the raw data and preprocessing flow of GRIDSAT satellite multiband data. (a) to (c) are schematic diagrams of the results of horizontal center calculation based on the raw brightness temperature spatial field of the 6.7 μm band, the raw brightness temperature spatial field of the 11 μm band, and the spatial field of the difference between the 6.7 μm and 11 μm brightness temperatures, respectively. Black asterisks in the diagrams indicate the location of disaster points, and blue asterisks indicate the calculated horizontal center of the weather system. (d) to (f) are schematic diagrams of the results of extracting data from the surrounding area of ​​the three types of target variable fields using the horizontal center of the weather system as a reference. (g) to (i) are schematic diagrams of the results of performing high-precision interpolation on the data from the surrounding area of ​​the center using the three types of target variable fields to obtain a high spatial resolution variable field. Figure 5The figure shows the extraction results of symmetric and asymmetric components of 6.7 μm band data, where: (a) is the full field result of the 6.7 μm band variable field; (b) is the asymmetric component field obtained by superimposing non-zero wavenumber components; (c) is the zero wavenumber component WN0, corresponding to the symmetric radial structure; (d) is the first-order non-zero wavenumber component WN1; (e) is the second-order non-zero wavenumber component WN2; (f) is the third-order non-zero wavenumber component WN3; the asterisks in the figure are used to indicate the horizontal center of the weather system corresponding to the origin of the polar coordinates. Figure 6 The figure shows the extraction results of symmetric and asymmetric components of 11-micron band data, where: (a) is the full field result of the 11-micron band variable field; (b) is the asymmetric component field obtained by superimposing non-zero wavenumber components; (c) is the zero wavenumber component WN0, corresponding to the symmetric radial structure; (d) is the first-order non-zero wavenumber component WN1; (e) is the second-order non-zero wavenumber component WN2; (f) is the third-order non-zero wavenumber component WN3; the asterisks in the figure are used to indicate the horizontal center of the weather system corresponding to the origin of the polar coordinates; Figure 7 The figure shows the extraction results of the symmetric and asymmetric components of the brightness temperature difference data of 6.7 μm brightness temperature minus 11 μm brightness temperature. Among them: (a) is the full field result of the brightness temperature difference variable field of 6.7 μm brightness temperature minus 11 μm brightness temperature; (b) is the asymmetric component field obtained by superimposing non-zero wavenumber components; (c) is the zero wavenumber component WN0, corresponding to the symmetric radial structure; (d) is the first-order non-zero wavenumber component WN1; (e) is the second-order non-zero wavenumber component WN2; (f) is the third-order non-zero wavenumber component WN3. The asterisks in the figure are used to mark the horizontal center of the weather system corresponding to the origin of the polar coordinates. Detailed Implementation

[0016] This invention aims to provide a method, medium, and program product for analyzing the symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition for meteorological spatial field data. This method is used to perform structured decomposition of downburst-related meteorological spatial fields and output symmetric and asymmetric components. To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions in the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. The described embodiments are some, but not all, embodiments of this invention, and are exemplary, intended to explain the invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0017] Example 1: Analytical Methods for Symmetric and Asymmetric Structures of Downbursts like Figure 1As shown in the embodiments of the present invention, the analytical method for symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition and oriented towards meteorological spatial field data constructs a central reference and polar coordinate sampling framework for meteorological spatial field data, using downburst events as constraints. Var(r,θ) is obtained through interpolation and resampling, and Fourier wavenumber decomposition is performed to output symmetric components, asymmetric modes, and their amplitude and phase parameters for structural diagnosis and comparative evaluation. Specifically, the method mainly includes the following steps in its implementation: SS1. Disaster Location Identification: Ground observation data at the moment of downburst occurrence is acquired, and the geographical location of the downburst disaster site is identified based on the condition that the maximum surface wind speed exceeds a preset threshold. The ground observation data includes surface wind speed, wind direction, air pressure, and temperature data recorded by an automatic weather station network at the moment of the downburst and in the preceding and following periods. Data synchronization, missing data removal, and anomaly peak verification are performed. When identifying the geographical location of the downburst disaster site, the maximum wind speed must exceed a preset threshold (e.g., 17.2 m / s) and the duration must meet a preset criterion (not less than a preset duration threshold T). th The site is selected as a candidate disaster point and aggregated within a preset spatial neighborhood (e.g., a radius of 5-10 km). The downburst disaster point is determined as the representative location of the aggregated candidate disaster points, providing a consistent spatial reference benchmark for the subsequent determination of the event constraint area and avoiding the disaster point location offset problem caused by multi-center or mobile downburst events.

[0018] It should be noted that step SS1, by introducing a multi-site spatiotemporal aggregation and quality control mechanism, solves the problem of unstable disaster point location in traditional single-site identification methods under complex terrain or uneven observation network conditions. By jointly setting threshold criteria and duration criteria, and performing spatial aggregation and quality control on candidate sites, the influence of single-site anomalies and observation noise can be reduced, representative disaster point coordinates can be output, and the disaster point location results can be verified, thereby providing a stable reference for event constraint areas and subsequent center calculations.

[0019] SS2. Data Collection: The preset time range is determined based on the occurrence time of the downburst, and the event constraint area is determined centered on the downburst disaster point. Meteorological spatial field data covering the event constraint area are collected, and the target variable field Var to be analyzed is determined from it. The preset time range is obtained by extending forward and backward from the time of downburst occurrence (e.g., extending forward and backward by 10-30 minutes each) to capture the complete life cycle of the downburst from development to decay. The event constraint area is centered on the downburst disaster point and is limited by a preset spatial radius threshold (e.g., 50-150 km), which is dynamically adjusted according to the radar detection range or the grid coverage capability of the numerical model. The meteorological spatial field data includes at least one or more of the following: Doppler radar inverted wind field or radial velocity spatial field, satellite brightness temperature spatial field, three-dimensional wind field and temperature and humidity element spatial field output by reanalysis or numerical model. The multi-source data is subjected to time alignment, horizontal resolution unification (e.g., resampling to a 1-5 km unified grid through bilinear interpolation or nearest neighbor method), and missing measurement label consistency processing to ensure the comparability and verifiability of the target variable field Var in subsequent steps and to support the extended application of multi-source data fusion analysis.

[0020] Preferably, the target variable field Var is a physical quantity characterizing the near-surface divergence and gust intensity of downbursts, including at least one of near-surface horizontal wind speed, radial wind speed, horizontal divergence, sinking velocity, surface gust, or reflectivity factor; and when the meteorological spatial field data is a three-dimensional spatial field, Var is obtained by performing vertical projection (e.g., taking the maximum value within the layer) or in-layer averaging on data within a preset height layer (e.g., 100-500 m above the ground) or a preset thickness layer, so that Var has a consistent physical orientation with the ground-contact divergence structure of the downburst and ensures the physical representativeness of the subsequent wavenumber decomposition input.

[0021] It should be noted that step SS2 ensures the spatial coverage integrity and temporal continuity of the target variable field Var by constructing a spatiotemporally constrained multi-source data acquisition and preprocessing workflow, providing high-quality input for polar coordinate resampling; the height layer selection strategy enables Var to focus on the main influence area of ​​the downburst near the ground, avoiding aliasing interference from upper atmospheric signals; and the unified processing of multi-source data supports the comparison and verification of radar observation and numerical simulation results, enhancing the applicability and verifiability of the method.

[0022] SS3. Calculation of horizontal center: Within the event-constrained region, horizontal center calculation is performed based on the target variable field Var to obtain the horizontal center of the weather system leading to the downburst. The horizontal center coordinates are obtained by weighting and normalizing the latitude and longitude plane coordinates of each sampling point using the amplitude of Var as the weight. The calculation of the horizontal center of the weather system leading to the downburst includes: within the event-constrained region, a set of valid sampling points is obtained by filtering based on the amplitude of the target variable field Var. The set of valid sampling points consists of sampling points that satisfy the Var amplitude not less than a preset amplitude threshold and meet the spatial connectivity criterion. The horizontal center coordinates of the weather system (lon) are then calculated. c ,lat c )according to and The weighted normalization method is used to calculate lon, where lon c lat c These are the meridional and latitudinal coordinates of the horizontal center of the weather system, respectively, where n is the number of valid sampling points in the set. i ,lat i Let be the latitude and longitude plane coordinates of the i-th valid sampling point, i = 1, 2, ..., n, lon i lat i These are the meridional and latitudinal coordinates of the i-th valid sampling point, respectively. i Let Var be the amplitude of the target variable field at the i-th effective sampling point, so as to achieve stable positioning of the horizontal center of the weather system and reduce the influence of noise sampling points on the center calculation results.

[0023] Preferably, the spatial connectivity criterion is achieved jointly through an adjacency distance threshold and a connected component scale threshold: an adjacency relationship is constructed based on the planar distance between any two sampling points in the effective sampling point set, and the spatial connectivity criterion is determined when the distance between the two sampling points is not greater than the preset adjacency distance threshold D. th When the distance is set to 3-10 km, the nodes are considered adjacent and connected; the adjacent relationships are partitioned into connected components (using depth-first search or breadth-first search algorithms) to obtain at least one connected component, and the scale parameter of each connected component is calculated, including at least the connected component coverage area A or the number of sampling points N within the connected component; only those satisfying A or N greater than the corresponding preset threshold (e.g., A > 100 km) are retained. 2 Connected components with N>20 sampling points are selected as valid connected components for horizontal centering calculation. Other connected components are removed from the set of valid sampling points. In cases where multiple valid connected components exist, the connected component closest to the disaster point or containing the global maximum value of Var is prioritized for centering calculation to ensure (lon) c ,lat c The calculation is based on a spatially continuous high-amplitude structure and suppresses the interference of discrete noise points, avoiding confusion of the identification of the main weather system center by multi-center or secondary vortex structures.

[0024] It should be noted that step SS3 achieves robust horizontal center localization against complex background noise and multi-center structures through a dual constraint mechanism of amplitude weighting and spatial connectivity; the connected domain scale screening effectively eliminates isolated high-value points and boundary pseudo-signals, ensuring that the center coordinates reflect the true location of the dominant weather system; this center calculation method provides a stable and physically meaningful origin for the subsequent polar coordinate sampling framework, which is a key prerequisite step for achieving accurate separation of symmetrical and asymmetrical structures.

[0025] SS4. Construction of the Polar Coordinate Sampling Framework: Using the horizontal center coordinates of the weather system as the origin of the polar coordinates, a polar coordinate sampling framework for structural analysis is constructed within the event constraint region. This framework includes at least a preset radius sequence and azimuth sequence. The set of central neighborhood sampling points for analysis is then determined based on the polar coordinate sampling framework.

[0026] Preferably, the radius sequence is a discrete sequence starting from zero and increasing with a preset radial step size (e.g., 0.5~5 km), and the maximum radius value of the radius sequence is determined according to the range of the event constraint region (e.g., 30~100). The system covers the main impact area of ​​the downburst (km). The azimuth sequence is a discrete sequence covering 0 to 2π and increasing in a preset angle step size (e.g., 1°~15°, corresponding to 24~360 azimuth sampling points). The selection of the angle step size must satisfy the Nyquist sampling theorem of Fourier wavenumber decomposition to ensure that the highest resolution wavenumber does not exceed half of the number of azimuth sampling points. The central neighborhood sampling point set is generated by the Cartesian product of the radius sequence and the azimuth sequence to form a regular polar coordinate grid. The total number of sampling points is the product of the radius sequence length and the azimuth sequence length. Sampling points with radii between the preset minimum radius and the preset maximum radius are used as effective sampling points for Fourier wavenumber decomposition. The minimum radius is set to avoid the interference of interpolation errors and boundary truncation effects near the central singularity on the wavenumber decomposition results. The maximum radius is used to exclude the influence of the data quality degradation area near the boundary of the event constraint area.

[0027] It should be noted that step SS4, by constructing a regularized polar coordinate sampling framework, transforms the irregular meteorological spatial field in the original Cartesian coordinate system into a polar coordinate expression with the center of the weather system as the reference, creating input conditions that meet the requirements of periodicity and uniform sampling for Fourier wavenumber decomposition; the parameterized design of the radius sequence and azimuth sequence takes into account both computational efficiency and wavenumber resolution, supporting multi-scale application needs from rapid analysis at coarse resolution to fine diagnosis at high resolution.

[0028] SS5. Interpolation and Polar Coordinate Resampling: At the central neighborhood sampling point set, sampling point data corresponding to the target variable field Var are extracted from the meteorological spatial field data, and two-dimensional interpolation and resampling are performed on them to generate a regularized polar coordinate variable field Var(r,θ) with radius r and azimuth angle θ as independent variables. The two-dimensional interpolation and resampling process includes at least the following sub-steps: Figure 2 As shown: S501. Sampling point data extraction: Extract target variable field Var sampling point data from meteorological spatial field data covering the central neighborhood sampling point set, and perform time synchronization and consistency, missing point removal, outlier identification and / or smoothing filtering to form an effective sample set for interpolation calculation; S502. Topology Construction: Based on the spatial distribution of the effective sample set, construct the Delaunay triangulation topology, connect the sample points into several non-overlapping triangles and ensure that there are no other sample points inside the circumcircle of each triangle, and determine the natural neighborhood relationship and the boundary of the interpolation support domain accordingly. S503. Location of interpolation point and determination of natural neighborhood: For each interpolation point in the central neighborhood sampling point set, based on the Delaunay triangular mesh, determine several triangular units containing the interpolation point or adjacent to the interpolation point, extract the vertex sample points of the triangular units and use them as the natural neighborhood sample point set, thereby obtaining candidate sample points and their spatial adjacency relationships for interpolation calculation. S504. Natural Neighbor Weighted Interpolation Calculation: Based on the target variable values ​​of each vertex sample point in the natural neighborhood sample point set, the interpolation result at the point to be interpolated is determined by a weighted average method. The interpolation weights are determined according to the natural neighbor criterion. The smaller the distance between the point to be interpolated and the vertex sample point, the larger the interpolation weight corresponding to the vertex sample point. Normalization is performed on each interpolation weight to ensure that the sum of the weights is 1. S505. Polar coordinate resampling and result generation: The interpolation results of each interpolation point in the central neighborhood sampling point set are organized according to their corresponding radius r and azimuth angle θ to generate a regularized polar coordinate variable field Var(r,θ). Minimum natural neighborhood sample point threshold and maximum extrapolation distance threshold are applied to the interpolation points located in sparse data regions or near the missing measurement boundary. When the threshold constraints are not met, the interpolation point is marked as an invalid point or does not participate in the subsequent Fourier wavenumber decomposition, so as to suppress high wavenumber pseudo signals caused by extrapolation and improve the consistency and verifiability of asymmetric mode representation.

[0029] It should be noted that step SS5 achieves high-precision data conversion from irregular Cartesian grids to regular polar grids through a natural neighbor interpolation method based on Delaunay triangulation. Compared with traditional bilinear interpolation, natural neighbor interpolation has better local adaptability and continuity, effectively suppressing interpolation oscillations caused by grid inhomogeneity. Extrapolation distance constraints and effective data masking mechanisms ensure the physical reliability of resampling results, providing high-quality and periodic regularized input data for subsequent Fourier wavenumber decomposition.

[0030] SS6. Fourier wavenumber decomposition: Perform a Fourier series expansion along the azimuth angle θ on the discrete Var(r,θ) sequence corresponding to each radius r, decomposing it into a zero wavenumber component WN0 with a wavenumber of 0 and multiple non-zero wavenumber components WN. j Where WN0 represents the symmetric radial structure, WN j Characterizing the asymmetric wave mode structure, where j is a non-zero wavenumber order, and forming a symmetric radial structure and an asymmetric mode structure that vary with the radius. The Fourier series expansion includes at least the following sub-steps: Figure 3 As shown: S601. Construction of Discrete Azimuth Sequence and Periodic Closure Verification: For each radius r, extract the corresponding discrete sample {Var(r,θ)} along the azimuth sequence in the regularized polar coordinate variable field Var(r,θ). k The azimuth sequence is subjected to equal-interval verification and periodic closure consistency verification to ensure that the discrete azimuth sequence meets the input requirements of a periodic sequence in Fourier series expansion, where θ k Let k be the k-th discrete azimuth sample value in the azimuth sequence, where k = 1, 2, ..., N θ N θ The number of sampling points in the azimuth sequence is used to characterize the discrete sampling resolution along the azimuth direction at each radius r. S602. Setting the highest wavenumber order: Determine the highest wavenumber order m used for mode decomposition, where m is a preset positive integer satisfying m≥4 and m does not exceed N. θ Half of it, and define the set of wavenumber orders as {0,1,2,…,m}; S603. Fourier coefficient solution and zero wavenumber component determination: For each radius r, based on the azimuth discrete sequence {Var(r,θ)} k )} Calculate the zero wavenumber coefficients respectively and non-zero wavenumber coefficients and , j is a non-zero wavenumber order, and the zero wavenumber component is defined as WN0(r,θ)=a0(r) / 2, so that WN0 characterizes a symmetric radial structure that varies only with the radius; S604. Construction of Non-zero Wavenumber Components and Extraction of Modal Parameters: For each non-zero wavenumber, construct the non-zero wavenumber component WN. j (r,θ)=a j (r)cos(jθ)+b j (r)sin(jθ), and calculate the set of asymmetric modal parameters, including at least the magnitude function A. j (r)=[a j 2 (r)+b j 2 (r)] 1 / 2 With phase function ; S605. Structural Component Reconstruction and Output Organization: Based on WN0 and Each WN j Reconstruct the symmetric and asymmetric component fields, where the asymmetric component field is composed of... Give and output the set of modal parameters organized by wavenumber order. It is used to form a reproducible characterization of symmetric radial structures and asymmetric modal structures that vary with radius.

[0031] Preferably, in step S601, during the equal interval verification and periodic closure consistency verification, for each radius r corresponding to the azimuth sequence {θ}... k Calculate the difference between adjacent azimuth angles Δθ k =θ k+1 -θ k , where θ k θ k+1 These are the k-th and (k+1)-th discrete azimuth angle sample values ​​in the azimuth angle sequence, respectively, and are compared for consistency with the preset azimuth angle step size Δθ. When any Δθ... k When the deviation from Δθ exceeds a preset tolerance threshold (e.g., 5% to 10% of Δθ), for {θ k The process involves reordering the azimuth values ​​monotonically in ascending order and resampling Var(r,θ) to an equally spaced azimuth sequence (using linear or spline interpolation methods) to ensure that the discrete azimuth sequence at each radius r satisfies the uniform sampling condition. The azimuth sequence is then closed at its beginning and end according to the periodic boundary, covering [0,2π) and satisfying the periodic extension consistency constraint. Specifically, it is checked whether Var(r,0) and Var(r,2π) are equal or the difference is within an acceptable range (e.g., relative error less than 5%). If not, the boundary values ​​are smoothed by using the sampled values ​​at the beginning as the periodic extension reference at the end and constructing a closed azimuth sequence for Fourier series expansion. This ensures that the wavenumber decomposition input satisfies the boundary conditions and reproducibility requirements of the periodic sequence, avoiding high wavenumber pseudo-signal pollution caused by boundary discontinuities.

[0032] It should be noted that step SS6 decomposes the polar coordinate variable field Var(r,θ) into wavenumber spectrum expressions of symmetric and asymmetric components through Fourier series expansion, realizing the hierarchical decomposition of the complex spatial structure of downbursts; the zero wavenumber component WN0 provides the axisymmetric radial profile reference, and the non-zero wavenumber component WN j Quantification reveals the intensity and spatial orientation of asymmetric wave modes such as eccentricity, ellipticity, and spirality; periodic closure verification and equal interval constraints ensure the mathematical rigor of Fourier decomposition.

[0033] SS7. Parsing result output: The output structured analysis results include at least the symmetric component field, the asymmetric component field, and the asymmetric modal parameter set organized by wavenumber. Specific output contents may include, for example: (1) radial profile data of the symmetric component field and its visualization graphics; (2) two-dimensional polar coordinate distribution map of the asymmetric component field; (3) independent distribution map of each wavenumber component; (4) numerical table and radial variation curve of the modal parameter set; (5) bar chart or pie chart of energy contribution rate of each wavenumber; (6) comparison and overlay map of the original field, symmetric component and asymmetric component, used to characterize the spatial distribution of the symmetric and asymmetric structure of downburst and its wave modes, and to support subsequent physical mechanism diagnosis, numerical simulation verification and operational early warning applications.

[0034] In this embodiment of the invention, when the meteorological spatial field data is a three-dimensional meteorological spatial field, during the execution of steps SS2 to SS7, the target variable field Var and its Var(r,θ) are constructed for multiple preset height layers (for example, selecting a height layer every 500 m or 1 km from the ground to a height of 6 km), and Fourier wavenumber decomposition is performed to obtain WN0 and WN for each height layer. j Based on this, a set of vertical profile parameters for the symmetric and asymmetric components as a function of height is constructed to characterize the evolution of the symmetric and asymmetric structures of downbursts in the vertical direction and the height distribution characteristics of low-level wind shear.

[0035] In this embodiment of the invention, steps SS3 to SS7 further include a center consistency calibration step: the horizontal center of the weather system is calculated for multiple time periods within a preset time range, and the center trajectory is smoothed and calibrated based on the continuity constraint of the center displacement of adjacent time periods. Specifically, Kalman filtering, moving average filtering, or spline curve fitting methods are used. When the center displacement velocity of adjacent time periods exceeds a preset threshold (e.g., greater than 30 m / s), anomaly detection is triggered and correction is performed. After the center trajectory is calibrated, the polar coordinate sampling frame is reconstructed with the calibration center of each time period and Var(r,θ) is generated to form the temporal evolution results of symmetric and asymmetric structural components. The evolution curves of wavenumber component amplitude and phase over time, the evolution diagram of the dominant wavenumber generation and dissipation, and the time series of symmetric / asymmetric structural intensity are output to characterize the enhancement, movement, and decay process of the downburst structure over time.

[0036] In summary, Example 1 achieves objective and quantitative analysis of the symmetric and asymmetric structures of the meteorological spatial field of downbursts through steps such as disaster point identification, data acquisition, horizontal center positioning, polar coordinate sampling, interpolation resampling, and Fourier wavenumber decomposition. This method breaks through the limitations of traditional subjective identification and qualitative description, and provides a verifiable and quantifiable wavenumber spectrum diagnostic framework, offering new methodological tools and scientific basis for the study of the physical mechanisms of downbursts, numerical simulation verification, and operational early warning technologies.

[0037] Example 2: Application Case Building upon Example 1 above, Example 2 further provides an application example for a real-world downburst event. Using a downburst event occurring in a specific location as the research object, it demonstrates the application effect and technical value of the method of this invention in the analysis of actual downburst meteorological data. The selection of this downburst as the application case is primarily based on the following considerations: this downburst exhibits typical small-to-medium scale characteristics (horizontal scale approximately 4-40 km), a short lifespan (not exceeding 1 hour), and extremely high surface wind speeds (reaching over 25 m / s). Its sudden onset and rapid evolution provide a rigorous testing scenario for verifying the applicability of the method of this invention. Simultaneously, the location of the disaster point is clearly defined, laying a reliable positioning benchmark for subsequent center localization and structure extraction.

[0038] In practice, the geographical location of the disaster site is first determined based on disaster records and ground observation information, serving as the disaster point in step SS1. Then, using this disaster point as a reference, GRIDSAT spatial field data covering the event constraint area is collected in step SS2, and time alignment and missing data marker consistency processing are completed, forming three types of Var inputs. These three Var inputs are the 6.7-micron brightness temperature field, the 11-micron brightness temperature field, and the difference field between the 6.7-micron and 11-micron brightness temperatures, respectively. This allows the input variables to simultaneously characterize the mid-to-upper-level water vapor and cloud top radiation features, deep convection cloud top structure features, and stratification thermal differences, thereby supporting multi-view diagnosis of symmetrical and asymmetrical structures.

[0039] Next, in step SS3, a horizontal center calculation is performed for each type of Var, and the horizontal center of the weather system causing the downburst is obtained by weighting the Var amplitude; such as Figure 4 As shown in Figures (a) to (c), black asterisks indicate the locations of disasters and accidents, while blue asterisks indicate the calculated horizontal center of the weather system, used to provide a consistent origin benchmark for subsequent polar coordinate sampling. Then, in steps SS4 to SS5, data around the center is extracted using the horizontal center of the weather system as the polar coordinate origin, and high-precision interpolation and polar coordinate resampling are performed: as follows... Figure 4 Figures (d) to (f) show the data extraction of the region surrounding the center. Figure 4 Figures (g) to (i) show that the extracted results are interpolated to a higher spatial resolution to generate a regularized Var(r,θ), providing isoazimuth sampling input for subsequent wavenumber decomposition. In this process, the set of sampling points in the central neighborhood is determined by both the radius sequence and the azimuth sequence, and validity constraints are applied near data sparsity or missing measurement boundaries to suppress interference from high-wavenumber pseudo-signals introduced by extrapolation on asymmetric modes.

[0040] Finally, in step SS6, a Fourier series expansion is performed on the discrete azimuth sequence at each radius r to obtain the zero wavenumber component WN0 and the set of non-zero wavenumber components WN. j In step SS7, the symmetric and asymmetric component fields are output. Figure 5 The results of symmetric and asymmetric extraction in the 6.7 micrometer band are presented. Figure 6 The results of symmetric and asymmetric extraction in the 11-micron band are presented. Figure 7 Symmetric and asymmetric extraction results of the 6.7-11 micrometer band difference are presented; WN0 is used to characterize the symmetric radial structure that varies with the radius, and the non-zero wavenumber component is used to characterize the directional shift and multimodal asymmetric perturbation structure, thereby realizing the decompositional characterization and parameterized expression of the downburst spatial structure.

[0041] Oriented Figure 5 In the 6.7-micron band results shown, wave 0 ( Figure 5 Figure (c) shows a symmetrical cold center structure with approximately concentric circles (central brightness temperature ~220 K), reflecting the axially symmetrical distribution of mid-to-high-level water vapor convergence and upward motion; wave 1 ( Figure 5 The middle (d) diagram (WN1) shows that the system is tilted towards the southeast with an amplitude of 20 K, indicating the asymmetric influence of the upper-level steering flow; wave 2 ( Figure 5 (e) Figure 2 shows an elliptical stretching in a northwest-southeast direction, possibly related to the directional effect of vertical wind shear; wave 3 ( Figure 5 Figure (f) shows a trilobal fine disturbance with an amplitude of about 5 K, which reflects the non-uniform distribution characteristics of local convective cells.

[0042] Oriented Figure 6 In the 11-micron band results shown, wave 0 ( Figure 6 Figure (c) reveals an extremely strong cold cloud top symmetrical core (central brightness temperature ~240 K), corresponding to the main structure of a deep convective cloud; wave 2 ( Figure 6 The WN2 anomaly in Figure (e) is significant, exhibiting a spiral asymmetric shape with an amplitude exceeding 10 K. This feature suggests that there may be mesoscale vortices or non-uniform downdraft distribution within the parent convective system of the downburst, which is highly consistent with the spatial asymmetry of strong wind disasters.

[0043] Oriented Figure 7 In the results showing the difference between 6.7 micrometers and 11 micrometers, 0 wave ( Figure 7 In Figure (c), WN0 shows a negative symmetry center (difference of approximately -30 K), representing an unstable vertical stratification with relatively dry and cold middle and upper levels and relatively humid and warm lower levels; the superposition of wave 1 and wave 2 ( Figure 7 Figures (d) to (e) show an asymmetric gradient zone running from southwest to northeast, which quantitatively characterizes the key thermally unstable asymmetric distribution region that triggers downbursts.

[0044] Comprehensive analysis shows that the method of this invention successfully separated the symmetrical and asymmetrical structural features of downbursts from multi-band satellite data. The zero-wave symmetrical component accurately located the core concentration area of ​​system energy and water vapor, while the higher-order asymmetrical components (especially the 2-wave spiral structure) revealed the non-uniform dynamic mechanism leading to local extreme winds. This structural decomposition result provides a quantitative physical diagnostic basis for understanding the causes, evolution, and disaster-causing mechanisms of downbursts, verifying the practicality and scientific value of the technology of this invention in the fine structural analysis of downbursts.

[0045] The objectives of this invention have been fully and effectively achieved through the above embodiments. Those skilled in the art will understand that this invention includes, but is not limited to, the contents described in the accompanying drawings and the specific embodiments described above. Although the invention has been described with reference to what is currently considered the most practical and preferred embodiments, it should be understood that the invention is not limited to the disclosed embodiments, and any modifications that do not depart from the functional and structural principles of the invention will be included within the scope of the claims.

Claims

1. An analytical method for the symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition and oriented towards meteorological spatial field data, characterized in that, It should include at least the following steps: SS1. Obtain ground observation data at the moment of downburst occurrence, and identify the geographical location of downburst disaster points based on the condition that the maximum ground wind speed exceeds a preset threshold; SS2. Determine the preset time range based on the occurrence time of the downburst flow, and determine the event constraint area centered on the downburst flow disaster point. Collect meteorological spatial field data covering the event constraint area, and determine the target variable field Var to be analyzed from it. SS3. Calculate the horizontal center based on the target variable field Var within the event constraint region to obtain the horizontal center of the weather system that caused the downburst. The amplitude of Var is used as the weight to perform a weighted summation and normalization of the latitude and longitude plane coordinates of each sampling point to obtain the coordinates of the horizontal center of the weather system. SS4. Using the horizontal center coordinates of the weather system as the origin of the polar coordinates, construct a polar coordinate sampling framework for structural analysis within the event constraint region, including at least a preset radius sequence and azimuth sequence, and determine the set of central neighborhood sampling points for analysis based on the polar coordinate sampling framework; SS5. At the central neighborhood sampling point set, extract the sampling point data corresponding to the target variable field Var from the meteorological spatial field data and perform two-dimensional interpolation and resampling on it to generate a regularized polar coordinate variable field Var(r,θ) with radius r and azimuth angle θ as independent variables; SS6. Perform a Fourier series expansion along the azimuth angle θ on the discrete Var(r,θ) sequence corresponding to each radius r, decomposing it into a zero wavenumber component WN0 with a wavenumber of 0 and multiple non-zero wavenumber components WN. j Where WN0 represents a symmetrical radial structure, WN j Characterizes the asymmetric wave mode structure, where j is the non-zero wavenumber order; SS7. Output structured analytical results, including at least the symmetric component field, the asymmetric component field, and the set of asymmetric modal parameters organized by wavenumber, to characterize the spatial distribution of the symmetric and asymmetric structures of downbursts and their wave modes.

2. The method according to claim 1, characterized in that, In step SS1, the ground observation data includes ground wind speed, wind direction, air pressure and temperature data recorded by the automatic weather station network at the time of downburst occurrence and the period before and after, and the data is processed for time synchronization, missing data removal and abnormal peak verification. When identifying the geographical location of downburst disaster points, sites that meet the preset threshold for maximum wind speed and the preset criterion for duration are selected as candidate disaster points. These candidate disaster points are then aggregated within a preset spatial neighborhood to determine the representative location of the aggregated downburst disaster points.

3. The method according to claim 1, characterized in that, In step SS2, the preset time range is obtained by extending forward and backward from the time of the downburst occurrence, and the event constraint area is limited by a preset spatial radius threshold centered on the downburst disaster point; the meteorological spatial field data includes at least one or more of the following: Doppler radar inverted wind field or radial velocity spatial field, satellite brightness temperature spatial field, three-dimensional wind field and temperature and humidity element spatial field output by reanalysis or numerical model, and time alignment, horizontal resolution unification and missing measurement label consistency processing are performed on the multi-source data.

4. The method according to claim 1 or 3, characterized in that, In step SS2, the target variable field Var is a physical quantity characterizing the near-surface divergence and gust intensity of a downburst, including at least one of near-surface horizontal wind speed, radial wind speed, horizontal divergence, sinking velocity, surface gust, or reflectivity factor; and when the meteorological spatial field data is a three-dimensional spatial field, Var is obtained by performing vertical projection or intra-layer averaging on data within a preset height layer or preset thickness layer, so that Var has a consistent physical orientation with the ground-contact divergence structure of the downburst.

5. The method according to claim 1, characterized in that, In step SS3, the calculation of the horizontal center of the weather system causing the downburst includes: within the event constraint region, a set of valid sampling points is obtained by filtering based on the amplitude of the target variable field Var. This set of valid sampling points consists of sampling points that satisfy both a Var amplitude not less than a preset amplitude threshold and a spatial connectivity criterion. The coordinates of the horizontal center of the weather system (lon) are then calculated. c ,lat c )according to and The weighted normalization method is used to calculate lon, where lon c lat c These are the meridional and latitudinal coordinates of the horizontal center of the weather system, respectively, where n is the number of valid sampling points in the set. i ,lat i Let be the latitude and longitude plane coordinates of the i-th valid sampling point, i = 1, 2, ..., n, lon i lat i These are the meridional and latitudinal coordinates of the i-th valid sampling point, respectively. i Let Var be the magnitude of the target variable field at the i-th valid sampling point.

6. The method according to claim 5, characterized in that, The spatial connectivity criterion is achieved through a combination of an adjacency distance threshold and a connected component scale threshold: an adjacency relationship is constructed based on the planar distance between any two sampling points in the effective sampling point set; when the distance between two sampling points is not greater than a preset adjacency distance threshold D... th When an adjacency is identified as adjacent, a connection is established; the adjacency relationship is partitioned into at least one connected domain, and the scale parameter of each connected domain is calculated, including at least the connected domain coverage area A or the number of sampling points N within the connected domain; Only connected components that satisfy A or N greater than the corresponding preset threshold are retained as valid connected components for horizontal center calculation, and the remaining connected components are removed from the set of valid sampling points.

7. The method according to claim 1, characterized in that, In step SS4, the radius sequence is a discrete sequence starting from zero and increasing with a preset radial step size, and the azimuth sequence is a discrete sequence covering 0~2π and increasing with a preset angular step size; the central neighborhood sampling point set is generated by the Cartesian product of the radius sequence and the azimuth sequence, and the sampling points whose radii are between the preset minimum radius and the preset maximum radius are used as effective sampling points for Fourier wavenumber decomposition.

8. The method according to claim 1, characterized in that, In step SS5, the two-dimensional interpolation and resampling, when implemented, includes at least the following sub-steps: S501. Sampling point data extraction: Extract target variable field Var sampling point data from meteorological spatial field data covering the central neighborhood sampling point set, and perform time synchronization and consistency, missing point removal, outlier identification and / or smoothing filtering to form an effective sample set for interpolation calculation; S502. Topology Construction: Based on the spatial distribution of the effective sample set, construct the Delaunay triangulation topology, connect the sample points into several non-overlapping triangles and ensure that there are no other sample points inside the circumcircle of each triangle, and determine the natural neighborhood relationship and the boundary of the interpolation support domain accordingly. S503. Location of interpolation point and determination of natural neighborhood: For each interpolation point in the central neighborhood sampling point set, based on the Delaunay triangular mesh, determine several triangular units containing the interpolation point or adjacent to the interpolation point, and extract the vertex sample points of the triangular units as the natural neighborhood sample point set. S504. Natural Neighbor Weighted Interpolation Calculation: Based on the target variable values ​​of each vertex sample point in the natural neighborhood sample point set, the interpolation result at the point to be interpolated is determined by a weighted average method. The interpolation weights are determined according to the natural neighbor criterion, and normalization is performed on each interpolation weight. S505. Polar coordinate resampling and result generation: The interpolation results of each interpolation point in the central neighborhood sampling point set are organized according to their corresponding radius r and azimuth angle θ to generate a regularized polar coordinate variable field Var(r,θ), and the minimum natural neighborhood sample point threshold and the maximum extrapolation distance threshold are applied to the interpolation points located in the sparse data region or near the missing measurement boundary.

9. The method according to claim 1, characterized in that, Step SS6, when performing the Fourier series expansion, includes at least the following sub-steps: S601. Construction of Discrete Azimuth Sequence and Periodic Closure Verification: For each radius r, extract the corresponding discrete sample {Var(r,θ)} along the azimuth sequence in the regularized polar coordinate variable field Var(r,θ). k )}, and perform equal-interval verification and periodic closure consistency verification on the azimuth sequence, where θ k Let k be the k-th discrete azimuth sample value in the azimuth sequence, where k = 1, 2, ..., N θ N θ This represents the number of sampling points in the azimuth sequence; S602. Setting the highest wavenumber order: Determine the highest wavenumber order m used for mode decomposition, where m is a preset positive integer satisfying m≥4 and m does not exceed N. θ Half of it, and define the set of wavenumber orders as {0,1,2,…,m}; S603. Fourier coefficient solution and zero wavenumber component determination: For each radius r, based on the azimuth discrete sequence {Var(r,θ)} k )} Calculate the zero wavenumber coefficients respectively and non-zero wavenumber coefficients and , j is a non-zero wavenumber order, and the zero wavenumber component is defined as WN0(r,θ)=a0(r) / 2, so that WN0 characterizes a symmetric radial structure that varies only with the radius; S604. Construction of Non-zero Wavenumber Components and Extraction of Modal Parameters: For each non-zero wavenumber, construct the non-zero wavenumber component WN. j (r,θ)=a j (r)cos(jθ)+b j (r)sin(jθ), and calculate the set of asymmetric modal parameters, including at least the magnitude function A. j (r)=[a j 2 (r)+b j 2 (r)] 1 / 2 With phase function ; S605. Structural Component Reconstruction and Output Organization: Based on WN0 and Each WN j Reconstruct the symmetric and asymmetric component fields, where the asymmetric component field is composed of... Give and output the set of modal parameters organized by wavenumber order. It is used to form a reproducible characterization of symmetric radial structures and asymmetric modal structures that vary with radius.

10. The method according to claim 9, characterized in that, In step S601, during the equal interval check and periodic closure consistency check, for each radius r, the azimuth sequence {θ} is... k Calculate the difference between adjacent azimuth angles Δθ k =θ k+1 -θ k , where θ k θ k+1 These are the k-th and (k+1)-th discrete azimuth angle sample values ​​in the azimuth angle sequence, respectively, and are compared for consistency with the preset azimuth angle step size Δθ. When any Δθ... k When the deviation from Δθ exceeds the preset tolerance threshold, for {θ k } Perform a reordering based on monotonically increasing azimuth angles and resample Var(r,θ) to an equally spaced azimuth angle sequence; then close the first and last ends of the azimuth angle sequence according to the periodic boundary to cover [0,2π) and satisfy the periodic extension consistency constraint, that is, use the first end sampled value as the periodic extension reference of the last end and construct a closed azimuth sequence for Fourier series expansion.

11. A computer program product comprising computer instructions, characterized in that, The computer instructions are used to execute the analytical method for symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition for meteorological space field data, as described in any one of claims 1 to 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the analytical method for symmetric and asymmetric structures of downbursts based on Fourier wavenumber decomposition for meteorological spatial field data as described in any one of claims 1 to 10.

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