A differential reflectance factor column identification method, system, medium, and device

Abstract

The present application relates to the technical field of meteorological identification, more particularly, to a differential reflectivity factor column identification method, system, medium and equipment. The scheme comprises importing radar-based data, performing quality control, and generating basic radar data; performing three-dimensional grid point processing according to the basic radar data to generate non-uniform grid point data; performing Cartesian coordinate conversion according to the non-uniform grid point data to generate three-dimensional grid point data; performing Zdr section identification according to the three-dimensional grid point data to generate Zdr section characteristic quantities, performing two-dimensional component identification to generate Zdr two-dimensional components; and performing Zdr column synthesis at different altitudes starting from the zero-degree layer height according to the Zdr two-dimensional components, and generating the maximum value of Zdr in real time. The scheme is a differential reflectivity factor column identification method based on dual-polarization weather radar observation data, which realizes quantitative analysis of severe convective storms and provides a powerful means for monitoring and early warning of severe convective storms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of meteorological identification, and more particularly to a differential reflectivity factor column identification method, system, medium and device. BACKGROUND

[0002] The differential reflectivity factor column (Zdr column) is one of the most significant dual-polarization radar features in convective storms, and its morphological characteristics are that the development height of the Zdr column corresponding to the Zdr area (Zdr >= 1dB) in the vertical direction exceeds the zero layer, which has important indicative significance for the development of convective storms.

[0003] Before the present application, there is still a lack of effective objective identification method, resulting in that whether the differential reflectivity factor column is an important indicator, but in the actual forecast business, the Zdr column cannot be automatically identified, and only post-analysis can be performed, and online quantitative analysis cannot be performed. SUMMARY

[0004] In view of the above problems, the present application provides a differential reflectivity factor column identification method, system, medium and device, a differential reflectivity factor column identification method based on dual-polarization weather radar observation data, to realize quantitative analysis of strong convective storms, and provide a powerful means for monitoring and early warning of strong convective storms.

[0005] According to a first aspect of an embodiment of the present application, a differential reflectivity factor column identification method is provided.

[0006] In one or more embodiments, preferably, the differential reflectivity factor column identification method comprises:

[0007] Importing radar-based data, performing quality control, and generating basic radar data;

[0008] According to the basic radar data, three-dimensional grid processing is performed to generate non-uniform grid data;

[0009] According to the non-uniform grid data, Cartesian coordinate conversion is performed to generate three-dimensional grid data;

[0010] According to the three-dimensional grid data, Zdr section identification is performed to generate Zdr section characteristic quantities;

[0011] According to the Zdr section characteristic quantities, two-dimensional component identification is performed to generate Zdr two-dimensional components;

[0012] According to the Zdr two-dimensional components, Zdr column synthesis at different layer heights starting from the zero layer height is performed, and the maximum value of Zdr is generated in real time.

[0013] In one or more embodiments, preferably, the imported radar-based data is subjected to quality control to generate the basic radar data, specifically including:

[0014] The imported radar-based data is subjected to calculation of a correlation coefficient and screening of updated radar-based data through a calculation formula of the correlation coefficient.

[0015] On the basis of the updated radar-based data, the dual-polarization radar-based data subjected to quality control is screened according to a signal-to-noise ratio parameter as the basic radar data.

[0016] The calculation formula of the correlation coefficient is:

[0017]

[0018] wherein pm(0) is a zero-order correlation coefficient of a horizontal channel and a vertical channel, Rhv(0) is a zero-order cross-correlation coefficient of the horizontal channel and the vertical channel, Rhh(0) is a zero-order autocorrelation coefficient of the horizontal channel, and Rvv(0) is a zero-order autocorrelation coefficient of the vertical channel.

[0019] In one or more embodiments, preferably, the three-dimensional grid processing is performed according to the basic radar data to generate the non-uniform grid data, specifically including:

[0020] The basic radar data is obtained, and an elevation angle is extracted therefrom.

[0021] The basic radar data is obtained, and an azimuth angle is extracted therefrom.

[0022] The basic radar data is obtained, and a radial distance is extracted therefrom.

[0023] The same time, the same coordinates, the elevation angle, the azimuth angle, and the radial distance are taken as a complete coordinate information to form the non-uniform grid data.

[0024] In one or more embodiments, preferably, the Cartesian coordinate conversion is performed according to the non-uniform grid data to generate the three-dimensional grid data, specifically including:

[0025] The non-uniform grid data is obtained, and each grid data is read therefrom, wherein the grid data includes a slant range from a radar, an azimuth angle, and an elevation angle.

[0026] Linear interpolation is performed on three adjacent elevation angles to replace the original non-uniform grid data to form polar coordinate data of information, which is stored as the three-dimensional grid data, wherein the three-dimensional grid data has a horizontal and vertical distribution rate of 250 meters within 150 kilometers around the radar.

[0027] In one or more embodiments, preferably, the Zdr segment identification according to the three-dimensional grid data, generating Zdr segment characteristic quantity, specifically includes:

[0028] The three-dimensional grid data is obtained to identify Zdr segment, and it is judged whether the first calculation formula is met. If met, a continue judgment instruction is generated;

[0029] After the continue judgment instruction appears, the Zdr segment is judged by the second calculation formula. If met, it is regarded as Zdr segment. After the retrieval of all three-dimensional grid data is completed, Zdr segment characteristic quantity is generated;

[0030] The first calculation formula is:

[0031] Zdr>Z y1

[0032] Wherein, Zdr is Zdr value, Z y1 is the first Zdr margin;

[0033] The second calculation formula is:

[0034] Z j <Z y2

[0035] Wherein, Z j is the distance not meeting the first calculation formula, Z y2 is the interruption distance margin.

[0036] In one or more embodiments, preferably, the two-dimensional component identification according to the Zdr segment characteristic quantity, generating Zdr two-dimensional component, specifically includes:

[0037] According to the Zdr segment characteristic quantity, corresponding grid points meeting the first calculation formula in y-axis direction are judged one by one;

[0038] The area of two-dimensional component needs to reach a preset threshold value, to generate a Zdr region, and all Zdr regions are stored as the Zdr two-dimensional component.

[0039] In one or more embodiments, preferably, the Zdr two-dimensional component is used to synthesize Zdr column of different layer heights starting from zero degree layer height, and the maximum value of Zdr is generated in real time, specifically including:

[0040] Starting from zero degree layer height, Zdr two-dimensional component of each height layer is retrieved from bottom to top;

[0041] Three search radii are set, specifically including first search radius, second search radius and third search radius;

[0042] According to three search radii in sequence, the two-dimensional components of adjacent height layers are sequentially associated and verified, it is judged whether the two-dimensional components of the previous height layer are located within the search radius, if yes, it is considered that the two-dimensional components of the adjacent height layer are associated, and the two-dimensional components are bound as three-dimensional Zdr columns;

[0043] The three-dimensional Zdr columns are analyzed in a plan view to form a plurality of plan views;

[0044] It is judged whether the projection in each three-dimensional Zdr column corresponding to the plan view is less than a preset search radius between the center of the collection, if yes, the volume, top height, bottom height, vertical direction extension height, Zdr maximum value and corresponding coordinates of the three-dimensional Zdr column are saved in real time;

[0045] If the distance between the center of the collection is greater than the preset search radius, the corresponding three-dimensional Zdr column is removed.

[0046] According to the second aspect of the embodiment of the present application, a differential reflectivity factor column identification system is provided.

[0047] In one or more embodiments, preferably, the differential reflectivity factor column identification system comprises:

[0048] A data import module is configured to import radar-based data, perform quality control, and generate basic radar data;

[0049] A three-dimensional grid module is configured to perform three-dimensional grid processing according to the basic radar data, and generate non-uniform grid data;

[0050] A data preprocessing module is configured to perform Cartesian coordinate conversion according to the non-uniform grid data to generate three-dimensional grid data;

[0051] A one-dimensional segment module is configured to perform Zdr segment identification according to the three-dimensional grid data, and generate Zdr segment characteristic quantities;

[0052] A two-dimensional segment module is configured to perform two-dimensional component identification according to the Zdr segment characteristic quantities, and generate Zdr two-dimensional components;

[0053] A data synthesis module is configured to perform Zdr column synthesis from the zero-degree layer height to different layer heights according to the Zdr two-dimensional components, and generate the maximum value of Zdr in real time.

[0054] According to the third aspect of the embodiment of the present application, a computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method according to any one of the first aspect of the embodiment of the present application.

[0055] According to a fourth aspect of the embodiments of the present application, an electronic device is provided, comprising a memory and a processor, the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of the first aspect of the embodiments of the present application.

[0056] The technical scheme provided by the embodiments of the present application can have the following beneficial effects.

[0057] In the scheme of the present application, a differential reflectivity factor column identification method based on dual-polarization weather radar observation data is provided to realize quantitative analysis of strong convective storms.

[0058] In the scheme of the present application, quantitative data of strong convective storms are obtained online, which is predictive for the development of hailstorms, and the extreme value of the development height is in advance of the hail falling process.

[0059] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application will be realized and achieved by means of the structures particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0060] The technical scheme of the present application will be further described in detail below with the help of the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0061] In order to more clearly illustrate the technical scheme in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0062] Figure 1 is a flow chart of a differential reflectivity factor column identification method according to an embodiment of the present application.

[0063] Figure 2 is a flow chart of importing radar base data, performing quality control, and generating basic radar data in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0064] Figure 3 is a flow chart of performing three-dimensional grid processing according to the basic radar data to generate non-uniform grid data in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0065] Figure 4A flow chart of a process of generating three-dimensional grid point data according to the non-uniform grid point data in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0066] Figure 5 A flow chart of a process of generating Zdr segment feature quantity according to the three-dimensional grid point data in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0067] Figure 6 A flow chart of a process of generating Zdr two-dimensional component according to the Zdr segment feature quantity in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0068] Figure 7 A flow chart of a process of generating the maximum value of Zdr according to the Zdr two-dimensional component in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0069] Figure 8 A structure diagram of a differential reflectivity factor column identification system according to an embodiment of the present application.

[0070] Figure 9 A structure diagram of an electronic device according to an embodiment of the present application.

[0071] Figure 10 A schematic diagram of selection of polar coordinate data in a vertical direction.

[0072] Figure 11 A schematic diagram of Zdr segment identification.

[0073] Figure 12 A schematic diagram of Zdr two-dimensional component identification. DETAILED DESCRIPTION

[0074] In some of the processes described in this specification and in the accompanying drawings, multiple operations are described in a particular, sequential order. However, it should be understood that these operations can be performed in an order different than that which is described, or these operations can be performed in parallel, or some operations can be omitted altogether. In addition, these processes can include more operations than are recited in the corresponding description. The order in which the operations are described is not intended to be a limitation unless specifically so stated in this document. Moreover, the descriptions "first," "second," "third," etc. are used herein for clarity, and do not necessarily have to imply a serial or chronological order, for example, unless otherwise indicated.

[0075] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0076] The differential reflectivity factor column (Zdr column) is one of the most significant dual-polarization radar features in a convective storm, and the morphological feature of the Zdr column is that the development height of the Zdr column corresponding to a Zdr area (Zdr is greater than or equal to 1 dB) in the vertical direction exceeds the zero layer, which is of important indicative significance for the development of the convective storm.

[0077] Before the present application technology, there is still a lack of effective objective identification method, resulting in that whether the differential reflectivity factor column is an important index, but in the actual prediction business, the Zdr column cannot be automatically identified, and only post-analysis can be performed, and online quantitative analysis cannot be performed.

[0078] In the embodiments of the present application, a differential reflectivity factor column identification method, system, medium and equipment are provided. The scheme is a differential reflectivity factor column identification method based on dual-polarization weather radar observation data, so as to realize quantitative analysis of a strong convective storm, and provide a powerful means for monitoring and early warning of the strong convective storm.

[0079] According to a first aspect of the embodiments of the present application, a differential reflectivity factor column identification method is provided.

[0080] Figure 1 It is a flowchart of the differential reflectivity factor column identification method of one embodiment of the present application.

[0081] In one or more embodiments, preferably, the differential reflectivity factor column identification method comprises:

[0082] S101, importing radar-based data, performing quality control, and generating basic radar data;

[0083] S102, performing three-dimensional grid processing according to the basic radar data, and generating non-uniform grid data;

[0084] S103, performing Cartesian coordinate conversion according to the non-uniform grid data to generate three-dimensional grid data;

[0085] S104, performing Zdr section identification according to the three-dimensional grid data, and generating Zdr section characteristic quantities;

[0086] S105, performing two-dimensional component identification according to the Zdr section characteristic quantities, and generating Zdr two-dimensional components;

[0087] S106, according to the Zdr two-dimensional component, Zdr column synthesis of different layer heights from zero layer height is carried out, and the maximum value of Zdr is generated in real time.

[0088] In the embodiment of the application, the severe convective weather has the characteristics of small spatial scale, strong burst, rapid development and evolution, and is difficult to defend and has strong destructive power. Identifying the convective storm that causes the severe convective weather and extracting its morphological features can effectively improve the prediction and early warning level of the severe convective weather, and is of great significance for disaster prevention and mitigation. In order to realize the objective recognition of the three-dimensional morphological features of the dual-polarization radar based on the convective storm, realize the objective recognition of the Zdr column, and output the feature quantity, a basis is provided for the quantitative analysis of the convective storm.

[0089] Figure 2 The flow chart of importing radar base data, quality control and generating basic radar data in a differential reflectivity factor column recognition method of one embodiment of the application.

[0090] As shown in Figure 2 in one or more embodiments, preferably, the imported radar base data, quality control and generating basic radar data specifically include:

[0091] S201, importing radar base data, calculating a correlation coefficient through a correlation coefficient calculation formula, and screening and updating the radar base data;

[0092] S202, on the basis of the updated radar base data, screening the quality-controlled dual-polarization radar base data according to a signal-to-noise ratio parameter as the basic radar data;

[0093] The correlation coefficient calculation formula is:

[0094]

[0095] pm(0) is the horizontal channel and vertical channel zero-order correlation coefficient; Rhv(0) is the horizontal channel and vertical channel zero-order cross-correlation coefficient, Rhh(0) is the horizontal channel zero-order autocorrelation coefficient, and Rvv(0) is the vertical channel zero-order autocorrelation coefficient.

[0096] In the embodiment of the present application, the base data of the dual-polarization radar is subjected to quality control, and the non-meteorological echo is removed based on the correlation coefficient and the signal-to-noise ratio parameter. The correlation coefficient is abbreviated as CC (correlation coefficient) in the dual-polarization radar system software, and it is one of the most important dual-polarization base products. In the mathematical meaning, P represents the linear correlation between two variables, and on the dual-polarization radar, it represents the correlation of the detection pulse changes of the horizontal channel and the vertical channel. The detection pulse variable is the amplitude and the phase. If the changes of the two variables are consistent, the correlation coefficient product outputs a high CC value; otherwise, if the changes of one of the variables are inconsistent, the correlation coefficient product outputs a low CC.

[0097] Figure 3 FIG. 1 is a flowchart of a method for generating non-uniform grid data according to base radar data in a differential reflectivity factor column recognition method according to an embodiment of the present application.

[0098] As shown in FIG. 1, in one or more embodiments, preferably, the method for generating non-uniform grid data according to the base radar data specifically includes the following steps. Figure 3 S301, obtaining the base radar data and extracting the elevation angle therein;

[0099] S302, obtaining the base radar data and extracting the azimuth angle therein;

[0100] S303, obtaining the base radar data and extracting the radial distance therein;

[0101] S304, taking the elevation angle, the azimuth angle, and the radial distance at the same time and the same coordinates as a complete coordinate information to form the non-uniform grid data.

[0102] In the embodiment of the present application, the base radar data is subjected to three-dimensional grid point processing. This process is mainly a process of storing and sorting the radar observation data in a spherical coordinate mode to form the non-uniform grid data.

[0103]

[0104] FIG. 2 is a flowchart of a method for generating three-dimensional grid data by Cartesian coordinate conversion according to the non-uniform grid data in a differential reflectivity factor column recognition method according to an embodiment of the present application. Figure 4 As shown in FIG. 2, in one or more embodiments, preferably, the method for generating three-dimensional grid data by Cartesian coordinate conversion according to the non-uniform grid data specifically includes the following steps.

[0105] Figure 4

[0106] ​​S401. Obtain the non-uniform grid data and read the data of each grid point, wherein the grid data includes the slant range, azimuth angle and elevation angle of the radar.

[0107] S402. Perform linear interpolation on three consecutive adjacent elevation angles to replace the original non-uniform grid data, forming polar coordinate data of the information, and store it as three-dimensional grid data, wherein the horizontal and vertical distribution rate of the three-dimensional grid data within 150 kilometers around the radar is 250 meters.

[0108] In this embodiment of the invention, the original radar observation data is stored in spherical coordinates (elevation, azimuth, and radial distance), which results in relatively uneven spatial resolution of the radar base data. For example, in the volume scan mode commonly used in operational weather radar, there are only 9 elevation angles between 0.5 and 19.5°, and the radar beamwidth is approximately 1°. Therefore, there will be certain gaps in the vertical direction of the beams at each elevation angle, and they cannot fill the entire detection space. If the positions within the beamwidth are strictly matched one-to-one during the radar data gridding process, some grid points will lack data, resulting in reduced spatial continuity of the echo. Therefore, the radar differential reflectivity factor Zdr data in spherical coordinates is interpolated into Cartesian coordinates to form three-dimensional grid data with uniform spatial resolution.

[0109] Figure 5 This is a flowchart illustrating the process of identifying Zdr segments and generating Zdr segment feature quantities based on the three-dimensional grid data in a differential reflectance factor column identification method according to an embodiment of the present invention.

[0110] like Figure 5 As shown, in one or more embodiments, preferably, the step of identifying Zdr segments based on the three-dimensional grid data and generating Zdr segment feature quantities specifically includes:

[0111] S501. Obtain the three-dimensional grid data and perform Zdr segment identification. Determine whether the first calculation formula is satisfied. If satisfied, generate a continue judgment instruction.

[0112] S502. After the continued judgment instruction appears, the Zdr segment is judged using the second calculation formula. If it meets the requirements, it is considered as the Zdr segment. After all the three-dimensional grid data retrieval is completed, the Zdr segment feature quantity is generated.

[0113] The first calculation formula is:

[0114] Zdr>Z y1

[0115] Where Zdr is the Zdr value, Z y1 This represents the first Zdr margin;

[0116] The second calculation formula is:

[0117] Z j <Z y2

[0118] wherein, Z j is a distance that does not satisfy the first calculation formula, Z y2 is an interruption distance margin.

[0119] In the embodiment of the present application, the Zdr segment is defined as a Zdr value reaching a certain threshold in the x-axis direction, having a certain length, and having no interruption or a very small interruption distance in the middle of the Zdr large value area, wherein the Zdr segment characteristic quantity includes a Zdr segment starting coordinate, an ending coordinate, a Zdr segment length, and a maximum value in the Zdr segment.

[0120] Figure 6 is a flowchart of a method for identifying a Zdr two-dimensional component according to the Zdr segment characteristic quantity in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0121] As Figure 6 shown, in one or more embodiments, preferably, the Zdr two-dimensional component is generated by identifying the Zdr two-dimensional component according to the Zdr segment characteristic quantity, and specifically includes the following steps.

[0122] S601, judging corresponding grid points satisfying the first calculation formula in the y-axis direction according to the Zdr segment characteristic quantity;

[0123] S602, generating a Zdr area when the area of the two-dimensional component reaches a preset threshold, and storing the Zdr area as the Zdr two-dimensional component.

[0124] In the embodiment of the present application, when the identification of the Zdr segment is completed, the Zdr two-dimensional component can be obtained by combining the Zdr segments in the y-axis direction according to a preset rule.

[0125] Figure 7 is a flowchart of a method for synthesizing Zdr columns of different heights starting from a zero-degree layer height and generating a maximum value of Zdr in real time according to the Zdr two-dimensional component in a differential reflectivity factor column identification method according to an embodiment of the present application.

[0126] As Figure 7 shown, in one or more embodiments, preferably, the Zdr two-dimensional component is generated by identifying the Zdr two-dimensional component according to the Zdr segment characteristic quantity, and specifically includes the following steps.

[0127] S701, searching the Zdr two-dimensional component of each height layer from the bottom to the top starting from a zero-degree layer height;

[0128] S702, three search radii are set, specifically including a first search radius, a second search radius and a third search radius;

[0129] S703, the two-dimensional components of adjacent height layers are sequentially associated and verified according to the three search radii, whether the two-dimensional component of the last height layer is located within the search radius is judged, if yes, it is considered that the two-dimensional component of the adjacent height layer is associated, and it is bound as a three-dimensional Zdr column;

[0130] S704, the three-dimensional Zdr column is analyzed in a plan view, and a plurality of plan views are formed;

[0131] S705, whether the projection corresponding to each three-dimensional Zdr column in the plan view is less than a preset search radius between the center of the collection is judged, if yes, the volume, top height, bottom height, vertical direction extension height, Zdr maximum value and corresponding coordinates of the three-dimensional Zdr column are saved in real time;

[0132] S706, if the projection corresponding to each three-dimensional Zdr column in the plan view is greater than the preset search radius between the center of the collection, the corresponding three-dimensional Zdr column is removed.

[0133] In the embodiment of the application, the Zdr column is composed of Zdr two-dimensional components on different height layers which satisfy a certain spatial distance correlation. Therefore, the three-dimensional structure information of the Zdr column can be obtained by matching the Zdr two-dimensional components of each height layer according to certain rules. The specific process is as follows: starting from the zero-degree layer height, the Zdr two-dimensional components of each height layer are searched from bottom to top, and the two-dimensional components of adjacent height layers are associated and verified. The geometric center of the first two-dimensional component is taken as the center, and the two-dimensional components of the last height layer are sequentially searched according to three search radii (5.0km, 7.5km and 10km respectively), when the two-dimensional component of the last height layer is located within the search radius, it is considered that the two-dimensional components exist correlation.

[0134] According to the second aspect of the embodiment of the application, a differential reflectivity factor column identification system is provided.

[0135] Figure 8 It is a structure diagram of a differential reflectivity factor column identification system according to an embodiment of the application.

[0136] In one or more embodiments, preferably, the differential reflectivity factor column identification system comprises:

[0137] The data import module 801 is used for importing radar-based data, performing quality control, and generating basic radar data;

[0138] The three-dimensional grid module 802 is configured to perform three-dimensional grid processing according to the basic radar data to generate non-uniform grid data.

[0139] The data preprocessing module 803 is configured to perform Cartesian coordinate conversion to generate three-dimensional grid data according to the non-uniform grid data.

[0140] The one-dimensional segment module 804 is configured to perform Zdr segment identification according to the three-dimensional grid data to generate Zdr segment characteristic quantities.

[0141] The two-dimensional segment module 805 is configured to perform two-dimensional component identification according to the Zdr segment characteristic quantities to generate Zdr two-dimensional components.

[0142] The data synthesis module 806 is configured to perform Zdr column synthesis at different layer heights starting from the zero-degree layer height according to the Zdr two-dimensional components, and generate the maximum value of Zdr in real time.

[0143] In the embodiment of the application, a system suitable for different structures is realized through a series of modular designs, and the system can realize closed-loop, reliable and efficient execution through acquisition, analysis and control.

[0144] According to a third aspect of the embodiment of the application, a computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions realize the method according to any one of the first aspect of the embodiment of the application when executed by a processor.

[0145] According to a fourth aspect of the embodiment of the application, an electronic device is provided. Figure 9 FIG. 1 is a structural diagram of an electronic device according to an embodiment of the application. Figure 9 The electronic device shown in the figure is a general differential reflectivity factor column identification device. The electronic device can be a smart phone, a tablet computer or the like. As shown, the electronic device 900 includes a processor 901 and a memory 902. The processor 901 is electrically connected to the memory 902. The processor 901 is the control center of the terminal 900, and connects all parts of the terminal through various interfaces and lines. By running or calling the computer program stored in the memory 902 and calling the data stored in the memory 902, the terminal performs various functions and processes data, thereby overall monitoring the terminal.

[0146] In this embodiment, the processor 901 in the electronic device 900 loads the instructions corresponding to the processes of one or more computer programs into the memory 902 according to the following steps, and the processor 901 runs the computer programs stored in the memory 902 to realize various functions: importing radar base data, performing quality control, and generating basic radar data; performing three-dimensional grid processing based on the basic radar data to generate non-uniform grid data; performing Cartesian coordinate transformation based on the non-uniform grid data to generate three-dimensional grid data; performing Zdr segment identification based on the three-dimensional grid data to generate Zdr segment feature quantities; performing two-dimensional component identification based on the Zdr segment feature quantities to generate two-dimensional Zdr components; and performing Zdr column synthesis from the zero-degree layer height to different layer heights based on the two-dimensional Zdr components, and generating the maximum value of Zdr in real time.

[0147] Memory 902 can be used to store computer programs and data. The computer programs stored in memory 902 contain instructions that can be executed in the processor. Computer programs can be composed of various functional modules. Processor 901 executes various functional applications and data processing by calling the computer programs stored in memory 902.

[0148] Figure 10 This is a schematic diagram illustrating the selection of polar coordinate data in the vertical direction. For example... Figure 10 As shown, the grid point (r,a,e) indicates that the grid point is located at a distance of slant range r, azimuth a, and elevation e from the radar, with its adjacent elevation layers being e2 and e1, respectively. When e < 20°, r changes little with the value of e; therefore, the intersection points of this point with the upper and lower elevation layers are (r,a,e2) and (r,a,e1), respectively. In the horizontal direction, the intersection points of this point with the upper and lower elevation layers are (r1,a,e2) and (r2,a,e1), respectively. The value of this grid point can be obtained by linear interpolation of the values ​​of these four grid points. Finally, a 40×1200×1200 three-dimensional grid data is obtained within a 150km radius around the radar, with a horizontal and vertical distribution rate of 250 meters.

[0149] Figure 11 This is a schematic diagram for Zdr segment identification. (Example) Figure 11 As shown, the identification algorithm searches for grid points with Zdr values ​​≥ 1 dB along the x-axis. The duration L of these grid points must meet a certain threshold (1 km). Due to quality control and other reasons, Zdr segments within the storm may experience brief interruptions. When the distance between two Zdr segments is less than 0.5 km, they are merged into one Zdr segment. For the identified Zdr segments, their characteristic quantities are calculated and saved. These characteristic quantities include the Zdr segment's start and end coordinates, length, maximum value within the Zdr segment, and its coordinates.

[0150] Figure 12 An identification diagram of Zdr two-dimensional components is shown. Figure 12 As shown, the identification algorithm searches each Zdr section along the y-axis direction to form a Zdr two-dimensional component. There needs to be a certain overlap distance (0.5 km) between two adjacent Zdr sections on the y-axis, a Zdr two-dimensional component should contain a certain number (2) of Zdr sections, and the area of the two-dimensional component needs to reach a certain threshold (1 km 2 ).

[0151] The technical scheme provided by the embodiments of the present application can have the following beneficial effects:

[0152] In the present application, a differential reflectivity factor column identification method based on dual-polarization weather radar observation data is provided to realize quantitative analysis of severe convective storms.

[0153] In the present application, quantitative data of severe convective storms are obtained online, which is predictive for the development of hailstorms, and the extreme value of the development height is in advance of the hail falling process.

[0154] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage, etc.) containing computer-usable program code.

[0155] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0156] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1the function specified in the one or more blocks.

[0157] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the flows Figure 1 the flows or the flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0158] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for identifying differential reflectance factor columns, characterized in that, The method includes: Import radar base data, perform quality control, and generate basic radar data; Three-dimensional grid processing is performed on the basic radar data to generate non-uniform grid data. Based on the non-uniform grid data, a Cartesian coordinate transformation is performed to generate three-dimensional grid data; Based on the three-dimensional grid data, Zdr segment identification is performed, and Zdr segment feature quantities are generated; Based on the Zdr segment feature values, perform two-dimensional component identification to generate Zdr two-dimensional components; Based on the two-dimensional Zdr components, Zdr columns of different heights are synthesized starting from the zero-degree layer height, and the maximum value of Zdr is generated in real time. Specifically, the step of identifying Zdr segments based on the three-dimensional grid data and generating Zdr segment feature quantities includes: The three-dimensional grid data is obtained for Zdr segment identification. It is determined whether the first calculation formula is satisfied. If it is satisfied, a further judgment instruction is generated. After the continued judgment instruction appears, the Zdr segment is judged using the second calculation formula. If it meets the requirements, it is considered as the Zdr segment. After all the three-dimensional grid data retrieval is completed, the Zdr segment feature quantity is generated. The first calculation formula is: Health>Z y1 Where Zdr is the Zdr value, Z y1 This represents the first Zdr margin; The second calculation formula is: WITH j < Z y2 Among them, Z j For the spacing that does not satisfy the first calculation formula, Z y2 This is the margin for interruption spacing; Specifically, the step of performing two-dimensional component identification based on the Zdr segment feature values ​​to generate Zdr two-dimensional components includes: Based on the Zdr segment feature quantity, determine the corresponding grid points that satisfy the first calculation formula in the y-axis direction one by one; The area of ​​the two-dimensional component needs to reach a preset threshold to generate a Zdr region, and the entire Zdr region is stored as the Zdr two-dimensional component.

2. The differential reflectance factor column identification method as described in claim 1, characterized in that, The process of importing radar base data, performing quality control, and generating basic radar data specifically includes: Import radar base data, calculate the correlation coefficient using the formula, and then filter and update the radar base data. Based on the updated radar base data, dual-polarization radar base data with quality control is selected according to the signal-to-noise ratio parameter and used as the base radar data. The formula for calculating the correlation coefficient is: Where pm(0) is the zero-order correlation coefficient between the horizontal and vertical channels; Rhv(0) is the zero-order cross-correlation coefficient between the horizontal and vertical channels; Rhh(0) is the zero-order autocorrelation coefficient of the horizontal channel; and Rvv(0) is the zero-order autocorrelation coefficient of the vertical channel.

3. The differential reflectance factor column identification method as described in claim 1, characterized in that, The step of performing three-dimensional grid processing based on the basic radar data to generate non-uniform grid data specifically includes: Obtain the basic radar data and extract the elevation angle; Obtain the basic radar data and extract the azimuth angle from it; Obtain the basic radar data and extract the radial distance from it; The elevation angle, azimuth angle, and radial distance at the same time and coordinates are used as a complete coordinate information to form the non-uniform grid data.

4. The differential reflectivity factor column identification method as described in claim 1, characterized in that, The step of generating three-dimensional grid data by performing Cartesian coordinate transformation based on the non-uniform grid data specifically includes: Acquire the non-uniform grid data, and read the data of each grid point, wherein the grid data includes the slant range, azimuth angle and elevation angle of the radar; Linear interpolation is performed on three consecutive adjacent elevation angles to replace the original non-uniform grid data, forming polar coordinate data of the information, which is stored as three-dimensional grid data. The three-dimensional grid data has a horizontal and vertical distribution rate of 250 meters within a 150-kilometer radius around the radar.

5. The differential reflectivity factor column identification method as described in claim 1, characterized in that, The process of synthesizing Zdr columns at different heights starting from the zero-degree layer height based on the two-dimensional Zdr components, and generating the maximum value of Zdr in real time, specifically includes: Starting from the zero-degree layer height, retrieve the Zdr two-dimensional components of each height layer from bottom to top; Set three search radii, specifically including the first search radius, the second search radius, and the third search radius; Based on the three search radii, the association test of the two-dimensional components of adjacent height layers is performed in turn. It is determined whether the two-dimensional component of the previous height layer is within the search radius. If it is, the two-dimensional components of the adjacent height layers are considered to be associated and are bound to a three-dimensional Zdr column. A top-view analysis is performed on the three-dimensional Zdr column to generate several top views; Determine whether the projection of each three-dimensional Zdr column in the top view is less than the preset search radius between the centers. If it is less than the preset search radius, then save the volume, top height, bottom height, vertical extension height, maximum Zdr value and corresponding coordinates of the three-dimensional Zdr column in real time. If the distance between the centers of the set is expected to be greater than the preset search radius, then the corresponding three-dimensional Zdr column will be removed.

6. A differential reflectivity factor column identification system, characterized in that, The system is used to implement the method as described in any one of claims 1-5, the system comprising: The data import module is used to import radar base data, perform quality control, and generate basic radar data. The three-dimensional grid module is used to perform three-dimensional grid processing on the basic radar data to generate non-uniform grid data. The data preprocessing module is used to perform Cartesian coordinate transformation to generate three-dimensional grid data based on the non-uniform grid data; The one-dimensional segment module is used to identify Zdr segments based on the three-dimensional grid data and generate Zdr segment feature quantities. The two-dimensional segment module is used to identify two-dimensional components based on the Zdr segment feature values ​​and generate Zdr two-dimensional components. The data synthesis module is used to synthesize Zdr columns of different heights starting from the zero-degree layer height based on the two-dimensional Zdr components, and to generate the maximum value of Zdr in real time.

7. A computer-readable storage medium storing computer program instructions thereon, characterized in that, The computer program instructions, when executed by a processor, implement the method as described in any one of claims 1-5.

8. An electronic device comprising a memory and a processor, characterized in that, The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of claims 1-5.

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