A short-time heavy rainfall identification method, device and equipment

Abstract

This invention relates to the field of electromagnetic wave technology, specifically to a method, apparatus, and device for identifying short-duration heavy precipitation. The method includes: acquiring radar base data; wherein the radar base data is acquired based on X-band radar; parsing the radar base data to obtain dual polarization parameters corresponding to the target spatial region, the dual polarization parameters including differential reflectivity and differential phase shift rate; determining a portion of the target spatial region that meets preset requirements as a warning area; wherein the preset requirements include: the differential reflectivity is less than a first preset threshold and the differential phase shift rate is greater than a second preset threshold. This addresses the problem in existing technologies that require complex operations such as data correction using other band radars to quickly and accurately identify precipitation.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic wave technology, specifically to a method, apparatus, and equipment for identifying short-duration heavy rainfall. Background Technology

[0002] Phased array weather radar adopts the latest technologies in the radar field in recent years. Compared with conventional radar, it has a faster scanning speed, higher detection accuracy and reliability, stronger detection capability, higher sensitivity, and is convenient to use and maintain.

[0003] However, X-band dual-polarization phased array weather radar, currently used as a spatiotemporal gap-filling radar in meteorological operational radar observation networks, has a strong advantage in temporal and spatial resolution. However, the X-band dual-polarization attenuation characteristic is significant, especially the differential reflectivity, where excessive attenuation of the horizontal polarized wave during severe weather events leads to numerical distortion of the differential reflectivity, making it difficult to use normally. In fusion applications with operational radars, intensity correction is often required, and most fusion applications are in reflectivity networking and wind field applications. This is mainly because the significant attenuation characteristic of X-band dual polarization necessitates attenuation correction in these methods, resulting in large errors and complex operations, thus limiting its application. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus and equipment for identifying short-term heavy precipitation, so as to solve the problem in the prior art that it is necessary to rely on other band radars for data correction and other complex operations in order to quickly and accurately identify precipitation.

[0005] According to a first aspect of the present invention, a method for identifying short-duration heavy rainfall includes:

[0006] Acquire radar-based data; wherein the radar-based data is acquired based on X-band radar;

[0007] By analyzing radar base data, the dual polarization parameters corresponding to the target spatial region are obtained. The dual polarization parameters include differential reflectivity and differential phase shift rate.

[0008] The target spatial region is designated as the warning zone if it meets the preset requirements.

[0009] The preset requirements include: the differential reflectivity is less than a first preset threshold and the differential phase shift rate is greater than a second preset threshold.

[0010] Furthermore, the first preset threshold is 0; the second preset threshold is 0;

[0011] The determination of a portion of the target spatial region that meets preset requirements as a warning zone includes:

[0012] The region with negative differential reflectance less than 0 in the target spatial region is defined as the first spatial region;

[0013] The region in the target spatial region with a differential phase shift rate greater than 0 is defined as the second spatial region.

[0014] The overlapping area of ​​the first and second spatial regions is designated as the warning area.

[0015] Furthermore, the target spatial region includes multiple grids;

[0016] The determination of a portion of the target spatial region that meets preset requirements as a warning zone includes:

[0017] For each grid, determine whether the grid meets the preset requirements; if it does, determine that the grid belongs to the warning area.

[0018] Furthermore, by analyzing the radar base data, the dual polarization parameters corresponding to the target spatial region are obtained. These dual polarization parameters include differential reflectivity and differential phase shift rate.

[0019] By analyzing the radar base data, the dual polarization parameters corresponding to each discrete point in the target space region are obtained at the first resolution.

[0020] Based on spatial interpolation, the dual polarization parameters corresponding to each discrete point in the target spatial region are obtained at the second resolution.

[0021] In this case, the second resolution is greater than the first resolution.

[0022] Furthermore, the differential reflectivity is the ratio of the estimated reflectivity of the horizontally polarized wave to the estimated reflectivity of the vertically polarized wave, and the calculation formula is as follows:

[0023] ZDR = 10 * lg(ZHH / ZVV)

[0024] Wherein, ZDR is the differential reflectivity, ZHH is the horizontally polarized wave reflectivity, and ZVV is the vertically polarized wave reflectivity; the differential reflectivity characterizes the morphology of precipitation particles.

[0025] The differential phase shift rate characterizes the size and density of precipitation particles.

[0026] Furthermore, including:

[0027] The radar base data includes low elevation angles.

[0028] Furthermore, it also includes:

[0029] Identify the warning area and display the warning on the map.

[0030] Furthermore, it also includes:

[0031] The first and second preset thresholds are set based on the climate characteristics and radar performance of the region.

[0032] According to a second aspect of the present invention, a short-term heavy rainfall identification device includes:

[0033] An acquisition module is used to acquire radar base data; wherein the radar base data is acquired based on an X-band radar.

[0034] The analysis module is used to analyze radar base data to obtain the dual polarization parameters corresponding to the target spatial region. The dual polarization parameters include differential reflectivity and differential phase shift rate.

[0035] The determination module is used to identify a portion of the target spatial region that meets preset requirements as a warning zone;

[0036] The preset requirements include: the differential reflectivity is within a first preset threshold and the differential phase shift rate is within a second preset threshold.

[0037] According to a third aspect of the present invention, a smart device includes:

[0038] A processor, and a memory connected to the processor;

[0039] The memory is used to store a computer program, which is at least used to execute the short-term heavy precipitation identification method described in the first aspect of the embodiments of the present invention.

[0040] The processor is used to call and execute the computer program in the memory.

[0041] This invention employs the above technical solution. It acquires X-band radar base data and analyzes it to obtain the dual polarization parameters corresponding to the target spatial region: differential reflectivity and differential phase shift rate. Utilizing the significant attenuation characteristics of the X-band dual polarization parameters, it directly identifies regions that satisfy both a differential reflectivity less than a first preset threshold and a differential phase shift rate greater than a second preset threshold, achieving rapid identification and accurate positioning of warning areas. This method leverages the large attenuation characteristics of X-band differential reflectivity and differential phase shift rate for rapid identification of warning areas, thus quickly and accurately identifying areas of heavy precipitation. Therefore, this method avoids the large estimation errors caused by the significant attenuation characteristics of X-band dual polarization parameters in traditional methods, and the complex operations such as relying on data correction based on low-attenuation data generated by other band radars. Based on the attenuation characteristics of the X-band and the powerful advantages of X-band dual polarization phased array weather radar in terms of temporal and spatial resolution, this invention achieves the identification of short-term heavy precipitation, effectively improving the ability to issue warnings for severe weather.

[0042] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0043] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0044] Figure 1 This is a flowchart illustrating a short-duration heavy rainfall identification method according to an exemplary embodiment;

[0045] Figure 2 This is a data processing flowchart illustrating a method for identifying short-duration heavy rainfall according to an exemplary embodiment;

[0046] Figure 3 This is a schematic block diagram illustrating a short-duration heavy rainfall identification device according to an exemplary embodiment;

[0047] Figure 4 This is a schematic diagram of the structure of a smart device according to an exemplary embodiment;

[0048] Figure 5 This is a flowchart illustrating a method for identifying short-duration heavy rainfall according to another exemplary embodiment. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0050] In traditional technologies, there are three methods for precipitation estimation using phased array weather radar:

[0051] The first method is to estimate the echo intensity and precipitation level using the ZI relationship. This method is one of the precipitation estimation methods of weather radar. Based on statistical characteristics, the echo intensity is used to quantitatively estimate the precipitation level. The estimation result can be used as one of the reference standards for short-term heavy precipitation. This method is mainly used in S-band and C-band radars.

[0052] The second method is precipitation estimation based on dual polarization parameters of dual polarization radar. Introducing dual polarization parameters (ZDR, KDP, etc.) into the precipitation estimation process effectively improves the accuracy of precipitation estimation and has a significant advantage in phase identification. This method is mostly used in S-band and C-band dual polarization radars.

[0053] The third method involves using existing radar parameters for heavy precipitation identification, and this method is mainly applied to S-band and C-band operational radar products.

[0054] This invention is applied to phased array weather radar. While conventional weather radar requires 6 minutes to complete an 11-layer volumetric scan, this radar can do so in just 1 minute. Employing distributed transmission and reception technology, its reliability is increased from 600 hours for conventional radar to over 3000 hours. Phased array weather radar comprehensively improves ground clutter suppression, anti-interference capabilities, and automated detection capabilities. It can detect hazardous weather affecting aviation safety, such as thunderstorms, strong winds, downbursts, and wind shear, more quickly and accurately. It can also capture and analyze the internal structure of hazardous weather with greater precision, providing detection data for accurate aviation forecasting services. It can promptly detect hazardous weather and monitor its development, making it of great significance in air traffic control and meteorological hazardous weather monitoring.

[0055] Example 1

[0056] refer to Figure 1 , Figure 1 This is a flowchart illustrating a short-duration heavy rainfall identification method according to an exemplary embodiment, such as... Figure 1 As shown, the method may specifically include the following steps:

[0057] Step S11: Acquire radar base data; wherein, the radar base data is acquired based on X-band radar.

[0058] In practice, radar base data includes elevation angle, azimuth angle, reflectivity factor, etc. The elevation angle in radar base data is low elevation angle, mainly because severe convective weather systems often develop to more than 10 kilometers, and water condensate will circulate in the system. Low elevation angle is more of the area where raindrops fall.

[0059] Step S12: Analyze the radar base data to obtain the dual polarization parameters corresponding to the target spatial region. The dual polarization parameters include differential reflectivity and differential phase shift rate.

[0060] In practice, the dual-polarization technology of weather radar mainly involves simultaneously transmitting horizontally polarized waves and vertically polarized waves. This allows it to receive the backscattered electromagnetic signals from precipitation particles in both the horizontal and vertical directions. By analyzing the differences in the received echo signals from the horizontal and vertical directions, the morphology of the water condensate particles can be determined, such as flat, round, or rhomboid shapes. The main dual-polarization parameters involved in this invention include: differential reflectivity (ZDR) and differential phase shift rate (ZDR).

[0061] Preferably, the differential reflectivity is the ratio of the estimated reflectivity of the horizontally polarized wave to the estimated reflectivity of the vertically polarized wave, calculated using the following formula:

[0062] ZDR = 10 * lg(ZHH / ZVV)

[0063] Wherein, ZDR is the differential reflectivity, ZHH is the horizontally polarized wave reflectivity, and ZVV is the vertically polarized wave reflectivity; the differential reflectivity characterizes the morphology of precipitation particles.

[0064] The differential phase shift rate characterizes the size and density of precipitation particles.

[0065] ZDR (Differential Reflectivity): A parameter that primarily reflects the morphology of hydrophobic condensates. It is the ratio of the estimated horizontal to vertical echo power, and the calculation formula is shown below:

[0066] It's worth noting that the larger the water droplet particles and the more pronounced their flattening, the larger the ZDR value. However, if the attenuation rate of the horizontally polarized wave ZHH is much greater than ZHH (i.e., ZHH is less than ZVV), the ZDR will be negative. The differential phase shift rate represents the phase change of the electromagnetic wave per unit distance during the propagation of water droplets. It indicates the size and density of the precipitation particles, and its value is unaffected by the attenuation of the electromagnetic wave intensity (except in cases of complete attenuation).

[0067] Preferably, the analytical radar base data yields dual polarization parameters corresponding to the target spatial region, wherein the dual polarization parameters include differential reflectivity and differential phase shift rate, including:

[0068] By analyzing the radar base data, the dual polarization parameters corresponding to each discrete point in the target space region are obtained at the first resolution.

[0069] Based on spatial interpolation, the dual polarization parameters corresponding to each discrete point in the target spatial region are obtained at the second resolution.

[0070] In this case, the second resolution is greater than the first resolution.

[0071] In practice, the analytical radar base data yields the dual polarization parameters corresponding to the target spatial region, as detailed below:

[0072] Reference Figure 2 , Figure 2 The figure shows a data processing flowchart for a method of identifying short-duration heavy precipitation, which includes:

[0073] A weather lightning antenna emits electromagnetic waves in one direction to obtain parameters of condensate passing along that direction. Scanning a plane involves rotating 360° and continuously emitting and receiving electromagnetic waves to obtain data for that surface. The data is stored according to the antenna's direction, storing parameters for each distance, and so on; therefore, it is stored in polar coordinates.

[0074] Analyzing radar base data involves converting polar coordinates to PPI data and then to CAPPI data.

[0075] In practice, radar base data uses polar coordinates, meaning azimuth, elevation, and range determine a location. Our grid points are rectangular, with coordinates X, Y, and Z, or Earth coordinates such as longitude, latitude, and altitude. PPI refers to a top-down view at the same elevation angle, such as data obtained by scanning a circle at an antenna elevation angle of 0.5° or 1.5°. Spatially, it is arranged in a cone shape. CAPPI data refers to data on a contour surface, which is essentially three-dimensional grid data. Conical data at different elevation angles constitute PPI data. Spatial interpolation of PPI data yields CAPPI data. The specific implementation method is existing technology and will not be elaborated here.

[0076] It is understandable that the PPI data on a top-down view at the same elevation angle is... Figure 2 The PPI data of the planar display after polar coordinate transformation is the first resolution. The dual polarization parameters corresponding to each discrete point are the PPI data. Based on the PPI data of the planar display and spatial interpolation, more discrete points are obtained to obtain the contour surface data at the second resolution. The dual polarization parameters in this contour surface data are then obtained, namely differential reflectivity and differential phase shift rate. The second resolution has a denser discrete point density than the first resolution, so it has a higher resolution.

[0077] Preferably, both the first preset threshold and the second preset threshold are 0. The first and second preset thresholds are set based on the climate characteristics and radar performance of the region.

[0078] In some embodiments, differential reflectivity and differential phase shift rate need to be threshold-controlled, and unnecessary clutter needs to be filtered out to prevent clutter information from affecting the calculation results. The threshold for differential phase shift rate needs to be set according to the climate characteristics and radar performance of each region, and is generally greater than 3.1; the threshold for differential reflectivity is less than 0.

[0079] Step S13: Identify the portion of the target space region that meets the preset requirements as the warning area.

[0080] The preset requirements include: the differential reflectivity is within a first preset threshold and the differential phase shift rate is within a second preset threshold.

[0081] In some embodiments, the target spatial region includes multiple grids;

[0082] The determination of a portion of the target spatial region that meets preset requirements as a warning zone includes:

[0083] For each grid, determine whether the grid meets the preset requirements; if it does, determine that the grid belongs to the warning area.

[0084] Understandably, a grid refers to the process of converting radar base data into an equidistant or equilatal grid for three-dimensional spatial calculation and display. Figure 2 The flowchart depicts the data processing flow, from reading and parsing radar base data to spatial interpolation, then dividing the interpolated target spatial region into grids, extracting data from each grid, and forming a process grid data.

[0085] Specifically, refer to Figure 2 For each grid, it is determined whether the grid meets preset requirements; if it does, the grid that meets the requirements is marked as a warning area. Figure 2 The product data grid in the image is the grid that meets the requirements, and all of these grids need to be identified.

[0086] Preferably, the portion of the target space region that meets the preset requirements is designated as the warning zone, including:

[0087] The region with negative differential reflectance less than 0 in the target spatial region is defined as the first spatial region;

[0088] The region in the target spatial region with a differential phase shift rate greater than 0 is defined as the second spatial region.

[0089] The overlapping area of ​​the first and second spatial regions is designated as the warning area.

[0090] Understandably, the preset requirement is to identify the region where the differential phase shift rate is greater than 0 (that is, the second spatial region) and the region where the differential reflectivity is less than 0 (that is, the first spatial region); therefore, the warning region is the overlapping region of the first spatial region and the second spatial region.

[0091] It is worth noting that this invention provides a short-term heavy precipitation identification method based on the attenuation characteristics of X-band dual polarization parameters. The greatest value of this method lies in proposing a new approach to applying X-band dual polarization parameters, utilizing the significant attenuation characteristics of dual polarization parameters. In contrast, traditional methods that utilize the attenuation characteristics of dual polarization parameters require data correction based on data from other band radars. For example, C-band radar has low attenuation, and data can be corrected using C-band radar data to reduce the large data errors caused by the significant attenuation of X-band dual polarization parameters.

[0092] The present invention provides a method for identifying short-term heavy precipitation based on the attenuation characteristics of X-band dual polarization parameters. This method can be used as one of the reference indicators for identifying short-term heavy precipitation. It does not require complex operations such as data correction and quality control by other band radars. Instead, it utilizes the attenuation characteristics of X-band electromagnetic waves to quickly identify, locate, and warn of short-term heavy precipitation, which can effectively improve the early warning capability for severe weather.

[0093] For example, in step S12, a contour surface with a second resolution of 1 km is obtained, the target spatial region is determined, the target spatial region is divided into multiple grids to form a grid matrix, the differential reflectivity and differential phase shift rate in each grid matrix are obtained, and a numerical judgment is performed on each grid point in the grid matrix, as follows:

[0094] First, the differential phase shift rate and differential reflectance values ​​for each grid point are extracted. Then, based on a set threshold, it is determined whether the grid point meets the requirements. This process is repeated for all grid points. If the identification result indicates that the grid point does not meet the requirements, there is no warning information; if it meets the requirements, it is assumed that short-term heavy rainfall has occurred at the location of that grid point.

[0095] Preferably, it further includes: identifying the warning area and displaying the warning on a map.

[0096] It is understood that this invention acquires X-band radar base data and analyzes it to obtain the dual polarization parameters corresponding to the target spatial region: differential reflectivity and differential phase shift rate. Utilizing the significant attenuation characteristics of the X-band dual polarization parameters, it directly identifies regions that satisfy both a differential reflectivity less than a first preset threshold and a differential phase shift rate greater than a second preset threshold, achieving rapid identification and accurate positioning of the warning area. This method leverages the large attenuation characteristics of X-band differential reflectivity and differential phase shift rate for rapid identification of warning areas, thus quickly and accurately identifying areas of heavy precipitation. Therefore, this method avoids the large estimation errors caused by the significant attenuation characteristics of X-band dual polarization parameters in traditional methods, and the complex operations such as relying on data correction based on low-attenuation data generated by other band radars. Based on the attenuation characteristics of the X-band and the powerful advantages of X-band dual polarization phased array weather radar in terms of temporal and spatial resolution, the method of this invention achieves the identification of short-term heavy precipitation, effectively improving the ability to issue warnings for severe weather.

[0097] Reference Figure 3 , Figure 3 This is a schematic block diagram of a short-term heavy rainfall identification device, such as... Figure 3 As shown, it includes:

[0098] Acquisition module 1 is used to acquire radar base data; wherein, the radar base data is acquired based on X-band radar;

[0099] Analysis module 2 is used to analyze radar base data to obtain the dual polarization parameters corresponding to the target spatial region. The dual polarization parameters include differential reflectivity and differential phase shift rate.

[0100] Module 3 is used to identify a portion of the target space region that meets preset requirements as a warning zone;

[0101] The preset requirements include: the differential reflectivity is within a first preset threshold and the differential phase shift rate is within a second preset threshold.

[0102] Specifically, the specific implementation method of a short-term heavy precipitation identification device can refer to the specific implementation method of a short-term heavy precipitation identification device described in any of the above embodiments, and will not be repeated here.

[0103] It is understood that this invention acquires X-band radar base data and analyzes it to obtain the dual polarization parameters corresponding to the target spatial region: differential reflectivity and differential phase shift rate. Utilizing the significant attenuation characteristics of the X-band dual polarization parameters, it directly identifies regions that satisfy both a differential reflectivity less than a first preset threshold and a differential phase shift rate greater than a second preset threshold, achieving rapid identification and accurate positioning of the warning area. This method leverages the large attenuation characteristics of X-band differential reflectivity and differential phase shift rate for rapid identification of warning areas, thus quickly and accurately identifying areas of heavy precipitation. Therefore, this method avoids the large estimation errors caused by the significant attenuation characteristics of X-band dual polarization parameters in traditional methods, and the complex operations such as relying on data correction based on low-attenuation data generated by other band radars. Based on the attenuation characteristics of the X-band and the powerful advantages of X-band dual polarization phased array weather radar in terms of temporal and spatial resolution, the method of this invention achieves the identification of short-term heavy precipitation, effectively improving the ability to issue warnings for severe weather.

[0104] This invention also provides a smart device; please refer to [link / reference]. Figure 4 , Figure 4 This is a structural diagram of a smart device, as shown in the figure, including:

[0105] Processor 202, and memory 201 connected to processor 202.

[0106] The memory 201 is used to store a computer program, which is used to execute at least the method for identifying short-term heavy precipitation as described in any one of the embodiments of the present invention.

[0107] The processor 202 is used to call and execute the computer program in the memory 201. Specifically, a specific implementation method of a smart device can refer to the specific implementation method of the short-term heavy precipitation identification method described in any of the above embodiments, and will not be repeated here.

[0108] It is understood that this invention acquires X-band radar base data and analyzes it to obtain the dual polarization parameters corresponding to the target spatial region: differential reflectivity and differential phase shift rate. Utilizing the significant attenuation characteristics of the X-band dual polarization parameters, it directly identifies regions that satisfy both a differential reflectivity less than a first preset threshold and a differential phase shift rate greater than a second preset threshold, achieving rapid identification and accurate positioning of the warning area. This method leverages the large attenuation characteristics of X-band differential reflectivity and differential phase shift rate for rapid identification of warning areas, thus quickly and accurately identifying areas of heavy precipitation. Therefore, this method avoids the large estimation errors caused by the significant attenuation characteristics of X-band dual polarization parameters in traditional methods, and the complex operations such as relying on data correction based on low-attenuation data generated by other band radars. Based on the attenuation characteristics of the X-band and the powerful advantages of X-band dual polarization phased array weather radar in terms of temporal and spatial resolution, the method of this invention achieves the identification of short-term heavy precipitation, effectively improving the ability to issue warnings for severe weather.

[0109] Example 2

[0110] Reference Figure 5 , Figure 5 This is a flowchart illustrating a method for identifying short-duration heavy rainfall, such as... Figure 5 As shown, the method includes:

[0111] 1) Data processing section:

[0112] Analyze the radar base data to obtain various radar parameters; obtain the spatial information Z1, Z2…ZN of the differential reflectivity of the low elevation angle dual polarization parameter; obtain the spatial information K1, K2…KN of the differential phase shift rate of the low elevation angle dual polarization parameter.

[0113] It is worth noting that the main radar parameters include differential reflectivity and differential phase shift rate, and the spatial information is the coordinates or values ​​of differential reflectivity and differential phase shift rate.

[0114] Understandably, radar-based data processing involves performing basic data processing based on the characteristics of meteorological radar detection data and the features of severe convective weather systems, mainly extracting data on dual-polarization parametric differential reflectivity and differential phase shift rate.

[0115] 2) Spatial calculation section:

[0116] The main task is to perform spatial matching of the coordinates of differential reflectivity and differential phase shift rate.

[0117] Determine whether a region with negative ZDR values ​​exists within a strong KDP region;

[0118] Establish corresponding relationships W1, W2...WN, and store them as early warning areas.

[0119] It is worth noting that the strong KDP region is the region where the differential phase shift rate is greater than the second preset threshold, that is, the second spatial region where KDP is greater than 0. The negative ZDR region is the region where the differential reflectance is less than the first preset threshold, that is, the first spatial region where ZDR is less than 0. Identifying the region where ZDR is less than 0 within the region where KDP is greater than 0, that is, the region where the first spatial region and the first spatial region overlap, is W1, W2...WN in the above text. The overlapping region is the warning region.

[0120] Understandably, this part mainly involves identifying each KDP area within the radar detection range that reaches the threshold, determining whether there are areas within the corresponding area where the ZDR becomes negative due to attenuation, and establishing early warning areas.

[0121] By using the above calculation process, the warning area for the area affected by short-term heavy rainfall can be quickly calculated.

[0122] It is understood that this invention acquires X-band radar base data and analyzes it to obtain the dual polarization parameters corresponding to the target spatial region: differential reflectivity and differential phase shift rate. Utilizing the significant attenuation characteristics of the X-band dual polarization parameters, it directly identifies regions that satisfy both a differential reflectivity less than a first preset threshold and a differential phase shift rate greater than a second preset threshold, achieving rapid identification and accurate positioning of the warning area. This method leverages the large attenuation characteristics of X-band differential reflectivity and differential phase shift rate for rapid identification of warning areas, thus quickly and accurately identifying areas of heavy precipitation. Therefore, this method avoids the large estimation errors caused by the significant attenuation characteristics of X-band dual polarization parameters in traditional methods, and the complex operations such as relying on data correction based on low-attenuation data generated by other band radars. Based on the attenuation characteristics of the X-band and the powerful advantages of X-band dual polarization phased array weather radar in terms of temporal and spatial resolution, the method of this invention achieves the identification of short-term heavy precipitation, effectively improving the ability to issue warnings for severe weather.

[0123] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0124] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0125] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0126] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0127] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0128] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0129] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0130] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, result, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, results, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0131] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A short-time heavy rain recognition method characterized by comprising: The method comprises: acquiring radar-based data, wherein the radar-based data is acquired based on an X-band radar; analyzing the radar-based data to obtain dual-polarization parameters corresponding to a target spatial region, wherein the dual-polarization parameters comprise a differential reflectivity and a differential phase shift rate; determining a part of the target spatial region that meets preset requirements as a pre-warning region; wherein the preset requirements comprise that the differential reflectivity is less than a first preset threshold and the differential phase shift rate is greater than a second preset threshold; the first preset threshold is 0, and the second preset threshold is 0; the determining of the part of the target spatial region that meets the preset requirements as the pre-warning region comprises: determining a negative value region in the target spatial region where the differential reflectivity is less than 0 as a first spatial region; determining a region in the target spatial region where the differential phase shift rate is greater than 0 as a second spatial region; determining an overlapping region of the first spatial region and the second spatial region as the pre-warning region.

2. The method of claim 1, wherein, The target spatial region comprises a plurality of grids; the determining of the part of the target spatial region that meets the preset requirements as the pre-warning region comprises: for each grid, judging whether the grid meets the preset requirements; if yes, determining that the grid belongs to the pre-warning region.

3. The method of claim 1, wherein, The analyzing of the radar-based data to obtain the dual-polarization parameters corresponding to the target spatial region comprises: analyzing the radar-based data to obtain dual-polarization parameters corresponding to each discrete point in the target spatial region at a first resolution; obtaining, based on a spatial interpolation method, dual-polarization parameters corresponding to each discrete point in the target spatial region at a second resolution; wherein the second resolution is greater than the first resolution.

4. The method of claim 3, wherein, The differential reflectivity is a ratio of a horizontally polarized wave reflectivity and a vertically polarized wave reflectivity, and a calculation formula is: ZDR = 10 * lg (ZHH / ZVV) wherein ZDR is the differential reflectivity, ZHH is the horizontally polarized wave reflectivity, and ZVV is the vertically polarized wave reflectivity; the differential reflectivity represents a shape of a precipitation particle; the differential phase shift rate represents a size and a density of the precipitation particle.

5. The method of claim 1, wherein, The method comprises: the radar-based data comprises a low elevation angle.

6. The method of claim 1, wherein, The method further comprises: identifying the pre-warning region and performing a pre-warning display on a map.

7. The method of claim 1, wherein, The method further comprises: the first preset threshold and the second preset threshold are set based on climate characteristics of a region and radar performance.

8. A short-time heavy rain recognition device characterized by comprising: The method is applied to a short-time heavy precipitation identification method, a short-time heavy precipitation identification device, or a short-time heavy precipitation identification apparatus as claimed in any one of claims 1 to 7, and the method comprises: an acquisition module configured to acquire radar-based data, wherein the radar-based data is acquired based on an X-band radar; an analysis module configured to analyze the radar-based data to obtain dual-polarization parameters corresponding to a target spatial region, wherein the dual-polarization parameters comprise a differential reflectivity and a differential phase shift rate; a determination module configured to determine a part of the target spatial region that meets preset requirements as a pre-warning region; wherein the preset requirements comprise that the differential reflectivity is within a first preset threshold and the differential phase shift rate is within a second preset threshold.

9. A smart device, comprising: The apparatus comprises: a processor, and a memory connected to the processor; The memory is configured to store a computer program, and the computer program is configured to at least execute the short-time heavy rain identification method in any one of claims 1-7. The processor is configured to invoke and execute the computer program in the memory.

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