A method, system, and equipment for quality control of meteorological radar data based on fuzzy logic

Abstract

This invention discloses a method, system, and device for quality control of meteorological radar data based on fuzzy logic. The method includes the following steps: constructing a radar reflectivity intensity product in a three-dimensional Cartesian coordinate system; rendering the three-dimensional reflectivity intensity product as texture data using OpenGL; acquiring elevation data of the effective radar scanning range and using the elevation values ​​as texture data for rendering three-dimensional terrain; providing a constant height plane position indication map product and a combined reflectivity product to determine the echo type, and providing an arbitrary vertical profile product to verify the accuracy of the echo type determination; after determining the echo type, providing interactive selection of the target echo region, statistically analyzing and visualizing the radar parameter characteristics of the target echo region; and using localized fuzzy logic to identify the membership function of ground clutter, thus completing the quality control of the radar reflectivity intensity data. This invention can improve the quality of radar reflectivity intensity data and reduce the error in precipitation inversion results.

Description

Technical Field

[0001] This invention relates to the field of meteorological radar data quality control technology, specifically to a meteorological radar data quality control method and equipment based on fuzzy logic. Background Technology

[0002] Fuzzy logic algorithms extract physical quantities from radar data to distinguish different radar echoes, such as precipitation echoes, ground feature echoes, and ocean wave echoes. Then, membership functions are set based on the characteristics of precipitation echoes, ground feature echoes, and ocean wave echoes. These physical quantities are then fuzzified to obtain criteria for the 0-1 value range of all physical quantities for different types of echoes. The larger the criterion, the greater the probability that the echo point belongs to that type of echo. These criteria are weighted and accumulated. When the criterion for a ground feature echo at a certain point exceeds a pre-defined threshold, that point is identified as a ground feature.

[0003] Fuzzy logic algorithms are used for removing ground clutter from weather radar. They determine membership functions based on the characteristics of precipitation echoes and ground clutter. However, there is currently a lack of methods for screening and extracting precipitation echoes and ground clutter, statistically analyzing echo parameter characteristics, and developing localized quality control algorithms. Some methods rely on experience to identify and classify ground clutter and precipitation echoes, while others use radar's three-dimensional visualization products or only provide two-dimensional products to study precipitation inversion, strong echo identification, tracking and prediction, hail and gale warnings, etc. Existing implementations mostly rely on weather radar operators' experience to determine echo types, lacking reference data to corroborate the echo types. Furthermore, for different precipitation cases, not only are the locations of heavy precipitation echoes random, but when the temperature and humidity in the observation area change, such as when a high-humidity inversion layer appears, radar waves bend more towards the ground than during normal propagation, resulting in more and stronger ground object echoes (APs) than usual. This makes it more difficult to determine the echo type based on experience, and it is not possible to accurately and conveniently distinguish between ground object echoes and precipitation echoes. Consequently, the membership function in the localized fuzzy logic algorithm is more difficult to obtain, thus affecting the quality of radar reflectivity intensity data and leading to larger errors in precipitation inversion results. Summary of the Invention

[0004] To overcome the shortcomings and deficiencies of existing technologies, this invention provides a meteorological radar data quality control method based on fuzzy logic. This method distinguishes between ground object echo and precipitation echo areas through three-dimensional visualization of terrain and radar reflectivity intensity products, statistically analyzes the radar data characteristics of these areas, localizes the membership functions in the fuzzy logic algorithm, and ultimately completes the quality control of radar reflectivity products. This results in more accurate echo type identification, improved radar reflectivity intensity data quality, and reduced errors in precipitation inversion results.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] This invention provides a meteorological radar data quality control method based on fuzzy logic, comprising the following steps:

[0007] Construct radar reflectivity intensity products in a three-dimensional Cartesian coordinate system to achieve three-dimensional visualization of terrain and radar reflectivity intensity;

[0008] The three-dimensional reflectivity intensity product is used as texture data for OpenGL rendering;

[0009] Acquire elevation data of the effective scanning range of the radar and use the elevation values ​​as texture data for rendering the 3D terrain;

[0010] It provides constant height planar position indication map CAPPI product and combined reflectivity CR product to determine echo type, and provides VCS product with arbitrary vertical profile combined with terrain to verify whether the echo type is accurately determined;

[0011] After determining the echo type, it provides interactive selection of target echo areas and statistical analysis of radar parameter characteristics of the target echo areas;

[0012] By visualizing radar parameter characteristics and using localized fuzzy logic to identify the membership function of ground clutter, the quality control of radar reflectivity intensity data is achieved.

[0013] As a preferred technical solution, the construction of radar reflectivity intensity products in a three-dimensional Cartesian coordinate system specifically includes:

[0014] The reflectivity intensity obtained from weather radar scanning in polar coordinates will be converted into three-dimensional Cartesian coordinates.

[0015] Approximate the Earth as a sphere and calculate the elevation angle, azimuth angle, and radial distance from the radar for any spatially distributed grid point G in radar polar coordinates.

[0016] As a preferred technical solution, the elevation angle, azimuth angle, and radial distance from the radar for any spatially distributed grid point G in radar polar coordinates are calculated, specifically including:

[0017] Let the latitude and longitude (λ) of radar station R be... R ,φ R ), latitude and longitude (λ) of grid point G G ,φ G The azimuth angle of grid point G in radar polar coordinates is calculated as follows:

[0018] Convert latitude and longitude from degrees to radians:

[0019]

[0020] Calculate the difference in longitude:

[0021] Δλ=λG -λ R

[0022] Calculate intermediate variables X and Y:

[0023] X = cos(φ) G sin(Δλ)

[0024] Y = cos(φ) R sin(φ) G )-sin(φ R cos(φ) G cos(Δλ)

[0025] Calculate the azimuth angle θ:

[0026] θ = arctan2(X,Y)

[0027] Convert azimuth to degrees:

[0028]

[0029] Adjust the azimuth angle to the range of 0-360 degrees:

[0030]

[0031] Calculate the great circle distance S between the two points based on the latitude and longitude of the radar station and the grid points. AG′ :

[0032]

[0033] S AG' =R earth ·c

[0034] ∠AOG' is calculated using the arc length formula:

[0035]

[0036] Among them, R earth Indicates the Earth's radius;

[0037] Construct a triangle OGR from the target grid point G, the radar antenna R, and the geocenter O. Calculate the radial distance RG between the grid point and the radar antenna using the law of cosines.

[0038]

[0039] ∠GRO is calculated using the law of sines:

[0040]

[0041] If line RG is perpendicular to line OR, then the elevation angle of grid point G relative to radar R is:

[0042] β=∠GRO-90°

[0043] Where β represents the elevation angle of grid point G relative to radar antenna R.

[0044] As a preferred technical solution, the three-dimensional reflectivity intensity product is used as texture data for OpenGL rendering, specifically using a GPU-accelerated single-channel volumetric ray casting rendering method.

[0045] As a preferred technical solution, the GPU-accelerated single-channel volumetric ray casting rendering method adopts isosurface projection, maximum intensity projection, and alpha hybrid projection.

[0046] As a preferred technical solution, the elevation value is used as the texture data for rendering the three-dimensional terrain. Based on the tessellation subdivision shader and dynamically adjusting the level of detail of the rendered three-dimensional terrain, the geometry is first initially subdivided using the tessellation subdivision shader to generate subdivided vertices. Then, the level of detail of the subdivided geometry is dynamically adjusted according to the viewpoint distance. Finally, the adjusted geometry is sent to the rasterization stage for final rendering to obtain the final rendering result.

[0047] As a preferred technical solution, the radar parameter characteristics of the statistical target echo region specifically include:

[0048] Physical quantities reflecting the differences between ground features and precipitation echoes are extracted from echo intensity, specifically including: echo intensity texture, vertical variation, radial variation, and inter-reservoir variation along the radial direction. The regional average and variance of radial velocity and the regional average of the velocity spectral width are extracted from radial velocity and velocity spectral width, and are specifically expressed as follows:

[0049]

[0050] G DBZ =W(R)(Z) up -Z low )

[0051]

[0052] Where, N A N R Z represents the calculation range defined in the azimuth and distance directions. up Z low T represents the echo intensity of the corresponding PPI of this layer and the upper layer. dBZ The local variation in echo intensity is represented by W(R), which represents the distance-related weight, and G... dBZ Z represents the vertical variation of echo intensity. thresh S represents the threshold for changes in inter-reservoir echo intensity. PINThis indicates the uniformity of echo intensity variation along the radial direction; S IGN Indicates the radial variation of echo intensity; M DVEi,j This represents the radial velocity value at a point after median filtering. S represents the average radial velocity within a set range. DVE Let V be the variance of the radial velocity.

[0053] As a preferred technical solution, the radar parameter characteristics are visualized, and the membership function of ground clutter is identified using localized fuzzy logic. Specifically, this includes:

[0054] Select four points sequentially on the CAPPI product map to form a quadrilateral region, and count the membership function parameters within this quadrilateral region.

[0055] This invention also provides a meteorological radar data quality control system based on fuzzy logic, including: a 3D conversion module, an OpenGL rendering module, an elevation data acquisition module, a 3D terrain rendering module, an echo type judgment module, an echo type verification module, a target echo area determination module, a radar parameter feature statistics module, a parameter visualization module, a membership function setting module, and an output module;

[0056] The 3D conversion module is used to construct radar reflectivity intensity products in a 3D Cartesian coordinate system, thereby enabling 3D visualization of terrain and radar reflectivity intensity.

[0057] The OpenGL rendering module is used to render the three-dimensional reflectivity intensity product as texture data using OpenGL.

[0058] The elevation data acquisition module is used to acquire elevation data within the effective scanning range of the radar.

[0059] The three-dimensional terrain rendering module is used to use elevation values ​​as texture data for rendering three-dimensional terrain.

[0060] The echo type determination module is used to determine the echo type by providing constant height plane position indication map CAPPI products and combined reflectivity CR products;

[0061] The echo type verification module is used to verify whether the echo type of VCS products with arbitrary vertical profiles combined with terrain is accurately determined.

[0062] The target echo region determination module is used to provide interactive selection of the target echo region after determining the echo type;

[0063] The radar parameter feature statistics module is used to statistically analyze the radar parameter features of the target echo area.

[0064] The parameter visualization is used to visualize radar parameter characteristics;

[0065] The membership function setting module is used to localize the membership function of fuzzy logic identification of ground clutter;

[0066] The output module is used to output the quality control results of radar reflectivity intensity data.

[0067] This invention provides a computer device, including a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the above-described fuzzy logic-based meteorological radar data quality control method.

[0068] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0069] (1) Existing methods rarely focus on the quality control of radar reflectivity intensity and lack the screening and extraction of precipitation echoes and ground clutter. This invention achieves the differentiation of ground echo and precipitation echo areas through three-dimensional visualization of terrain and radar reflectivity intensity products, statistically analyzes the radar data characteristics of ground echo and precipitation echo areas, localizes the membership function in the fuzzy logic algorithm, completes the quality control of radar reflectivity intensity products, makes the identification of echo types more accurate, improves the quality of radar reflectivity intensity data, and reduces the error of precipitation inversion results.

[0070] (2) The three-dimensional visualization of radar reflectivity intensity for determining echo type in this invention adopts OpenGL GPU-accelerated single-channel volumetric ray casting rendering technology, which provides three rendering methods: isosurface projection, maximum intensity projection and alpha blending projection. The three-dimensional terrain is combined with tessellation subdivision shader and dynamic adjustment of the level of detail of the rendered terrain, which improves performance and visual quality.

[0071] (3) Most quality control technologies currently only support processing radar data in a single format. This invention supports the localization of fuzzy logic algorithm membership functions for multiple different types of meteorological radars. It uses C++ virtual functions and polymorphism technology to process radar data in different formats and provides a unified external interface.

[0072] (4) Most quality control technologies currently only provide two-dimensional radar products. This invention uses OpenGL to render three-dimensional radar reflectivity intensity in parallel on the GPU, providing three rendering methods: isosurface projection, maximum intensity projection, and alpha blending projection. In addition, it combines tessellation subdivision shaders and dynamically adjusts the level of detail of the rendered terrain, thereby improving performance and visual quality.

[0073] (5) This invention not only realizes the discrimination of echo regions, but also supports the feature statistics of regional echoes, realizes the localization of membership functions in fuzzy logic algorithms, and improves the quality control performance of radar reflectivity intensity data. Attached Figure Description

[0074] Figure 1 This is a schematic diagram illustrating the overall implementation process of the fuzzy logic-based meteorological radar data quality control method of the present invention.

[0075] Figure 2 This is a schematic diagram illustrating the specific implementation process of the meteorological radar data quality control method based on fuzzy logic of the present invention.

[0076] Figure 3 This is a schematic diagram of the grid point coordinate transformation of the present invention;

[0077] Figure 4 This is a schematic diagram of single-channel volumetric light projection according to the present invention;

[0078] Figure 5 This is a schematic diagram of the isosurface rendering of three-dimensional radar reflectivity intensity according to the present invention;

[0079] Figure 6 This is a schematic diagram of the maximum intensity projection rendering of three-dimensional radar reflectivity of the present invention;

[0080] Figure 7 This is a schematic diagram of the 3D radar reflectivity intensity rendered by alpha hybrid projection according to the present invention;

[0081] Figure 8 This is a schematic diagram of the dynamically detailed terrain rendering of the present invention;

[0082] Figure 9 This is a schematic diagram of the CAPPI, CR, and VCS products of the present invention;

[0083] Figure 10 This is a schematic diagram of ground clutter characteristics in a mountainous area in the northwest of a city selected for this invention;

[0084] Figure 11 This is a schematic diagram illustrating the characteristics of heavy precipitation echoes in the southeastern plain area of ​​a certain city, selected for this invention. Detailed Implementation

[0085] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0086] Example 1

[0087] like Figure 1 As shown, this embodiment provides a meteorological radar data quality control method based on fuzzy logic, including the following steps:

[0088] S1: Construct radar reflectivity intensity products in a three-dimensional Cartesian coordinate system;

[0089] In this embodiment, a visualization algorithm based on three-dimensional terrain and radar reflectivity intensity is used to achieve three-dimensional visualization of terrain and radar reflectivity intensity, in order to distinguish between ground feature echoes and precipitation echoes, such as... Figure 2 As shown, the radar reflectivity intensity product in a three-dimensional Cartesian coordinate system is first constructed. The reflectivity intensity obtained from meteorological radar scanning is in a polar coordinate system. Before volumetric rendering of the data, the coordinate system needs to be transformed, i.e., the polar coordinates are converted to three-dimensional Cartesian coordinates. The maximum scanning radius of the operational S-band radar is 460km, and the maximum effective scanning radius is 230km. Due to the sufficiently long detection range, the curvature of the Earth needs to be considered during the coordinate system transformation. The algorithm design approximates the Earth as a sphere and calculates the elevation angle, azimuth angle, and radial distance from the radar to any spatially distributed grid point G in the radar polar coordinate system, such as... Figure 3 As shown, the algorithm takes the longitude, latitude, and altitude of a grid point as input and outputs the elevation angle, azimuth angle, and radial distance of that point relative to the radar antenna.

[0090] In this embodiment, the azimuth angle θ of the grid point relative to the radar is calculated:

[0091] Let the latitude and longitude (λ) of radar station R be... R ,φ R ), latitude and longitude (λ) of grid point G G ,φ G The formula for calculating the azimuth angle of grid point G in radar polar coordinates is as follows:

[0092] Convert latitude and longitude from degrees to radians:

[0093]

[0094] Calculate the difference in longitude:

[0095] Δλ=λ G -λ R

[0096] Calculate intermediate variables X and Y:

[0097] X = cos(φ) G sin(Δλ)

[0098] Y = cos(φ) R sin(φ) G )-sin(φ R cos(φ) G cos(Δλ)

[0099] Calculate the azimuth angle θ:

[0100] θ = arctan2(X,Y)

[0101] Convert azimuth to degrees:

[0102]

[0103] Adjust the azimuth angle to the range of 0-360 degrees:

[0104]

[0105] Calculate the elevation angle β and radial distance RG:

[0106] Given the latitude and longitude of the radar station and grid points, calculate the great circle distance S between the two points. AG′ (Great-circle distance), the formula used in this embodiment is the "Haversine formula":

[0107]

[0108] S AG' =R earth ·c

[0109] Combining the great circle distance SAG' calculated from the two points and the Earth's radius R earth According to the arc length formula, ∠AOG' can be calculated:

[0110]

[0111] Construct triangle OGR from the target grid point G, radar antenna R, and geocenter O. Let the elevation of the grid point be Hg, the elevation of the radar antenna be a known value, and the angle ∠AOG' of triangle OGR be calculated. The sides OR and OG of triangle OGR are also determined. Therefore, the radial distance RG between the grid point and the radar antenna can be calculated using the law of cosines.

[0112]

[0113] Then, calculate ∠GRO using the law of sines:

[0114]

[0115] Given that line RG is perpendicular to line OR, the elevation angle β of grid point G relative to radar R is:

[0116] β=∠GRO-90°

[0117] S2: After constructing the reflectivity intensity product in a three-dimensional Cartesian coordinate system, use the three-dimensional reflectivity intensity product as texture data for OpenGL rendering.

[0118] This invention employs a GPU-accelerated single-channel volumetric ray casting rendering method. Ray casting is a visualization technique used for volumetric rendering. It involves casting rays from the camera through each pixel of the viewport. Each ray draws a path in the volume, and intensity samples are collected along this path to render the pixel corresponding to that ray, such as... Figure 4 As shown, this embodiment employs three methods for single-channel volumetric ray projection: isosurface projection, maximum intensity projection, and alpha mixing projection. For example... Figure 5 , Figure 6 , Figure 7 As shown, the present invention designs and implements the above three projection methods to visualize radar reflectivity intensity products.

[0119] S3: Terrain data download and rendering;

[0120] This invention utilizes a Python script to call the PyGMT API to download elevation data within the effective scanning range of a radar. The elevation data source used is IGPP Earth Relief. IGPP Earth Relief is a global topographic and seafloor topographic dataset provided by the Institute of Geophysics and Planetary Physics (IGPP) at the University of California, San Diego (UCSD).

[0121] This invention designs a function to take the latitude, longitude, and elevation data resolution of the region as input, and calls an elevation data download script to generate an elevation data nc file. Then, the NetCDF C++ API is used to read the elevation nc file and obtain the elevation data, which is then used as texture data for rendering 3D terrain. Rendering extremely high-resolution elevation data is time-consuming. To optimize terrain rendering performance, this invention combines a tessellation tessellation shader with dynamically adjusting the level of detail of the rendered terrain to improve performance and visual quality. The tessellation tessellation shader is a shader stage on the GPU used for dynamically subdividing geometry. It consists of three main stages: the Tessellation Control Shader (TCS), the Tessellator, and the Tessellation Evaluation Shader (TES). Dynamic level of detail technology optimizes rendering performance by dynamically adjusting the level of detail of objects based on the viewpoint distance. This invention first uses the tessellation tessellation shader to initially subdivide the geometry, generating subdivided vertices, and then dynamically adjusts the level of detail of the subdivided geometry based on the viewpoint distance. Finally, the adjusted geometry is sent to the rasterization stage for final rendering, such as... Figure 8 As shown, the final rendering result is obtained.

[0122] S4: Provides 3D visualization products of terrain and radar reflectivity intensity to identify areas of ground echo and heavy precipitation echo;

[0123] In this embodiment, CAPPI (Constant Altitude Plan Position Indicator) and CR (Composite Reflectivity) products are provided to further determine the echo type, and arbitrary VCS (Vertical Cross Section) products combined with terrain are provided to help users verify whether the echo type is accurately determined.

[0124] In this embodiment, the weather radar CR (Composite Reflectivity) is the maximum value of multiple elevation angle reflectivity data obtained from radar scanning. It is the highest value among all reflectivity values ​​from the ground to the highest elevation angle within the radar coverage area. This product is used to display the overall intensity and coverage of precipitation systems and can comprehensively reflect the structure and intensity of storms.

[0125] In this embodiment, CAPPI (Constant Altitude Plan Position Indicator) provides a horizontal profile of a specific altitude layer by displaying radar reflectivity data at a fixed altitude. CAPPI products can help meteorologists monitor weather conditions at specific altitudes. By analyzing CAPPI images at different altitudes, the structure and vertical distribution of storms can be understood, which helps to determine the intensity and development trend of storms.

[0126] In this embodiment, VCS (Vertical Cross Section) products are an important tool in weather radar technology, used to display the vertical structure and reflectivity distribution in a specific direction of the atmosphere. VCS products can reveal the vertical structure of storms, including the height of convective cloud towers, the location of heavy precipitation cores, and the reflectivity intensity at different levels within the storm, helping meteorologists understand weather phenomena at different altitudes.

[0127] like Figure 9 As shown, the implementation results of CR, CAPPI, and VCS products are obtained.

[0128] S5: After determining the echo type, provide interactive selection of target echo area and statistical analysis of radar parameter characteristics of the area;

[0129] In this embodiment, four physical quantities are extracted from the echo intensity as physical quantities reflecting the differences between ground features and precipitation echoes, specifically including: echo intensity texture (T...dBZ Vertical variation (G) dBZ ), variation along the radial direction (S) IGN ), the degree of inter-basin variation along the radial direction (S) PIN Three physical quantities extracted from radial velocity and velocity spectrum width: regional average value of radial velocity. Variance (S) DVE ), regional average velocity spectrum width (M) DSW Specifically, it is expressed as follows:

[0130]

[0131] G DBZ =W(R)(Z) up -Z low )

[0132]

[0133]

[0134] Where, N A N R This indicates the calculation range defined in the azimuth and range directions, where Z is the echo intensity at any point, and T... dBZ It mainly reflects the magnitude of local variations in echo intensity; Z up Z low The echo intensity is the corresponding PPI of the current layer and the upper layer, W(R) represents the distance-related weight, and G is the echo intensity of the current layer and the upper layer. dBZ This reflects the vertical variation in echo intensity; Z thresh The threshold for the change in inter-reservoir echo intensity is typically taken as 2-5 dB. PIN This reflects the consistency of echo intensity variation along the radial direction; S IGN Indicates the radial variation of echo intensity; M DVEi,j This represents the radial velocity value at a point after median filtering. S represents the average value of this radial velocity over this range. DVE Let N be the variance of the radial velocity. For physical quantities related to echo intensity, this embodiment specifies N as... A =3, N R =3; for radial velocity and velocity spectrum width N A =3, N R =9.

[0135] Select a city region composed of two major landforms: the northwest mountains and the southeast plains, such as Figure 10 and Figure 11As shown, ground feature echoes from the northwestern mountainous region of Beijing and heavy precipitation echoes from the southeastern plain region were selected respectively. The radar parameter characteristics of each region were statistically analyzed to assist users in localizing the membership function parameters.

[0136] S6: After visualizing the radar parameter characteristics mentioned above, localized fuzzy logic is used to identify the membership function of ground clutter, thereby achieving quality control of radar reflectivity intensity data.

[0137] In this embodiment, four points are selected sequentially on the CAPPI product map to form a quadrilateral region. The membership function parameters within this region are then statistically analyzed, and the final results are visualized in a pop-up window.

[0138] In this embodiment, by visualizing the terrain and radar reflectivity intensity products in three dimensions, the identification of ground object echo and precipitation echo regions is realized. The radar data characteristics of ground object echo and precipitation echo regions are statistically analyzed, the membership function in the local fuzzy logic algorithm is localized, and the quality control of radar reflectivity products is completed. This makes the identification of echo types more accurate, improves the quality of radar reflectivity intensity data, and reduces the error of precipitation inversion results.

[0139] Example 2

[0140] This embodiment provides a meteorological radar data quality control system based on fuzzy logic, including: a 3D conversion module, an OpenGL rendering module, an elevation data acquisition module, a 3D terrain rendering module, an echo type judgment module, an echo type verification module, a target echo area determination module, a radar parameter feature statistics module, a parameter visualization module, a membership function setting module, and an output module.

[0141] In this embodiment, the 3D conversion module is used to construct radar reflectivity intensity products in a 3D Cartesian coordinate system to achieve 3D visualization of terrain and radar reflectivity intensity;

[0142] In this embodiment, the OpenGL rendering module is used to render the three-dimensional reflectivity intensity product as texture data using OpenGL.

[0143] In this embodiment, the elevation data acquisition module is used to acquire elevation data of the effective scanning range of the radar;

[0144] In this embodiment, the 3D terrain rendering module is used to render 3D terrain by using elevation values ​​as texture data.

[0145] In this embodiment, the echo type determination module is used to determine the echo type by providing the constant height plane position indication map CAPPI product and the combined reflectivity CR product;

[0146] In this embodiment, the echo type verification module is used to verify whether the echo type of the VCS product combined with the terrain is accurately determined.

[0147] In this embodiment, the target echo region determination module is used to provide interactive selection of the target echo region after determining the echo type;

[0148] In this embodiment, the radar parameter feature statistics module is used to statistically analyze the radar parameter features of the target echo region;

[0149] In this embodiment, parameter visualization is used to visualize radar parameter characteristics;

[0150] In this embodiment, the membership function setting module is used to localize the membership function of fuzzy logic identification of ground clutter;

[0151] In this embodiment, the output module is used to output the quality control results of the radar reflectivity intensity data.

[0152] Example 3

[0153] This embodiment provides a computing device, which may be a desktop computer, laptop computer, smartphone, PDA handheld terminal, tablet computer or other terminal device with display function. The computing device includes a processor and a memory. The memory stores one or more programs. When the processor executes the program stored in the memory, it implements the fuzzy logic-based meteorological radar data quality control method of Embodiment 1.

[0154] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A quality control method for meteorological radar data based on fuzzy logic, characterized in that, Includes the following steps: Construct radar reflectivity intensity products in a three-dimensional Cartesian coordinate system to achieve three-dimensional visualization of terrain and radar reflectivity intensity; The three-dimensional reflectivity intensity product is used as texture data for OpenGL rendering; Acquire elevation data of the effective scanning range of the radar, and use the elevation values ​​as texture data for rendering 3D terrain. It provides constant height planar location indication map CAPPI product and combined reflectivity CR product to determine the type of ground object echo and heavy precipitation echo, and provides arbitrary vertical profile VCS product combined with terrain to verify whether the echo type is accurately determined; After determining the echo type, it provides interactive selection of target echo areas and statistical analysis of radar parameter characteristics of the target echo areas; Visualize radar parameter characteristics, use localized fuzzy logic to identify the membership function of ground clutter, and complete the quality control of radar reflectivity intensity data; Visualizing radar parameter characteristics and using localized fuzzy logic to identify the membership functions of ground clutter, specifically including: Select four points sequentially on the CAPPI product map to form a quadrilateral region, and count the membership function parameters within this quadrilateral region.

2. The meteorological radar data quality control method based on fuzzy logic according to claim 1, characterized in that, The construction of the radar reflectivity intensity product in a three-dimensional Cartesian coordinate system specifically includes: The reflectivity intensity obtained from weather radar scanning in polar coordinates will be converted into three-dimensional Cartesian coordinates. Approximate the Earth as a sphere and calculate the elevation angle, azimuth angle, and radial distance from the radar for any spatially distributed grid point G in radar polar coordinates.

3. The meteorological radar data quality control method based on fuzzy logic according to claim 2, characterized in that, Calculate the elevation angle, azimuth angle, and radial distance from the radar for any spatially distributed grid point G in radar polar coordinates, specifically including: Let the latitude and longitude (λ) of radar station R be... R , R ), latitude and longitude (λ) of grid point G G , G The azimuth angle of grid point G in radar polar coordinates is calculated as follows: Convert latitude and longitude from degrees to radians: Calculate the difference in longitude: Dλ=λ G -l R Calculate intermediate variables X and Y: X=cos( G )sin(Δλ) Y=cos( R )sin( G )-sin( R )cos( G )cos(Δλ) Calculate the azimuth angle θ: θ = arctan2(X,Y) Convert azimuth to degrees: Adjust the azimuth angle to the range of 0-360 degrees: Calculate the great circle distance S between the two points based on the latitude and longitude of the radar station and the grid points. AG′ : S AG' =R earth ·c ∠AOG′ is calculated using the arc length formula: Among them, R earth Indicates the Earth's radius; Construct a triangle OGR from the target grid point G, the radar antenna R, and the geocenter O. Calculate the radial distance RG between the grid point and the radar antenna using the law of cosines. ∠GRO is calculated using the law of sines: If line RG is perpendicular to line OR, then the elevation angle of grid point G relative to radar R is: β=∠GRO-90° Where β represents the elevation angle of grid point G relative to radar antenna R.

4. The meteorological radar data quality control method based on fuzzy logic according to claim 1, characterized in that, The three-dimensional reflectivity intensity product is used as texture data for OpenGL rendering, specifically using a GPU-accelerated single-channel volumetric ray casting rendering method.

5. The meteorological radar data quality control method based on fuzzy logic according to claim 4, characterized in that, The GPU-accelerated single-channel volumetric ray casting rendering method uses isosurface projection, maximum intensity projection, and alpha blending projection.

6. The meteorological radar data quality control method based on fuzzy logic according to claim 1, characterized in that, The elevation value is used as texture data for rendering 3D terrain. Based on the tessellation subdivision shader and dynamically adjusting the level of detail of the rendered 3D terrain, the geometry is first initially subdivided using the tessellation subdivision shader to generate subdivided vertices. Then, the level of detail of the subdivided geometry is dynamically adjusted according to the viewpoint distance. Finally, the adjusted geometry is sent to the rasterization stage for final rendering to obtain the final rendering result.

7. The meteorological radar data quality control method based on fuzzy logic according to claim 1, characterized in that, The radar parameter characteristics of the statistical target echo region specifically include: Physical quantities reflecting the differences between ground features and precipitation echoes are extracted from echo intensity, specifically including: echo intensity texture, vertical variation, radial variation, and inter-reservoir variation along the radial direction. The regional average and variance of radial velocity and the regional average of the velocity spectral width are extracted from radial velocity and velocity spectral width, and are specifically expressed as follows: G dBZ =W(R)(Z up -Z low ) Where, N A N R Z represents the calculation range defined in the azimuth and distance directions. up Z low T represents the echo intensity of the corresponding PPI of this layer and the upper layer. dBZ W(R) represents the magnitude of the local variation in echo intensity, and G represents the distance-related weight. dBZ Z represents the vertical variation of echo intensity. thresh S represents the threshold for changes in inter-reservoir echo intensity. PIN This indicates the uniformity of echo intensity variation along the radial direction; S IGN Indicates the radial variation of echo intensity; M DVEi,j This represents the radial velocity value at a point after median filtering. S represents the average radial velocity within a set range. DVE Let V be the variance of the radial velocity.

8. A meteorological radar data quality control system based on fuzzy logic, characterized in that, The method for implementing the fuzzy logic-based meteorological radar data quality control method according to any one of claims 1-7 includes: a 3D conversion module, an OpenGL rendering module, an elevation data acquisition module, a 3D terrain rendering module, an echo type judgment module, an echo type verification module, a target echo area determination module, a radar parameter feature statistics module, a parameter visualization module, a membership function setting module, and an output module. The 3D conversion module is used to construct radar reflectivity intensity products in a 3D Cartesian coordinate system, thereby enabling 3D visualization of terrain and radar reflectivity intensity. The OpenGL rendering module is used to render the three-dimensional reflectivity intensity product as texture data using OpenGL. The elevation data acquisition module is used to acquire elevation data within the effective scanning range of the radar. The three-dimensional terrain rendering module is used to use elevation values ​​as texture data for rendering three-dimensional terrain. The echo type determination module is used to determine the echo type by providing constant height plane position indication map CAPPI products and combined reflectivity CR products; The echo type verification module is used to verify whether the echo type of VCS products with arbitrary vertical profiles combined with terrain is accurately determined. The target echo region determination module is used to provide interactive selection of the target echo region after determining the echo type; The radar parameter feature statistics module is used to statistically analyze the radar parameter features of the target echo area. The parameter visualization is used to visualize radar parameter characteristics; The membership function setting module is used to localize the membership function of fuzzy logic identification of ground clutter; The output module is used to output the quality control results of radar reflectivity intensity data.

9. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the meteorological radar data quality control method based on fuzzy logic as described in any one of claims 1-7.

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