Meteorological radar data quality control method, system and equipment based on fuzzy logic

Abstract

The invention discloses a meteorological radar data quality control method, a meteorological radar data quality control system and meteorological radar data quality control equipment based on fuzzy logic. The meteorological radar data quality control method comprises the following steps: constructing a radar reflectivity intensity product under a three-dimensional Cartesian coordinate system; performing OpenGL rendering by taking the three-dimensional reflectivity intensity product as texture data; acquiring elevation data of an effective scanning range of the radar, and performing three-dimensional terrain rendering by taking an elevation value as texture data for rendering the three-dimensional terrain; providing a constant-height plane position indicator diagram product and a combined reflectivity product to judge the echo type, and providing any vertical section product to check whether the echo type is judged accurately or not; after the echo type is determined, providing an interactive selection target echo region, counting radar parameter characteristics of the target echo region, and performing visualization; and identifying the membership function of the ground clutter by localized fuzzy logic, and completing the quality control of the radar reflectivity intensity data. According to the method, the quality of radar reflectivity intensity data can be improved, and errors of rainfall inversion results are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological radar data quality control, and in particular to a meteorological radar data quality control method and equipment based on fuzzy logic. Background Art

[0002] The fuzzy logic algorithm extracts physical quantities from radar data to distinguish different radar echoes, such as precipitation echoes, ground objects, and sea wave echoes, and then sets membership functions according to the characteristics of precipitation echoes, ground objects, and sea wave echoes, performs fuzzy processing on these physical quantities, and obtains the 0-1 value range of all physical quantities for different types of echoes. The larger the criterion, the greater the possibility that the echo point belongs to this type of echo. These criteria are weighted and accumulated, and when the criterion of a ground object echo at a certain point exceeds a predetermined threshold, the point is identified as a ground object.

[0003] The fuzzy logic algorithm is an algorithm for removing ground clutter from meteorological radar. It determines the membership function 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, and for statistically analyzing echo parameter characteristics to localize quality control algorithms. Currently, some methods identify and divide ground object echoes and precipitation echoes based on experience, while others study precipitation inversion, strong echo identification, tracking and prediction, hail and gale warnings, etc. based on the three-dimensional visualization products of radar, or only provide two-dimensional products. Most of the existing implementation methods are that meteorological radar business personnel judge the echo type based on experience, and lack reference materials to verify the echo type; and for different precipitation cases, not only is the position of the strong precipitation echo random, but when the temperature and humidity in the observation area change, such as when a high-humidity inversion layer appears, the radar wave bends toward the ground more than during normal propagation, resulting in more and stronger ground object echoes (AP) than usual, making it more difficult to judge the echo type based on experience, and it is not possible to accurately and conveniently distinguish ground object echoes from precipitation echoes, so that the membership function in the localized fuzzy logic algorithm is more difficult, thereby affecting the quality of the radar reflectivity intensity data and resulting in large errors in the precipitation inversion results. Summary of the invention

[0004] In order to overcome the defects and shortcomings of the prior art, the present invention provides a meteorological radar data quality control method based on fuzzy logic. The method of the present invention realizes the distinction between ground object echo and precipitation echo areas through three-dimensional visualization of terrain and radar reflectivity intensity products, statistics the radar data characteristics of the ground object echo and precipitation echo areas, localizes the membership function in the fuzzy logic algorithm, and finally completes the quality control of the radar reflectivity product, so that the identification of the echo type is more accurate, the quality of the radar reflectivity intensity data is improved, and the error of the precipitation inversion result is reduced.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention provides a weather 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] Use the three-dimensional reflectivity intensity product as texture data for OpenGL rendering;

[0009] Obtain the elevation data of the effective scanning range of the radar, and use the elevation value as the texture data for rendering the three-dimensional terrain;

[0010] Provide constant altitude plane position indication chart CAPPI product and combined reflectivity CR product to judge echo type, and provide any vertical profile VCS product combined with terrain to verify whether the echo type is judged accurately;

[0011] After determining the echo type, it provides interactive selection of the target echo area and statistics on the radar parameter characteristics of the target echo area;

[0012] Visualize radar parameter features, localize fuzzy logic to identify membership functions of ground clutter, and complete quality control of radar reflectivity intensity data.

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

[0014] Convert the reflectivity intensity obtained by weather radar scanning in the polar coordinate system into three-dimensional Cartesian coordinates;

[0015] The earth is approximated as a sphere, and the elevation angle, azimuth angle and radial distance from the radar of any spatially distributed grid point G in the radar polar coordinates are calculated.

[0016] As a preferred technical solution, calculating the elevation angle, azimuth angle and radial distance from the radar of the grid point G of arbitrary spatial distribution in the radar polar coordinates specifically includes:

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

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

[0019]

[0020] Calculate the longitude difference:

[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 site and the grid point AG′ :

[0032]

[0033] S AG' =R earth ·c

[0034] Calculate ∠AOG' according to the arc length formula:

[0035]

[0036] Among them, R earth represents the radius of the Earth;

[0037] The target grid point G, radar antenna R and the center of the earth O form a triangle OGR, and the radial distance RG between the grid point and the radar antenna is calculated according to the cosine theorem:

[0038]

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

[0040]

[0041] 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 the grid point G relative to the 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 single-channel volume ray casting rendering method based on GPU acceleration.

[0045] As a preferred technical solution, a single-channel volume ray casting rendering method based on GPU acceleration adopts isosurface projection, maximum intensity projection and alpha blending 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 shader and the dynamic adjustment of the detail level of the rendered three-dimensional terrain, the tessellation shader is first used to perform initial subdivision on the geometry to generate the subdivided vertices, and then the detail level 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 area specifically include:

[0048] The physical quantities reflecting the difference between the ground object and the precipitation echo are extracted from the echo intensity, including: the texture of the echo intensity, the vertical change, the change along the radial direction, and the radial variation between the reservoirs. The regional average value, variance, and regional average value of the radial velocity are extracted from the radial velocity and velocity spectrum width. The specific expression is:

[0049]

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

[0051]

[0052] Among them, N A 、N R Indicates the calculation range defined in the azimuth and distance directions, Z up , Z low is the echo intensity of the corresponding PPI in the current layer and the upper layer, T dBZ represents the local change of echo intensity, W(R) represents the weight related to distance, G dBZ Indicates the vertical change of echo intensity, Z thresh The threshold value of echo intensity variation between bins, S PINIndicates the consistency of echo intensity change along the radial direction; S IGN Indicates that the echo intensity varies along the radial direction; M DVEi,j It represents the radial velocity value of a point after median filtering. Indicates the average value of radial velocity in the set range, S DVE is the variance of the radial velocity.

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

[0054] Select four points on the CAPPI product graph in turn to form a quadrilateral area, and count the membership function parameters within the quadrilateral area.

[0055] The present invention also provides a weather radar data quality control system based on fuzzy logic, comprising: a three-dimensional conversion module, an OpenGL rendering module, an elevation data acquisition module, a three-dimensional terrain rendering module, an echo type judgment module, an echo type inspection module, a target echo area determination module, a radar parameter feature statistics module, a parameter visualization, a membership function setting module, and an output module;

[0056] The three-dimensional conversion module is used to construct a radar reflectivity intensity product in a three-dimensional Cartesian coordinate system to achieve three-dimensional visualization of terrain and radar reflectivity intensity;

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

[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 the elevation value as texture data for rendering the three-dimensional terrain, and perform three-dimensional terrain rendering;

[0060] The echo type judgment module is used to provide a constant altitude plane position indication image CAPPI product and a combined reflectivity CR product to judge the echo type;

[0061] The echo type verification module is used to provide any vertical profile VCS product combined with terrain to verify whether the echo type 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 count 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 for localizing the membership function of fuzzy logic identification of ground clutter;

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

[0067] The present invention provides a computer device, including a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned fuzzy logic-based weather radar data quality control method is implemented.

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

[0069] (1) Existing methods pay little attention to the quality control of radar reflectivity intensity and lack the screening and extraction of precipitation echoes and ground clutter. The present invention realizes the distinction between ground object echoes and precipitation echo areas through three-dimensional visualization of terrain and radar reflectivity intensity products, statistically analyzes the radar data characteristics of ground object echoes and precipitation echo areas, localizes the membership function in the fuzzy logic algorithm, and completes the quality control of radar reflectivity intensity products, making the identification of echo types more accurate, improving the quality of radar reflectivity intensity data, and reducing the error of precipitation inversion results.

[0070] (2) The three-dimensional visualization of the radar reflectivity intensity used to determine the echo type of the present invention adopts OpenGL GPU-accelerated single-channel volume ray casting rendering technology, and provides three rendering methods: isosurface projection, maximum intensity projection, and alpha blending projection. The three-dimensional terrain is combined with a tessellation shader and dynamically adjusts the detail level of the rendered terrain, thereby improving performance and visual quality.

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

[0072] (4) Currently, most quality control technologies only provide two-dimensional radar products. The present invention uses OpenGL to render three-dimensional radar reflectivity intensity in parallel on the GPU, and provides three rendering methods: isosurface projection, maximum intensity projection, and alpha blending projection. In addition, it combines a tessellation shader and dynamically adjusts the level of detail of the rendered terrain to improve performance and visual quality.

[0073] (5) The present invention not only realizes the discrimination of echo regions, but also supports the characteristic 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a schematic diagram of the overall implementation process of the weather radar data quality control method based on fuzzy logic of the present invention;

[0075] Figure 2 The figure is a schematic diagram of a specific implementation flow of the weather radar data quality control method based on fuzzy logic of the present invention;

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

[0077] Figure 4 It is a schematic diagram of single-channel volume ray casting of the present invention;

[0078] Figure 5 A schematic diagram of the three-dimensional radar reflectivity intensity rendered by an isosurface of the present invention;

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

[0080] Figure 7 This is a schematic diagram of the alpha blended projection rendering of the three-dimensional radar reflectivity intensity of the present invention;

[0081] Figure 8 A schematic diagram of terrain rendering for dynamic details of the present invention;

[0082] Fig. 9 It is a schematic diagram of CAPPI, CR and VCS products of the present invention;

[0083] Fig.10 A schematic diagram of ground clutter characteristics in a mountainous area in the northwest of a city is selected for the present invention;

[0084] Fig.11 A schematic diagram of heavy rainfall echo characteristics in the southeastern plain area of ​​a city is selected for the present invention. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0086] Example 1

[0087] like Figure 1 As shown, this embodiment provides a weather radar data quality control method based on fuzzy logic, comprising 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 realize three-dimensional visualization of terrain and radar reflectivity intensity to distinguish between ground object echoes and precipitation echoes, such as Figure 2 As shown, first construct the radar reflectivity intensity product in a three-dimensional Cartesian coordinate system. The reflectivity intensity obtained by the meteorological radar scan is in a polar coordinate system. Before volume rendering the data, the coordinate system needs to be converted, that is, the polar coordinates are converted to three-dimensional Cartesian coordinates. The maximum scanning radius of the S-band radar in business operation is 460km, and the maximum effective scanning radius is 230km. Since the detection distance is far enough, the curvature of the earth needs to be considered when converting the coordinate system. The algorithm design approximates the earth as a sphere, and calculates the elevation angle, azimuth angle and radial distance from the radar of any spatially distributed grid point G in the radar polar coordinates, as shown in Figure 3 As shown, the algorithm function is to input the longitude, latitude and altitude of a grid point, and output the elevation angle, azimuth and radial distance of the 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] Assume the longitude and latitude of radar station R (λ R ,φ R ), the longitude and latitude of the grid point G (λ G ,φ G ), the formula for calculating the azimuth of grid point G in radar polar coordinates is as follows:

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

[0093]

[0094] Calculate the longitude difference:

[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 site and the grid point, 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] Combine the great circle distance SAG' calculated from the two points and the radius of the earth R earth , according to the arc length formula, ∠AOG' can be calculated:

[0110]

[0111] The target grid point G, radar antenna R and the center of the earth O form a triangle OGR. Let the grid point's altitude be Hg. The radar antenna's altitude is a known value. The ∠AOG' of the triangle OGR has been calculated. The two sides of the triangle OGR, OR and OG, have been determined. Then, the radial distance RG between the grid point and the radar antenna can be calculated using the cosine theorem:

[0112]

[0113] Then calculate ∠GRO according to the sine theorem:

[0114]

[0115] It is known that line RG is perpendicular to line OR, then 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 the three-dimensional Cartesian coordinate system, the three-dimensional reflectivity intensity product is used as texture data for OpenGL rendering.

[0118] The present invention adopts a single-pass volume ray casting rendering method based on GPU acceleration. Ray casting is a visualization technique for volume rendering. It involves casting rays from the camera through each pixel of the viewport. Each ray draws a path in the volume and collects intensity samples along this path to render the pixel corresponding to the ray, such as Figure 4 As shown, the single-channel volume ray projection in this embodiment adopts three methods: isosurface projection, maximum intensity projection and alpha blending projection. 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] The present invention uses Python script to call PyGMT API to download the elevation data of the effective scanning range of the radar. The elevation data source used is IGPP Earth Relief. IGPP Earth Relief is a global topography and seafloor topography dataset provided by the Institute of Geophysics and Planetary Physics (IGPP) of the University of California, San Diego (UCSD).

[0121] The present invention designs a function to implement the input of regional longitude and latitude and elevation data resolution, and calls the elevation data download script to generate an elevation data nc file. Then use Netcdf c++API to read the elevation nc file and wait for the elevation data, and use the elevation value as the texture data for rendering the three-dimensional terrain. It takes a long time to render elevation data with extremely high resolution. In order to optimize the terrain rendering performance, the present invention is designed to combine tessellation shaders and dynamically adjust the level of detail of the rendered terrain to improve performance and visual quality. Tessellation shader is a shader stage on the GPU for dynamically subdividing geometry. It is divided into three main stages: Tessellation Control Shader (TCS), Tessellator, and Tessellation Evaluation Shader (TES). Dynamic level of detail technology optimizes rendering performance by dynamically adjusting the level of detail of objects according to the viewpoint distance. The present invention is designed to first use a tessellation shader to perform initial subdivision on the geometry, generate subdivided vertices, and then dynamically adjust the level of detail of the subdivided geometry according to 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 three-dimensional visualization products of terrain and radar reflectivity intensity to determine the area of ​​ground object 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 any VCS (Vertical Cross Section) product combined with the terrain is provided to help the user 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 reflectivity data obtained from the radar scan, which is the highest value of all reflectivity values ​​from the ground to the highest elevation within the radar coverage area. This product is used to display the overall intensity and coverage of the precipitation system, and can comprehensively reflect the structure and intensity of the storm.

[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. The CAPPI product can help meteorologists monitor weather conditions at a specific altitude. By analyzing CAPPI images at different altitudes, the structure and vertical distribution of the storm can be understood, which helps to judge the intensity and development trend of the storm.

[0126] In this embodiment, the VCS (Vertical Cross Section) product is an important tool in weather radar technology, which is used to display the vertical structure and reflectivity distribution in a specific direction in the atmosphere. The VCS product can reveal the vertical structure of the storm, including the height of the convective cloud tower, the location of the heavy precipitation core, and the reflectivity intensity at different levels in the storm, helping meteorologists understand weather phenomena at different altitude levels.

[0127] like Fig. 9 As shown, the CR, CAPPI, and VCS product implementation results are obtained.

[0128] S5: After determining the echo type, interactive selection of the target echo area is provided to count the radar parameter characteristics of the area;

[0129] In this embodiment, four physical quantities are extracted from the echo intensity as physical quantities reflecting the difference between the ground object and the precipitation echo, specifically including: the texture of the echo intensity (TdBZ ), vertical change (G dBZ ), along the radial direction (S IGN ), the radial variation between the libraries (S PIN ); Three physical quantities extracted from the radial velocity and velocity spectrum width: the regional average of the radial velocity Variance (S DVE ), the regional average of the velocity spectrum width (M DSW ), which is specifically expressed as follows:

[0130]

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

[0132]

[0133]

[0134] Among them, N A 、N R It represents the calculation range defined in the azimuth and distance directions, Z is the echo intensity at any point, T dBZ Mainly reflects the local change of echo intensity; Z up , Z low is the echo intensity of the corresponding PPI of the current layer and the upper layer, W(R) represents the weight related to the distance, G dBZ Reflects the vertical change of echo intensity; Z thresh is the threshold of echo intensity change between reservoirs, generally 2-5dB, S PIN It reflects the consistency of echo intensity change along the radial direction; S IGN Indicates that the echo intensity varies along the radial direction; M DVEi,j It represents the radial velocity value of a point after median filtering. represents the average value of the radial velocity in this range, S DVE is the variance of radial velocity. For the physical quantity related to echo intensity, this embodiment specifies N A =3,N R =3; for radial velocity and velocity spectrum width N A =3,N R =9.

[0135] Select a city area consisting of two major landforms: northwest mountains and southeast plains, such as Fig.10 and Fig.11As shown, the ground feature echoes in the northwest mountainous area of ​​Beijing and the heavy rainfall echoes in the southeast plain area are selected respectively, and the radar parameter characteristics of each area are counted to assist the user in localizing the membership function parameters.

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

[0137] In this embodiment, four points are selected in sequence on the CAPPI product map to form a quadrilateral area, and the membership function parameters in the area are counted, and the final result is visualized in a pop-up window.

[0138] In this embodiment, through the three-dimensional visualization of the terrain and radar reflectivity intensity products, the discrimination of the ground object echo and precipitation echo areas is realized, and the radar data characteristics of the ground object echo and precipitation echo areas are statistically analyzed, and the membership function in the localized fuzzy logic algorithm is used to complete the quality control of the radar reflectivity product, so that the identification of the echo type is more accurate, the quality of the radar reflectivity intensity data is improved, and the error of the precipitation inversion result is reduced.

[0139] Example 2

[0140] The present embodiment provides a weather radar data quality control system based on fuzzy logic, including: a three-dimensional conversion module, an OpenGL rendering module, an elevation data acquisition module, a three-dimensional terrain rendering module, an echo type judgment module, an echo type inspection module, a target echo area determination module, a radar parameter feature statistics module, a parameter visualization, a membership function setting module, and an output module;

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

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

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

[0144] In this embodiment, the three-dimensional terrain rendering module is used to render the three-dimensional terrain by using the elevation value as the texture data for rendering the three-dimensional terrain;

[0145] In this embodiment, the echo type judgment module is used to provide a constant altitude plane position indication image CAPPI product and a combined reflectivity CR product to judge the echo type;

[0146] In this embodiment, the echo type verification module is used to provide any vertical profile VCS product combined with the terrain to verify whether the echo type 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 count the radar parameter features of the target echo area;

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

[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 can be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer or other terminal device with a display function. The computing device includes a processor and a memory, and the memory stores one or more programs. When the processor executes the program stored in the memory, the fuzzy logic-based weather radar data quality control method of Example 1 is implemented.

[0154] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A weather radar data quality control method based on fuzzy logic, characterized in that: The steps include: Construct radar reflectivity intensity products in a three-dimensional Cartesian coordinate system to achieve three-dimensional visualization of terrain and radar reflectivity intensity; Use the three-dimensional reflectivity intensity product as texture data for OpenGL rendering; Obtain the elevation data of the effective scanning range of the radar, and use the elevation value as the texture data for rendering the three-dimensional terrain to render the three-dimensional terrain; Provide constant altitude plane position indication chart CAPPI product and combined reflectivity CR product to judge echo type, and provide any vertical profile VCS product combined with terrain to verify whether the echo type is judged accurately; After determining the echo type, it provides interactive selection of the target echo area and statistics on the radar parameter characteristics of the target echo area; Visualize radar parameter features, localize fuzzy logic to identify membership functions of ground clutter, and complete quality control of radar reflectivity intensity data.

2. The weather radar data quality control method based on fuzzy logic according to claim 1, characterized in that: The step of constructing a radar reflectivity intensity product in a three-dimensional Cartesian coordinate system specifically includes: Convert the reflectivity intensity obtained by weather radar scanning in the polar coordinate system into three-dimensional Cartesian coordinates; The earth is approximated as a sphere, and the elevation angle, azimuth angle and radial distance from the radar of any spatially distributed grid point G in the radar polar coordinates are calculated.

3. The weather 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 of any spatially distributed grid point G in the radar polar coordinates, including: Assume the longitude and latitude of radar station R (λ R ,φ R ), the longitude and latitude of the grid point G (λ G ,φ G ), the azimuth of grid point G in radar polar coordinates is calculated as follows: Convert longitude and latitude from degrees to radians: Calculate the longitude difference: Dλ=λ G -l R Calculate intermediate variables X and Y: X=cos(φ G )sin(Δλ) Y=cos(φ R )sin(φ G )-sin(φ R )cos(φ 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 site and the grid point AG′ : S AG' =R earth ·c Calculate ∠AOG' according to the arc length formula: Among them, R earth represents the radius of the Earth; The target grid point G, radar antenna R and the center of the earth O form a triangle OGR, and the radial distance RG between the grid point and the radar antenna is calculated according to the cosine theorem: ∠GRO is calculated using the law of sines: 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 the grid point G relative to the radar antenna R.

4. The weather 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 single-channel volume ray casting rendering method based on GPU acceleration.

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

6. The weather 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 three-dimensional terrain. Based on the tessellation shader and the dynamic adjustment of the detail level of the rendered three-dimensional terrain, the tessellation shader is first used to perform initial subdivision on the geometry to generate subdivided vertices, and then the detail level 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 weather 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 area specifically include: The physical quantities reflecting the difference between the ground object and the precipitation echo are extracted from the echo intensity, including: the texture of the echo intensity, the vertical change, the change along the radial direction, and the radial variation between the reservoirs. The regional average value, variance, and regional average value of the radial velocity are extracted from the radial velocity and velocity spectrum width. The specific expression is: G DBZ =W(R)(Z up -Z low ) Among them, N A 、N R Indicates the calculation range defined in the azimuth and distance directions, Z up , Z low is the echo intensity of the corresponding PPI in the current layer and the upper layer, T dBZ represents the local change of echo intensity, W(R) represents the weight related to distance, G dBZ Indicates the vertical change of echo intensity, Z thresh The threshold value of echo intensity variation between bins, S PIN Indicates the consistency of echo intensity change along the radial direction; S IGN Indicates that the echo intensity varies along the radial direction; M DVEi,j It represents the radial velocity value of a point after median filtering. Indicates the average value of radial velocity in the set range, S DVE is the variance of the radial velocity.

8. The weather radar data quality control method based on fuzzy logic according to claim 1, characterized in that: Visualize radar parameter features and localize fuzzy logic to identify membership functions of ground clutter, including: Select four points on the CAPPI product graph in turn to form a quadrilateral area, and count the membership function parameters within the quadrilateral area.

9. A weather radar data quality control system based on fuzzy logic, characterized in that: include: Three-dimensional conversion module, OpenGL rendering module, elevation data acquisition module, three-dimensional terrain rendering module, echo type judgment module, echo type inspection module, target echo area determination module, radar parameter feature statistics module, parameter visualization, membership function setting module, output module; The three-dimensional conversion module is used to construct a radar reflectivity intensity product in a three-dimensional Cartesian coordinate system to achieve three-dimensional visualization of terrain and radar reflectivity intensity; The OpenGL rendering module is used to perform OpenGL rendering using the three-dimensional reflectivity intensity product as texture data; 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 the elevation value as texture data for rendering the three-dimensional terrain, and perform three-dimensional terrain rendering; The echo type judgment module is used to provide a constant altitude plane position indication image CAPPI product and a combined reflectivity CR product to judge the echo type; The echo type verification module is used to provide any vertical profile VCS product combined with terrain to verify whether the echo type 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 count 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 for localizing the membership function of fuzzy logic identification of ground clutter; The output module is used to output the quality control results of the radar reflectivity intensity data.

10. A computer device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the fuzzy logic-based weather radar data quality control method as described in any one of claims 1 to 8 is implemented.

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