A weather radar base data quality analysis and over-standard early warning system

Abstract

The application discloses a weather radar base data quality analysis and over-standard early warning system, which comprises a weather radar base data collection and classification subsystem, a weather radar base data quality analysis subsystem and a visual service early warning subsystem; the weather radar base data collection and classification subsystem is used for acquiring weather radar base data from a real-time running weather radar and classifying; the weather radar base data quality analysis subsystem is used for quality analysis on the weather radar base data; the visual service early warning subsystem is used for displaying the weather radar base data and the classification results, the analysis results of the weather radar base data quality analysis subsystem, calculating comprehensive scores, identifying and displaying early warning information and generating solving suggestions. The application can analyze the quality of the weather radar base data in real time and accurately, and early warning and suggestions can be given to abnormal conditions in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of weather radar, more particularly, it relates to a weather radar base data quality analysis and over-standard early warning system. BACKGROUND

[0002] At present, the existing weather radar base data quality analysis system cannot realize real-time analysis effect, and has problems such as low analysis result accuracy, inability to timely early warn abnormal conditions and give suggestions.

[0003] Therefore, how to provide a weather radar base data quality analysis and over-standard early warning system, which can analyze the quality of weather radar base data in real time and accurately, and timely early warn abnormal conditions and give suggestions is a problem that those skilled in the art need to solve. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a weather radar base data quality analysis and over-standard early warning system.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] A weather radar base data quality analysis and over-standard early warning system, comprising a weather radar base data collection and classification subsystem, a weather radar base data quality analysis subsystem and a visual business early warning subsystem;

[0007] The weather radar base data collection and classification subsystem is used to obtain weather radar base data from a real-time running weather radar and classify it;

[0008] The weather radar base data quality analysis subsystem is used to analyze the quality of weather radar base data; wherein, the quality analysis includes isolated noise point elimination effect analysis, ground object echo suppression effect analysis, minimum measurable echo intensity analysis, same-site radar echo consistency analysis, differential reflectivity system bias analysis, radial velocity standard deviation analysis, antenna pointing accuracy analysis;

[0009] The visual business early warning subsystem is used to display the weather radar base data and its classification results, and the analysis results of the weather radar base data quality analysis subsystem;

[0010] The visual business early warning subsystem is also used to calculate a comprehensive score according to the analysis results of the weather radar base data quality analysis subsystem, identify early warning information and generate a solution suggestion;

[0011] The visual business early warning subsystem is also used to display the comprehensive score, the early warning information and the solution suggestion.

[0012] Via the technical solution, compared with the prior art, the weather radar base data quality analysis and over-standard early warning system is provided; the quality of the weather radar base data can be analyzed in real time and accurately, and abnormal conditions can be early warned and suggestions can be given in time. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0014] Figure 1 A structure schematic diagram of the weather radar base data quality analysis and over-standard early warning system provided by the present application is shown in the figure.

[0015] Figure 2 A radar distance library matching process schematic diagram with resolution of 0.25km and 0.15km provided by an embodiment of the present application is shown in the figure.

[0016] Figure 3 A differential reflectivity system deviation probability density diagram provided by an embodiment of the present application is shown in the figure.

[0017] Figure 4 A certain interface of the weather radar base data quality analysis and over-standard early warning system provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0019] As shown in the figures, Figure 1 and Figure 4 an embodiment of the present application discloses a weather radar base data quality analysis and over-standard early warning system, which comprises a weather radar base data collection and classification subsystem, a weather radar base data quality analysis subsystem and a visual business early warning subsystem.

[0020] The weather radar base data collection and classification subsystem is used to acquire weather radar base data from a real-time running weather radar and to classify the weather radar base data.

[0021] The weather radar base data quality analysis subsystem is configured to analyze the quality of the weather radar base data; wherein the quality analysis includes isolated noise point elimination effect analysis, ground object echo suppression effect analysis, minimum measurable echo intensity analysis, same-site radar echo consistency analysis, differential reflectivity system bias analysis, radial velocity standard deviation analysis, and antenna pointing accuracy analysis;

[0022] The visualized business early warning subsystem is configured to display the weather radar base data and its classification results, and the analysis results of the weather radar base data quality analysis subsystem;

[0023] The visualized business early warning subsystem is further configured to calculate a comprehensive score, identify early warning information, and generate a solution suggestion based on the analysis results of the weather radar base data quality analysis subsystem;

[0024] The visualized business early warning subsystem is further configured to display the comprehensive score, the early warning information, and the solution suggestion.

[0025] In an embodiment, the weather radar base data collection and classification subsystem includes a weather radar configuration module and a data collection module;

[0026] The weather radar configuration module is configured to manage and adjust the working parameters of the weather radar;

[0027] The data collection module is configured to collect and classify the weather radar base data of various weather conditions in real time.

[0028] It can be understood that the weather radar configuration module is an important component of the weather radar base data collection and classification subsystem, which is mainly responsible for managing and adjusting the working parameters of the weather radar, so that the weather radar can collect accurate weather radar base data under different weather and environmental conditions. The weather radar configuration module not only needs to handle the basic settings of the weather radar, but also needs to flexibly configure the weather radar according to different observation requirements and precipitation processes (such as clear sky, precipitation, etc.). The specific functions and effects are as follows:

[0029] (1) Working parameter management.

[0030] The weather radar configuration module is responsible for managing various operational parameters of the weather radar, including but not limited to: a) Scan mode: Selecting different scan modes (such as directional scanning, rotational scanning, etc.) to adapt to different observation needs. For example, when monitoring precipitation, a higher scan density may be required, while in clear sky monitoring, a more relaxed scan interval can be used. b) Beam angle and frequency: Setting the beam angle (horizontal, pitch angle) and frequency of the weather radar according to different targets (such as precipitation, weather phenomena, wind field, etc.) to optimize the accuracy and range of weather radar detection. c) Transmit power: Adjusting the transmit power of the weather radar according to the observation distance and precipitation intensity to ensure the reliability of the data. Generally, in long-distance or heavy precipitation environments, the transmit power of the weather radar may need to be adjusted.

[0031] (2) Adjustment of operational parameters:

[0032] Different weather and environmental conditions have different effects on the quality of weather radar base data, and the weather radar configuration module needs to adjust the operational parameters according to different weather and environmental conditions: a) Clear sky mode: When the weather radar is used for clear sky monitoring, it is necessary to reduce the interference to ground objects, select appropriate scan mode and beam frequency, and ensure that only valid meteorological data is collected. b) Precipitation mode: When the weather radar is used for precipitation monitoring, the weather radar configuration module may automatically select appropriate beam angle, transmit power, etc. according to the precipitation intensity and radar detection requirements, to ensure balanced detection of weak and heavy precipitation.

[0033] It can be understood that the data collection module plays a crucial role in the weather radar base data collection and classification subsystem, mainly responsible for collecting weather radar base data of various meteorological conditions in real time from the weather radar, and classifying and managing these data. Specifically, it involves the collection of weather radar base data under different precipitation processes, the establishment of data sets, the classification of base data, and the logical implementation based on different observation mode switching.

[0034] The main functions of the data collection module are:

[0035] (1) Collection of weather radar base data under different precipitation processes

[0036] The data collection module can select the appropriate scan mode according to the actual situation by real-time judgment of the weather radar scan mode. By analyzing the radar echo intensity, beam position and scan interval, the module can identify the current weather type and switch to the appropriate observation mode. For example, if a large area of precipitation is observed, the system will automatically switch to a strong convective observation mode.

[0037] The data collection module needs to determine the current weather conditions based on the observation mode of the weather radar (such as VCP31, VCP21, VCP11, etc.) and dynamically switch to the appropriate mode. VCP31 mode: This mode is usually used for clear sky or light precipitation monitoring, and the weather radar adopts a relatively loose scanning strategy, with a wide observation range, focusing on monitoring the meteorological background. VCP21 mode: This mode is suitable for large-scale precipitation monitoring, and the weather radar scans densely, covering a large area, aiming to capture a wide range of precipitation areas. VCP11 mode: Suitable for monitoring strong convective weather. The weather radar will conduct intensive scanning, focusing on strong echoes and high reflectivity areas, quickly capturing dangerous weather phenomena such as thunderstorms or tornadoes.

[0038] (2) Establishment of data sets

[0039] The data collection module also needs to establish data sets for different precipitation processes, which include not only basic data such as precipitation intensity and echo intensity, but also meteorological variables such as wind speed and direction. Each type of precipitation data set needs to be labeled and grouped differently for subsequent analysis and evaluation. a) Structure of data set: The data set will usually contain multiple dimensions of data, such as time, space (such as observation location), radar scanning mode, echo intensity, radar signal quality, etc. In addition, the data set may also include labels to distinguish different precipitation processes or weather phenomena. b) Data preprocessing: The collected raw data may contain noise or incomplete information, and the data collection module needs to perform necessary data cleaning, such as removing invalid data, filling in missing data, etc.

[0040] (3) Classification of base data

[0041] Another key function of the data collection module is to classify the collected base data. Base data classification is the process of dividing base data into different categories based on multiple dimensions such as weather radar observation patterns, precipitation processes, and echo characteristics. According to different weather processes, the logic of observation pattern switching in ROSE is used to determine the weather conditions by setting specific thresholds for radar echo intensity (Z), differential reflectivity (ZDR), and radial velocity (VR) parameters, combined with the actual observation values of these parameters. For example, when the echo intensity (Z) is less than 20 dBZ, it is considered to be clear sky; when Z is greater than 40 dBZ and ZDR is greater than 3 dB, it is considered to be strong convection; if Z is between 20 dBZ and 40 dBZ, and ZDR is between 0.5 dB and 3 dB, and the radial velocity changes little, it is considered to be large-area precipitation. Base data can be classified as follows: a) Clear sky data: This refers to weather radar base data when there is no significant precipitation or atmospheric disturbance. Clear sky data usually has low echo intensity and is mainly used to establish baseline data. b) Large-area precipitation data: This type of data refers to precipitation processes that cover a wide area, with relatively high and uniform echo intensity, usually accompanied by large-scale precipitation areas. c) Strong convection data: This type of data usually contains rapidly changing and intense echoes with very high echo intensity and rapid spatial changes, which may represent convective weather phenomena such as thunderstorms, tornadoes, etc.

[0042] (4) Output base data classification record file

[0043] In order to facilitate subsequent quality analysis of base data, the data collection module will output a record file of base data classification. This record file usually contains the following contents:

[0044] Data name: Record the name of the data set to facilitate identification of the data source or type.

[0045] Echo area: Record the spatial distribution area of echo signals to help determine the range and intensity of precipitation.

[0046] Maximum echo intensity: Record the maximum echo intensity value in the data set, which is usually used to identify areas of heavy precipitation or convective weather.

[0047] Precipitation type: According to different precipitation processes (such as clear sky, large-area precipitation, strong convection, etc.), record the precipitation type to which the data belongs.

[0048] Observation pattern: Record the radar scanning pattern used to collect the data, such as VCP31, VCP21, VCP11, etc.

[0049] In a certain embodiment, the weather radar base data quality analysis subsystem comprises an isolated noise point elimination effect analysis module, which comprises an isolated noise point elimination module and an isolated noise point elimination effect score mapping module;

[0050] The isolated noise point elimination module is configured to eliminate isolated noise points in the weather radar echo data that are less than an echo intensity threshold value; wherein the echo intensity threshold value is determined by meteorological conditions.

[0051] It can be understood that the intensity of radar echoes varies greatly depending on meteorological conditions. The present application dynamically adjusts the echo intensity threshold value according to the two meteorological conditions of clear sky and precipitation, in order to achieve the best noise elimination effect.

[0052] Specifically, when the sky is clear, the radar echo signal is weak. At this time, a higher echo intensity threshold value (for example, 30 dBZ) is set, with the purpose of avoiding the false elimination of weak real echoes. When it is raining, the radar echo is strong, especially the precipitation echo. At this time, a lower echo intensity threshold value (for example, 10 dBZ) is set, which can effectively eliminate low-intensity noise points while retaining effective precipitation echoes.

[0053] The isolated noise point elimination module is further configured to analyze the spatial distribution of the weather radar echo data, identify isolated noise points using a spatial clustering algorithm, and eliminate the identified isolated noise points.

[0054] It can be understood that after preliminary screening by the echo intensity threshold value, the present application further identifies isolated noise points by a spatial clustering analysis method. The core idea of spatial clustering analysis is that the distance between echo points and the echo intensity should be consistent. If the spatial position of some echo points is far away from other echo points and their intensity is much lower than that of the surrounding area, they can be determined as isolated noise points.

[0055] A commonly used spatial clustering method is a density-based spatial clustering algorithm (such as DBSCAN). DBSCAN can automatically identify isolated noise points according to the spatial position and density information of echo points. If an echo point is too far away from other echoes and its intensity is weak, it will be determined as an isolated noise point. The spatial clustering algorithm divides all echo points into different categories, wherein the echo points of a cluster are considered as effective echoes, and the isolated echo points are marked as isolated noise points and eliminated.

[0056] The isolated noise point elimination effect score mapping module is configured to compare the weather radar echo data after eliminating isolated noise points with the echo data of a standard radar, calculate the number of non-eliminated isolated noise points in the weather radar echo data after eliminating isolated noise points, and map the number of non-eliminated isolated noise points to the corresponding isolated noise point elimination effect score.

[0057] It can be understood that the isolated noise point elimination effect analysis module is used to evaluate the elimination effect of isolated noise points of the weather radar under clear sky and precipitation conditions.

[0058] The echo data of the standard radar has been strictly verified, and represents the real meteorological phenomenon.

[0059] Specifically, if the number of uneliminated isolated noise points is 0 (i.e., the weather radar echo data after eliminating the isolated noise points is completely consistent with the echo data of the standard radar), 100 points are mapped;

[0060] If the number of uneliminated isolated noise points is 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, 90 points, 80 points, 70 points, 60 points, 50 points, 40 points, 30 points, 20 points, 10 points, or 0 points are mapped, respectively.

[0061] If the number of uneliminated isolated noise points is greater than 10, 0 points are mapped.

[0062] In addition, the present application also judges whether there is a problem of false elimination (i.e., eliminating effective echoes) in the elimination process. And according to the existing problems (there are uneliminated isolated noise points or eliminated effective echo points), the echo intensity threshold, the parameters of the spatial clustering algorithm and the overall noise point elimination strategy are adjusted, which can further improve the accuracy of the elimination effect.

[0063] Finally, the present application also outputs an elimination result report.

[0064] The elimination result report contains the following contents:

[0065] Echo area: the spatial coverage range of the radar echo after eliminating the isolated noise points.

[0066] Maximum echo intensity: the maximum echo intensity in the weather radar echo data after eliminating the isolated noise points.

[0067] Echo distribution: the spatial distribution of the echo signal after eliminating the isolated noise points, which can effectively reflect the meteorological characteristics.

[0068] Meteorological condition classification: according to the characteristics (such as intensity distribution, shape, etc.) of the echo data, the current meteorological condition is judged, such as clear sky, precipitation, etc.

[0069] In an embodiment, the weather radar base data quality analysis subsystem further comprises a ground object echo suppression effect analysis module, the ground object echo suppression effect analysis module comprising a ground object echo suppression module and a ground object echo suppression effect score mapping module.

[0070] The ground object echo suppression module comprises a radial alignment unit, a fuzzification processing unit, a weighted summation unit and a ground object echo elimination unit.

[0071] The radial alignment unit is used for aligning weather radar echo radial data to a uniform azimuth distribution;

[0072] It can be understood that: weather radar echo radial data is usually recorded in polar coordinates, that is, along different azimuths and range gates. However, the radar azimuth distribution in actual observation may not be strictly uniform, so it is necessary to align the weather radar echo radial data to a uniform azimuth distribution (such as 0°-360°, one point per degree). This step helps to improve the accuracy of data processing and simplify subsequent analysis.

[0073] In order to realize the alignment, the application adopts an algorithm based on linear interpolation, the core idea of which is to remap the echo radial data of the original azimuth distribution to a uniform azimuth grid through an interpolation function.

[0074] The specific steps are as follows:

[0075] a) Generate a uniform azimuth array.

[0076] Specifically, use the linspace function of NumPy to generate a uniform array from 0° to 360°, and each point corresponds to an interpolated azimuth.

[0077] b) Construct an interpolation function.

[0078] Use the interpld function provided by SciPy to construct a mapping function through linear interpolation (kind='linear'), which can map the original echo data to a new azimuth.

[0079] c) Data interpolation and alignment.

[0080] Input the original echo radial data into the interpolation function and map it to a uniform azimuth distribution to obtain the aligned echo radial data.

[0081] The fuzzification processing unit is used for calculating the fuzzy membership of reflectivity, radial velocity, spectral width and differential phase in weather radar base data;

[0082] It can be understood that:

[0083] 1) The fuzzy membership of reflectivity is calculated using the fuzzy membership function sigmf, that is, the Sigmoid Membership Function;

[0084] The formula of the Sigmoid Membership Function is as follows:

[0085]

[0086] Wherein, f(x) represents the fuzzy membership degree; b represents the center value of the S-shaped function, b=30 represents that the fuzzy membership degree is 0.5 when the reflectivity is 30dBZ; c is the slope of the S-shaped function, indicating the steepness of the transition from low reflectivity to high reflectivity, c=1;

[0087] When the reflectivity is greater than 30dBZ, it is more likely to be ground clutter. When the reflectivity is less than 30dBZ, it is likely to be a meteorological target.

[0088] 2) The fuzzy membership degree of the radial velocity is calculated using the fuzzy membership function zmf, i.e. the Z-shaped membership function (Z-Shaped Membership Function);

[0089] The formula of the Z-shaped membership function is as follows:

[0090]

[0091] Wherein, a and b represent two thresholds of the Z-shaped function, specifically a=-2, b=2, indicating that the radial velocity is more likely to be ground clutter when it is in the range of [-2, 2]. Ground objects are usually stationary or move very slowly, so the radial velocity is close to 0. The area greater than 2m / s or less than -2m / s is more likely to be a weather target.

[0092] 3) The fuzzy membership degree of the spectrum width is also calculated using the fuzzy membership function: zmf, i.e. the Z-shaped function. Wherein, a=2, b=3 are set: indicating that the spectrum width is more likely to be ground clutter when it is between 2-3m / s. Ground objects usually have a narrow spectrum width because the ground object reflection signal is stable and consistent. A larger spectrum width usually indicates a weather target, such as wind shear or turbulence.

[0093] 4) The fuzzy membership degree of the differential phase is also calculated using the membership function: zmf, i.e. the Z-shaped function. Wherein, a=-50, b=50 are set: the differential phase is more likely to be ground clutter when it changes in the range of [-50°, 50°] with a small change. Ground echo usually has a relatively flat differential phase change, while precipitation echo has a larger phase change.

[0094] The weighted sum unit is configured to perform weighted sum on the fuzzy membership degrees of the reflectivity, the radial velocity, the spectrum width, and the differential phase to obtain a weighted sum result;

[0095] The weighted sum result is obtained based on the following formula:

[0096] Clutter score = w refl *reflectivity fuzzy +w vel *velocity fuzzy +w sw *spectrumwidth fuzzy +wphi phidp fuzzy

[0097] wherein, clutter score represents the weighted summation result; reflectivity fuzzy represents the fuzzy membership of reflectivity, w refl represents the weight of reflectivity fuzzy ; velocity fuzzy represents the fuzzy membership of radial velocity; w vel represents the weight of velocity fuzzy ; spectrum width fuzzy represents the fuzzy membership of spectrum width, w sw represents the weight of spectrum width fuzzy ; phidp fuzzy represents the fuzzy membership of differential phase, w phi represents the weight of phidp fuzzy ; specifically, the present application sets w refl as 0.4; w vel as 0.2; w sw as 0.2; w phi as 0.2.

[0098] The ground echo elimination unit is used to eliminate the ground echo whose weighted summation result is greater than the first threshold value;

[0099] It can be understood that: the present application sets the first threshold value as 0.7 (the first threshold value can be adjusted according to actual needs, and adapt to different radar detection scenes); if the clutter score is greater than 0.7, the corresponding echo point is marked as ground clutter; if chutter score is less than or equal to 0.7, it is considered that the corresponding echo point is a sky echo.

[0100] The ground echo suppression effect score mapping module is used to calculate the intensity difference between the ground echo intensity after eliminating the ground echo and the ground echo intensity of the standard radar, and map the intensity difference into the corresponding ground echo suppression effect score.

[0101] It can be understood that: the ground echo, such as mountains or buildings, may interfere with the radar echo and affect the accuracy of meteorological data. The ground echo suppression effect analysis module is used to check whether the weather radar can effectively suppress the influence of ground echo under clear sky conditions.

[0102] Specifically, when the intensity difference is equal to 0 dB, it is mapped to 100 points; when the intensity difference is in the range of (0-5 dB), 5 points are deducted for each 1 dB increase; when the intensity difference is in the range of [5-10 dB], 8 points are deducted for each 1 dB increase; when the intensity difference exceeds 10 dB, 10 points are deducted for each 1 dB increase, and the minimum score is 0.

[0103] In addition, the present application also carries out a comparison of spatial distribution, checks whether there is a problem of false rejection (effective echo is rejected) or missed rejection (ground object echo is not rejected), especially those areas that may be ground object echoes. And according to the existing false rejection (effective echo is rejected) or missed rejection (ground object echo is not rejected) problem, the following adjustment is made:

[0104] Intensity threshold (i.e. first threshold) adjustment: adjust the intensity threshold (i.e. first threshold) of the ground object echo to avoid false rejection of effective echo.

[0105] Spatial range adjustment: adjust the spatial rejection range of the ground object echo according to the comparison result to ensure that the effective echo is not falsely rejected.

[0106] In an embodiment, the weather radar base data quality analysis subsystem further comprises a minimum measurable echo intensity analysis module;

[0107] The minimum measurable echo intensity analysis module is configured to calculate the echo intensity measured by the weather radar at a specific distance, and map the measured echo intensity to a corresponding minimum measurable echo intensity score using a second threshold.

[0108] It can be understood that the minimum measurable echo intensity analysis module is configured to evaluate the detection capability of the weather radar for weak echoes at a specific distance.

[0109] The specific distance is 50 kilometers; and the second threshold is -4.5 dBZ.

[0110] Specifically, when the weather radar measures an echo intensity of less than -4.5 dBZ at a distance of 50 kilometers, it is mapped to 100 points; for each 1 dB increase in the difference between the measured echo intensity and the second threshold, 10 points are deducted, and the minimum score is 0.

[0111] The second threshold is obtained based on the following formula:

[0112]

[0113] wherein, represents the minimum detectable signal power; the noise bandwidth B n = signal bandwidth; B n = 1 / τ; noise factor F0 = 1 + T / 290; C represents a radar constant, in dB; λ represents the radar operating wavelength (specifically 10 cm), in cm; P trepresents the transmitting pulse power, unit: kW; G represents the antenna gain, unit: dB; τ represents the pulse width, unit: us; θ represents the horizontal beam width, unit: °; represents the vertical beam width, unit: °; L Σ represents the total loss of the system = the loss of the matched filter + the loss of the transmitting branch + the loss of the receiving branch + the pulse compression loss (pulse compression radar), unit: dB; Z represents the echo intensity; L at represents the atmospheric loss, 0.011 for the S band, 0.019 for the C band, and 0.025 for the X band; R represents the echo distance, unit: m, corresponding to the specific distance of 50 kilometers in the present application.

[0114] In an embodiment, the weather radar basic data quality analysis subsystem further comprises a same-site radar echo consistency analysis module;

[0115] The same-site radar echo consistency analysis module is used to screen the basic data of the weather radar and the standard radar with a body scan time difference less than a preset time difference threshold, to obtain a first group of basic data; wherein the weather radar is a standard radar;

[0116] The same-site radar echo consistency analysis module is used to screen the basic data of the weather radar and the standard radar with a body scan elevation angle difference less than a preset elevation angle difference threshold and the basic data with a body scan azimuth angle difference less than a preset azimuth angle difference threshold in the first group of basic data, to obtain a second group of basic data;

[0117] The same-site radar echo consistency analysis module is used to screen the basic data with a position difference less than a preset position difference threshold in the second group of basic data, to obtain a third group of basic data;

[0118] The same-site radar echo consistency analysis module is used to screen the meteorological echo data in the third group of basic data;

[0119] The same-site radar echo consistency analysis module is used to convert the observation coordinates of the weather radar and the standard radar, to obtain the geodetic coordinates of the weather radar and the geodetic coordinates of the standard radar; and to determine whether the distance difference between the weather radar and the standard radar is less than a preset distance difference threshold by using the distance library matching algorithm, the geodetic coordinates of the weather radar, and the geodetic coordinates of the standard radar in the geodetic coordinate system; if the distance difference is less than the preset distance difference threshold, to calculate the difference values of each type of echo product of the weather radar and each type of echo product of the standard radar in the matched distance library, and to map the difference values to the corresponding echo consistency effect scores; wherein each type of echo product of the weather radar and each type of echo product of the standard radar are selected from the meteorological echo data.

[0120] In an embodiment: each type of echo product includes echo intensity, radial velocity, differential reflectivity, spectral width, correlation coefficient, differential phase shift, and differential phase shift rate.

[0121] The corresponding difference values include a difference between echo intensity of the weather radar and echo intensity of the standard radar, a difference between differential reflectivity of the weather radar and differential reflectivity of the standard radar, a difference between radial velocity of the weather radar and radial velocity of the standard radar, and the like.

[0122] In the embodiment, the weather radar and the standard radar are of the same wave band.

[0123] Firstly, base data with a time difference between weather radar and standard radar body scanning less than 30s is screened to obtain a first group of base data.

[0124] Then, base data with an elevation angle difference between weather radar and standard radar body scanning less than 0.1° and base data with an azimuth angle difference between weather radar and standard radar body scanning less than 0.1° are screened from the first group of base data to obtain a second group of base data.

[0125] Next, base data with a position difference less than is screened from the second group of base data to obtain a third group of base data; wherein, Δr max is a maximum distance resolution of the weather radar and the standard radar; the weather radar and the standard radar are of the same wave band.

[0126] Next, meteorological echo data is screened from the third group of base data; the threshold values are as follows: (1) the distance range of the screened echo data is 50-230 kilometers to exclude ground object interference; (2) the correlation coefficient is between 0.97-1; (3) the differential reflectivity is between -2-2dB; (4) the signal-to-noise ratio is between 15-100dB; (5) the radial velocity range is between -1-1m / s; (6) the spectral width is between 0-3m / s; (7) the ground object echo suppression pre-reflectivity factor is less than 50dBT. When comparing the basic reflectivity factor, the echo intensity comparison range is increased to 15-45dBZ.

[0127] The distance library matching algorithm is specifically as follows:

[0128] Suppose R1 is a weather radar (or a standard radar) with large distance resolution, and R2 is a standard radar (or a weather radar) with small distance resolution. Each distance library of the radars R1 and R2 is halved (1 / 2 of the first distance library is subtracted from each distance library, M i = N i - Δr / 2, i = 1, 2, …, n) N is an initial distance library, M is a calculated distance library, Δr is a distance resolution, and n is a distance library number; after updating the distance libraries of the two radars, whether the difference between the distance libraries of the two radars is within a set threshold value (I, j = 1, 2, …, n) is judged, the set threshold value in this document is half of the small distance resolution of the two radars, and finally the same or similar distance libraries of the two radars are found. I, j = 1, 2, …, n) is judged, the set threshold value in this document is half of the small distance resolution of the two radars, and finally the same or similar distance libraries of the two radars are found.

[0129] For example,Figure 2 As shown, the process of distance library matching of two radars with resolutions of 0.25 km and 0.15 km is shown;

[0130] The same-site radar echo consistency analysis module is used for evaluating the consistency of various echo products of the weather radar and the standard radar; the present application sets corresponding deduction rules for the difference of various echo products;

[0131] Taking the echo intensity in various echo products as an example: when the difference of the echo intensity is within 2 dB, no score is deducted; 10 points are deducted for each 1 dB of deviation, and the score of the mapping is 0 at the lowest and 100 at the highest;

[0132] The final obtained echo consistency effect score is:

[0133] Wherein, N represents the number of various echo products, M i represents the score deducted by the i-th echo product; F represents the final obtained echo consistency effect score.

[0134] The same-site radar echo consistency analysis module can help to judge the stability and consistency of the radar output data, and ensure that the output of the radar will not be affected by abnormal fluctuations.

[0135] In an embodiment, the weather radar base data quality analysis subsystem further comprises a differential reflectivity system bias analysis module, the differential reflectivity system bias analysis module comprising a data acquisition module, a first data screening module, a second data screening module, a third data screening module, a median extraction module, a differential reflectivity factor system bias score mapping module;

[0136] The data acquisition module is used to read the differential reflectivity factor, correlation coefficient, signal-to-noise ratio, reflectivity factor before ground echo suppression, radial velocity, and echo distance in the weather radar base data;

[0137] The first data screening module is used to screen the differential reflectivity factor value of the required distance direction according to a preset distance threshold;

[0138] The second data screening module is used to remove non-precipitation echoes according to a preset screening condition to obtain the differential reflectivity factor value after the second screening; wherein the preset screening condition is 0.95≤correlation coefficient≤1, 15dB≤signal-to-noise ratio≤100dB, 5dB≤reflectivity factor≤30dB, -0.5m / s≤radial velocity≤0.5m / s;

[0139] The third data screening module is used to remove invalid data and null values to obtain a final differential reflectivity factor sequence;

[0140] The median extraction module is configured to extract a median of the differential reflectivity factor value deviation from the final differential reflectivity factor sequence;

[0141] The differential reflectivity factor system deviation score mapping module is configured to obtain a system deviation of the differential reflectivity factor, and map the system deviation of the differential reflectivity factor to a corresponding differential reflectivity factor system deviation score; wherein the system deviation of the differential reflectivity factor is the median.

[0142] It can be understood that the differential reflectivity (ZDR) is an important parameter for distinguishing different types of precipitation, especially the reflectivity under the condition of light rain.

[0143] Specifically,

[0144] When the system deviation is less than 0.2 dB, the score is 100 points; the system deviation increases by 0.05 dB, and 10 points are deducted, and the minimum is 0.

[0145] In addition, the present application also provides a differential reflectivity system deviation probability density diagram (as shown in Figure 3 The differential reflectivity system deviation probability histogram can more intuitively see the distribution of the differential reflectivity system deviation.

[0146] In an embodiment, the weather radar base data quality analysis subsystem further comprises a radial velocity standard deviation analysis module, the radial velocity standard deviation analysis module comprising a velocity ambiguity processing module, a distance folding data rejection module, a window definition and standard deviation calculation module, a percentile statistics module, a radial velocity standard deviation output module and a radial velocity standard deviation score mapping module;

[0147] The velocity ambiguity processing module is configured to restore the ambiguous radial velocity to the real radial velocity;

[0148] The distance folding data rejection module is configured to reject distance folding data in the weather radar echo data;

[0149] The window definition and standard deviation calculation module is configured to define a grid window for each radar echo data point, and iteratively calculate the standard deviation value of each valid radial velocity data in each grid window; wherein if the valid radial velocity data included in the grid window is less than 9, the grid window is skipped; the grid window size is 7*9, that is, 7 points are arranged along the azimuth direction, and 9 points are arranged along the distance direction;

[0150] The percentile statistics module is configured to sort all the calculated standard deviation values, and filter out the standard deviation value at the 75% position;

[0151] The radial velocity standard deviation output module is configured to output a standard deviation value at a 75% position;

[0152] The radial velocity standard deviation score mapping module is configured to map the standard deviation value at the 75% position to a corresponding radial velocity standard deviation score.

[0153] It can be understood that, in a weather radar, the radial velocity measurement is determined by the Doppler effect to measure the movement speed of the target along the direction of the radar beam. However, the radial velocity ambiguity and the range folding are common problems in radar measurement. The radial velocity ambiguity refers to the periodic folding phenomenon of the radar echo velocity when the target velocity exceeds the maximum velocity range that can be detected by the radar system, that is, the so-called "ambiguity" phenomenon. The range folding refers to the fact that the echo beyond a certain distance is incorrectly determined as a target with a shorter distance due to the observation distance limitation of the radar system.

[0154] In this case, the velocity debambiguity processing refers to using the system characteristics of the radar and related algorithms to restore the ambiguous velocity information. The data of the range folding is removed, and the erroneous data of the radar within a certain distance due to system problems is removed, so as to improve the accuracy of the radial velocity.

[0155] The radial velocity standard deviation analysis of the present application is based on a 7*9 grid window to calculate the radial velocity standard deviation in the window, and finally the percentiles of the standard deviations are counted, and the upper quartile (75% percentile) is calculated as the radial velocity standard deviation of the time.

[0156] Specifically,

[0157] Velocity ambiguity processing: the radar echo data is preliminarily preprocessed, and first, it is checked whether the radial velocity of each echo exceeds the effective measurement range of the radar system. When the radial velocity exceeds the range, the radar will be "ambiguous", that is, the radial velocity of the target is incorrectly folded back. In order to solve this problem, it is necessary to apply the ambiguity theorem to restore the ambiguous radial velocity. The ambiguity restoration process is based on the frequency shift of the radar echo and the known radar transmission frequency to infer the true radial velocity of the target. This process inversely infers the true velocity by calculating the frequency shift of the target and combining the parameters of the radar system, so as to reduce the data error caused by ambiguity.

[0158] Culling of range fold data: In the preprocessing of radar data, in addition to the blurring process, it is also necessary to cull "range fold" data. The measurement range of radar is limited, when the echo signal reflection exceeds the detection distance of the radar system, the system cannot accurately locate the true position of the echo, resulting in the phenomenon of range fold. In order to avoid this problem, first of all, according to the distance information of the echo, judge whether it exceeds the effective detection range of the system. If the distance of the echo is greater than the maximum measurement range of the radar, the data is marked as invalid data, and filtered out from the data set, to ensure that the final data set accurately reflects the real detection ability of the radar.

[0159] Window definition and standard deviation calculation: After data preprocessing, the next step is to perform window processing on each radar data point. A 7x9 grid window centered on the current data point is defined, with 7 points arranged along the azimuth direction and 9 points arranged along the range direction. The size of this window is designed to capture the adjacent radar measurement data around the point, ensuring that the calculation of standard deviation covers sufficient spatial and temporal range. Calculate the standard deviation of all valid radial velocity data in each window, the formula is as follows:

[0160]

[0161] Where, v i is the radial velocity of the i-th valid data point in the window, is the average value of all valid data in the window, N is the number of valid data points in the window. The standard deviation measures the dispersion of the radial velocity in the window, which is an important indicator for evaluating the accuracy of radar measurement.

[0162] Selection of valid data points: In order to ensure the accuracy of the standard deviation calculation, the number of valid data points in each 7x9 window must reach at least 9 valid values. If the number of valid data points in the window is less than 9, it is considered that the data in the window is not sufficient, and the standard deviation cannot be calculated, so the window is skipped. This step ensures that the calculation of standard deviation will not be affected by sparse data or poor quality, thereby improving the reliability of the evaluation results.

[0163] Percentile statistics: For each data point in the radar dataset, the standard deviation of radial velocity within the 7x9 window that the data point belongs to is calculated. The smaller the standard deviation value, the smaller the variation of radial velocity within the window, and the higher the accuracy of the radar measurement. The calculation process traverses the entire dataset, calculating the corresponding standard deviation at each data point's position to obtain a local accuracy assessment for each point. After calculating the standard deviation values for all windows, these values are sorted and analyzed using percentile statistics. A commonly used statistical method is to calculate the upper quartile (75th percentile), which is to arrange all standard deviation values in order of size and find the standard deviation value at the 75th position. The upper quartile reflects the deviation of most data and can help assess the measurement accuracy of the radar in most periods and areas. The calculation of this percentile can effectively suppress the influence of extreme values and provide a stable accuracy assessment.

[0164] Output radial velocity standard deviation: Finally, the upper quartile calculated by percentile statistics is the radial velocity standard deviation for this time. This value serves as the main indicator for assessing the radial velocity measurement accuracy of the radar system during this period. A lower standard deviation indicates that the radar system has higher consistency and better accuracy in measurement results during this period; a higher standard deviation indicates that the radar system's measurement results have greater fluctuations and may need further optimization or adjustment.

[0165] The radial velocity standard deviation analysis module evaluates the deviation between the radar's radial velocity data and the standard value

[0166] Specifically:

[0167] When the standard deviation value at the 75th position is less than 1 m / s, the score is 100 points; for every 1 m / s of deviation, deduct 10 points, with a minimum of 0 points.

[0168] In an embodiment, the weather radar base data quality analysis subsystem further includes an antenna pointing accuracy analysis module, which includes a fixed ground target selection module, a radar data acquisition module, an effective data screening module, an antenna pointing error calculation module, a statistical error analysis module, an error distribution and visualization module, and an antenna pointing error score mapping module.

[0169] The fixed ground target selection module is used to select stable and known location ground targets.

[0170] The radar data acquisition module is used to acquire the measured azimuth and measured elevation angles returned by the weather radar after scanning the ground target.

[0171] The effective data screening module is used to screen the effective echo data returned by the weather radar after scanning the ground target

[0172] The antenna pointing error calculation module is configured to calculate the azimuth error and the elevation error of the effective echo data; wherein the azimuth error is equal to the measured azimuth angle minus the expected azimuth angle; the elevation error is equal to the measured elevation angle minus the expected elevation angle.

[0173] The statistical error analysis module is configured to calculate the mean and the standard deviation of each azimuth error, and the mean and the standard deviation of each elevation error.

[0174] The error distribution and visualization module is configured to display the azimuth error and the elevation error of the effective echo data.

[0175] The antenna pointing error score mapping module is configured to map the azimuth error and the elevation error of the effective echo data to the corresponding antenna pointing error score.

[0176] It can be understood that:

[0177] Fixed ground target selection: Before performing antenna pointing accuracy analysis, it is necessary to select some stable and known position ground targets as reference. The selection of ground targets is very important, and high towers, mountains, bridges and other targets with strong reflected signals and in the radar scanning area are usually selected. The selected targets should have sufficient echo signals to ensure the accuracy of the measurement results. In addition, the actual positions of these ground targets should be calibrated in a known coordinate system to ensure the reference value of the analysis process. The selection of ground targets should cover multiple azimuth angles and elevation angles of the radar beam to ensure the comprehensiveness of the evaluation.

[0178] Radar data collection: The weather radar records the echo signals of each target by scanning these fixed ground targets. During the collection process, the radar system obtains two important parameters of the echo signals: azimuth (Azimuth) and elevation (Elevation). Azimuth is the horizontal angle between the radar antenna and the target, while elevation is the vertical angle. These two angles determine the pointing direction of the radar beam. The radar obtains a series of echo data by continuously rotating the antenna to scan the fixed ground targets at different times and positions.

[0179] Antenna pointing error calculation: After obtaining the radar echo data, the next step is to calculate the antenna pointing error. Assuming that the actual position of each selected fixed ground object is known (for example, through GPS positioning), the expected azimuth angle and the expected elevation angle of the ground target in the radar coordinate system can be calculated. ​are calculated based on the geometric relationship between the ground object and the radar, and are usually converted from the geographic coordinates of the ground object to the angle in the radar coordinate system. Specifically, the expected azimuth and elevation angles can be calculated by the straight-line distance between the radar and the ground object, the relative position, and the trigonometric functions. For each fixed ground object, the radar measured echo signal will provide the actual measured azimuth and measured elevation angle After comparing these measurements with the expected values, the error value of each echo signal is calculated, which is the difference between the expected value and the measured value. The calculation formulas of the azimuth error and the elevation error are as follows:

[0180]

[0181]

[0182] where Δθ az and Δθ el are the error values of the azimuth and the elevation, respectively.

[0183] Screening valid data: In practical applications, there may be some abnormal data or noise interference in the echo data measured by the radar. Therefore, the collected echo data needs to be screened to ensure that only valid echo data is used when calculating the error. The standard of valid data can be screened according to the size of the error. For example, only when the number of echo data points of each ground object is greater than or equal to 10 valid values, the error calculation can be performed. In order to eliminate outliers, the present application also sets an error threshold. If the azimuth or elevation error of a certain echo data exceeds the set maximum error range (for example, 10°), it is considered as an outlier and is eliminated.

[0184] Statistical error analysis: After calculating the azimuth error and the elevation error of each ground object, statistical analysis needs to be performed on these error values. Common statistical methods include calculating the mean, standard deviation, etc. First, the mean μ az and the mean μ el of the azimuth error and the elevation error are calculated, respectively:

[0185]

[0186] where N represents the number of valid echo data points of each ground object, and are the azimuth error and the elevation error of the i-th valid data point, respectively.

[0187] Secondly, the standard deviation σ az and the standard deviation σ el of the azimuth error and the elevation error are calculated, respectively:

[0188]

[0189] The standard deviation reflects the dispersion degree of errors, and a smaller standard deviation indicates that the pointing accuracy of the radar is higher, and the errors are more stable.

[0190] Error distribution and visualization: after the error calculation is completed and statistical analysis is performed, the results can be displayed in a graphical manner. Common visualization methods include error histograms, scatter plots, and error heat maps. Error histograms can show the distribution of azimuth and elevation angle errors, reflecting the concentration of errors. Scatter plots can display the measurement errors of each ground object target, providing a visual representation of the pointing accuracy of the radar system at different azimuth and elevation angles. Through error heat maps, the error distribution of the radar system in different regions can be analyzed to help identify areas with larger deviations, providing a basis for subsequent optimization.

[0191] In an embodiment, the visualization service warning subsystem includes a comprehensive scoring module, a warning information identification module, and a warning information solution suggestion generation module.

[0192] The comprehensive scoring module is used to standardize and then weight (weighted sum) the isolated noise point elimination effect score, the ground echo suppression effect score, the minimum measurable echo intensity score, the echo consistency effect score, the differential reflectivity factor system bias score, the radial velocity standard deviation score, and the antenna pointing error score to obtain a comprehensive score.

[0193] The warning information identification module is used to automatically identify data overruns and abnormal conditions based on the analysis results of the weather radar base data quality analysis subsystem to obtain the warning information.

[0194] The warning information solution suggestion generation module is used to provide corresponding solutions for the data overruns and abnormal conditions.

[0195] In an embodiment, the weights of the isolated noise point elimination effect score, the ground echo suppression effect score, the minimum measurable echo intensity score, the echo consistency effect score, the differential reflectivity factor system bias score, the radial velocity standard deviation score, and the antenna pointing error score after standardization are 15%, 20%, 30%, 25%, 20%, 15%, and 10%, respectively.

[0196] The present application will generate a detailed evaluation report, which includes:

[0197] 1) The standardized scores of each analysis indicator (i.e., the scores of isolated noise point removal effect, ground object echo suppression effect, minimum measurable echo intensity, echo consistency effect, differential reflectivity factor system deviation, radial velocity standard deviation, and antenna pointing error), the weighted scores, and the influence of each analysis indicator on the overall score;

[0198] 2) Overall Score: Displays the overall score of the radar system, reflecting its overall performance;

[0199] 3) Performance Analysis and Optimization Suggestions: Based on the analysis results, the system will provide specific performance analysis and optimization suggestions for each indicator. If the scores of certain indicators are low, the system will recommend adjustment measures, such as calibration and algorithm optimization.

[0200] In one embodiment, the visual business early warning subsystem includes a visualization module and a business early warning module.

[0201] 1) Visualization module

[0202] The visualization module is a crucial component of the visualized operational early warning subsystem. Its primary responsibility is to present complex radar base data and analysis results to users in an intuitive and easy-to-understand manner. This module not only enhances the system's operability but also helps technical personnel quickly understand and analyze radar performance, providing decision support. The main functions of the visualization module include data interaction, data transmission, and the display of system analysis results.

[0203] (1) Data interaction.

[0204] Data interaction is a core function of the visualization module. It primarily facilitates interaction between user actions and the system, providing flexible data display and analysis methods. Through data interaction, users can select different views and data display methods according to their needs, thereby gaining a deeper understanding of the analysis results.

[0205] Interactive Interface: Users can interact with the system through a graphical interface, such as selecting analysis data for different times, locations, or radar configurations. The system supports multiple interaction methods, such as clicking, zooming, and dragging, allowing users to freely choose the data range, precision, and display mode.

[0206] Dynamic Display: Users can view changes and analysis results of radar base data in real time, with dynamic updates supported. For example, the system can display changes in indicators such as echo intensity and radial velocity based on real-time collected radar base data, helping users quickly assess the radar's real-time performance.

[0207] Data filtering and screening: Users can set filtering conditions, such as specific time periods, types of precipitation, radar parameters, etc. The system will automatically filter the base data that meets the conditions and display them on the interface, making it easier to conduct more detailed analysis.

[0208] (2) Data transmission

[0209] Data transmission function refers to how the system efficiently transmits the collected radar base data and analysis results to the user end or other analysis systems, ensuring the timeliness, accuracy and integrity of the data.

[0210] Real-time data transmission: The system can transmit the real-time base data collected by the radar to the visualization interface and display the real-time updated analysis results on the interface. In this way, users can quickly obtain the latest data of current radar performance analysis, helping decision-makers to grasp the radar status in real time.

[0211] Data upload and download: Users can not only view real-time base data, but also download historical base data or import external base data for analysis. The system supports multiple data formats (such as CSV, JSON, XML, etc.) for uploading and downloading to ensure data compatibility.

[0212] Data synchronization and distribution: To support multi-user operation and remote collaboration, the system can synchronize the analysis results to multiple terminals, allowing different users to view and operate base data in different locations. Through cloud or local area network transmission, base data can be updated and distributed to each workstation in a timely manner.

[0213] (3) Interface display of system analysis results

[0214] The display of system analysis results is the final output of the visualization module, which aims to convert complex radar base data and analysis results into easy-to-understand visual graphics or tables to help users quickly obtain information and make decisions.

[0215] Analysis result chart display: Through graphical means (such as bar charts, line charts, radar charts, etc.), the analysis results of each analysis index are displayed intuitively. For example, echo intensity analysis can be represented by line charts to show the echo intensity changes at different times and different configurations, and radial velocity analysis can be displayed by heat maps to show the velocity distribution in different regions.

[0216] Thermal map and vector map display: Radar base data often involves the display of spatial distribution, so the visualization module supports the display of thermal maps and vector maps. For example, echo intensity can be displayed as a thermal map with different color depths, and radial velocity can be represented by the size and direction of the arrow in the vector map. In this way, users can quickly identify abnormal values or hotspots in a certain area.

[0217] Multi-dimensional display and comprehensive analysis: To help users comprehensively evaluate the performance of radar systems, the interface can display multiple analysis indicators simultaneously. For example, in the same view, multiple analysis indicators such as echo intensity, radial velocity, and antenna pointing accuracy can be displayed simultaneously to facilitate comprehensive performance analysis. Time series analysis can also be combined to show the trend of indicators over time, helping technicians analyze the dynamic changes in radar performance.

[0218] Analysis result score and rating: For each analysis indicator, the system calculates a comprehensive score based on scoring rules and weights, which is displayed on the interface. Analysis results can be marked by color, level, etc. to facilitate users to quickly understand the results. For example, radar systems with high comprehensive scores may be displayed in green, indicating good performance; while low-scoring systems may be displayed in red, prompting the need for adjustment or optimization.

[0219] (4) Features of visual design

[0220] Intuitiveness: Through graphical display, users can easily understand complex radar performance analysis results. Different graphics and colors can intuitively express the changes and trends of base data.

[0221] Interactivity: Users can freely select display content, adjust views, and filter and filter base data according to their needs, greatly enhancing user operation experience.

[0222] Real-time: The system can display base data and analysis results in real time, helping users make quick decisions and take necessary optimization measures.

[0223] Flexibility and customizability: The visualization module supports multiple display styles and views, which can be customized according to the needs of different users to meet different evaluation needs and scenarios.

[0224] 2) Business warning module

[0225] The business warning module is one of the core functions of the visual business warning subsystem, mainly used to monitor radar performance indicators in real time, and timely detect abnormalities and issue warnings. It can automatically trigger an alarm when radar indicators exceed the preset threshold, alerting the operator to the problem, and providing appropriate calibration suggestions based on the current analysis results to help technicians quickly take appropriate measures to correct.

[0226] (1) Early warning trigger mechanism. The business early warning module monitors multiple analysis indicators (such as echo intensity, radial velocity, antenna accuracy, etc.) to detect whether these indicators are outside the normal range in real time. The system sets a threshold for each indicator and determines whether it exceeds the predetermined threshold through continuous real-time data analysis. Once a certain indicator exceeds the threshold, the system will immediately trigger an early warning and display the abnormal information in a visual interface with a prominent color (e.g., red, orange).

[0227] (2) Early warning prompt. The module will divide the early warning levels according to the extent of the over-standard indicators, into "warning", "serious" and "critical" three levels. For slightly over-standard indicators, the system will prompt "warning" and suggest further checking; for more serious over-standard cases, the system will give "serious" warning and recommend calibration correction; and in the "critical" level, the system will recommend immediate measures to avoid large-scale failure of radar performance.

[0228] (3) Calibration suggestions and correction measures. When the system finds that the indicators exceed the threshold, the business early warning module will automatically generate calibration suggestions according to the type of abnormality. For example, if the echo intensity is too low, the system may suggest "adjusting the radar antenna gain or transmit power"; if the radial velocity fluctuates greatly, it suggests "re-calibrating the radar frequency". These calibration suggestions help technicians quickly understand the problem and handle it.

[0229] The generation of calibration suggestions not only depends on real-time analysis results, but also combines historical data and experience to give the correction value. For example, when the radial velocity deviates greatly, the system may give the suggestion "adjust the frequency calibration error ± 0.2 m / s" according to historical records, helping users to more accurately adjust and calibrate.

[0230] In addition, the business early warning module also records each early warning record in detail to form a warning log, which is convenient for technicians to track and trace problems. Each early warning log will contain the time of triggering, indicator name, over-standard value, analysis result and related suggestions, etc. to provide data support for subsequent analysis and optimization.

[0231] (4) Early warning record and tracking. After the problem is solved, the module will track the calibration measures to ensure that the correction measures are appropriate and effectively restore the normal value of the indicators. If the calibration adjustment fails to effectively solve the problem, the system will issue a second early warning or further remind the technician to conduct more checks or adjustments.

[0232] Finally, the business warning module can be linked with other system modules, such as the weather radar base data collection and classification subsystem, the weather radar base data quality analysis subsystem and the visualization module, to form a complete closed loop. For example, the base data transmitted in real time by the weather radar base data collection and classification subsystem can automatically trigger the weather radar base data quality analysis subsystem to perform analysis, and when the analysis result exceeds the standard, the visualization module will immediately display warning information, facilitating the operator to take measures.

[0233] The various embodiments described in the specification are progressive in nature, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be mutually referred to. For the device disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0234] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A weather radar-based data quality analysis and exceedance early warning system, characterized in that, It includes a weather radar-based data collection and classification subsystem, a weather radar-based data quality analysis subsystem, and a visualization-based business early warning subsystem; The weather radar base data collection and classification subsystem is used to acquire and classify weather radar base data from real-time operating weather radars. The weather radar base data quality analysis subsystem is used to perform quality analysis on weather radar base data; the quality analysis includes isolated noise point removal effect analysis, ground object echo suppression effect analysis, minimum measurable echo intensity analysis, co-located radar echo consistency analysis, differential reflectivity system deviation analysis, radial velocity standard deviation analysis, and antenna pointing accuracy analysis. The weather radar base data quality analysis subsystem includes an isolated noise point removal effect analysis module, which includes an isolated noise point removal module and an isolated noise point removal effect score mapping module. The isolated noise point removal module is used to remove isolated noise points in weather radar echo data that are less than the echo intensity threshold; wherein, the echo intensity threshold is determined by meteorological conditions. The isolated noise point removal module is also used to analyze the spatial distribution of weather radar echo data, identify isolated noise points using spatial clustering algorithms, and remove the identified isolated noise points. The isolated noise point removal effect score mapping module is used to compare the weather radar echo data after removing isolated noise points with the echo data of standard radar, calculate the number of unremoved isolated noise points included in the weather radar echo data after removing isolated noise points, and map the number of unremoved isolated noise points to the corresponding isolated noise point removal effect score. The visualization business early warning subsystem is used to display the weather radar base data and its classification results, as well as the analysis results of the weather radar base data quality analysis subsystem. The visualization business early warning subsystem is also used to calculate a comprehensive score, identify early warning information, and generate solution suggestions based on the analysis results of the weather radar base data quality analysis subsystem. The visualization business early warning subsystem is also used to display the comprehensive score, the early warning information, and the solution suggestions.

2. The weather radar-based data quality analysis and exceedance early warning system according to claim 1, characterized in that, The weather radar-based data collection and classification subsystem includes a weather radar configuration module and a data collection module; The weather radar configuration module is used to manage and adjust the operating parameters of the weather radar. The data collection module is used to collect weather radar base data under various meteorological conditions in real time and to classify and manage it.

3. The weather radar-based data quality analysis and exceedance early warning system according to claim 2, characterized in that, The weather radar base data quality analysis subsystem also includes a ground object echo suppression effect analysis module, which includes a ground object echo suppression module and a ground object echo suppression effect score mapping module. The ground feature echo suppression module includes a radial alignment unit, a blurring processing unit, a weighted summation unit, and a ground feature echo removal unit; The radial alignment unit is used to align the radial data of the weather radar echo to a uniform azimuth distribution. The fuzzification processing unit is used to calculate the fuzzy membership degree of reflectivity, radial velocity, spectral width, and differential phase in the weather radar base data; The weighted summation unit is used to perform weighted summation on the fuzzy membership degree of reflectivity, radial velocity, spectral width, and differential phase to obtain the weighted summation result; The ground feature echo removal unit is used to remove ground feature echoes whose weighted summation result is greater than a first threshold. The ground object echo suppression effect score mapping module is used to calculate the intensity difference between the ground object echo intensity after removing ground object echoes and the ground object echo intensity of the standard radar, and to map the intensity difference to the corresponding ground object echo suppression effect score.

4. The weather radar-based data quality analysis and exceedance early warning system according to claim 3, characterized in that, The weather radar base data quality analysis subsystem also includes a minimum measurable echo intensity analysis module; The minimum measurable echo intensity analysis module is used to calculate the echo intensity measured by the weather radar at a specific distance, and to map the measured echo intensity to the corresponding minimum measurable echo intensity score using a second threshold.

5. A weather radar-based data quality analysis and exceedance early warning system according to claim 4, characterized in that, The weather radar base data quality analysis subsystem also includes a co-located radar echo consistency analysis module; The co-located radar echo consistency analysis module is used to filter basic data where the volume scan time difference between weather radar and standard radar is less than a preset time difference threshold, and obtain the first set of basic data; wherein, the weather radar is the radar being targeted. The co-located radar echo consistency analysis module is used to filter the basic data in the first set of basic data where the volume scan elevation angle difference between weather radar and standard radar is less than a preset elevation angle difference threshold, and the volume scan azimuth angle difference is less than a preset azimuth angle threshold, to obtain the second set of basic data. The co-location radar echo consistency analysis module is used to filter basic data in the second set of basic data whose position difference is less than a preset position difference threshold, and obtain the third set of basic data. The co-located radar echo consistency analysis module is used to filter out meteorological echo data from the third set of base data. The co-location radar echo consistency analysis module is used to transform the observation coordinates of weather radar and standard radar to obtain the geodetic coordinates of weather radar and standard radar. Then, in the geodetic coordinate system, it uses a range database matching algorithm, the geodetic coordinates of weather radar and standard radar, and the geodetic coordinates of standard radar to determine whether the distance difference between weather radar and standard radar is less than a preset distance difference threshold. If it is less than the preset distance difference threshold, it calculates the difference between various echo products of weather radar and various echo products of standard radar in the matched range database, and uses the difference to map the corresponding echo consistency score. The various echo products of weather radar and standard radar are all selected from the meteorological echo data.

6. A weather radar-based data quality analysis and exceedance early warning system according to claim 5, characterized in that, The weather radar base data quality analysis subsystem also includes a differential reflectivity system deviation analysis module, which includes a data acquisition module, a first data filtering module, a second data filtering module, a third data filtering module, a median extraction module, and a differential reflectivity factor system deviation score mapping module. The data acquisition module is used to read the differential reflectivity factor, correlation coefficient, signal-to-noise ratio, reflectivity factor before ground object echo suppression, radial velocity, and echo distance from the weather radar base data; The first data filtering module is used to filter the differential reflectance factor values ​​for the required distance and orientation based on a preset distance threshold. The second data filtering module is used to remove non-precipitation echoes according to preset filtering conditions and obtain the differential reflectivity factor value after the second filtering. The third data filtering module is used to remove invalid data and null values ​​to obtain the final differential reflectivity factor sequence. The median extraction module is used to extract the median of the numerical deviation of the differential reflectance factors from the final differential reflectance factor series; The differential reflectivity factor system deviation score mapping module is used to obtain the system deviation of the differential reflectivity factor and map the system deviation of the differential reflectivity factor to the corresponding differential reflectivity factor system deviation score; wherein, the system deviation of the differential reflectivity factor is the median.

7. A weather radar-based data quality analysis and exceedance early warning system according to claim 6, characterized in that, The weather radar base data quality analysis subsystem also includes a radial velocity standard deviation analysis module, which includes a velocity fuzzy processing module, a distance folding data removal module, a window definition and standard deviation calculation module, a percentile statistics module, a radial velocity standard deviation output module, and a radial velocity standard deviation fraction mapping module. The velocity fuzzing processing module is used to restore the fuzzy radial velocity to the true radial velocity; The distance folding data removal module is used to remove distance folding data from weather radar echo data. The window definition and standard deviation calculation module is used to define a grid window for each radar echo data point and iterate through each grid window to calculate the standard deviation value of each effective radial velocity data. If the grid window includes less than 9 effective radial velocity data, the grid window is skipped. The grid window size is 7*9, that is, 7 points are arranged along the azimuth direction and 9 points are arranged along the range direction. The percentile statistics module is used to sort all calculated standard deviation values ​​and filter out the standard deviation values ​​ranked at the 75th percentile. The radial velocity standard deviation output module is used to output the standard deviation value at the 75th percentile. The radial velocity standard deviation fraction mapping module is used to map the standard deviation value at the 75th percentile to the corresponding radial velocity standard deviation fraction.

8. A weather radar-based data quality analysis and exceedance early warning system according to claim 7, characterized in that, The weather radar base data quality analysis subsystem also includes an antenna pointing accuracy analysis module, which includes a fixed ground target selection module, a radar data acquisition module, an effective data filtering module, an antenna pointing error calculation module, a statistical error analysis module, an error distribution and visualization module, and an antenna pointing error fraction mapping module. The fixed ground feature selection module is used to select stable ground features whose locations are known. The radar data acquisition module is used to acquire the measured azimuth and measured elevation angles returned by the weather radar after scanning the ground targets; The effective data filtering module is used to filter the effective echo data returned by the weather radar after scanning the ground target; The antenna pointing error calculation module is used to calculate the azimuth and elevation errors of the effective echo data; wherein, the azimuth error is equal to the measured azimuth minus the expected azimuth; the elevation error is equal to the measured elevation minus the expected elevation. The statistical error analysis module is used to calculate the mean and standard deviation of each azimuth error, and the mean and standard deviation of each pitch angle error. The error distribution and visualization module is used to display the azimuth and elevation errors of the effective echo data; The antenna pointing error fraction mapping module is used to map the azimuth and elevation errors of the effective echo data to the corresponding antenna pointing error fractions.

9. A weather radar-based data quality analysis and exceedance early warning system according to claim 8, characterized in that, The visualized business early warning subsystem includes a comprehensive scoring module, an early warning information identification module, and an early warning information solution suggestion generation module; The comprehensive scoring module is used to standardize the isolated noise point removal effect score, ground object echo suppression effect score, minimum measurable echo intensity score, echo consistency effect score, differential reflectivity factor system deviation score, radial velocity standard deviation score, and antenna pointing error score, and then perform weighted processing to obtain a comprehensive score. The early warning information identification module is used to automatically identify data exceeding standards and abnormal situations based on the analysis results of the weather radar base data quality analysis subsystem. The early warning information resolution suggestion generation module is used to provide corresponding resolution suggestions for the data exceeding the standard and abnormal situations.

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