X-band weather radar non-precipitation data quality control method and system

Abstract

This disclosure provides a method and system for quality control of non-precipitation data from X-band weather radar. Applied to the field of weather radar technology, the method includes acquiring X-band weather radar data; identifying and removing electromagnetic interference echoes based on the continuity characteristics, correlation coefficient (CC), and differential reflectivity factor (ZDR) distribution of electromagnetic interference echoes; determining whether the difference in reflectivity factor before and after hardware filtering at each echo point is greater than a preset threshold; if so, marking the echo point as a precipitation echo; otherwise, performing fuzzy logic recognition and elimination based on feature parameters; wherein, the probability P of non-precipitation echoes at each echo point is calculated based on the feature parameters. NPR and precipitation echo probability P PR Determine P NPR Is it greater than P? PR If so, the echo point is marked as a non-precipitation echo; otherwise, a weak non-precipitation echo is identified. This is determined by comparing the mean reflectivity factor, mean correlation coefficient, and the percentage of effective distances between the echo points at the entire elevation angle. CC与 The threshold value is used to determine whether an echo is non-precipitation. If it is, the echo point is marked as a non-precipitation echo; otherwise, the radial and azimuth reflectivity factors are continuously used to filter out scattered and patchy noise around the echo point to obtain precipitation echoes. In this way, the quality of weather radar data can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of weather radar technology, and in particular to a method and system for quality control of non-precipitation data from X-band weather radar. Background Technology

[0002] Currently, the deployment of X-band weather radar in China is becoming increasingly dense, and its role in short-term early warning and disaster prevention and mitigation is becoming more and more obvious. However, the radar echoes also include non-meteorological echoes such as clear sky echoes and ground object echoes, which affect the quality of radar data. Therefore, it is necessary to conduct quality control on X-band weather radar base data before application.

[0003] In non-meteorological echoes, clear-sky echoes are mainly generated by the scattering of radar waves by turbulent atmosphere and the specular reflection of radar waves by stratified atmosphere. X-band radar observations show that clear-sky echoes are generally below 15 dBZ, and their velocity is generally not zero. Ground object echoes are mainly generated by the scattering of electromagnetic waves by mountains and various buildings; the echo intensity is generally strong, and the location is relatively fixed. Electromagnetic interference echoes are usually caused by radar signal processor malfunctions or the reception of external electromagnetic waves. From the echo morphology, they are usually continuously distributed along a certain radial direction, with a significant difference in reflectivity distribution between them and adjacent radial directions.

[0004] Currently, methods for identifying and eliminating non-meteorological echoes from X-band radar include fuzzy logic, neural networks, and filtering. Overall, these methods are less effective at removing electromagnetic interference with poor continuity, and there is room for improvement in the recognition rate of non-precipitation echoes. Summary of the Invention

[0005] This disclosure provides a method and system for controlling the quality of non-precipitation data from X-band weather radar, solving the technical problem of how to improve the quality of weather radar data.

[0006] According to a first aspect of this disclosure, a method for quality control of non-precipitation data from X-band weather radar is provided. The method includes:

[0007] Acquire X-band weather radar data;

[0008] Based on the continuity characteristics of electromagnetic interference echoes, the distribution of correlation coefficient CC and differential reflectivity factor ZDR, electromagnetic interference echoes are identified and eliminated.

[0009] Determine whether the difference in reflectivity factor before and after hardware filtering at each echo point is greater than a preset threshold. If so, mark the echo point as a precipitation echo.

[0010] If not, then perform fuzzy logic recognition and elimination based on feature parameters; where,

[0011] The non-precipitation echo probability P of each echo point is calculated based on the aforementioned characteristic parameters. NPR and precipitation echo probability P PR Determine P NPR Is it greater than P? PR If so, then mark the echo point as a non-precipitation echo;

[0012] If not, then non-precipitation weak echo discrimination is performed; among which,

[0013] By comparing the mean reflectivity factor of the entire echo elevation angle. Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC The size of the discrimination threshold determines whether it is a non-precipitation echo; if so, the echo point is marked as a non-precipitation echo.

[0014] If not, the scattered and patchy noise around the echo point is filtered out using the continuity of radial and azimuth reflectivity factors to obtain the precipitation echo.

[0015] In addition to the aspects described above and any possible implementations, a further implementation is provided, wherein the feature parameters include:

[0016] Median reflectance factor M DBZ Median correlation coefficient M CC Differential propagation phase shift horizontal texture T φDP Reflectivity factor horizontal texture T DBZ Correlation coefficient horizontal texture T CC And depolarization ratio DR.

[0017] As described above and in any possible implementation, a further implementation is provided, wherein the non-precipitation echo probability P of each echo point is calculated based on the characteristic parameters. NPR and precipitation echo probability P PR include:

[0018] Based on the probability distributions of non-precipitation echoes and precipitation echoes, the membership functions of each characteristic parameter are determined, and the characteristic parameters are then fuzzified using the membership functions.

[0019] As described above, and in accordance with any possible implementation, a further implementation is provided, wherein the determination P NPR Is it greater than P? PR If so, then the echo point is marked as a non-precipitation echo, including:

[0020] Based on the probability distribution of different characteristic parameters and membership functions, ground feature echoes and clear sky echoes are identified and eliminated.

[0021] As described above and in any possible implementation, a further implementation is provided, wherein the mean reflectivity factor of the entire echo elevation angle is compared. Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC Determining whether an echo is non-precipitation echo, based on the threshold value, includes:

[0022] Comprehensive judgment of the mean reflectivity factor Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC Do they simultaneously meet the discrimination threshold?

[0023] The proportion of the effective distance database for the correlation coefficient is as follows:

[0024]

[0025] Val CC N represents the proportion of the distance database where the correlation coefficient on the current PPI surface is an effective value. Z and N CC These represent the number of positive effective values ​​for the current elevation angle reflectance and the number of effective values ​​for the correlation coefficient, respectively.

[0026] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0027] Using electromagnetic interference echo to identify POD accuracy RI Ground feature echo recognition accuracy (POD) AP Clear Sky Echo Recognition Accuracy POD CA Precipitation echo recognition accuracy (POD) PR The Critical Success Index (CSI) is used to evaluate the quality control results of weather radar data.

[0028] According to a second aspect of this disclosure, an X-band weather radar non-precipitation data quality control system is provided. The system includes:

[0029] The acquisition module is used to acquire X-band weather radar data;

[0030] The electromagnetic interference echo filtering module is used to identify and eliminate electromagnetic interference echoes based on the continuity characteristics, correlation coefficient CC, and differential reflectivity factor ZDR distribution of the electromagnetic interference echoes.

[0031] The echo detection module determines whether the difference in reflectivity factor before and after hardware filtering at each echo point is greater than a preset threshold. If so, the echo point is marked as a precipitation echo; otherwise, fuzzy logic recognition and elimination based on feature parameters are performed.

[0032] The non-precipitation echo probability P of each echo point is calculated based on the aforementioned characteristic parameters. NPR and precipitation echo probability P PR Determine P NPR Is it greater than P? PR If yes, then the echo point is marked as a non-precipitation echo; otherwise, a non-precipitation weak echo discrimination is performed; where,

[0033] By comparing the mean reflectivity factor of the entire echo elevation angle. Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC The size of the discrimination threshold determines whether it is a non-precipitation echo. If it is, the echo point is marked as a non-precipitation echo; if not, the scattered and patchy noise around the echo point is filtered out by the continuity of the radial and azimuth reflectivity factors to obtain the precipitation echo.

[0034] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0035] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods according to the first and / or second aspects of this disclosure.

[0036] Using X-band weather radar data, and based on the continuity characteristics of electromagnetic interference echoes, the distribution of correlation coefficient (CC) and differential reflectivity factor (ZDR), electromagnetic interference echoes are identified and eliminated. It is determined whether the difference in reflectivity factor before and after hardware filtering at each echo point exceeds a preset threshold. If so, the echo point is marked as a precipitation echo; otherwise, fuzzy logic identification and elimination of characteristic parameters are performed. Residual weak non-precipitation echoes are eliminated based on the mean reflectivity factor, mean correlation coefficient, and the proportion of effective distance coulombs in the correlation coefficient. Finally, the continuity of radial and azimuth reflectivity factors is used to filter out scattered and patchy noise to obtain precipitation echoes.

[0037] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0038] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0039] Figure 1 A flowchart is shown for a method for quality control of non-precipitation data from an X-band weather radar according to an embodiment of the present disclosure;

[0040] Figure 2 The electromagnetic interference echo distribution diagrams at different elevation angles of the Fangshan radar according to embodiments of the present disclosure are shown.

[0041] Figure 3 The electromagnetic interference echo distribution at a 7.4° elevation angle of the Yanqing radar is shown according to an embodiment of the present disclosure;

[0042] Figure 4 The diagram shows the parameter distribution of electromagnetic interference echoes and precipitation echoes corresponding to those of a Beijing X-band weather radar according to an embodiment of the present disclosure.

[0043] Figure 5 The diagram illustrates the distribution of the difference in precipitation echo reflectivity factor in a mixed area and the distribution of the corresponding correlation coefficients according to embodiments of the present disclosure.

[0044] Figure 6 The probability distribution diagrams of various characteristic parameters of ground feature echo, clear sky echo and precipitation echo according to embodiments of the present disclosure are shown.

[0045] Figure 7 Membership function diagrams of various characteristic parameters of ground feature echo, clear sky echo, and precipitation echo according to embodiments of the present disclosure are shown.

[0046] Figure 8 A statistical graph of characteristic parameters of non-precipitation echo and snowfall echo according to an embodiment of the present disclosure is shown;

[0047] Figure 9 A comparison chart of the quality control effects of electromagnetic interference echoes according to embodiments of the present disclosure is shown;

[0048] Figure 10 A comparison diagram of echo quality control effects in a mixed precipitation zone containing ground features, according to an embodiment of the present disclosure, is shown.

[0049] Figure 11 A comparison chart of the quality control effects of ground features and clear-sky echoes according to embodiments of the present disclosure is shown;

[0050] Figure 12 A comparison chart of quality control effects for weak non-precipitation echoes according to embodiments of the present disclosure is shown;

[0051] Figure 13 A comparison chart of the quality control effects of scattered and patchy clutter according to embodiments of the present disclosure is shown;

[0052] Figure 14 A block diagram of an X-band weather radar non-precipitation data quality control system according to an embodiment of the present disclosure is shown;

[0053] Figure 15 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0055] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0056] This disclosure utilizes X-band weather radar data and identifies and removes electromagnetic interference echoes based on the continuity characteristics of the electromagnetic interference echoes, the distribution of the correlation coefficient (CC), and the differential reflectivity factor (ZDR). It determines whether the difference in reflectivity factor before and after hardware filtering at each echo point exceeds a preset threshold; if so, the echo point is marked as a precipitation echo; otherwise, fuzzy logic identification and elimination of characteristic parameters are performed. Residual weak non-precipitation echoes are eliminated based on the mean reflectivity factor, the mean correlation coefficient, and the proportion of effective distance channels for the correlation coefficient. Finally, the continuity of the radial and azimuth reflectivity factors is used to filter out scattered and patchy noise to obtain precipitation echoes. This improves the quality of weather radar data.

[0057] Figure 1 A flowchart of a non-precipitation data quality control method for X-band weather radar according to an embodiment of the present disclosure is shown, such as... Figure 1 As shown, a process for quality control of non-precipitation data from X-band weather radar includes:

[0058] In one embodiment, X-band weather radar data is acquired; electromagnetic interference echoes are identified and removed based on the continuity characteristics, correlation coefficient (CC), and differential reflectivity factor (ZDR) distribution of the electromagnetic interference echoes; it is determined whether the difference in reflectivity factor before and after hardware filtering at each echo point is greater than a preset threshold. If so, the echo point is marked as a precipitation echo; otherwise, fuzzy logic identification and elimination of feature parameters are performed; wherein, the probability P of non-precipitation echoes at each echo point is calculated based on the feature parameters. NPR and precipitation echo probability P PR Determine P NPR Is it greater than P? PR If so, the echo point is marked as a non-precipitation echo; otherwise, a weak non-precipitation echo is identified. This is determined by comparing the average reflectivity factor of the entire elevation angle of the echoes from each echo point. Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC The size of the discrimination threshold determines whether it is a non-precipitation echo. If it is, the echo point is marked as a non-precipitation echo; if not, the scattered and patchy noise around the echo point is filtered out by the continuity of the radial and azimuth reflectivity factors to obtain the precipitation echo.

[0059] S101, acquire X-band weather radar data.

[0060] In some embodiments, taking Beijing as an example, historical data of X-band weather radar in Beijing is first obtained, and statistical analysis is performed on the characteristics of non-precipitation echoes such as electromagnetic interference, clear sky, and ground objects, as well as precipitation echoes in the historical weather radar data.

[0061] Specifically, using baseline data from nine X-band weather radars in Fangshan, Tongzhou, and Miyun districts from February 2023 to August 2024, a database containing echoes of ground features, clear sky, and precipitation was established to statistically analyze the differences in characteristic parameters of different echoes. Volume scan data at each elevation angle were manually determined. Ground feature and clear sky echoes primarily selected from observations during clear weather, with a 15 dBz threshold for differentiation. Precipitation echoes generally had a larger echo range or exhibited an irregular, blocky structure. Based on manual experience, 200 PPI data points for electromagnetic interference echoes, 350 PPI data points for clear sky echoes, 400 PPI data points for ground feature echoes, and 320 PPI data points for precipitation echoes were collected. The collected baseline data files for electromagnetic interference echoes, ground feature echoes, clear sky echoes, and precipitation echoes were randomly divided into a statistical database and a validation database. Each database was organized into a azimuth-range database unit. The statistical and validation databases are shown in Table 1. Each database contains clear-sky echoes, ground object echoes, and precipitation echoes, used to statistically analyze the differences in echo characteristics and verify the recognition effect of the algorithm. The collected ground object echoes generally have strong reflectivity, their location does not change over time, and their echo top height is relatively low; clear-sky echoes have low reflectivity, extend from near the radar to distant areas, and have a relatively uniform coverage area; precipitation echoes include stratiform cloud precipitation and convective cloud precipitation. The former is mostly blocky in structure with strong echo center intensity, while the latter is mostly continuous, with lower intensity and more uniform distribution than the former.

[0062]

[0063] Table 1

[0064] S102. Based on the continuity characteristics of electromagnetic interference echoes, the distribution of correlation coefficient CC and differential reflectivity factor ZDR, electromagnetic interference echoes are identified and eliminated.

[0065] In some embodiments, electromagnetic interference echoes are usually caused by radar signal processor malfunctions or the reception of external electromagnetic waves. Based on X-band radar observations in the Beijing area, electromagnetic interference can be mainly classified into the following two categories:

[0066] (1) Type I electromagnetic interference echo, Figure 2 The diagram shows the electromagnetic interference echo distribution at different elevation angles of a Fangshan radar according to an embodiment of the present disclosure, where (a) is the reflectivity factor at a 0.5° elevation angle and (b) is the reflectivity factor at a 1.5° elevation angle. Figure 2 It is known that Type I electromagnetic interference echoes typically have good continuity in the current radial direction, but poor continuity in the upper or lower layers, with reflectivity ranging from 5-60 dBz. Based on these characteristics, the filtering method is as follows:

[0067]

[0068] In the formula, R validThe ratio of the effective range library number to the total range library number in the radial reflectivity factor at the current elevation angle. N represents the proportion of the effective range of reflectivity factors in the same radial direction of adjacent upper or lower layers to the total range of layers. R Z represents the total distance to the target at a certain azimuth angle of the elevation angle. j Val represents the reflectivity factor of a certain distance library at this azimuth angle. It should be noted that during filtering, the effective distance library ratio of the reflectivity factor at the current elevation angle is compared with the effective distance library ratio of the reflectivity factor at the adjacent upper or lower elevation angle. If either of them meets the equation (1), it is identified as an electromagnetic interference echo and is removed.

[0069] (2) Type II electromagnetic interference echo, Figure 3 The diagram shows the electromagnetic interference echo distribution at a 7.4° elevation angle of the Yanqing radar according to an embodiment of the present disclosure, where (a) is the reflectivity factor at a 7.4° elevation angle and (b) is the correlation coefficient at a 7.4° elevation angle. Figure 3 It can be seen that the second type of electromagnetic interference echo is usually intermittent in the radial direction and has a large affected area. Through extensive observational data, it has been found that, unlike the first type of electromagnetic interference echo, this type of echo has a larger ZDR, mainly distributed in the ranges of -8 to -7 dB and 7 to 8 dB, with a CC value above 1. Figure 4 As shown. By Figure 4 It can be seen that although the CC of precipitation echo is relatively large, the differential reflectivity ZDR is generally distributed between -4 and 4 dB. The filtering method is shown in equation (4):

[0070]

[0071]

[0072] In the formula, ZDR valid This is the average of the absolute values ​​of the ZDR in the radial direction at the current elevation angle. N is the average of CC in the current radial direction at this elevation angle. ZDR N represents the current effective distance library for ZDR in the radial direction. CC N represents the current effective distance library in the radial direction (CC). R ZDR is the total number of distance units at a certain azimuth angle for that elevation angle. j Let ZDR and CC be the distances along a certain radial direction. j Let CC be the distance library along this radial direction.

[0073] S103, determine whether the difference in reflectivity factor before and after hardware filtering of each echo point is greater than the preset threshold. If so, mark the echo point as precipitation echo; otherwise, perform fuzzy logic recognition and elimination based on feature parameters.

[0074] In some embodiments, in areas where ground features and precipitation echoes mix, although precipitation echoes are preserved after hardware filtering, the correlation coefficient (CC) is lower due to the influence of surrounding ground features, making subsequent identification prone to confusion. Therefore, comparing the reflectivity factor (Z) before and after hardware filtering is crucial. T (Unit: dBZ) and Z H (Unit: dBZ) Differences were observed, revealing a mixing region (Z). T -Z H ) is relatively large, and Z H Generally greater than 15 dBz, while the value of ground feature echoes after filtering varies between effective and ineffective values ​​depending on the intensity of the reflectivity factor before filtering. Precipitation echoes usually appear in patches, Z H The CC echo is in good agreement with the surrounding echo points, so this characteristic can be used for identification.

[0075] Figure 5 The diagram illustrates the distribution of the difference in precipitation echo reflectivity factor in a mixed area according to embodiments of the present disclosure, along with the corresponding correlation coefficient distribution. Statistical results for the mixed area show that the most echo points are located in the 0-15 dBz difference range, with CC values ​​mostly higher than 0.9. When the difference exceeds 15 dBz, the CC value decreases rapidly and becomes uniformly distributed between 0 and 1. Therefore, (Z...) T -Z H A threshold of 15 dBz was set to preserve precipitation echoes.

[0076] In some embodiments, for simultaneously satisfying (Z) T -Z H >15dBz, Z H For echo points meeting the conditions of >15dBz and CC<0.9, a window is set radially around the echo point, and Z is statistically analyzed. H If the proportion of both CC and RGB values ​​is greater than a threshold, then that point is identified as a precipitation echo. The proportion is expressed by the formula:

[0077] P DBZ >0.7∩P CC >0.2 (9)

[0078] In the formula, P DBZ Z represents the echo point within the window H The percentage of >15dBz, P CC This indicates the percentage of echo points with a CC > 0.8 within the window. Considering the continuity of precipitation echoes, the radial window is set to 0.825 km.

[0079] S104, Calculate the non-precipitation echo probability P of each echo point based on the aforementioned characteristic parameters. NPR and precipitation echo probability P PR Determine P NPR Is it greater than P?PR If yes, then mark the echo point as a non-precipitation echo; otherwise, perform a non-precipitation weak echo discrimination.

[0080] In some embodiments, based on the analysis of clear sky, ground object echoes, and precipitation echoes, the different characteristics of clear sky, ground object echoes, and precipitation echoes are found to be: clear sky echoes generally extend from near the radar to distant locations, with relatively uniform echo intensity and area, exhibiting a certain degree of continuity; ground object echoes have clear edges, fixed positions that do not change over time, and radial velocities close to zero. Clear Sky Echo Z H Generally below 20 dBz, while ground object echoes are between 20-60 dBz; ground object echo differential propagation phase shift. (Unit: °) and ZDR have relatively large fluctuations and large texture feature values. Using these dual polarization parameters, the two types of echoes can be distinguished well.

[0081] Specifically, from the reflectivity factor (Z) H ), correlation coefficient (CC), differential propagation phase shift ( Six characteristic parameters reflecting the differences between precipitation echoes and clear sky / ground object echoes were extracted, including the median reflectivity factor M. DBZ Median correlation coefficient M CC Differential propagation phase shift standard deviation T φDP Standard deviation of reflectivity factor T DBZ The standard deviation T of the correlation coefficient CC The definition is as shown in formula (10); DR is the depolarization ratio at the current distance, indicating that precipitation particles are uniform in shape (high CC) and nearly spherical (linear unit Z). DR The degree of =1) is defined as in formula (11).

[0082]

[0083] In equation (10), T X Let X be the standard deviation of the current radial X characteristic parameter, and n represent the window size for radial processing. j Z is the filtered value of the j-th radial distance from the median in the current distance library. k Z represents the value of the feature parameter within the current radial distance processing window. In equation (11), Z DR DR is the differential reflectance on a linear scale, and the calculated values ​​are distributed between 0 and 1. The DR values ​​of meteorological targets are generally small.

[0084] In some embodiments, characteristic parameters of ground object echoes, clear-sky echoes, and precipitation echoes are statistically analyzed based on an established statistical database. Figure 6 The diagram shows the probability distribution of various characteristic parameters of ground feature echo, clear sky echo, and precipitation echo according to embodiments of the present disclosure.

[0085] In some embodiments, the membership function of each feature parameter is determined based on the probability distribution of non-precipitation echoes and precipitation echoes. The feature parameters are then fuzzified using the membership function to obtain the 0-1 value criteria for each feature parameter for different types of echoes. The criteria values ​​are then weighted and accumulated. If the sum exceeds a preset threshold, the echo is judged as a non-precipitation echo and is removed.

[0086] Specifically, based on the characteristic parameter M CC M DBZ T CC T DBZ T φDP The membership functions of each parameter can be determined by the DR probability distribution. The membership functions used include trapezoidal membership functions (including standard trapezoidal membership functions and trapezoidal membership functions combined with the sigmoid function) and triangular membership functions. Figure 7 Membership function graphs of various characteristic parameters of ground feature echo, clear sky echo, and precipitation echo according to embodiments of the present disclosure are shown.

[0087] S105, by comparing the mean reflectivity factor of the entire echo elevation angle. Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC The size of the discrimination threshold determines whether it is a non-precipitation echo. If it is, the echo point is marked as a non-precipitation echo; if not, the scattered and patchy noise around the echo point is filtered out by the continuity of the radial and azimuth reflectivity factors to obtain the precipitation echo.

[0088] In some embodiments, after filtering out non-precipitation echoes using the above method, some echo points of clear sky and ground object echoes lacking dual polarization may be difficult to distinguish. Data analysis revealed that precipitation echoes typically have a reflectivity greater than 20 dBz and a high correlation coefficient, while non-precipitation echoes, after processing using the above method, only retain scattered echo points with low reflectivity and significantly missing correlation coefficients. Therefore, residual non-precipitation echoes can be effectively removed based on reflectivity and correlation coefficients. Furthermore, considering that winter snowfall echoes also have low reflectivity, the parameter distributions of residual non-precipitation and snowfall echoes were statistically analyzed. Figure 8 The diagram illustrates statistical graphs of characteristic parameters for non-precipitation echoes and snowfall echoes according to embodiments of the present disclosure. Figure 8 It can be seen that the average reflectance of the remaining non-precipitation echoes is below 10 dBz, mainly distributed in the range of 2-8 dBz, with an average correlation coefficient of less than 0.9; the average reflectance of snowfall echoes is above 10 dBz, mainly distributed in the range of 11-16 dBz, with an average correlation coefficient of more than 0.95.

[0089] In some embodiments, the mean reflectivity factor is determined comprehensively. Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC Does it simultaneously meet the discrimination threshold?

[0090] Specifically, based on the above analysis, the identified PPI surface is judged according to two conditions: (1) Calculate the mean of the reflectivity factor of the entire PPI surface with a positive effective value. The mean of the correlation coefficient is less than 7.5. When the value is less than 0.83, the entire plane is determined to be a non-precipitation echo and is discarded; (2) For some points with low reflectivity, and invalid values ​​for correlation coefficient and differential propagation phase shift, when the mean value of the entire PPI surface is less than 0.83, the entire plane is determined to be a non-precipitation echo and is discarded; Less than 7.5 and the effective value Val of CC CC When the proportion is less than 0.2, the entire plane is determined to be a non-precipitation echo and is removed. Val CC N CC N Z The definition is as shown in equations (12)-(18).

[0091]

[0092] In the formula, and Z represents the average of the reflectivity factor and correlation coefficient for the current PPI surface that are positive. j (Unit: dBz) is the reflectivity factor at a certain distance in the azimuth angle where the value is positive. (CC) j Val is the correlation coefficient of a distance database at a certain azimuth angle; CC The distance to the database is the percentage of the PPI with a valid correlation coefficient, +Val (unit: dBz) represents the valid value with a positive value, and N is the distance to the database. Z and N CC These represent the number of positive effective values ​​for the current elevation angle reflectance and the number of effective values ​​for the correlation coefficient, respectively.

[0093] In some embodiments, after processing by the above method, there may be scattered, patchy noise echoes or non-precipitation echoes. These echoes are characterized by strong isolation, poor continuity in the radial and azimuth directions, and sporadic distribution. Therefore, when removing them, the effective value ratio within the window is calculated with the distance database as the center. If the effective value ratio is less than 30%, then the distance database is filtered out as noise echoes. The window size is set to 5° × 0.675 km.

[0094] In some embodiments, the accuracy of POD is determined by electromagnetic interference echo. RI Ground feature echo recognition accuracy (POD) AP Clear Sky Echo Recognition Accuracy PODCA Precipitation echo recognition accuracy (POD) PR The Critical Success Index (CSI) is used to evaluate the quality control results of weather radar data.

[0095] Specifically, the recognition rate POD can be expressed by formula (19):

[0096] Point of Detection (POD) = Number of accurately identified echo points / Total number of echo samples * 100% (19)

[0097] Table 2 shows the statistical results of the algorithm's quality control effect on weather radar data of ground features, clear sky, and precipitation echoes. According to the evaluation results in Table 2, the algorithm has a good quality control effect on weather radar data of ground features, clear sky, and precipitation echoes, with a CSI score of over 0.88. The accuracy rate for identifying precipitation echoes can reach 0.982, while the identification rates for ground feature echoes, clear sky echoes, and electromagnetic interference are 0.984, 0.977, and 0.935, respectively. This indicates that the algorithm can effectively preserve precipitation echoes and eliminate non-precipitation echoes.

[0098]

[0099] Table 2

[0100] In some embodiments, the method disclosed herein evaluates the case processing effect based on five aspects: electromagnetic interference elimination, clear sky and ground object echo elimination, ground object precipitation mixed zone discrimination, non-precipitation weak echo discrimination, and patch clutter filtering.

[0101] Specifically, Figure 9 A comparison chart of quality control effects including electromagnetic interference echoes according to embodiments of the present disclosure is shown. Figure 9 (a) shows the echo distribution around the Fangshan radar at 13:36 on March 24, 2024. The area around the station experiences relatively dense electromagnetic interference, with noticeable patchy precipitation echoes at the 270° azimuth. After processing using the method disclosed in this paper, as shown... Figure 10 (b) Large-area electromagnetic interference was effectively removed, and precipitation echoes were effectively preserved.

[0102] Specifically, Figure 10 The diagram shows a comparison of echo quality control effects in a precipitation mixing zone containing ground features according to embodiments of the present disclosure. Figure 10 (a) Echo distribution observed by the Miyun station radar at an elevation angle of 0.5° at 17:39 on August 9, 2024, combined with... Figure 10 (b) Correlation coefficient distribution: It can be seen that in the area circled in red, due to the overlap between ground features and precipitation echoes before filtering, the correlation coefficient of precipitation echoes in this area is relatively small after hardware filtering. Without mixing zone discrimination, after quality control, as shown... Figure 10As shown in (c), some precipitation echoes were incorrectly rejected, but after adding them to the mixing zone discrimination, as shown in [the diagram], [the text continues with further details about precipitation echoes and their rejection]. Figure 10 As shown in (d), precipitation echoes in this area are effectively preserved, and the echo morphology is relatively complete.

[0103] Specifically, Figure 11 The diagram shows a comparison of the quality control effects of ground features and clear-sky echoes according to embodiments of the present disclosure. Figure 11 (a) shows the echo distribution around the Miyun radar at 14:03 on July 29, 2023. Ground features and clear-sky echoes exist around the station. After processing using the method disclosed herein, the echoes are as follows: Figure 12 (b) Non-precipitation echoes near the station were effectively eliminated.

[0104] Specifically, Figure 12 A comparison chart of quality control effects for weak non-precipitation echoes according to embodiments of the present disclosure is shown. Figure 12 (a) shows the echo distribution around the Fangshan radar at 10:18 on May 10, 2023. The correlation coefficient distribution before quality control is shown in [reference needed]. Figure 12 (b) As can be seen from the figure, the correlation coefficient is partially missing, resulting in residual clear-sky echoes after processing by the method disclosed in this paper. Figure 12 (c) After adding the non-precipitation weak echo quality control algorithm, clear-sky echoes are completely eliminated, such as... Figure 12 (d)

[0105] Specifically, Figure 13 A comparison chart of the quality control effects of scattered and patchy clutter according to embodiments of the present disclosure is shown. Figure 13 This is a comparison of reflectivity factors before and after filtering out scattered and patchy clutter around the Changping station radar at 10:27 on July 7, 2024. The figure shows that noise points and patchy clutter with strong or weak reflectivity within the radar detection range were effectively removed.

[0106] According to embodiments of this disclosure, the quality of weather radar data is effectively improved.

[0107] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0108] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0109] Figure 14A block diagram of an X-band weather radar non-precipitation data quality control system 140 according to an embodiment of the present disclosure is shown. Figure 14 As shown, the device 1400 includes:

[0110] Acquisition module 141 is used to acquire X-band weather radar data;

[0111] Electromagnetic interference echo filtering module 142 is used to identify and eliminate electromagnetic interference echoes based on the continuity characteristics of electromagnetic interference echoes, the distribution of correlation coefficient CC and differential reflectivity factor ZDR.

[0112] The echo judgment module 143 is used to determine whether the difference in reflectivity factor before and after hardware filtering at each echo point is greater than a preset threshold. If so, the echo point is marked as a precipitation echo; otherwise, fuzzy logic recognition and elimination based on feature parameters are performed.

[0113] The non-precipitation echo probability P of each echo point is calculated based on the aforementioned characteristic parameters. NPR and precipitation echo probability P PR Determine P NPR Is it greater than P? PR If yes, then the echo point is marked as a non-precipitation echo; otherwise, a non-precipitation weak echo discrimination is performed; where,

[0114] By comparing the mean reflectivity factor of the entire echo elevation angle. Mean correlation coefficient and the proportion of the effective distance database in the correlation coefficient Val CC The size of the discrimination threshold determines whether it is a non-precipitation echo. If it is, the echo point is marked as a non-precipitation echo; if not, the scattered and patchy noise around the echo point is filtered out by the continuity of the radial and azimuth reflectivity factors to obtain the precipitation echo.

[0115] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0116] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0117] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0118] Figure 15A schematic block diagram of an electronic device 150 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0119] Electronic device 150 includes a computing unit 151, which can perform various appropriate actions and processes according to a computer program stored in ROM 152 or a computer program loaded into RAM 153 from storage unit 158. RAM 153 may also store various programs and data required for the operation of electronic device 150. The computing unit 151, ROM 152, and RAM 153 are interconnected via bus 154. I / O interface 155 is also connected to bus 154.

[0120] Multiple components in electronic device 150 are connected to I / O interface 155, including: input unit 156, such as keyboard, mouse, etc.; output unit 157, such as various types of displays, speakers, etc.; storage unit 158, such as disk, optical disk, etc.; and communication unit 159, such as network card, modem, wireless transceiver, etc. Communication unit 159 allows electronic device 150 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] The computing unit 151 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 151 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 151 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 158. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 150 via ROM 152 and / or communication unit 159. When the computer program is loaded into RAM 153 and executed by the computing unit 151, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 151 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0122] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0126] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0127] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0128] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0129] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for non-precipitation data quality control of X-band weather radar, comprising: obtaining X-band weather radar data; identifying and removing electromagnetic interference echoes according to the continuity characteristics of the electromagnetic interference echoes, the correlation coefficient (CC) and the distribution of the differential reflectivity factor (ZDR) ; judging whether the difference of the reflectivity factor before and after hardware filtering of each echo point is greater than a preset threshold, and if yes, marking the echo point as a precipitation echo; if not, performing fuzzy logic identification elimination based on characteristic parameters; wherein calculating a non-precipitation echo probability of each echo point according to the characteristic parameter and a precipitation echo probability , determining whether the non-precipitation echo probability is greater than , if yes, marking the echo point as a non-precipitation echo if not, performing non-precipitation weak echo discrimination; wherein By comparing the average of the reflectivity factor of the echo whole layer elevation angle , the average of the correlation coefficient , and the proportion of the number of the effective distance library of the correlation coefficient with the size of the discrimination threshold value to determine whether it is a non-precipitation echo, if so, mark the echo point as a non-precipitation echo; if not, filtering the scatter points and patchy noise around the echo point using the reflectivity factor continuity in the radial and azimuth directions to obtain precipitation echoes.

2. The method of claim 1, wherein, The characteristic parameters include: Reflectance factor median Correlation coefficient median Difference propagation phase shift level texture Reflectance factor level texture Correlation coefficient level texture And depolarization ratio DR.

3. The method of claim 1, wherein, The non-precipitation echo probability of each echo point is calculated according to the characteristic parameter and the precipitation echo probability : determining the membership function of each characteristic parameter according to the probability distribution of non-precipitation echoes and precipitation echoes, and fuzzy processing the characteristic parameters through the membership function.

4. The method of claim 1, wherein, The judging whether greater than If yes, marking the echo point as non-precipitation echo includes: According to different characteristic parameter probability distributions and membership functions, identify and remove ground echoes and clear air echoes.

5. The method of claim 1, wherein, The proportion of the number of valid distance libraries of the correlation coefficient is: the proportion of the distance library whose correlation coefficient on the current PPI surface is valid, and respectively represent the number of current elevation reflectivity positive valid values and the number of correlation coefficient valid values.

6. The method of claim 1, wherein, The mean value of the reflectivity factor of the echo layer elevation angle of each echo point by comparison The mean value of the correlation coefficient And the proportion of the number of effective distance libraries of the correlation coefficient And the size of the discriminant threshold determines whether it is a non-precipitation echo. The comprehensive judgment reflectivity factor mean value The correlation coefficient mean value The correlation coefficient effective distance library number proportion Whether the threshold is satisfied simultaneously.

7. The method of claim 1, wherein, The method further comprises: Identification accuracy of electromagnetic interference echoes Identification accuracy of ground echoes Identification accuracy of clear air echoes Identification accuracy of precipitation echoes and critical success index, CSI, to evaluate weather radar data quality control results. 8.A non-precipitation data quality control system for X-band weather radar, comprising: an acquisition module configured to obtain X-band weather radar data; an electromagnetic interference echo filtering module configured to identify and remove electromagnetic interference echoes according to the continuity characteristics of the electromagnetic interference echoes, the correlation coefficient (CC) and the distribution of the differential reflectivity factor (ZDR) ; an echo judgment module configured to judge whether the difference of the reflectivity factor before and after hardware filtering of each echo point is greater than a preset threshold, and if yes, marking the echo point as a precipitation echo; if not, performing fuzzy logic identification elimination based on characteristic parameters; wherein According to the characteristic parameter, a non-precipitation echo probability of each echo point is calculated and a precipitation echo probability , it is judged whether the non-precipitation echo probability is greater than the precipitation echo probability; if yes, the echo point is marked as a non-precipitation echo; if no, a non-precipitation weak echo discrimination is performed; wherein, By comparing the average of the reflectivity factor of the echo whole layer elevation angle , the average of the correlation coefficient , and the proportion of the number of the effective distance library of the correlation coefficient with the size of the discrimination threshold value to determine whether it is a non-precipitation echo, if so, mark the echo point as a non-precipitation echo; if not, filter out the scattered points and patchy noise around the echo point by using the continuity of the radial and azimuth reflectivity factor to obtain the precipitation echo. 9.An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method according to any one of claims 1-7.

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