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

Abstract

The embodiment of the invention provides an X-band weather radar non-rainfall data quality control method and system. The method is applied to the technical field of weather radars and comprises the following steps: acquiring X-band weather radar data; identifying and eliminating the electromagnetic interference echo according to the continuity characteristic of the electromagnetic interference echo and the distribution condition of the correlation coefficient CC and the differential reflectivity factor ZDR; judging whether the difference value of the reflectivity factors of each echo point before and after hardware filtering is greater than a preset threshold value or not, and if so, marking the echo point as a rainfall echo; if not, fuzzy logic recognition elimination based on characteristic parameters is carried out; wherein the non-precipitation echo probability PNPR and the precipitation echo probability PPR of each echo point are calculated according to the characteristic parameters, whether the PNPR is larger than the PPR or not is judged, and if yes, the echo point is marked as a non-precipitation echo; if not, non-rainfall weak echo discrimination is carried out; whether the echo is a non-rainfall echo is determined by comparing the reflectivity factor mean value # imgabs0 #, the correlation coefficient mean value # imgabs1 # of the elevation angle of the whole layer of the echo and the ratio ValCC of the effective distance library number of the correlation coefficients with a judgment threshold value, and if the echo is the non-rainfall echo, the echo point is marked as the non-rainfall echo; and if not, continuously filtering scatter and plaque noise around the echo point by using the reflectivity factors in the radial direction and the azimuth to obtain the rainfall echo. In this way, the quality of weather radar data can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of weather radars, and in particular to a method and system for quality control of non-precipitation data of an X-band weather radar. Background Art

[0002] At present, the deployment rate of X-band weather radar in China is becoming more and more dense, and its role in short-term warning and disaster prevention and mitigation is becoming more and more obvious. However, the echoes observed by radar will 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 perform quality control on X-band weather radar basic data before application.

[0003] Among non-meteorological echoes, clear sky echoes are mainly caused by the scattering of radar waves by the turbulent atmosphere and the mirror reflection of radar waves by the stratified atmosphere. From the X-band radar observation results, clear sky echoes are generally below 15dBZ and the speed is generally not 0; ground object echoes are mainly caused by the scattering of electromagnetic waves by mountains and various buildings, and the echo intensity is generally strong and the position is relatively fixed. Electromagnetic interference echoes are usually caused by abnormal radar signal processors or the reception of external electromagnetic waves. From the echo morphology, they are usually continuously distributed in a certain radial direction, and have obvious differences in the reflectivity distribution of the adjacent radial direction.

[0004] At present, there are fuzzy logic method, neural network method, filtering method and other methods for identifying and eliminating non-meteorological echoes of X-band radar. Overall, the effect of removing electromagnetic interference with poor continuity is poor, and there is still room for improvement in the recognition rate of non-precipitation echoes. Summary of the invention

[0005] The present invention provides a method and system for quality control of non-precipitation data of an X-band weather radar, which solves the technical problem of how to improve the quality of weather radar data.

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

[0007] Obtain X-band weather radar data;

[0008] According to 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 of each echo point before and after hardware filtering is greater than a preset threshold. If so, mark the echo point as a precipitation echo;

[0010] If not, then perform fuzzy logic identification elimination based on characteristic parameters;

[0011] Calculate the non-precipitation echo probability P of each echo point according to the characteristic parameters NPR and precipitation echo probability P PR , judge P NPR Is it greater than P PR , if yes, then mark the echo point as a non-precipitation echo;

[0012] If not, non-precipitation weak echo identification is performed; among them,

[0013] By comparing the reflectivity factor average of the entire layer of echo elevation Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC The value 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 radial and azimuth reflectivity factors are used to continuously filter out the scattered and patchy noises around the echo point to obtain the precipitation echo.

[0015] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the characteristic parameters include:

[0016] Reflectivity factor median 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] According to the above aspects and any possible implementation, a further implementation is provided, wherein the probability P of a non-precipitation echo at each echo point is calculated according to the characteristic parameters. NPR and precipitation echo probability P PR include:

[0018] According to the probability distribution of non-precipitation echoes and precipitation echoes, the membership function of each characteristic parameter is determined, and the characteristic parameters are fuzzy processed through the membership function.

[0019] According to the above aspects and any possible implementation, further provided is an implementation, wherein the determination P NPR Is it greater than P PR If yes, then the echo point is marked as a non-precipitation echo including:

[0020] According to the probability distribution of different characteristic parameters and membership functions, ground object echoes and clear sky echoes are identified and eliminated.

[0021] According to the above aspects and any possible implementation, a further implementation is provided, wherein the reflectivity factor mean of the entire layer of echo is compared. Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC The size of the discrimination threshold determines whether it is a non-precipitation echo, including:

[0022] Comprehensive judgment of the mean reflectivity factor Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether the discrimination threshold is met at the same time;

[0023] Among them, the percentage of correlation coefficient effective distance libraries is:

[0024]

[0025] Val CC N is the proportion of distance libraries with effective correlation coefficients on the current PPI surface. Z and N CC They respectively represent the number of positive effective values ​​of the current elevation reflectivity and the number of effective values ​​of the correlation coefficient.

[0026] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0027] Using electromagnetic interference echo to identify the accuracy of POD RI , ground object echo recognition accuracy POD AP , Clear sky echo recognition accuracy POD CA , precipitation echo recognition accuracy POD PR And the critical success index CSI is used to evaluate the quality control results of weather radar data.

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

[0029] Acquisition module, used to obtain X-band weather radar data;

[0030] The electromagnetic interference echo filtering module is used to identify and remove the electromagnetic interference echo according to the continuity characteristics of the electromagnetic interference echo, the distribution of the correlation coefficient CC and the differential reflectivity factor ZDR;

[0031] The echo judgment module is used to judge whether the difference of the reflectivity factor of each echo point before and after the hardware filtering is greater than the preset threshold. If so, the echo point is marked as a precipitation echo; if not, fuzzy logic recognition and elimination based on characteristic parameters are performed; wherein,

[0032] Calculate the non-precipitation echo probability P of each echo point according to the characteristic parameters NPR and precipitation echo probability P PR , judge P NPR Is it greater than P PR If yes, then the echo point is marked as a non-precipitation echo; if no, then a non-precipitation weak echo is identified; where,

[0033] By comparing the reflectivity factor average of the entire layer of echo elevation Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether it is a non-precipitation echo is determined by the size of the discrimination threshold. If so, the echo point is marked as a non-precipitation echo; if not, the scattered points and patchy noise around the echo point are continuously filtered out using the radial and azimuth reflectivity factors to obtain the precipitation echo.

[0034] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0035] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect and / or the second aspect of the present disclosure is implemented.

[0036] Using X-band weather radar data, and according to the continuity characteristics of electromagnetic interference echoes, the distribution of correlation coefficient CC and differential reflectivity factor ZDR, the electromagnetic interference echoes are identified and eliminated; it is judged whether the difference of reflectivity factor before and after hardware filtering of each echo point is greater than the preset threshold. If so, the echo point is marked as precipitation echo; if not, the fuzzy logic identification and elimination of characteristic parameters are performed; and the residual weak non-precipitation echoes are eliminated by comprehensive judgment based on the mean value of reflectivity factor, the mean value of correlation coefficient and the proportion of correlation coefficient effective distance library; finally, the continuity of reflectivity factor in radial and azimuth is used to filter out scattered points and patchy noise to obtain precipitation echoes.

[0037] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

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

[0040] Figure 2 The electromagnetic interference echo distribution diagram of the Fangshan radar at different elevation angles according to an embodiment of the present disclosure is shown;

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

[0042] Figure 4 A parameter distribution diagram corresponding to the electromagnetic interference echo and precipitation echo of the Beijing X-band weather radar according to an embodiment of the present disclosure is shown;

[0043] Figure 5 The diagram shows the distribution of the difference of the reflectivity factor of the mixed area precipitation echo and the distribution diagram of the correlation coefficient corresponding to the difference according to the embodiment of the present disclosure;

[0044] Figure 6 shows a probability distribution diagram of each characteristic parameter of ground object echo, clear sky echo and precipitation echo according to an embodiment of the present disclosure;

[0045] Figure 7 The figure shows the membership function diagram of each characteristic parameter of the ground object echo, the clear sky echo and the precipitation echo according to the embodiment of the present disclosure;

[0046] Figure 8 shows a statistical diagram of characteristic parameters of non-precipitation echoes and snowfall echoes according to an embodiment of the present disclosure;

[0047] Fig. 9 A comparison diagram of quality control effects of electromagnetic interference echoes according to an embodiment of the present disclosure is shown;

[0048] Fig.10 A comparison diagram of the echo quality control effect in a mixed area containing ground objects and precipitation according to an embodiment of the present disclosure is shown;

[0049] Fig.11 A comparison diagram of the quality control effects of ground object-containing and clear sky echoes according to an embodiment of the present disclosure is shown;

[0050] Fig.12 A comparison diagram of quality control effects including weak non-precipitation echoes according to an embodiment of the present disclosure is shown;

[0051] Fig.13 A comparison diagram of quality control effects containing scattered point and patchy clutter according to an embodiment of the present disclosure is shown;

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

[0053] Fig.15 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0055] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0056] In the present disclosure, X-band weather radar data is used, and according to 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 of reflectivity factor before and after hardware filtering of each echo point is greater than a preset threshold, if so, the echo point is marked as a precipitation echo; if not, fuzzy logic identification and elimination of characteristic parameters are performed; and residual weak non-precipitation echoes are eliminated by comprehensive judgment based on the mean value of reflectivity factor, the mean value of correlation coefficient and the proportion of correlation coefficient effective distance library; finally, scattered points and patchy noises are filtered out by using the continuity of reflectivity factor in radial and azimuth directions to obtain precipitation echoes. In this way, the quality of weather radar data can be improved.

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

[0058] In one embodiment, X-band weather radar data is obtained; electromagnetic interference echoes are identified and eliminated according to the continuity characteristics of electromagnetic interference echoes, the distribution of correlation coefficient CC and differential reflectivity factor ZDR; it is determined whether the reflectivity factor difference before and after hardware filtering of each echo point is greater than a preset threshold, and if so, the echo point is marked as a precipitation echo; if not, fuzzy logic identification and elimination of characteristic parameters are performed; wherein the non-precipitation echo probability P of each echo point is calculated according to the characteristic parameters. NPR and precipitation echo probability P PR , judge P NPR Is it greater than P PR If yes, the echo point is marked as a non-precipitation echo; if no, a non-precipitation weak echo is identified; wherein, the reflectivity factor average of the entire layer elevation angle of each echo point is compared. Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether it is a non-precipitation echo is determined by the size of the discrimination threshold. If so, the echo point is marked as a non-precipitation echo; if not, the scattered points and patchy noise around the echo point are continuously filtered out using the radial and azimuth reflectivity factors to obtain the precipitation echo.

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

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

[0061] Specifically, the basic data of 9 X-band weather radars in Fangshan, Tongzhou, Miyun and other places from February 2023 to August 2024 are used to establish a database containing ground objects, clear sky and precipitation echoes to statistically analyze the differences in characteristic parameters of different echoes. The volume scanning data of each elevation layer are manually determined, among which the ground objects and clear sky echoes mainly select the observation data of sunny weather and use 15DBz as the distinction. The precipitation echo generally has a large echo range or an irregular block structure. According to manual experience, a total of 200 electromagnetic interference echo PPI data, 350 clear sky echo PPI data, 400 ground object echo PPI data, and 320 precipitation echo PPI data are collected. The collected electromagnetic interference echo, ground object echo, clear sky echo and precipitation echo basic data files are randomly divided into statistical database and verification database, where the database takes one azimuth-distance library as one unit. The statistical database and verification database are shown in Table 1. Each database contains clear sky echoes, ground object echoes and precipitation echoes, which are used to count the differences in different echo characteristics and verify the recognition effect of the algorithm. The collected ground object echoes generally have a strong reflectivity, their position does not change with time, and the echo top height is low; the clear sky echo has a low reflectivity, extending from the radar to the distance, and the coverage area is relatively uniform; precipitation echoes include stratiform cloud precipitation and convective cloud precipitation. The former is mostly blocky in structure and has a strong echo center intensity, while the latter is mostly continuous in pieces, with lower intensity than the former and more uniform distribution.

[0062]

[0063] Table 1

[0064] S102, identifying and eliminating electromagnetic interference echoes according to the continuity characteristics of the electromagnetic interference echoes, the distribution of the correlation coefficient CC and the differential reflectivity factor ZDR.

[0065] In some embodiments, electromagnetic interference echoes are usually caused by an abnormal radar signal processor or the reception of external electromagnetic waves. From the observation results of X-band radar in Beijing, electromagnetic interference can be mainly divided into the following two categories:

[0066] (1) The first type of electromagnetic interference echo, Figure 2 The electromagnetic interference echo distribution diagram of the Fangshan radar at different elevation angles according to the embodiment of the present disclosure is shown, where (a) is the reflectivity factor at an elevation angle of 0.5°, and (b) is the reflectivity factor at an elevation angle of 1.5°. Figure 2 It can be seen that the first type of electromagnetic interference echo usually has good continuity in the current radial direction, poor continuity in the upper or lower layers, and the reflectivity is distributed in 5-60dBz. According to these characteristics, the filtering method is:

[0067]

[0068] In the formula, R validis the ratio of the effective distance library of reflectivity factor in the radial direction of the current elevation angle to the total distance library, N is the ratio of the effective distance bins of reflectivity factor in the same radial direction of the adjacent upper or lower layer elevation angle to the total distance bins. R is the total number of distance bins at a certain azimuth angle at this elevation angle, Z j is the reflectivity factor of a distance library at this azimuth, and Val represents the effective value. It should be noted that when filtering, the effective distance library ratio of the reflectivity factor of the current elevation angle will be compared with the effective distance library ratio of the reflectivity factor of the adjacent upper elevation angle or lower elevation angle. If one of them meets formula (1), it is identified as electromagnetic interference echo rejection.

[0069] (2) The second type of electromagnetic interference echo, Figure 3 The figure shows the distribution diagram of electromagnetic interference echo at an elevation angle of 7.4° of the Yanqing radar according to an embodiment of the present disclosure, where (a) is the reflectivity factor at an elevation angle of 7.4°, and (b) is the correlation coefficient at an elevation angle of 7.4°. Figure 3 It can be seen that the second type of electromagnetic interference echo is usually intermittent in the radial direction and the interfered area is large. Through a large amount of observation data, it is found that, unlike the first type of electromagnetic interference echo, this type of echo has a larger ZDR, mainly distributed in the range of -8 to -7dB and 7 to 8dB, and the CC is distributed above 1, such as Figure 4 As shown. 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 formula (4):

[0070]

[0071]

[0072] Where ZDR valid is the average absolute value of ZDR on the current radial direction at this elevation angle, is the average of CCs on the current radial direction at this elevation angle, N ZDR N is the number of effective ZDR distance libraries in the current radial direction. CC N is the number of CC effective distance libraries in the current radial direction. R is the total number of range bins at a certain azimuth angle at the elevation angle, ZDR j is the ZDR of a certain distance library on the radial direction, CC j is the CC of a certain distance library on the radial direction.

[0073] S103, determining whether the difference in reflectivity factor of each echo point before and after hardware filtering is greater than a preset threshold, if so, marking the echo point as a precipitation echo; if not, performing fuzzy logic identification and elimination based on characteristic parameters.

[0074] In some embodiments, in the area where ground objects and precipitation echoes are mixed, although the precipitation echoes are retained after hardware filtering, the correlation coefficient CC is low due to the influence of surrounding ground objects, which is easy to confuse in subsequent identification. Therefore, the reflectivity factor Z before and after hardware filtering is compared. T (Unit: dBZ) and Z H (Unit: dBZ) difference, found the mixed zone (Z T -Z H ) is larger, and Z H Generally greater than 15dBz, and the ground echo varies between valid and invalid values ​​after filtering, depending on the intensity of the reflectivity factor before filtering. Precipitation echoes usually appear in pieces, Z H , CC has good consistency with the surrounding echo points, so it can be identified based on this feature.

[0075] Figure 5 The distribution of the difference in reflectivity factor of precipitation echoes in the mixed zone and the distribution of the correlation coefficient corresponding to the difference according to the embodiment of the present disclosure are shown. The statistical results for the mixed zone show that the echo points with a difference of 0-15dBz are the most, and the CC value is mostly higher than 0.9; when the difference exceeds 15dBz, the CC value decreases rapidly and is evenly distributed between 0 and 1. Therefore, setting (Z T -Z H )>15dBz is the discrimination threshold to retain the precipitation echo.

[0076] In some embodiments, for simultaneously satisfying (Z T -Z H )>15dBz、Z H >15dBz and CC<0.9, take the echo point as the center, set a window along the radial direction and count Z H If the ratio is greater than the threshold at the same time, the point is marked as a precipitation echo. The ratio is expressed as follows:

[0077] P DBZ >0.7∩P CC >0.2 (9)

[0078] Where P DBZ Z represents the echo point in the window H >15dBz, P CC It indicates the proportion of echo points with CC>0.8 in the window. Considering the continuity of precipitation echo, the radial window is set to 0.825km.

[0079] S104, calculating the non-precipitation echo probability P of each echo point according to the characteristic parameters NPR and precipitation echo probability P PR , judge P NPR Is it greater than PPR If yes, the echo point is marked as a non-precipitation echo; if no, a non-precipitation weak echo is identified.

[0080] In some embodiments, based on the analysis of clear sky, ground object echoes and precipitation echoes, it is found that the different characteristics of clear sky, ground object echoes and precipitation echoes are: clear sky echoes generally extend from the vicinity of the radar to the far distance, and the echo intensity and echo area are relatively uniform and have a certain continuity; the ground object echo has a clear edge, a fixed position that does not change with time, and a radial velocity close to zero. H Generally lower than 20dBz, while ground object echo is between 20-60dBz; ground object echo differential propagation phase shift (Unit: °) and ZDR fluctuations are relatively large, and the texture eigenvalues ​​are relatively large. The application of these dual polarization parameters can better distinguish the two echoes.

[0081] Specifically, from the reflectivity factor (Z H ), correlation coefficient (CC), differential propagation phase shift ( ) to extract 6 characteristic parameters that can reflect the difference between precipitation echo and clear sky and ground echo, including: reflectivity factor median M DBZ , median correlation coefficient M CC , differential propagation phase shift standard deviation T φDP , reflectivity factor standard deviation T DBZ and the standard deviation of the correlation coefficient T CC , defined as in formula (10); DR is the depolarization ratio on the current distance bin, indicating that the precipitation particles are uniform in shape (high CC) and nearly spherical (linear unit Z DR =1), defined as formula (11).

[0082]

[0083] In formula (10), T X is the standard deviation of the X feature parameter in the current radial direction, n represents the window size during radial processing, and X j is the value after median filtering of the j-th distance library on the current radial direction, Z k is the value of the characteristic parameter within the j-th distance library processing window on the current radial direction. In formula (11), Z DR is the differential reflectivity on a linear scale, DR is the depolarization ratio, and the calculated value is distributed between 0 and 1. The DR value of meteorological targets is generally small.

[0084] In some embodiments, characteristic parameters of ground object echo, clear sky echo and precipitation echo are statistically analyzed based on the established statistical database. Figure 6 Probability distribution diagrams of characteristic parameters of ground object echo, clear sky echo and precipitation echo according to an embodiment of the present disclosure are shown.

[0085] In some embodiments, the membership function of each characteristic parameter is determined according to the probability distribution of non-precipitation echoes and precipitation echoes, and the characteristic parameters are fuzzy processed through the membership function to obtain a 0-1 value criterion for each characteristic parameter for different types of echoes, and the criterion values ​​are weighted and accumulated. If the value exceeds a preset threshold, it is judged as a non-precipitation echo and is eliminated.

[0086] Specifically, according to the characteristic parameter M CC 、M DBZ , T CC , T DBZ , T φDP , DR probability distribution, the membership function of each parameter can be determined, where the membership functions used include trapezoidal membership function (including standard trapezoidal membership function and trapezoidal membership function combined with sigmoid function) and triangular membership function, where, Figure 7 The figure shows the membership function diagram of each characteristic parameter of the ground object echo, the clear sky echo and the precipitation echo according to the embodiment of the present disclosure.

[0087] S105, by comparing the reflectivity factor mean of the entire layer elevation of the echo Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether it is a non-precipitation echo is determined by the size of the discrimination threshold. If so, the echo point is marked as a non-precipitation echo; if not, the scattered points and patchy noise around the echo point are continuously filtered out using the radial and azimuth reflectivity factors to obtain the precipitation echo.

[0088] In some embodiments, after filtering out non-precipitation echoes by the above method, for clear sky and ground object echoes with missing dual polarization quantities, some echo points will be difficult to distinguish. By analyzing the data, it is found that precipitation echoes usually have a reflectivity greater than 20dBz and a large correlation coefficient, while after the non-precipitation echoes are processed by the above method, only echo points with sporadic missing data will remain, and the reflectivity is small and the correlation coefficient is missing more. Therefore, the residual non-precipitation echoes can be effectively eliminated according to the reflectivity and correlation coefficient. At the same time, considering that the reflectivity of snowfall echoes in winter is also small, the parameter distribution of the remaining non-precipitation echoes and snowfall echoes is statistically analyzed. Figure 8 FIG. 4 shows a statistical diagram of characteristic parameters of non-precipitation echoes and snowfall echoes according to an embodiment of the present disclosure, wherein Figure 8 It can be seen that the average reflectivity of the remaining non-precipitation echo is distributed below 10dBz, mainly distributed in 2-8dBz, and the average correlation coefficient is lower than 0.9; the average reflectivity of the snowfall echo is distributed above 10dBz, mainly distributed in 11-16dBz, and the average correlation coefficient is higher than 0.95.

[0089] In some embodiments, the reflectivity factor mean is comprehensively determined. Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether the discrimination threshold is met at the same time.

[0090] Specifically, through the above analysis, the PPI surface after identification is judged. There are two judgment conditions: (1) Calculate the mean value of the reflectivity factor of the entire PPI surface with a positive effective value. Less than 7.5 mean correlation coefficient When it is less than 0.83, the entire plane is judged to be a non-precipitation echo and is removed; (2) For some points with low reflectivity, where the correlation coefficient and differential propagation phase shift are invalid values, when the mean of the entire PPI surface is Less than 7.5 and CC effective value Val 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 of is as shown in equations (12)-(18).

[0091]

[0092] In the formula, and are the average values ​​of the reflectivity factor and correlation coefficient with positive values ​​on the current PPI surface, Z j (Unit: dBz) is the reflectivity factor at a certain distance library value in azimuth that is positive, CC j is the correlation coefficient of a certain distance library on the azimuth; Val CC is the proportion of distance libraries with valid correlation coefficients on the current PPI surface, +Val (unit: dBz) is the valid value with a positive value, N Z and N CC They respectively represent the number of positive effective values ​​of the current elevation reflectivity and the number of effective values ​​of the correlation coefficient.

[0093] In some embodiments, after being processed by the above method, there will be scattered points, patchy noise echoes or non-precipitation echoes. The characteristics of such echoes are strong isolation, poor continuity in radial and azimuth, and sporadic distribution. Therefore, when removing them, the distance library is taken as the center, and the effective value ratio in the window is calculated. If the effective value ratio is less than 30%, the distance library is filtered out as a noise echo, and the window size is set to 5°×0.675km.

[0094] In some embodiments, the electromagnetic interference echo recognition accuracy POD is used RI , ground object echo recognition accuracy POD AP , Clear sky echo recognition accuracy PODCA , precipitation echo recognition accuracy POD PR And 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] Recognition rate (POD) = number of echo accurate recognition points / total number of echo samples * 100% (19)

[0097] Table 2 shows the statistics of the algorithm's weather radar data quality control effect on ground objects, clear sky and precipitation echoes. According to the evaluation results in Table 2, the algorithm has good quality control effects on weather radar data for ground objects, clear sky and precipitation echoes, and the CSI score can reach above 0.88. The recognition accuracy of precipitation echoes can reach 0.982, and the recognition rates of ground object echoes, clear sky echoes and electromagnetic interference are 0.984, 0.977 and 0.935 respectively, indicating that the algorithm can better retain precipitation echoes and effectively eliminate non-precipitation echoes.

[0098]

[0099] Table 2

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

[0101] Specifically, Fig. 9 A comparison diagram of the quality control effect of the electromagnetic interference echo according to an embodiment of the present disclosure is shown. Fig. 9 (a) is the echo distribution around the Fangshan radar at 13:36 on March 24, 2024. There is relatively dense electromagnetic interference around the station, and there is a relatively obvious block precipitation echo at the 270° azimuth. After being processed by the method disclosed in the present invention, Fig.10 (b), the electromagnetic interference in a large area is effectively removed and the precipitation echo is effectively retained.

[0102] Specifically, Fig.10 A comparison diagram of the echo quality control effect in a mixed area containing ground objects and precipitation according to an embodiment of the present disclosure is shown. Fig.10 (a) is the echo distribution of the radar observation at 0.5° elevation angle at Miyun Station at 17:39 on August 9, 2024. Fig.10 (b) Correlation coefficient distribution. It can be seen that the red circle has a small correlation coefficient of precipitation echo after hardware filtering due to the overlap of ground objects and precipitation echo before filtering. When the mixed area is not judged, after quality control, Fig.10As shown in (c), some precipitation echoes are mistakenly removed, while after adding the mixed area discrimination, Fig.10 As shown in (d), the precipitation echo in this area is effectively preserved and the echo morphology is relatively complete.

[0103] Specifically, Fig.11 A comparison diagram of the quality control effects of ground object and clear sky echo according to an embodiment of the present disclosure is shown. Fig.11 (a) is the echo distribution around the Miyun radar at 14:03 on July 29, 2023. There are ground objects and clear sky echoes around the station. After being processed by the disclosed method, Fig.12 (b), non-precipitation echoes near the station are effectively eliminated.

[0104] Specifically, Fig.12 A comparison diagram of the quality control effect of weak non-precipitation echoes according to an embodiment of the present disclosure is shown. Fig.12 (a) is the echo distribution around the Fangshan radar at 10:18 on May 10, 2023. The correlation coefficient distribution before quality control is shown in Fig.12 (b) It can be seen from the figure that the correlation coefficient is partially missing, resulting in the remaining clear sky echo after processing by the disclosed method. Fig.12 (c), after adding the non-precipitation weak echo quality control algorithm, the clear sky echo is completely eliminated, such as Fig.12 (d).

[0105] Specifically, Fig.13 A comparison diagram of the quality control effect of scattered point and patchy clutter according to an embodiment of the present disclosure is shown. Fig.13 This is a comparison chart of the reflectivity factors before and after filtering out scattered points and patchy clutter around the Changping station radar at 10:27 on July 7, 2024. From the figure, it can be seen that the noise points and patchy clutter with strong or weak reflectivity within the radar detection range are well removed.

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

[0107] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0108] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0109] Fig.14FIG. 1 is a block diagram of an X-band weather radar non-precipitation data quality control system 140 according to an embodiment of the present disclosure. Fig.14 As shown, the apparatus 1400 includes:

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

[0111] The electromagnetic interference echo filtering module 142 is used to identify and remove the electromagnetic interference echo according to the continuity characteristics of the electromagnetic interference echo, the distribution of the correlation coefficient CC and the differential reflectivity factor ZDR;

[0112] The echo judgment module 143 is used to judge whether the difference of the reflectivity factor of each echo point before and after the hardware filtering is greater than a preset threshold. If so, the echo point is marked as a precipitation echo; if not, fuzzy logic recognition and elimination based on characteristic parameters is performed; wherein,

[0113] Calculate the non-precipitation echo probability P of each echo point according to the characteristic parameters NPR and precipitation echo probability P PR , judge P NPR Is it greater than P PR If yes, then the echo point is marked as a non-precipitation echo; if no, then a non-precipitation weak echo is identified; where,

[0114] By comparing the reflectivity factor average of the entire layer of echo elevation Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether it is a non-precipitation echo is determined by the size of the discrimination threshold. If so, the echo point is marked as a non-precipitation echo; if not, the scattered points and patchy noise around the echo point are continuously filtered out using the radial and azimuth reflectivity factors to obtain the precipitation echo.

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

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

[0117] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0118] Fig.15A schematic block diagram of an electronic device 150 that can be used to implement an embodiment 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 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

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

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

[0121] The computing unit 151 may be a variety of general and / or special 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 dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 151 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a 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 the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 151 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0122] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0124] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0126] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0127] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

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

[0129] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for quality control of non-precipitation data of X-band weather radar, comprising: Obtain X-band weather radar data; According to 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; Determine whether the difference in reflectivity factor of each echo point before and after hardware filtering is greater than a preset threshold. If so, mark the echo point as a precipitation echo; If not, then perform fuzzy logic identification elimination based on characteristic parameters; Calculate the non-precipitation echo probability P of each echo point according to the characteristic parameters NRP and precipitation echo probability P PR , judge P NPR Is it greater than P PR , if yes, then mark the echo point as a non-precipitation echo; If not, non-precipitation weak echo identification is performed; among them, By comparing the reflectivity factor average of the entire layer of echo elevation Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC The value of the discrimination threshold determines whether it is a non-precipitation echo. If so, the echo point is marked as a non-precipitation echo. If not, the radial and azimuth reflectivity factors are used to continuously filter out the scattered and patchy noises around the echo point to obtain the precipitation echo.

2. The method according to claim 1, characterized in that The characteristic parameters include: Reflectivity factor median 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.

3. The method according to claim 1, characterized in that The non-precipitation echo probability P of each echo point is calculated according to the characteristic parameters NPR and precipitation echo probability P PR include: According to the probability distribution of non-precipitation echoes and precipitation echoes, the membership function of each characteristic parameter is determined, and the characteristic parameters are fuzzy processed through the membership function.

4. The method according to claim 1, characterized in that: The judgment P NPR Is it greater than P PR If yes, then the echo point is marked as a non-precipitation echo including: According to the probability distribution of different characteristic parameters and membership functions, ground object echoes and clear sky echoes are identified and eliminated.

5. The method according to claim 1, characterized in that The percentage of the correlation coefficient effective distance library is: Val CC N is the proportion of distance libraries with effective correlation coefficients on the current PPI surface. Z and N CC They respectively represent the number of positive effective values ​​of the current elevation reflectivity and the number of effective values ​​of the correlation coefficient.

6. The method according to claim 1, characterized in that By comparing the reflectivity factor average of the entire layer elevation angle of each echo point Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC The size of the discrimination threshold determines whether it is a non-precipitation echo, including: Comprehensive judgment of the mean reflectivity factor Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether the discrimination threshold is met at the same time.

7. The method according to claim 1, characterized in that The method further comprises: Using electromagnetic interference echo to identify the accuracy of POD RI , ground object echo recognition accuracy POD AP , Clear sky echo recognition accuracy POD CA , precipitation echo recognition accuracy POD PR And the critical success index CSI is used to evaluate the quality control results of weather radar data.

8. An X-band weather radar non-precipitation data quality control system, comprising: Acquisition module, used to obtain X-band weather radar data; The electromagnetic interference echo filtering module is used to identify and remove the electromagnetic interference echo according to the continuity characteristics of the electromagnetic interference echo, the distribution of the correlation coefficient CC and the differential reflectivity factor ZDR; The echo judgment module is used to judge whether the difference of the reflectivity factor of each echo point before and after the hardware filtering is greater than the preset threshold. If so, the echo point is marked as a precipitation echo; if not, fuzzy logic recognition and elimination based on characteristic parameters are performed; wherein, Calculate the non-precipitation echo probability P of each echo point according to the characteristic parameters NRP and precipitation echo probability P PR , judge P NPR Is it greater than P PR If yes, then the echo point is marked as a non-precipitation echo; if no, then a non-precipitation weak echo is identified; where, By comparing the reflectivity factor average of the entire layer of echo elevation Mean correlation coefficient And the correlation coefficient effective distance library ratio Val CC Whether it is a non-precipitation echo is determined by the size of the discrimination threshold. If so, the echo point is marked as a non-precipitation echo; if not, the scattered points and patchy noise around the echo point are continuously filtered out using the radial and azimuth reflectivity factors to obtain the precipitation echo.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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