A real-time evaluation method and system for dual-polarization weather radar data quality

Abstract

The present invention provides a real-time data quality assessment method for dual-polarization weather radars, comprising: S1. Obtaining a base data file for a dual-polarization weather radar; if the base data file is VOL data, executing S2 to S5 below; if the base data file is PPI data, executing S2 to S3 and S5 below; S2. Performing echo identification; S3. If the base data file is a meteorological echo, calculating a system deviation factor of differential reflectivity, a reflectivity sensitivity factor, a system deviation factor of horizontal reflectivity, and a phase correlation quality factor; S4. If the base data file is a meteorological echo, calculating an over-suppression factor for ground clutter; S5. If the base data file is not a meteorological echo, calculating an under-suppression factor for ground clutter; and outputting each calculation result as an assessment result. The present invention assesses the data quality of base data obtained by the radar in real time from multiple perspectives, utilizing the inherent relationships between parameters, and thus providing more accurate assessment results.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time assessment of weather radar data quality, and in particular to a method and system for real-time assessment of dual-polarization weather radar data quality. Background Art

[0002] Weather radar is a crucial piece of equipment for precipitation observation and plays a vital role. Radar boasts precise and reliable meteorological target detection, monitoring, and early warning capabilities, making it essential for minimizing losses and ensuring flight safety. Currently, single-polarization Doppler weather radars, widely used in China, can detect information such as the intensity, radial velocity, and spectral width of precipitation systems, but are unable to further analyze the shape and phase of precipitation particles. Beginning in 2019, dual-polarization radar deployment has been gradually expanding in key areas along China's eastern and southeastern coasts. These radars are expected to play a significant role in future operational applications.

[0003] While dual-polarization radars can detect more parameters, they also introduce greater uncertainty into the radar system. Dual-polarization weather radars have more stringent performance requirements than single-polarization weather radars. Furthermore, radar performance can fluctuate over time due to radar device aging, internal system noise, and external noise. Therefore, real-time assessment of radar data quality is necessary, providing both qualitative and quantitative evaluations to inform radar data applications and reduce misjudgments and missed detections in specific scenarios.

[0004] Currently, there is a weather radar intensity data quality test method, which includes: corresponding the beam broadening factor to the radar beam broadening quality index Frange; corresponding the beam broadening factor to the radar beam broadening quality index Fshield; corresponding the electromagnetic wave attenuation factor to the radar electromagnetic wave attenuation quality index Fatt; corresponding the vertical profile unevenness factor to the radar vertical profile unevenness quality index Fvpr; and weightedly summing the radar beam broadening quality index Frange, the radar beam blocking quality index Fshield, the radar electromagnetic wave attenuation quality index Fatt, and the radar vertical profile unevenness quality index Fvpr according to corresponding weight coefficients to obtain the radar average quality index FZ.

[0005] However, this method has the following problems:

[0006] 1. Only the radar intensity parameter data was quality assessed, but not the radar phase parameter data;

[0007] 2. The inherent constraints between radar parameters are not utilized.

[0008] Therefore, it is necessary to provide a real-time evaluation method and system for dual-polarization weather radar data quality. Summary of the Invention

[0009] The present invention provides a method and system for real-time evaluation of dual-polarization weather radar data quality, which utilizes the intrinsic relationship between parameters from multiple angles to evaluate radar data quality in real time, and the evaluation results are more accurate.

[0010] An embodiment of the present invention discloses a method for real-time assessment of dual-polarization weather radar data quality, comprising:

[0011] Step S1. Obtain the base data file of the dual-polarization weather radar and determine the radar data type;

[0012] If the base data file is VOL data, then execute the following steps S2 to S5;

[0013] If the base data file is a single-layer PPI data, then execute the following steps S2 to S3 and S5;

[0014] Step S2. performing echo recognition on the base data file to determine whether the echo of the base data file is a weather echo;

[0015] Step S3. If the echo in the base data file is a weather echo, then calculate the system deviation factor of the differential reflectivity, the reflectivity sensitivity factor, the system deviation factor of the horizontal reflectivity, and the phase correlation quality factor, and output the calculation results;

[0016] Step S4. If the echo in the base data file is a weather echo, calculate the ground clutter suppression excess factor and output the calculation result;

[0017] Step S5. If the echo of the base data file does not belong to a weather echo, then calculate the ground clutter suppression deficiency factor and output the calculation result;

[0018] Here, each calculation result is output as an evaluation result.

[0019] In some embodiments, in step S2, the method for performing echo identification includes:

[0020] Extract the horizontal reflectivity Z of the volume scan base data of the base data file h , differential reflectivity Z dr , radial velocity V, differential phase shift Φ dp and the mutual correlation coefficient ρ hv , and calculate the differential reflectivity Z dr The standard deviation of the differential phase shift Φ dp The standard deviation and cross-correlation coefficient ρ hv The standard deviation of the differential reflectivity Z dr The standard deviation of the differential phase shift Φdp The standard deviation and cross-correlation coefficient ρ hv The standard deviation is calculated based on the data collected along the radial direction with three optional range distance library windows of 3x3, 5x5, and 7x7;

[0021] Differential reflectivity Z dr The standard deviation std dev(Z dr ), differential phase shift Φ dp The standard deviation std dev (Φ dp ) and the mutual correlation coefficient ρ hv The standard deviation std dev (ρ hv ) is calculated as follows:

[0022]

[0023] Among them, N A and N R Defined as the calculation range in distance and azimuth directions, and In N A ×N R After eliminating invalid data within the range library, the mean differential phase shift, mean differential reflectivity and mean cross-correlation coefficient within the range library are calculated.

[0024] In some embodiments, in step S2, the method for performing echo identification further includes:

[0025] Based on the base data file, the data is processed by fuzzy logic to calculate the differential reflectivity Z on each distance library window in the base data. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The mean of the distance library window is calculated based on the mean value. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The standard deviation of the data Z on each distance library window is then h , ρ hv 、V、std dev(Φ dp )、std dev(Z dr )、std dev(ρ hv ) to perform weighted averaging.

[0026] In some embodiments, the calculation process of the system deviation factor of the differential reflectivity includes:

[0027] Statistics ρ hv Greater than 0.97 and 5≤Z h Z for ≤20 samplesdr and Z dr , and make a scatter plot, and fit a straight line at the same time, take the straight line Z h =Z at 10dBZ dr The value is used as the system deviation factor Zdrbias of the differential reflectivity.

[0028] In some embodiments, the calculation process of the reflectivity sensitivity factor includes:

[0029] Calculate the minimum measurable Zmin value of the dual-polarization weather radar, count the minimum reflectivity values in all radial distance libraries at every 20km, subtract them from the nominal values, and calculate the average difference, which is used as the reflectivity sensitivity factor ZhSensivitybias.

[0030] In some embodiments, the calculation process of the system deviation factor of the horizontal reflectivity includes:

[0031] Using Z h and Z dr K reconstructed through consistency relations dpn Compared with the K actually measured by radar dpm Comparison is used to determine the system deviation factor Zhbias of the horizontal reflectivity. The specific formula that needs to be satisfied is as follows:

[0032]

[0033] And select 15<Z h <50, K dp >0,ρ hv >0.95, calculate the results of all distance libraries that meet the conditions and find the average to obtain the system deviation factor Zhbias of the horizontal reflectivity.

[0034] In some embodiments, the calculation process of the phase correlation quality factor includes:

[0035] Calculate the zero-lag correlation coefficient ρ for 9 consecutive distance bins hv Greater than T p And the differential phase standard deviation is less than T σ1 The average value of the differential phase of the distance library segment is used as the initial differential phase of the radial direction, and then the average differential phase of all valid radial directions at each elevation angle is calculated as the initial differential phase.

[0036] In some embodiments, the process of calculating the ground clutter suppression transition factor includes:

[0037] After echo identification is performed on the base data file, the number of meteorological echoes in the near-field distance library of the low-level volume scan file is counted, and the number of meteorological echoes in the near-field distance library of the high-level volume scan file is counted. It is determined whether the difference between the number of meteorological echoes in the high-level volume scan file and the number of meteorological echoes in the low-level volume scan file exceeds a threshold. If so, excessive ground clutter suppression exists in the base data file; if not, excessive ground clutter suppression does not exist in the base data file.

[0038] In some embodiments, the process of calculating the ground clutter suppression deficiency factor includes:

[0039] After performing echo identification on the base data file, the number of non-meteorological echoes in each distance library at the bottom elevation angle is counted, and it is determined whether the number of non-meteorological echoes exceeds a threshold. If so, the base data file has insufficient ground clutter suppression; if not, the base data file does not have insufficient ground clutter suppression.

[0040] Another aspect of the present invention discloses a real-time assessment system for dual-polarization weather radar data quality, comprising:

[0041] The data reading module is used to obtain the base data file of the dual-polarization weather radar and determine the radar data type;

[0042] an echo recognition module, configured to perform echo recognition on the base data file and determine whether the echo in the base data file is a weather echo;

[0043] The data processing module is used to calculate the system deviation factor of the differential reflectivity, the reflectivity sensitivity factor, the system deviation factor of the horizontal reflectivity, the phase correlation quality factor, and the ground clutter suppression excess factor, as well as to determine the ground clutter suppression deficiency factor;

[0044] The result output module is used to output the calculation results and judgment results as evaluation results.

[0045] In summary, the present invention has at least the following beneficial effects:

[0046] The present invention evaluates the quality of basic data obtained by radar from multiple perspectives and utilizes the intrinsic connections between parameters to evaluate the data quality in real time. The evaluation results are more accurate and directly presented in qualitative and quantitative forms. This not only helps researchers engaged in radar data applications to quickly understand the quality of radar data, but also helps radar manufacturers improve radars and enhance radar application effects. It also provides a reference for the application of radar data and reduces the occurrence of misjudgments or missed judgments in application scenarios. The present invention has certain applicability and can evaluate radar data of most systems and different models. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 The figure is a flow chart of the method for real-time assessment of dual-polarization weather radar data quality involved in the present invention.

[0049] Figure 2 Schematic diagram of the application of the real-time assessment system for dual-polarization weather radar data quality involved in the present invention.

[0050] Figure 3 Z in the present invention h Schematic diagram of the membership function.

[0051] Figure 4 is the ρ involved in the present invention hv Schematic diagram of the membership function.

[0052] Figure 5 Schematic diagram of the membership function of V involved in the present invention.

[0053] Figure 6 is the std dev(Φ dp ) is a diagram of the membership function.

[0054] Figure 7 is the std dev(Z dr ) is a diagram of the membership function.

[0055] Figure 8 is the std dev (ρ hv ) is a diagram of the membership function.

[0056] Figure 9 It is a schematic diagram of a processing flow chart of echo recognition involved in the present invention.

[0057] Figure 10 Schematic diagram of a flow chart for calculating the system deviation factor of differential reflectivity involved in the present invention.

[0058] Figure 11 Schematic diagram of a flow chart for calculating the reflectivity sensitivity factor involved in the present invention.

[0059] Figure 12 Schematic diagram of a flow chart for calculating the system deviation factor of horizontal reflectivity involved in the present invention.

[0060] Figure 13 Schematic diagram of a flow chart for calculating the initial phase factor involved in the present invention.

[0061] Figure 14 Schematic diagram of a flow chart for calculating the phase folding factor involved in the present invention.

[0062] Figure 15 Schematic diagram of a flow chart for calculating the phase noise factor involved in the present invention.

[0063] Figure 16 Schematic diagram of a flow chart for calculating the ground clutter suppression transition factor involved in the present invention.

[0064] Figure 17 Schematic diagram of a flow chart for calculating the ground clutter suppression deficiency factor involved in the present invention. DETAILED DESCRIPTION

[0065] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0066] The disclosure below provides many different embodiments or examples for implementing different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the components and configurations of specific examples are described below. Of course, these are merely examples and are not intended to limit the embodiments of the present invention. In addition, the embodiments of the present invention may repeat reference numerals and / or reference letters in different examples. Such repetition is for the purpose of simplicity and clarity and does not in itself indicate the relationship between the various embodiments and / or configurations discussed.

[0067] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0068] like Figure 1 As shown, this embodiment provides a real-time evaluation method for dual-polarization weather radar data quality, including:

[0069] Step S1. Obtain the base data file of the dual-polarization weather radar and determine the radar data type;

[0070] If the base data file is VOL (Volume scan) data, then the following steps S2 to S5 are executed;

[0071] If the base data file is a single layer of PPI (Plan Position Indicator) data, then the following steps S2 to S3 and S5 are executed;

[0072] Step S2. performing echo recognition on the base data file to determine whether the echo of the base data file is a weather echo;

[0073] Step S3. If the echo in the base data file is a weather echo, then calculate the system deviation factor of the differential reflectivity, the reflectivity sensitivity factor, the system deviation factor of the horizontal reflectivity, and the phase correlation quality factor, and output the calculation results;

[0074] Step S4. If the echo in the base data file is a weather echo, calculate the ground clutter suppression excess factor and output the calculation result;

[0075] Step S5. If the echo of the base data file does not belong to a weather echo, then calculate the ground clutter suppression deficiency factor and output the calculation result;

[0076] Here, each calculation result is output as an evaluation result.

[0077] In some embodiments, in step S2, the method for performing echo identification includes:

[0078] Extract the horizontal reflectivity Z of the volume scan base data of the base data file h , differential reflectivity Z dr , radial velocity V, differential phase shift Φ dp and the mutual correlation coefficient ρ hv , and calculate the differential reflectivity Z dr The standard deviation of the differential phase shift Φ dp The standard deviation and cross-correlation coefficient ρ hv The standard deviation of the differential reflectivity Z dr The standard deviation of the differential phase shift Φ dp The standard deviation and cross-correlation coefficient ρ hv The standard deviation is calculated based on the data collected along the radial direction with three optional range distance library windows of 3x3, 5x5, and 7x7;

[0079] Differential reflectivity Z dr The standard deviation std dev(Z dr ), differential phase shift Φ dp The standard deviation std dev (Φ dp ) and the mutual correlation coefficient ρ hv The standard deviation std dev (ρ hv ) is calculated as follows:

[0080]

[0081] Among them, N A and N R Defined as the calculation range in distance and azimuth directions, and In N A ×N R After eliminating invalid data within the range library, the mean differential phase shift, mean differential reflectivity and mean cross-correlation coefficient within the range library are calculated.

[0082] In some embodiments, in step S2, the method for performing echo identification further includes:

[0083] Based on the base data file, the data is processed by fuzzy logic to calculate the differential reflectivity Z on each distance library window in the base data. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The mean of the distance library window is calculated based on the mean value. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The standard deviation of the data Z on each distance library window is then h , ρ hv 、V、std dev(Φ dp )、std dev(Z dr )、std dev(ρ hv ) to perform weighted averaging.

[0084] In some embodiments, the calculation process of the system deviation factor of the differential reflectivity includes:

[0085] Statistics ρ hv Greater than 0.97 and 5≤Z h Z for ≤20 samples dr and Z dr , and make a scatter plot, and fit a straight line at the same time, take the straight line Z h =Z at 10dBZ dr The value is used as the system deviation factor Zdrbias of the differential reflectivity.

[0086] In some embodiments, the calculation process of the reflectivity sensitivity factor includes:

[0087] Calculate the minimum measurable Zmin value of the dual-polarization weather radar, count the minimum reflectivity values in all radial distance libraries at every 20km, subtract them from the nominal values, and calculate the average difference, which is used as the reflectivity sensitivity factor ZhSensivitybias.

[0088] In some embodiments, the calculation process of the system deviation factor of the horizontal reflectivity includes:

[0089] Using Z h and Z dr K reconstructed through consistency relations dpn Compared with the K actually measured by radar dpm Comparison is used to determine the system deviation factor Zhbias of the horizontal reflectivity. The specific formula that needs to be satisfied is as follows:

[0090]

[0091] And select 15<Z h <50, K dp >0,ρ hv >0.95, calculate the results of all distance libraries that meet the conditions and find the average to obtain the system deviation factor Zhbias of the horizontal reflectivity.

[0092] In some embodiments, the calculation process of the phase correlation quality factor includes:

[0093] Calculate the zero-lag correlation coefficient ρ for 9 consecutive distance bins hv Greater than T p And the differential phase standard deviation is less than T σ1 The average value of the differential phase of the distance library segment is used as the initial differential phase of the radial direction, and then the average differential phase of all valid radial directions at each elevation angle is calculated as the initial differential phase.

[0094] In some embodiments, the process of calculating the ground clutter suppression transition factor includes:

[0095] After echo identification is performed on the base data file, the number of meteorological echoes in the near-field distance library of the low-level volume scan file is counted, and the number of meteorological echoes in the near-field distance library of the high-level volume scan file is counted. It is determined whether the difference between the number of meteorological echoes in the high-level volume scan file and the number of meteorological echoes in the low-level volume scan file exceeds a threshold. If so, excessive ground clutter suppression exists in the base data file; if not, excessive ground clutter suppression does not exist in the base data file.

[0096] In some embodiments, the process of calculating the ground clutter suppression deficiency factor includes:

[0097] After performing echo identification on the base data file, the number of non-meteorological echoes in each distance library at the bottom elevation angle is counted, and it is determined whether the number of non-meteorological echoes exceeds a threshold. If so, the base data file has insufficient ground clutter suppression; if not, the base data file does not have insufficient ground clutter suppression.

[0098] like Figure 2 As shown, another embodiment of the present invention discloses a real-time assessment system for dual-polarization weather radar data quality, comprising:

[0099] The data reading module is used to obtain the base data file of the dual-polarization weather radar and determine the radar data type;

[0100] an echo recognition module, configured to perform echo recognition on the base data file and determine whether the echo in the base data file is a weather echo;

[0101] The data processing module is used to calculate the system deviation factor of the differential reflectivity, the reflectivity sensitivity factor, the system deviation factor of the horizontal reflectivity, the phase correlation quality factor, and the ground clutter suppression excess factor, as well as to determine the ground clutter suppression deficiency factor;

[0102] The result output module is used to output the calculation results and judgment results as evaluation results.

[0103] In summary, the concept of the present invention is as follows:

[0104] By reading the radar volume scan data file, the radar data can be automatically processed. After reading the data file, it will automatically determine whether the data file is radar echo data with evaluation value, and apply different evaluation processing processes based on the judgment result. Finally, the data quality evaluation result will be output through the output module.

[0105] The quality assessment process of radar base data is as follows: after the device receives the radar base data file, it will judge the radar data type. If the data is judged to be single-layer PPI data, the ground clutter suppression factor calculation will not be performed in the process (see the complete process for details). Figure 1 ) After the judgment is completed, the algorithm will determine whether the data file has evaluation value (that is, whether there is enough weather echo distance library for evaluation). When the echo data meets the evaluation conditions (the echo belongs to the weather echo), the sensitivity quality factor, Z dr Quality factor, Z h Quality factor, Φ dpThe quality factor and the ground clutter suppression excess factor (when the data type belongs to a multi-layer volume scan file and meets the evaluation conditions) are calculated. If the echo data does not meet the evaluation conditions, only the ground clutter suppression deficiency factor is judged, and the result is finally saved to the data server.

[0106] The details include:

[0107] 1. Weather echo identification

[0108] Failure to correctly distinguish between meteorological and non-meteorological echoes can contaminate radar data, negatively impacting assessment results and even leading to errors. Therefore, to address this issue, we studied radar echo data from dual-polarization radars and applied fuzzy logic processing to identify and classify meteorological and non-meteorological echoes in real time, which is of great significance to radar-based data quality assessment.

[0109] (a) Fuzzy logic processing

[0110] First, extract the horizontal reflectivity Z of the volume scan data of the radar echo h , differential reflectivity Z dr , radial velocity V, differential phase shift Φ dp and the mutual correlation coefficient ρ hv Afterwards, the differential reflectivity Z is calculated from the volume scan data. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The standard deviation of the variable is calculated based on the data collected along the radial direction with three optional range distance library windows of 3x3, 5x5, and 7x7. dp ), standard deviation of differential reflectivity std dev(Z dr ) and the standard deviation of the cross-correlation coefficient std dev(ρ hv ) is calculated as follows:

[0111]

[0112]

[0113]

[0114] Among them, N A and N R Defined as the calculation range in distance and azimuth directions, and In N A ×N RAfter eliminating invalid data within the range library, the mean differential phase shift, mean differential reflectivity and mean cross-correlation coefficient within the range library are calculated.

[0115] In the fuzzy logic algorithm, the horizontal reflectivity Z h , mutual correlation coefficient ρ hv , radial velocity V, standard deviation of differential phase std dev(Φ dp ), standard deviation of differential reflectivity std dev(Z dr ) and the standard deviation of the cross-correlation coefficient std dev(ρ hv ) Six basic variables with different weights, as shown in Table 1.

[0116] Table 1: Weights assigned to the six variables in the fuzzy logic process

[0117] variable Weight <![CDATA[Horizontal reflectivity Z h > 1.0 <![CDATA[Cross-correlation coefficient ρ hv > 1.0 Radial velocity V 1.0 <![CDATA[Standard deviation of differential phase, std dev(Φ dp )]]> 2.0 <![CDATA[Standard deviation of differential reflectivity std dev(Z dr )]]> 2.0 <![CDATA[Standard deviation of the cross-correlation coefficient std dev(ρ hv )]]> 1.0

[0118] The weighted average value A is determined in each distance library window, and the formula is as follows:

[0119]

[0120] Where W j is the weight, PV j is the value of the variable for the jth input. After fuzzy processing, these physical quantities are converted into criteria ranging from 0 to 1. The larger the value of the criterion for a point, the greater the likelihood that the point belongs to this type of echo. That is, when the criterion for a radar echo at a point exceeds a certain threshold, the point is identified as a weather echo; when the criterion for a radar echo at a point is less than a certain threshold, the point is identified as a non-weather echo.

[0121] like Figures 3 to 8 As shown, the six membership functions of fuzzy logic processing: Z h , ρ hv 、V、std dev(Φ dp )、stddev(Z dr ) and std dev(ρ hv ), the number in brackets at the top of the graph is the weight used for that variable, and PV(·) is the value of the membership function for a given input.

[0122] (b) Fuzzy logic processing of data

[0123] If fuzzy logic processing is performed on the data, the differential reflectivity Z on each range library window in the base data must be calculated first. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The mean of the distance library window is calculated based on the mean value.dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The standard deviation of the data Z on each distance library window is then h , ρ hv 、V、std dev(Φ dp )、std dev(Z dr )、std dev(ρ hv ) to perform weighted averaging. After processing, the echoes can be classified into weather echoes and clear sky echoes, and subsequent algorithms can perform different processing based on different echo types.

[0124] To sum up, the processing flow chart of echo recognition is as follows: Figure 9 As shown, including:

[0125] Find ρ for the values of the 5*5 distance library that are not NAN (Not a Number) hv , Z h , the average value of V;

[0126] Find ρ for the values of 5*5 distance libraries that are not NAN hv , Z dr , Φ dp The standard deviation of

[0127] ρ hv , Z h , the average value of V and ρ hv , Z dr , Φ dp The standard deviation of is converted into the corresponding membership function;

[0128] The values of each membership function are weighted and averaged;

[0129] Determine whether the weighted average value is greater than the set threshold. If so, the distance library is a weather echo; if not, determine whether the weighted average value is == NAN. If so, the distance library is a non-echo. If not, the distance library is a non-meteorological echo;

[0130] The meteorological echo distance library is counted to determine whether the number of meteorological echo distance libraries is greater than a set threshold. If so, the data is a meteorological echo; if not, the data is a clear sky echo.

[0131] 2. System deviation factor Zdrbias of differential reflectivity

[0132] For raindrops with a diameter less than 0.5 mm, the ideal Z dr This is equal to 0dB, which is because the shape of a tiny water droplet can be approximated as a sphere. This method can be applied to conventional dual-polarization radar volume scanning when the radar is detecting at low elevation angles. drData calculation Z dr System deviation ("low elevation angle light rain method"). dr Among the system deviation methods, this method is simple and reliable, and it is easy to obtain a large amount of data for bias correction. Therefore, the “low elevation angle light rain” method is used to determine Z dr Since the deviation value can usually directly reflect the performance status of the radar and thus analyze the quality of the radar base data, the deviation value can be directly used as the differential reflectivity system deviation factor. hv Greater than 0.95, Z h Estimated average Z in the range of 5-20dBz dr The deviation is used as the system deviation factor (Zdrbias) of the differential reflectivity.

[0133] Statistics 5≤Z h Z for ≤20 samples dr and Z dr , and make a scatter plot, and fit a straight line at the same time, take the straight line Z h =Z at 10dBZ dr The value is taken as Zdrbias, and the flow chart is as follows Figure 10 As shown, including:

[0134] Starting from the i=1 distance library, determine whether 5<Z on the distance library h <20, ρ hv > 0.95; if not, assign i = i + 1 and continue to judge; if so, count all points that meet the conditions and fit Z h -Z dr straight line;

[0135] Take the straight line Z h =10, Z dr The value of is used as the system bias factor of differential reflectivity (Zdrbias).

[0136] 3. Reflectivity sensitivity factor ZhSensivitybias

[0137] The relationship between precipitation echo power and radar technical parameters as well as the nature, intensity, and distance of precipitation is often expressed by the radar meteorological equation:

[0138]

[0139] Where, P r is the average value of the echo power received by the radar (W); P t is the pulse power emitted by the radar (W); G e is the effective gain of the radar antenna; θ, are the horizontal beam width and vertical beam width of the antenna (radians), respectively; τ is the transmit pulse width (s); λ is the radar operating wavelength (m); C is the propagation speed of electromagnetic waves (3×10 8 (m / s); ψ is the fill coefficient of the precipitation area in the space composed of the entire radar beam width and the pulse space length (1 / 2τc); K is the attenuation factor of electromagnetic waves during spatial propagation; m is the complex refractive index of rain; R is the distance to the target (m); Z is the precipitation reflection factor (m3); ln2 is the natural logarithm of 2, which is 0.69315.

[0140] P in the equation r This is the average power returned by a meteorological target to the receiver. Random sampling of received echoes has no definite relationship to radar parameters. The equation already accounts for antenna needle beam scanning loss in the calculation process and does not require additional correction for use. In the radar meteorological equation, received echo power is inversely proportional to the square of the distance and the square of the wavelength.

[0141] Weather radars often use circular rotating parabolic antennas with needle-shaped beam width θ = φ. When K = 1, and quantitative detection is performed within a range of 200 km, the beam filling coefficient ψ = 1. Based on the radar meteorological equation and the radar's technical parameters, the minimum measurable Z of the dual-polarization weather radar can be calculated. min value.

[0142] Therefore, the above formula can be simplified and rewritten as:

[0143]

[0144] According to the radar meteorological equation and the radar technical parameters, the minimum measurable Z of the dual polarization weather radar can be calculated. min The nominal detection sensitivity can be calculated from the basic parameters and used as a comparison condition to analyze the actual radar sensitivity. The actual radar performance can be judged by the difference between the two, thereby analyzing the quality of the radar base data. The radar measured sensitivity is obtained from the volume scan data obtained. The minimum reflectivity value of all radial distances at every 20km is counted and subtracted from the nominal value. The average difference is calculated and used as the reflectivity sensitivity factor (ZhSensivitybias). The flow chart is as follows: Figure 11 As shown, including:

[0145] Input radar equation parameters and calculate the theoretical radar minimum measurable Z min curve;

[0146] Calculate the minimum reflectivity factor of actual echo every 20km;

[0147] Calculate the difference between the theoretical curve and the actual echo at every 20 km and take the average.

[0148] Output reflectivity sensitivity factor (ZhSensivitybias).

[0149] 4. System deviation factor Zhbias of horizontal reflectivity

[0150] Systematic deviations are usually caused by difficulties in accurately calibrating radar hardware and errors in operation. h The systematic deviation of can be evaluated based on the principle of parameter consistency. h and Z dr K reconstructed through consistency relations dpn Compared with the K actually measured by radar dpm Compare to determine Z h The system deviation is as follows:

[0151]

[0152] Among them, a, b, and c are consistency relationship parameters, which can be obtained by forward modeling radar parameters through raindrop spectrum measurement. h is the measured radar horizontal reflectivity factor, Z dr is the measured differential radar reflectivity factor.

[0153] First, K dpn , Z h , Z dr Convert to linear units:

[0154]

[0155]

[0156] Z h Z in hbias It can be obtained using the following relationship:

[0157]

[0158] Through the consistency relation K dpn And calculate the deviation Z h(bias) , the effective value of the statistical calculation result (i.e. non-NAN calculation result), select 15<Z h <50, K dp >0,ρ hv The result of >0.95 is used to ensure the reliability of the echo calculation results. The system deviation factor Zhbias of the horizontal reflectivity can be obtained by calculating the results of all distance libraries that meet the conditions and finding their average. The flow chart is as follows Figure 12 As shown, including:

[0159] Get a, b, c, Z h , Zdr , K dpn ;

[0160] By Z h , Z dr , K dpn Calculate the consistency relationship Z h(bias) , we get Z h(bias) .

[0161] 5. Phase-correlated quality factor

[0162] Φ dp The quality factor is mainly used to evaluate Φ dp Initial phase, discrete and distributed noise characteristics. Normal Φ dp Random noise fluctuates within a certain reasonable range, but when the phase noise of the system becomes more unstable, Φ dp The initial phase and random fluctuation noise will be greatly affected, so we can evaluate Φ dp The initial phase, Φ dp Phase folding and Φ dp The phase noise characteristics are used to reflect the phase stability of the system.

[0163] (a) Initial phase factor InitialPdp evaluation method

[0164] Usually the initial differential phase is obtained by taking the differential phase close to the edge of the precipitation echo. dp The data is affected by ground objects and noise. Even if there is precipitation echo, the fluctuation is large. Nine consecutive range bins in the radial direction (each range bin is 300m long) are used as a window. The zero-lag correlation coefficient (ρ hv ) is greater than T p (The zero-lag correlation coefficient threshold within the window is 0.7 in this method and can be modified by yourself) and Φ dp Standard deviation is less than T σ1 (Window Φ dp The standard deviation threshold of the distance window is Φ in the second half. This method takes 10 and can be modified by yourself. dp The average value of the effective initial Φ in the radial direction dp , then calculate the effective initial Φ for all radial directions of elevation angle dp And calculate the average, and use this value as the initial phase factor (InitialPdp). The flow chart is as follows Figure 13 As shown, including:

[0165] Get Φ dp , ρ hv , set T p (The zero-lag correlation coefficient threshold within the window is 0.7 in this method and can be set by yourself) and Tσ1 (Window Φ dp The standard deviation threshold is 10 in this method and can be set by yourself);

[0166] Start from the first distance library i=1;

[0167] Find the Φ of the distance library from i to i+9 dp The standard deviation of

[0168] Judgment Φ dp Is the standard deviation σ less than T? σ1 And the ρ of the i+4th to i+5th distance library hv Is it greater than T p If not, assign i=i+1 and find Φ from i to i+9 dp Continue to judge after the standard deviation;

[0169] If so, find Φ from i+4 to i+9 dp The average value is taken as the initial phase R of the radial direction;

[0170] Find the initial phase R of all radial directions;

[0171] The initial phase factor InitialPdp is obtained by averaging all radial initial phases R.

[0172] (b) Phase folding factor FoldPdp evaluation method

[0173] Because the radar's Φ dp The measurable range is limited to 180° intervals, that is, the interval between the maximum and minimum measurable values is 180°. When there is heavy rainfall over a large area, the Φ at the far end of the rainfall echo dp It may be greater than the maximum measurable value of 180°. In this case, Φ dp The value will be folded, which is manifested as the Φ of the two adjacent reservoirs in the radial direction. dp The value is close to the measurable value with opposite sign and maximum absolute value; then there is phase folding. dp It is a distance cumulant, and its distribution with distance should be continuous and have an increasing trend. Therefore, the radial continuity check can be used to check the Φ dp Perform unfolding. Use the method in Yanting Wang. (2009) to identify phase folding. The flowchart is as follows Figure 14 As shown, including:

[0174] Get Φ dp , ρ hv 、T p and T σ1 ;

[0175] Start from the first distance library i=1;

[0176] Find Φ from i to i+9 dp The standard deviation σ;

[0177] Judgment Φ dp Is the standard deviation σ less than T? σ1 And the ρ of the i+4th to i+5th distance library hv Is it greater than T p If not, assign i=i+1 and find Φ from i to i+9 dp Continue to judge after the standard deviation;

[0178] If so, find Φ from i+4 to i+9 dp The average value is taken as the initial phase R of the radial direction;

[0179] Find Φ from i to i+9 dp The standard deviation of Φ from i+4 to i+9 dp Slope of the fitted line S;

[0180] Determine Φ from i to i+9 dp If the standard deviation σ is less than 15° and -5° / Km<S<20° / Km, then assign R=R+S*Δr. If not, proceed to the next step, where Δr is the length of a single distance library.

[0181] Judgment Φ dp -R<-80°, if so, phase folding occurs and Foldpd is assigned a value of 1; if not, determine whether it is the last library;

[0182] If it is the last bank, no phase folding occurs and Foldpd is assigned 0. If it is not the last bank, then i=i+1 is assigned and Φ from i to i+9 is calculated. dp The standard deviation of Φ from i+4 to i+9 dp Fit the slope S of the straight line and continue the subsequent judgment until the last library or the result of whether distance folding occurs is obtained.

[0183] (c) Phase noise factor NoisePdp evaluation method

[0184] By counting the PPI data, we can determine the radial direction with the most valid data and evaluate the radial direction. dp It is a distance accumulator. Its distribution with distance should be continuous and have an increasing trend. Therefore, a curve can be fitted according to the data to calculate the radial Φ. dp The difference between the fitted curve (i.e. residual) and the phase noise factor can be obtained by calculating the standard deviation of the residual. The specific implementation method is as follows: Figure 15 As shown, including:

[0185] Input data Φdp ;

[0186] Starting from the distance outside the ground clutter range (about 10 km), Φ dp Fitting curves;

[0187] Calculate the residuals of the curve fitted to the data;

[0188] Calculate the standard deviation of the residual sequence;

[0189] The residual standard deviation is output as the phase noise factor NoisePdp.

[0190] 6. Quality Factors Related to Ground Clutter Suppression

[0191] Ground clutter suppression is often performed during the quality control phase of base data production. However, improper filtering methods during this process can lead to excessive clutter suppression. Similarly, improper filtering methods or significant instability in system phase noise can also lead to significant residual clutter. Both of these phenomena can seriously impact radar data quality. Therefore, determining whether these conditions exist in the base data is an important basis for evaluating radar data quality.

[0192] (a) Ground clutter suppression excess factor ClutterOverSupRec

[0193] The bottom elevation angle usually can observe more echoes, and ground clutter often appears at the bottom elevation angle. Generally speaking, if weather echoes appear at the high elevation angle, they will also appear at the bottom elevation angle. If weather echoes appear at the high elevation angle but not at the bottom elevation angle, it may be that ground clutter is over-suppressed. In the data belonging to the weather echo classification, by comparing the echo recognition results of the high and low elevation angles, it can be determined whether ground clutter is over-suppressed. The flow chart is as follows: Figure 16 As shown, including:

[0194] After echo identification is performed on the base data file, the number of distance libraries in which the near-field of the bottom volume scan file belongs to meteorological echoes is counted, and the number of distance libraries in which the near-field of the high-level volume scan file belongs to meteorological echoes is counted. It is determined whether the difference between the number of meteorological echoes in the high-level volume scan file and the number of meteorological echoes in the low-level volume scan file exceeds a threshold. If so, excessive ground clutter suppression exists in the base data file, and ClutterOverSupRec is assigned a value of 1. If not, excessive ground clutter suppression does not exist in the base data file, and ClutterOverSupRec is assigned a value of 0.

[0195] (b) Ground clutter suppression deficiency factor ClutterLessSupRec

[0196] When the filtering method is inappropriate, it may also cause insufficient suppression of ground clutter. When the data file belongs to clear sky echo, the distance library number of non-meteorological echo at the bottom elevation angle can be counted. When it exceeds a certain threshold, it is considered that the volume scan has insufficient suppression of ground clutter. The process is as follows: Figure 17 As shown, including:

[0197] After echo identification is performed on the base data file, the number of non-meteorological echoes in each distance library at the bottom elevation angle is counted, and it is determined whether the number of non-meteorological echoes exceeds the threshold. If so, insufficient ground clutter suppression exists in the base data file, and ClutterLessSupRec is assigned a value of 1. If not, insufficient ground clutter suppression exists in the base data file, and ClutterLessSupRec is assigned a value of 0.

[0198] 7. Result Output

[0199] During the above evaluation process, each step will output an evaluation result. Based on the above evaluation results and the current radar hardware working status, a comprehensive judgment is made, and the radar-based data quality of this evaluation is finally given in the form of a score. That is, a real-time evaluation method for dual-polarization weather radar data quality also includes step S6 (which can be executed by the result output module). Step S6 is specifically as follows:

[0200] The scoring formula is:

[0201] P=∑S i ×W i

[0202] Where P is the total score, S i is the quality factor score, W i is the scoring weight.

[0203] The quality factors involved in the scoring include:

[0204] Serial number Quality Factor Scoring weight 1 Dual-channel balance 0.2 2 Detection sensitivity 0.2 3 Systematic bias 0.15 4 Ground clutter suppression quality 0.05 5 Radar Status 0.5

[0205] If there is no updated rating, the most recent rating is used and the initial default rating is zero.

[0206] In the scoring rules, the quality factor weight can be adjusted, the quality factor scoring interval can be adjusted, and the quality factor scoring standard can be adjusted.

[0207] 1. Dual-channel balance

[0208] Evaluation results Scoring 0≤abs(Zdrbias)≤2 100 2<abs(Zdrbias)≤4 90 4<abs(Zdrbias)≤6 80 6<abs(Zdrbias)≤8 70 8<abs(Zdrbias)≤10 60 10<abs(Zdrbias)≤14 40 14<abs(Zdrbias)≤16 20 abs(Zdrbias)>16 0

[0209] 2. Systematic Bias

[0210] Evaluation results Scoring 0≤Zhbias≤3 100 3<Zhbias≤6 90 6<Zhbias≤9 80 9<Zhbias≤12 70 12<Zhbias≤15 60 15<Zhbias≤20 40 20<Zhbias≤25 20 Zhbias>25orZhbias=N / A 0

[0211] 3. Detection sensitivity

[0212]

[0213]

[0214] 4. Ground clutter suppression quality factor

[0215]

[0216] 5. Radar status

[0217]

[0218] The above embodiments are intended to illustrate the present invention, not to limit the present invention. Therefore, changes in illustrative values or substitutions of equivalent components should still fall within the scope of the present invention.

[0219] From the above detailed description, it will be clear to those skilled in the art that the present invention can indeed achieve the aforementioned objectives and is in compliance with the provisions of the Patent Law.

[0220] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as covering the preferred embodiments and all changes and modifications that fall within the scope of the invention. The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

[0221] It should be noted that the above description of the relevant processes is for illustration and purpose only and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the processes under the guidance of this specification. However, such modifications and changes are still within the scope of this specification.

[0222] The basic concepts have been described above. It will be apparent to those skilled in the art after reading this application that the above disclosures are merely illustrative and do not constitute limitations on this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to this application. Such modifications, improvements, and amendments are suggested in this application and remain within the spirit and scope of the exemplary embodiments of this application.

[0223] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or more in different places in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0224] In addition, it will be understood by those skilled in the art that various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful combination of processes, machines, products or substances, or any new and useful improvements thereto. Therefore, various aspects of the present application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software can all be referred to as "units", "modules" or "systems". In addition, various aspects of the present application can take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0225] The computer program code required for the operation of each part of the application can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, conventional procedural programming languages such as C programming language, VisualBasic, Fortran2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy or other programming languages. The program code can be run completely on the user's computer, or run on the user's computer as an independent software package, or run partly on the user's computer and partly on a remote computer, or run completely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or be connected to an external computer (such as by the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0226] In addition, unless expressly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some embodiments of the invention that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a pure software solution, for example, installation on an existing server or mobile device.

[0227] Similarly, it should be noted that in order to simplify the presentation of this disclosure and thereby facilitate understanding of one or more of the invention's embodiments, the foregoing descriptions of the embodiments of this disclosure sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this approach should not be interpreted as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject matter of the invention may possess fewer features than the single embodiment described above.

Claims

1. A real-time evaluation method for dual-polarization weather radar data quality, characterized in that: include: Step S1. Obtain the base data file of the dual-polarization weather radar and determine the radar data type; If the base data file is VOL data, then execute the following steps S2 to S5; If the base data file is a single-layer PPI data, then execute the following steps S2 to S3 and S5; Step S2. performing echo recognition on the base data file to determine whether the echo of the base data file is a weather echo; Step S3. If the echo in the base data file is a weather echo, then calculate the system deviation factor of the differential reflectivity, the reflectivity sensitivity factor, the system deviation factor of the horizontal reflectivity, and the phase correlation quality factor, and output the calculation results; Step S4. If the echo in the base data file is a weather echo, calculate the ground clutter suppression excess factor and output the calculation result; Step S5. If the echo of the base data file does not belong to a weather echo, then calculate the ground clutter suppression deficiency factor and output the calculation result; Among them, each calculation result is output as an evaluation result; In step S2, the method for performing echo identification includes: Extract the horizontal reflectivity Z of the volume scan base data of the base data file h , differential reflectivity Z dr , radial velocity V, differential phase shift Φ dp and the mutual correlation coefficient ρ hv , and calculate the differential reflectivity Z dr The standard deviation of the differential phase shift Φ dp The standard deviation and cross-correlation coefficient ρ hv The standard deviation of the differential reflectivity Z dr The standard deviation of the differential phase shift Φ dp The standard deviation and cross-correlation coefficient ρ hv The standard deviation is calculated based on the data collected along the radial direction with three optional range distance library windows of 3x3, 5x5, and 7x7; Differential reflectivity Z dr The standard deviation std dev(Z dr ), differential phase shift Φ dp The standard deviation std dev (Φ dp ) and the mutual correlation coefficient ρ hv The standard deviation std dev (ρ hv ) is calculated as follows: Among them, N A and N R Defined as the calculation range in distance and azimuth directions, and In N A ×N R After eliminating invalid data within the range library, the average differential phase shift, average differential reflectivity and average cross-correlation coefficient within the range library are calculated; In step S2, the method for performing echo identification further includes: Based on the base data file, the data is processed by fuzzy logic to calculate the differential reflectivity Z on each distance library window in the base data. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The mean of the distance library window is calculated based on the mean value. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The standard deviation of the data Z on each distance library window is then h , ρ hv 、V、std dev(Φ dp )、std dev(Z dr )、std dev(ρ hv ) to perform weighted averaging.

2. The method for real-time assessment of dual-polarization weather radar data quality according to claim 1, characterized in that: The calculation process of the system deviation factor of differential reflectivity includes: Statistics ρ hv Greater than 0.97 and 5 ≤Z h Z for ≤20 samples dr and Z dr , and make a scatter plot, and fit a straight line at the same time, take the straight line Z h =Z at 10dBZ dr The value is used as the system deviation factor Zdrbias of the differential reflectivity.

3. The method for real-time assessment of dual-polarization weather radar data quality according to claim 2, characterized in that: The calculation process of the reflectivity sensitivity factor includes: Calculate the minimum measurable Zmin value of the dual-polarization weather radar, count the minimum reflectivity values in all radial distance libraries at every 20km, subtract them from the nominal values, and calculate the average difference, which is used as the reflectivity sensitivity factor ZhSensivitybias.

4. The method for real-time assessment of dual-polarization weather radar data quality according to claim 3, characterized in that: The calculation process of the systematic deviation factor of horizontal reflectivity includes: Using Z h and Z dr K reconstructed through consistency relations dpn Compared with the K actually measured by radar dpm Comparison is used to determine the system deviation factor Zhbias of the horizontal reflectivity. The specific formula that needs to be satisfied is as follows: And select 15<Z h <50, K dp >0,ρ hv >0.95, calculate the results of all distance libraries that meet the conditions and find the average to obtain the system deviation factor Zhbias of the horizontal reflectivity.

5. The method for real-time assessment of dual-polarization weather radar data quality according to claim 4, characterized in that: The calculation process of the phase-correlated quality factor includes: Calculate the zero-lag correlation coefficient ρ for 9 consecutive distance bins hv Greater than T p And the differential phase standard deviation is less than T σ1 The average value of the differential phase of the distance library segment is used as the initial differential phase of the radial direction, and then the average differential phase of all valid radial directions at each elevation angle is calculated as the initial differential phase.

6. The method for real-time assessment of dual-polarization weather radar data quality according to claim 5, characterized in that: The calculation process of the ground clutter suppression transition factor includes: After echo identification is performed on the base data file, the number of meteorological echoes in the near-field distance library of the low-level volume scan file is counted, and the number of meteorological echoes in the near-field distance library of the high-level volume scan file is counted. It is determined whether the difference between the number of meteorological echoes in the high-level volume scan file and the number of meteorological echoes in the low-level volume scan file exceeds a threshold. If so, excessive ground clutter suppression exists in the base data file; if not, excessive ground clutter suppression does not exist in the base data file.

7. The method for real-time assessment of dual-polarization weather radar data quality according to claim 6, characterized in that: The calculation process of the ground clutter suppression deficiency factor includes: After performing echo identification on the base data file, the number of non-meteorological echoes in each distance library at the bottom elevation angle is counted, and it is determined whether the number of non-meteorological echoes exceeds a threshold. If so, the base data file has insufficient ground clutter suppression; if not, the base data file does not have insufficient ground clutter suppression.

8. A dual-polarization weather radar data quality real-time assessment system, characterized in that: The system performs the steps of the evaluation method according to any one of claims 1 to 7; The system comprises: A data reading module is used to obtain the base data file of the dual-polarization weather radar and determine the radar data type; an echo recognition module is used to perform echo recognition on the base data file and determine whether the echo of the base data file is a meteorological echo; A data processing module is used to calculate the system deviation factor of differential reflectivity, reflectivity sensitivity factor, system deviation factor of horizontal reflectivity, phase correlation quality factor, ground clutter over-suppression factor, and ground clutter under-suppression factor; A result output module is used to output the calculation results and judgment results as evaluation results; The echo recognition module performs an echo recognition method including: Extract the horizontal reflectivity Z of the volume scan base data of the base data file h , differential reflectivity Z dr , radial velocity V, differential phase shift Φ dp and the mutual correlation coefficient ρ hv , and calculate the differential reflectivity Z dr The standard deviation of the differential phase shift Φ dp The standard deviation and cross-correlation coefficient ρ hv The standard deviation of the differential reflectivity Z dr The standard deviation of the differential phase shift Φ dp The standard deviation and cross-correlation coefficient ρ hv The standard deviation is calculated based on the data collected along the radial direction with three optional range distance library windows of 3x3, 5x5, and 7x7; Differential reflectivity Z dr The standard deviation std dev(Z dr ), differential phase shift Φ dp The standard deviation std dev (Φ dp ) and the mutual correlation coefficient ρ hv The standard deviation std dev (ρ hv ) is calculated as follows: Among them, N A and N R Defined as the calculation range in distance and azimuth directions, and In N A ×N R After eliminating invalid data within the range library, the average differential phase shift, average differential reflectivity and average cross-correlation coefficient within the range library are calculated; The echo recognition module further comprises: Based on the base data file, the data is processed by fuzzy logic to calculate the differential reflectivity Z on each distance library window in the base data. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The mean of the distance library window is calculated based on the mean value. dr , differential phase shift Φ dp and the mutual correlation coefficient ρ hv The standard deviation of the data Z on each distance library window is then h , ρ hv 、V、std dev(Φ dp )、std dev(Z dr )、std dev(ρ hv ) to perform weighted averaging.

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