CN116047524A

CN116047524A - Dual-polarization weather radar data quality real-time evaluation method and system

Info

Publication number: CN116047524A
Application number: CN202211392137.7A
Authority: CN
Inventors: 马勇; 史朝; 郭财政; 龙子豪
Original assignee: China Civil Aviation Southwest Region Air Traffic Authority; Chengdu University of Information Technology
Current assignee: China Civil Aviation Southwest Region Air Traffic Authority; Chengdu University of Information Technology
Priority date: 2022-11-08
Filing date: 2022-11-08
Publication date: 2023-05-02

Abstract

The invention provides a dual-polarization weather radar data quality real-time assessment method, which comprises the following steps: s1, acquiring a base data file of a dual-polarization weather radar; if the base data file is VOL data, executing the following S2-S5; if the base data file is PPI data, executing the following S2-S3 and S5; s2, carrying out echo identification; s3, if the data file belongs to the 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 data file belongs to the meteorological echo, calculating a ground clutter suppression transition factor; s5, if the data file does not belong to the meteorological echo, calculating insufficient factors of clutter suppression of the ground object; and outputting each calculation result as an evaluation result. According to the invention, the data quality is estimated in real time from multiple angles by utilizing the inter-parameter internal connection on the base data obtained by the radar, and the estimation result is more accurate.

Description

Dual-polarization weather radar data quality real-time evaluation method and system

Technical Field

The invention relates to the technical field of weather radar data quality real-time evaluation, in particular to a dual-polarization weather radar data quality real-time evaluation method and system.

Background

Weather radar is an important device for precipitation observation, and plays an important role. The radar has the accurate and reliable meteorological target detection, detection and early warning capability, and is necessary equipment for reducing loss and guaranteeing the safety of flying. The single polarization Doppler weather radar widely applied in China at present can detect the information of intensity, radial speed, spectrum width and the like of a precipitation system, but the shape and phase state of precipitation particles cannot be further analyzed, and the network deployment double polarization radar is gradually expanded from 2019 in key areas of eastern and southeast coasts of China, so that the double polarization radar plays a great role in future business application.

More parameters can be detected by the double-polarization radar, and meanwhile, more parameters can bring more uncertainty to a radar system, and the double-polarization weather radar has more strict requirements on performance compared with the single-polarization weather radar. Meanwhile, the radar performance can be changed along with the aging of radar devices, the influence of internal noise and external noise of a system, and the accuracy of radar observation values can be changed at any time, so that the quality of radar data is required to be evaluated in real time, qualitative and quantitative evaluation is given, a reference is provided for the application of the radar data, and the situations of misjudgment or missed judgment in application scenes are reduced.

At present, a weather radar intensity data quality testing method comprises the following steps: the beam broadening factors are corresponding to radar beam broadening quality indexes Frange; the beam broadening factors are corresponding to radar beam broadening quality indexes Fshield; the electromagnetic wave attenuation factor corresponds to the radar electromagnetic wave attenuation quality index Fatt; the vertical profile non-uniformity factor is corresponding to the radar vertical profile non-uniformity quality index Fvpr; and carrying out weighted summation on the radar beam broadening quality index Frange, the radar beam blocking quality index Fshield and the radar electromagnetic wave attenuation quality index Fatt radar vertical profile non-uniform quality index Fvpr according to corresponding weight coefficients to obtain a radar average quality index FZ.

However, this method has the following problems:

1. only the radar intensity parameter data is subjected to quality evaluation, and the radar phase parameter data is not subjected to quality evaluation;

2. no inherent constraint relationship between radar parameters is utilized.

Therefore, it is necessary to provide a method and a system for evaluating the quality of dual-polarization weather radar data in real time.

Disclosure of Invention

The invention provides a real-time evaluation method and a real-time evaluation system for the data quality of a dual-polarization weather radar, which are used for evaluating the data quality of the radar in real time from multiple angles by utilizing the internal relation among parameters, and the evaluation result is more accurate.

The embodiment of the invention discloses a dual-polarization weather radar data quality real-time evaluation method, which comprises the following steps:

s1, acquiring a data file of a dual-polarization weather radar, and judging the type of radar data;

if the base data file is VOL data, executing the following steps S2 to S5;

if the base data file is single-layer PPI data, executing the following steps S2 to S3 and S5;

s2, carrying out echo identification on the base data file, and judging whether the echo of the base data file belongs to a meteorological echo or not;

s3, if the echo of the base data file belongs to 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-related quality factor, and outputting a calculation result;

s4, if the echo of the base data file belongs to a meteorological echo, calculating a ground clutter suppression transition factor, and outputting a calculation result;

s5, if the echo of the base data file does not belong to the meteorological echo, calculating a ground clutter suppression deficiency factor, and outputting a calculation result;

wherein each calculation result is output as an evaluation result.

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

extracting the horizontal reflectivity Z of the body sweep base data of the base data file _h Differential reflectance Z _dr Radial velocity V, differential phase shift Φ _dp And cross-correlation coefficient ρ _hv And calculate the differential reflectance Z _dr Standard deviation, differential phase shift Φ _dp Standard deviation and cross-correlation coefficient ρ of (2) _hv Standard deviation of differential reflectivity Z _dr Standard deviation, differential phase shift Φ _dp Standard deviation and cross-correlation coefficient ρ of (2) _hv Is calculated from the radially collected data in three alternative ranges of 3x3, 5x5, 7x7 from the library window;

differential reflectivity Z _dr Standard deviation std dev (Z) _dr ) Differential phase shift phi _dp Standard deviation std dev (phi) _dp ) And cross-correlation coefficient ρ _hv Standard deviation std dev (ρ _hv ) The calculation formula of (2) is as follows:

wherein N is _A And N _R Defined as the calculated range in distance and azimuth direction,

and->

Respectively at N _A ×N _R And after invalid data is removed from the range of the range bin, calculating a differential phase shift average value, a differential reflectivity average value and a cross correlation coefficient average value of the range bin.

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

based on the base data file, performing fuzzy logic processing on the data, and calculating the differential reflectivity Z of each distance library window in the base data _dr Differential phase shift phi _dp And cross-correlation coefficient ρ _hv On the basis of the calculated average, calculating the differential reflectivity Z on each distance library window _dr Differential phase shift phi _dp And cross-correlation coefficient ρ _hv Standard deviation of (2) and then for each distance library window data Z _h 、ρ _hv 、V、std dev(Φ _dp )、std dev(Z _dr )、std dev(ρ _hv ) And carrying out weighted average processing.

In some embodiments, the calculation of the systematic deviation factor of differential reflectivity includes:

statistics ρ _hv Z is more than or equal to 0.97 and is more than or equal to 5 _h Z of 20 samples or less _dr Z is as follows _dr And making a scatter diagram, simultaneously fitting a straight line and taking a straight line Z _h Z at =10dbz _dr The value is taken as a systematic deviation factor ZDrbias of the differential reflectivity.

In some embodiments, the calculation of the reflectivity sensitivity factor includes:

the minimum measurable Zmin value of the dual-polarization weather radar is calculated, the minimum value of the reflectivity of all radial directions on a distance base at each 20km is counted, the minimum value is different from the nominal value, the average difference value is calculated, and the average difference value is used as a reflectivity sensitivity factor ZhSensivitybias.

In some embodiments, the calculation of the systematic deviation factor for the horizontal reflectivity includes:

by using the slave Z _h And Z _dr K reconstructed by consistency relationship _dpn K actually measured with radar _dpm The systematic deviation factor Zhbias for the comparison to determine the horizontal reflectivity is specifically formulated as follows:

And choose Z being less than 15 _h ＜50、K _dp ＞0、ρ _hv And calculating results of more than 0.95, calculating results of all distance libraries meeting the conditions, and obtaining an average value to obtain a systematic deviation factor Zhbias of the horizontal reflectivity.

In some embodiments, the process of calculating the phase-related quality factor includes:

calculating zero-lag correlation coefficient rho of continuous 9 distance libraries _hv Greater than T _p And the differential phase standard deviation is less than T _σ1 As the initial differential phase for that radial, and then calculate the average differential phase for all effective radial for each elevation angle as the initial differential phase.

In some embodiments, the process of calculating the clutter suppression transition factor includes:

and after the base data file is subjected to echo identification, counting the number of meteorological echoes of a near-field distance library of the low-layer body scanning file, counting the number of meteorological echoes of a near-field distance library of the high-layer body scanning file, judging whether the difference value between the number of meteorological echoes of the high-layer body scanning file and the number of meteorological echoes of the low-layer body scanning file exceeds a threshold value, if so, the base data file has excessive clutter suppression, and if not, the base data file does not have excessive clutter suppression.

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

After the base data file is subjected to echo identification, the number of non-meteorological echoes of each distance library under the elevation angle of the bottom layer is counted, whether the number of the non-meteorological echoes exceeds a threshold value is judged, if yes, the base data file is insufficient in ground clutter suppression, and if not, the base data file is insufficient in ground clutter suppression.

The embodiment of the invention also discloses a dual-polarization weather radar data quality real-time evaluation system, which comprises:

the data reading module is used for acquiring a data file of the dual-polarization weather radar and judging the radar data type;

the echo identification module is used for carrying out echo identification on the base data file and judging whether the echo of the base data file belongs to meteorological echo or not;

the data processing module is used for calculating a system deviation factor of differential reflectivity, a reflectivity sensitivity factor, a system deviation factor of horizontal reflectivity, a phase correlation quality factor and a ground clutter suppression excessive factor and judging a ground clutter suppression insufficient factor;

and the result output module is used for outputting the calculation result and the judgment result as an evaluation result.

In summary, the invention has at least the following advantages:

According to the invention, the data quality is estimated in real time from multiple angles by utilizing the inter-parameter internal connection on the base data obtained by the radar, the estimation result is more accurate, and the base data is directly displayed in qualitative and quantitative modes, so that not only can researchers engaged in radar data application be helped to quickly know the radar data quality, but also radar manufacturers can be helped to improve the radar, and the radar application effect is improved; the method also provides reference for the application of radar data, and reduces the situations of misjudgment or missed judgment in application scenes; the method has certain applicability and can evaluate the radar data of most systems and different models.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flow chart of a real-time evaluation method for data quality of dual-polarization weather radar according to the present invention.

Fig. 2 is an application schematic diagram of the dual-polarization weather radar data quality real-time evaluation system related to the invention.

FIG. 3 is Z as referred to in the present invention _h Membership function of (C)Schematic representation of the numbers.

FIG. 4 is a graph of ρ in accordance with the present invention _hv Is a schematic diagram of membership functions of (a).

Fig. 5 is a schematic diagram of membership functions of V as referred to in the present invention.

FIG. 6 is a graph of std dev (. Phi.) in accordance with the present invention _dp ) Is a schematic diagram of membership functions of (a).

FIG. 7 shows std dev (Z) according to the present invention _dr ) Is a schematic diagram of membership functions of (a).

FIG. 8 is a graph showing std dev (. Rho.) of the present invention _hv ) Is a schematic diagram of membership functions of (a).

Fig. 9 is a schematic diagram of a process flow chart of echo identification according to the present invention.

Fig. 10 is a schematic diagram of a flow chart of calculating a systematic deviation factor of differential reflectivity according to the present invention.

Fig. 11 is a schematic diagram of a flow chart of calculating a reflectance sensitivity factor according to the present invention.

FIG. 12 is a schematic diagram of a flow chart of calculating a systematic deviation factor for horizontal reflectivity in accordance with the present invention.

Fig. 13 is a schematic diagram of a flowchart for calculating an initial phase factor according to the present invention.

Fig. 14 is a schematic diagram of a flowchart for calculating a phase folding factor according to the present invention.

Fig. 15 is a schematic diagram of a flowchart for calculating a phase noise factor according to the present invention.

Fig. 16 is a schematic diagram of a flowchart for calculating clutter suppression transition factors according to the present invention.

Fig. 17 is a schematic diagram of a flowchart for calculating ground clutter suppression deficiency factors according to the present invention.

Detailed Description

Hereinafter, only certain exemplary embodiments are briefly described. As will be recognized by those of skill in the pertinent art, the described embodiments may be modified in numerous different ways without departing from the spirit or scope of the embodiments of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

The following disclosure provides many different implementations, or examples, for implementing different configurations of embodiments of the invention. In order to simplify the disclosure of embodiments of the present invention, components and arrangements of specific examples are described below. Of course, they are merely examples and are not intended to limit embodiments of the present invention. Furthermore, embodiments of the present invention may repeat reference numerals and/or letters in the various examples, which are for the purpose of brevity and clarity, and which do not themselves indicate the relationship between the various embodiments and/or arrangements discussed.

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

As shown in fig. 1, the embodiment provides a method for evaluating the quality of dual-polarization weather radar data in real time, which includes:

if the base data file is VOL (Volume scan) data, executing the following steps S2 to S5;

if the base data file is single-layer PPI (Plan Position Indicator plane position display) data, executing the following steps S2-S3 and S5;

Wherein each calculation result is output as an evaluation result.

and->

Based on the instituteThe base data file performs fuzzy logic processing on the data to calculate the differential reflectivity Z of each distance base window in the base data _dr Differential phase shift phi _dp And cross-correlation coefficient ρ _hv On the basis of the calculated average, calculating the differential reflectivity Z on each distance library window _dr Differential phase shift phi _dp And cross-correlation coefficient ρ _hv Standard deviation of (2) and then for each distance library window data Z _h 、ρ _hv 、V、std dev(Φ _dp )、std dev(Z _dr )、std dev(ρ _hv ) And carrying out weighted average processing.

and choose Z being less than 15 _h ＜50、K _dp ＞0、ρ _hv Calculation result of > 0.95And calculating results of all distance libraries meeting the conditions and obtaining an average value to obtain a systematic deviation factor Zhbias of the horizontal reflectivity.

As shown in fig. 2, another aspect of the embodiment of the present invention discloses a dual-polarization weather radar data quality real-time evaluation system, which includes:

In summary, the concept of the invention is as follows:

the radar data can be automatically processed by reading the radar body scan data file, after the data file is read, whether the data file belongs to radar echo data with evaluation value is automatically judged, different evaluation processing flows are applied according to the judgment result, and finally, the data quality evaluation result is output through the output module.

The quality evaluation flow of the radar-based data is as follows: after the device receives the radar data file, the radar data type is judged, if the judging data is single-layer PPI data, the process does not carry out clutter suppression overfactor calculation (the complete process is shown in figure 1), the algorithm judges whether the data file has evaluation value (namely, whether a sufficient weather echo distance library is used for evaluation), and when the echo data meets the evaluation condition (the echo belongs to weather echo), the sensitivity quality factor and Z are carried out _dr Quality factor, Z _h Quality factor, phi _dp And calculating a quality factor and a ground clutter suppression excess factor (under the condition that the data type belongs to a multilayer body scanning file and the evaluation condition is met), judging only ground clutter suppression deficiency factors if echo data does not meet the evaluation condition, and finally storing the result into a data server.

The method specifically comprises the following steps:

1. weather echo identification

If the weather echo and the non-weather echo cannot be correctly distinguished, radar data is polluted, and the evaluation result is greatly and negatively influenced and even wrong. Therefore, according to the problem, the radar echo data of the dual-polarized radar is researched and subjected to fuzzy logic processing, so that the weather echo and the non-weather echo are identified and classified in real time, and the method has important significance for radar-based data quality evaluation.

(a) Fuzzy logic processing

First, the volume-scan data level reflectivity Z of the radar returns is extracted _h Differential reflectance Z _dr Radial velocity V, differential phase shift Φ _dp And cross-correlation coefficient ρ _hv . Thereafter, differential reflectivity Z is calculated from the volume-scan data _dr Differential phase shift phi _dp And cross-correlation coefficient ρ _hv The standard deviation of the variables is calculated from the radially collected data in three alternative ranges of 3x3, 5x5, 7x7 from the library window. Standard deviation std dev (phi) of differential phase _dp ) Standard deviation std dev of differential reflectivity (Z _dr ) And standard deviation std dev (ρ) of the cross correlation coefficient _hv ) The calculation formula of (2) is as follows:

And->

Respectively at N _A ×N _R After invalid data is removed from the range of the distance library, the distance library is calculatedThe range of the differential phase shift mean value, the differential reflectivity mean value and the cross correlation coefficient mean value.

In the fuzzy logic algorithm, respectively allocated to the horizontal reflectivity Z _h Cross-correlation coefficient ρ _hv Radial velocity V, standard deviation std dev of differential phase (Φ) _dp ) Standard deviation std dev of differential reflectivity (Z _dr ) And standard deviation std dev (ρ) of the cross correlation coefficient _hv ) The six basic variables are weighted differently as shown in table 1.

Table 1: weights assigned to six variables in fuzzy logic processing

Variable(s)	Weighting of
		Horizontal reflectivity Z _h	1.0
Cross-correlation coefficient ρ _hv	1.0
		Radial velocity V	1.0
Standard deviation std dev (phi) of differential phase _dp )	2.0
		Standard deviation std dev of differential reflectivity (Z _dr )	2.0
Standard deviation std dev (ρ) of cross correlation coefficient _hv )	1.0

A weighted average a is determined at each range bin window as follows:

wherein W is _j Is the weight, PV _j Is the value of the variable for the j-th input. These physical quantities are subjected to blurring processing and then converted into criteria with values between 0 and 1, and if the numerical value of the criteria of the point is larger, the probability that the point belongs to the echo is higher. I.e. when the criterion of the radar echo at a point exceeds a certain threshold value, the point is identified as a weather echo; when the criterion of the radar echo for the point is less than a certain threshold value, the point is identified as a non-weather echo.

As shown in fig. 3 to 8, the fuzzy logic processes six membership functions: z is Z _h 、ρ _hv 、V、std dev(Φ _dp )、std dev(Z _dr ) And std dev (ρ _hv ) The number in brackets at the top of the graph is the weight for that variable, and PV (·) is the value of the membership function for a given input.

(b) Fuzzy logic processing of data

If the data is processed by fuzzy logic, the differential reflectivity Z of each distance base window in the base data is calculated _dr Differential phase shift phi _dp And cross-correlation coefficient ρ _hv On the basis of the calculated average, calculating the differential reflectivity Z on each distance library window _dr Differential phase shift phi _dp And cross-correlation coefficient ρ _hv Standard deviation of (2) and then for each distance library window data Z _h 、ρ _hv 、V、std dev(Φ _dp )、std dev(Z _dr )、std dev(ρ _hv ) And carrying out weighted average processing. After the processing is completed, the echo can be classified into a meteorological echo and a clear sky echo, and the subsequent algorithm can perform different processing according to different echo types.

In summary, as shown in fig. 9, the processing flow chart of echo identification includes:

ρ is found for values of 5*5 distance bins Not being NAN (Not Number of Not a Number) _hv 、Z _h Average value of V;

ρ is found for values of 5*5 distance bins that are not NAN _hv 、Z _dr 、Φ _dp Standard deviation of (2);

will ρ _hv 、Z _h Average value of V and ρ _hv 、Z _dr 、Φ _dp The standard deviation of (2) is converted into a corresponding membership function;

Weighting and averaging the values of all membership functions;

judging whether the weighted average value is larger than a set threshold value, if so, the distance library is a meteorological echo; if not, it is determined whether the weighted average value is = =nan, if so, the range bin is non-echo, if not, the range bin is non-meteorological echo;

and counting a meteorological echo distance library, judging whether the number of the meteorological echo distance library is larger than a set threshold, if so, judging that the data are meteorological echoes, and if not, judging that the data are clear sky echoes.

2. Systematic deviation factor Zdrbias of differential reflectivity

For raindrops with a diameter of less than 0.5mm, the ideal Z _dr Equal to 0dB, because the shape of a minute water droplet can be approximated as a sphere. The method can be applied to the conventional dual-polarization radar body sweep Z when the radar is detected at a low elevation angle _dr Data calculation Z _dr Systematic deviation ("low elevation rain method"). In a plurality of corrections Z _dr In the system deviation method, the method is simple and reliable, and a large amount of data is easy to obtain for correcting the deviation, so that the method of 'low elevation angle and small rain' is adopted to determine Z _dr Is a systematic deviation of (2). Since the bias value can generally reflect the performance state of the radar directly, thereby analyzing radar-based data quality, the bias value can be directly used as a differential reflectivity system bias factor. At ρ _hv Greater than 0.95, Z _h Estimating the average Z in the range of 5-20dBz _dr Deviation, which is taken as a systematic deviation factor of differential reflectivity(Zdrbias)。

Counting Z which is not less than 5 _h Z of 20 samples or less _dr Z is as follows _dr And making a scatter diagram, simultaneously fitting a straight line and taking a straight line Z _h Z at =10dbz _dr The value is taken as Zdrbias, and the flow chart is shown in fig. 10 and includes:

starting from the i=1th distance library, judging that the distance library is 5 < Z _h ＜20、ρ _hv > 0.95; if not, after i=i+1 is given, continuing to judge; if yes, counting all points meeting the condition, and fitting Z _h -Z _dr A straight line;

taking the straight line Z _h When=10, Z _dr As a systematic deviation factor (Zdrbias) of the differential reflectivity.

3. Reflectivity sensitivity factor ZhSensivitybias

The relationship between the precipitation echo power and radar technical parameters and the properties, strength, distance and the like of the precipitation are expressed by a radar weather equation:

wherein P is _r Is the average value (W) of the echo power received by the radar; p (P) _t Is the pulse power (W) emitted by the radar; g _e Is the effective gain of the radar antenna; θ, a,

The horizontal beam width and the vertical beam width (radians) of the antenna, respectively; τ is the transmit pulse width(s); lambda is the radar operating wavelength (m); c is the propagation velocity of electromagnetic wave 3×10 ⁸ (m/s); psi is the filling coefficient of the dewatering area in the space formed by the whole radar beam width and the pulse space length (1/2 tau c); k is an attenuation factor of electromagnetic waves when the electromagnetic waves propagate in space; m is the complex refractive index of rain; r is the distance (m) of the target; z is a precipitation reflection factor (m 3); ln2 is the natural logarithm of 2, taken 0.69315.

P in the equation _r Is the average power of the meteorological target returned to the receiver, and random receptionThe echo sampling and radar parameters have no definite relation, and the equation considers the scanning loss of the needle beam of the antenna in the calculation process, so that the equation is not required to be additionally corrected when in use. The received echo power in the radar weather equation is inversely proportional to the square of the distance and inversely proportional to the square of the wavelength.

The method is characterized in that a circular rotating parabolic antenna is commonly used for a weather radar, the needle-shaped beam width theta=phi of the circular rotating parabolic antenna is K=1, when quantitative detection is carried out within a range of 200km, the beam filling coefficient psi=1, and the minimum measurable Z of the dual-polarization weather radar can be calculated according to radar weather equation and radar technical parameters _min Values.

Therefore, the above method can be simplified to rewrite:

the minimum measurable Z of the dual-polarization weather radar can be calculated according to the radar weather equation and the technical parameters of the radar _min And calculating the nominal detection sensitivity according to the basic parameters, analyzing the actual detection sensitivity of the radar by taking the nominal detection sensitivity as a comparison condition, and judging the actual radar working performance condition according to the difference between the nominal detection sensitivity and the actual detection sensitivity, thereby analyzing the radar base data quality. The radar measured sensitivity is obtained by statistics of obtained body sweep data, the minimum reflectivity values of all radial distances at each 20km are counted, the minimum reflectivity values are different from the nominal values, the average difference value is obtained, the difference value is taken as a reflectivity sensitivity factor (ZHSensivitybias), and the flow chart is shown in figure 11 and comprises the following steps:

inputting radar equation parameters, and calculating the minimum measurable Z of the theoretical radar _min A curve;

counting the minimum reflectivity factor of each 20Km of the actual echo;

the difference between the theoretical curve and the actual echo at every 20Km is calculated and averaged.

The reflectance sensitivity factor (ZhSensivitybias) is output.

4. System bias factor Zhbias for horizontal reflectivity

Systematic deviations are often due to difficulty in accurately calibrating radar hardAnd errors during operation. Z is Z _h Can be evaluated based on the principle of parametric consistency. Can utilize the slave Z _h And Z _dr K reconstructed by consistency relationship _dpn K actually measured with radar _dpm Comparison to determine Z _h Is a systematic deviation of (2). The following formula is satisfied:

the a, the b and the c are consistency relation parameters, and can be obtained by measuring forward radar parameters through raindrop spectrum. Z is Z _h For the measured radar level reflectivity factor, Z _dr Is the measured differential radar reflectivity factor.

First, K is taken up _dpn 、Z _h 、Z _dr Converted into linear units:

Z _h z in (a) _hbias The following relationship may be used to obtain:

by consistency relation K _dpn And calculate the deviation Z _h(bias) Counting the effective value of the calculated result (namely, the non-NAN calculated result), and selecting 15 < Z _h ＜50，K _dp ＞0，ρ _hv The result of > 0.95 is used to ensure the reliability of the echo calculation result, and the system deviation factor Zhbias of the horizontal reflectivity can be obtained by calculating the results of all the distance libraries meeting the conditions and averaging them, and the flow chart is shown in fig. 12, and comprises:

acquisition of a、b、c、Z _h 、Z _dr 、K _dpn ；

By Z _h 、Z _dr 、K _dpn Is calculated by the consistency relation of Z _h(bias) Obtaining Z _h(bias) 。

5. Phase dependent quality factor

Φ _dp Quality factor is mainly used for estimating phi _dp Is a characteristic of initial phase, discrete and distributed noise. Normal phi _dp Random noise is subject to fluctuations within a reasonable range, but when the phase noise of the system is subject to large instabilities Φ _dp Both the initial phase and the random heave noise of (c) are greatly affected, so that phi can be estimated _dp Initial phase of phi _dp Phase folding of (c) and phi _dp To reflect the degree of stability of the system in phase.

(a) Initial phase factor InitialPdp evaluation method

The initial differential phase is typically obtained using a differential phase near the edge of the precipitation echo. A range radar of less than 3km _dp The data are affected by ground objects and noise, and even if there is a rainfall echo, the fluctuation is larger. The radial continuous 9 distance libraries (each distance library length is 300 m) are used as a window, and the zero-lag correlation coefficient (ρ in the second half of the window _hv ) Greater than T _p (zero hysteresis correlation coefficient threshold in window, the method takes 0.7, which can be modified by self) and Φ _dp Standard deviation is less than T _σ1 (within window Φ) _dp The method takes 10, which can be modified by itself), phi of the second half of the distance window _dp As the effective initial phi in the radial direction _dp Then calculate the effective initial phi for all radial directions of elevation _dp And averaging, this value is taken as an initial phase factor (InitialPdp), and the flowchart is shown in fig. 13, and includes:

acquiring phi _dp 、ρ _hv Setting T _p (zero hysteresis correlation coefficient threshold in window, 0.7, self-settable) and T _σ1 (within window Φ) _dp 10, which can be set by itself);

Starting from the first range bin i=1;

solving phi of the i-th to i+9-th distance library _dp Standard deviation of (2);

judging phi _dp Whether the standard deviation sigma is smaller than T _σ1 And ρ of the i+4 to i+5 distance bins _hv Whether or not it is greater than T _p If not, then i=i+1 is given and Φ from i to i+9 is found _dp Continuing to judge after the standard deviation of (2);

if yes, find Φ of i+4 to i+9 _dp And taking the value as the initial phase R of the radial direction;

solving all radial initial phases R;

and (5) averaging all radial initial phases R to obtain an initial phase factor InitialPdp.

(b) Phase folding factor FoldPdp evaluation method

Because of phi of radar _dp The measurable range is limited to a 180 ° interval, that is to say a 180 ° interval between the maximum and minimum measurable values. When there is large-scale strong precipitation, the phi of the far end of the precipitation echo _dp May be greater than 180 deg. from the maximum measurable value, where Φ _dp The values will fold, expressed as phi of two radially adjacent bins _dp A value near the opposite sign, maximum absolute measurable value; at this point there is a phase folding. Due to phi _dp Is a distance accumulation whose distribution over distance should be continuous and have a tendency to increase, so that the radial continuity check pair Φ _dp And performing unfolding treatment. Phase folding identification is performed by a method in Yanting wang (2009), and a flowchart is shown in fig. 14, including:

Acquiring phi _dp 、ρ _hv 、T _p And T _σ1 ；

Starting from the first range bin i=1;

solving phi from i to i+9 _dp Standard deviation sigma of (2);

find Φ of i to i+9 _dp Phi of i+4 to i+9 _dp Fitting a straight line slope S;

determine Φ of i to i+9 _dp Wherein-5 °/Km < S < 20 °/Km, if r=r+s Δr is given, if the next step is not performed, wherein Δr is the single distance bin length;

judging phi _dp -R < -80 °, if so, phase folding Foldpd assignment 1 occurs; if not, judging whether the last library is the last library;

if the last bin is not phase folding, foldpd is assigned 0, if not the last bin is assigned i=i+1 and Φ of i to i+9 _dp Phi of i+4 to i+9 _dp Fitting the slope S of the straight line, and continuing the subsequent judgment until the last library or obtaining the result of whether the distance folding occurs.

(c) Phase noise factor Noisedp evaluation method

The most significant radial is determined by counting the PPI data and evaluated. Due to phi _dp Is a distance accumulation, the distribution of which along with the distance should be continuous and have an increasing trend, so a curve can be fitted according to the data, and the radial phi can be counted _dp The phase noise factor can be obtained by calculating the standard deviation of the difference value (i.e. residual) between the phase noise factor and the fitting curve, and the specific implementation method is shown in fig. 15, and comprises the following steps:

input data phi _dp ；

Starting from a distance outside the clutter range (about 10 Km), for Φ _dp Fitting a curve;

solving residual errors of a curve fitted with the data;

standard deviation is calculated for the residual sequence;

the residual standard deviation is output as the phase noise factor noiseprp.

6. Correlation quality factor for clutter suppression of ground objects

In the quality control link of the base data output, ground clutter is often inhibited, however, an improper filtering mode in the inhibiting process may cause excessive clutter inhibition, and also serious clutter residues may be caused by the occurrence of larger instability of the improper filtering mode or system phase noise. Both phenomena have serious influence on the data quality of the radar, so that judging whether the situation occurs in the base data or not is taken as one of important basis for judging the data quality of the radar.

(a) Ground clutter suppression transition factor ClutterOverSupRec

More echoes can be observed at the bottom elevation, ground clutter often appears at the bottom elevation, in general, weather echoes appear at the high elevation, weather echoes also appear at the bottom elevation, and under the condition that weather echoes appear at the high elevation but no weather echoes appear at the bottom elevation, the situation of excessive ground clutter suppression may occur. In the data belonging to the meteorological echo classification, by comparing the echo recognition results of the high and low elevation angles, it can be judged whether or not the ground clutter suppression is excessive, and the flow chart is as shown in fig. 16, and includes:

after the base data file is subjected to echo identification, counting the number of distance banks of the near field of the base data file belonging to the meteorological echo, counting the number of distance banks of the near field of the high-layer body scanning file belonging to the meteorological echo, judging whether the difference value between the number of the meteorological echo of the high-layer body scanning file and the number of the meteorological echo of the low-layer body scanning file exceeds a threshold value, if so, carrying out ground clutter suppression on the base data file excessively, carrying out ClutterOverSupRec assignment 1, and if not, carrying out ground clutter suppression excessively on the base data file, carrying out ClutterOverSupRec assignment 0.

(b) Ground clutter suppression deficiency factor ClutterLessSupRec

When the filtering mode is improper, insufficient clutter suppression of the ground object may be caused. When the data file belongs to a clear sky echo, the distance library number belonging to a non-meteorological echo under the elevation angle of the bottom layer can be counted, and when the distance library number exceeds a certain threshold value, the body sweep is considered to have the phenomenon of insufficient ground clutter suppression, and the flow is shown in a figure 17 and comprises the following steps:

after echo identification is carried out on the base data file, the number of non-meteorological echoes of each distance library under the elevation angle of the bottom layer is counted, whether the number of the non-meteorological echoes exceeds a threshold value is judged, if yes, the base data file is insufficient in clutter suppression, if not, the base data file is insufficient in clutter suppression, and if yes, the base data file is not sufficient in clutter suppression, and if yes, the base data file is 0.

7. Result output

In the process of performing the above evaluation, each step will output an evaluation result, and comprehensive judgment needs to be made by combining with the current radar hardware working state according to the evaluation result, and finally the evaluated radar-based data quality is given out in a scoring form, that is, the real-time evaluation method of the dual-polarization weather radar data quality further includes step S6 (which may be executed by the result output module), where step S6 is specifically as follows:

The score calculation formula is:

P＝∑S _i ×W _i

wherein P is the total score, S _i For quality factor score, W _i Is the scoring weight.

The quality factors involved in scoring include:

sequence number	Quality factor	Scoring weight
				1	Dual channel balance	0.2
2	Detection sensitivity	0.2
			3	Systematic deviation	0.15
4	Ground clutter suppression quality	0.05
			5	Radar status	0.5

The last score without updated score, the initial default score is zero score.

In the scoring rule, the quality factor weight is adjustable, the quality factor scoring interval is adjustable, and the quality factor scoring standard is adjustable.

1. Dual channel balance

Evaluation results	Scoring device
			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

2. Systematic deviation

Evaluation results	Scoring device
			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

3. Detection sensitivity

4. Ground clutter suppression quality factor

5. Radar status

The above embodiments are provided to illustrate the present invention and not to limit the present invention, so that the modification of the exemplary values or the replacement of equivalent elements should still fall within the scope of the present invention.

From the foregoing detailed description, it will be apparent to those skilled in the art that the present invention can be practiced without these specific details, and that the present invention meets the requirements of the patent statutes.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention. The foregoing description of the preferred embodiment of the invention is not intended to be limiting, but rather to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.

It should be noted that the above description of the flow is only for the purpose of illustration and description, and does not limit the application scope of the present specification. Various modifications and changes to the flow may be made by those skilled in the art under the guidance of this specification. However, such modifications and variations are still within the scope of the present description.

While the basic concepts have been described above, it will be apparent to those of ordinary skill in the art after reading this application that the above disclosure is by way of example only and is not limiting of the present application. Although not explicitly described herein, various modifications, improvements, and adaptations of the present application are possible for those of ordinary skill in the art. Such modifications, improvements, and modifications are intended to be suggested within this application, and are therefore within the spirit and scope of the exemplary embodiments of this application.

Meanwhile, the present application uses specific words to describe embodiments of the present application. For example, "one embodiment," "an embodiment," and/or "some embodiments" means a particular feature, structure, or characteristic in connection with at least one embodiment of the present application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present application may be combined as suitable.

Furthermore, those of ordinary skill in the art will appreciate that aspects of the invention may be illustrated and described in terms of several patentable categories or circumstances, including any novel and useful processes, machines, products, or materials, or any novel and useful improvements thereof. Thus, aspects of the present application may be implemented entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or a combination of hardware and software. The above hardware or software may be referred to as a "unit," module, "or" system. Furthermore, aspects of the present application may take the form of a computer program product embodied in one or more computer-readable media, wherein the computer-readable program code is embodied therein.

Computer program code required for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb.net, python, etc., a conventional programming language such as C programming language, visualBasic, fortran2103, perl, COBOL2102, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer, or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.

Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application and are not intended to limit the order in which the processes and methods of the application are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present application. For example, while the implementation of the various components described above may be embodied in a hardware device, it may also be implemented as a purely software solution, e.g., an installation on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed herein and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, the inventive subject matter should be provided with fewer features than the single embodiments described above.

Claims

1. The real-time evaluation method for the quality of the dual-polarization weather radar data is characterized by comprising the following steps of:

if the base data file is VOL data, executing the following steps S2 to S5;

wherein each calculation result is output as an evaluation result.

2. The method for real-time assessment of dual-polarization weather radar data quality according to claim 1, wherein in step S2, the method for echo identification comprises:

and->

3. The method for real-time assessment of dual-polarization weather radar data quality according to claim 2, wherein in step S2, the method for echo identification further comprises:

4. The method for real-time assessment of dual-polarization weather radar data quality according to claim 3, wherein the calculation process of the system deviation factor of differential reflectivity comprises:

5. The method for real-time assessment of dual-polarization weather radar data quality according to claim 4, wherein the calculation process of the reflectivity sensitivity factor comprises:

6. The method for real-time assessment of dual-polarization weather radar data quality according to claim 5, wherein the calculation process of the systematic deviation factor of the horizontal reflectivity comprises:

7. The method for real-time assessment of dual-polarization weather radar data quality according to claim 6, wherein the calculation process of the phase-dependent quality factor comprises:

8. The method for real-time assessment of dual-polarization weather radar data quality according to claim 7, wherein the process of calculating the ground clutter suppression transition factor comprises:

9. The method for real-time assessment of dual-polarization weather radar data quality according to claim 8, wherein the calculation process of the ground clutter suppression deficiency factor comprises:

10. A dual polarization weather radar data quality real-time assessment system, comprising:

the data processing module is used for calculating a system deviation factor of differential reflectivity, a reflectivity sensitivity factor, a system deviation factor of horizontal reflectivity, a phase correlation quality factor, a ground clutter suppression excessive factor and a ground clutter suppression insufficient factor;