CN116559820A - X-band-based dual-polarization weather radar particle phase state identification method - Google Patents
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Abstract
The invention discloses a phase state identification method of weather radar particles based on X-band double polarization, which comprises the following steps: preprocessing radar variables, and calculating the credibility of the radar variables based on the radar variables obtained by preprocessing; calculating the height of the melting layer according to the radar variable; respectively calculating aggregation values corresponding to all radar variables obtained by preprocessing; based on the aggregate values, radar returns are classified. On the basis of the fuzzy logic principle, the method adds the credibility probability and the hard threshold value, and improves the accuracy of phase identification of X-band dual-polarization weather radar particles.
Description
Technical Field
The invention relates to the technical field of weather radar, in particular to a particle phase state identification method based on X-band dual-polarization weather radar.
Background
The Doppler weather radar in China is being updated, and has the function of double polarization gradually. The updated Doppler radar can provide information about the shape, composition, phase and the like of the hydrogel, which is beneficial to further understanding, quantification and pre-predictionAnd (5) measuring weather. Single polarization doppler radar can only measure the reflectivity factor Z, radial velocity V and velocity spectrum width SW. The radial velocities V and SW measured by doppler radar represent the mean and standard deviation, respectively, of the radial velocity of the scatterer. The reflectivity factor Z, while providing microscopic physics information directly, clearly does not fully characterize complex clouds and precipitation microscopic physics, and therefore efforts and attempts have been made to increase the number of radar measurements to better understand and characterize weather conditions through frequency, wavelength, polarization diversity. The upgraded double-polarization radar also has the following products: differential reflectanceCross-correlation coefficient->Differential phase shift->Differential phase shift->Etc., these radar data have not been fully utilized. If the radar data can accurately classify the condensate, severe weather detection and early warning and quantitative precipitation estimation and prediction can be improved to a great extent.
In China, with the development of radar cooperative networking technology, an X-band dual-polarization radar is gradually put into service application, and the X-band dual-polarization radar has an increasing effect on quantitative precipitation estimation, particle phase identification and the like. How to classify the condensate accurately, scholars at home and abroad put forward a lot of solutions, wherein the fuzzy logic principle forms the basis of the particle phase state recognition algorithm. In the application of the particle phase recognition algorithm, an inaccurate particle phase recognition result is caused due to errors possibly existing in radar measurement and antenna beam broadening at a longer distance.
Disclosure of Invention
In view of the above, the present invention provides a method for identifying phase states of particles based on X-band dual-polarization weather radar, so as to solve the above technical problems.
The invention discloses a method for manufacturing a semiconductor device, which comprises the following steps:
step 1: preprocessing radar variables, and calculating the credibility of the radar variables based on the radar variables obtained by preprocessing;
step 2: calculating the height of the melting layer according to the radar variable;
step 3: respectively calculating aggregation values corresponding to all radar variables obtained by preprocessing;
step 4: based on the aggregate values, radar returns are classified.
Further, the radar variable includes: horizontal polarization reflectance factor Z, differential reflectanceCross-correlation coefficient->Differential phase shift->Differential phase shift->Radial velocity V; the horizontal polarization reflectivity factor Z represents the sum of 6 times of the diameter of precipitation particles in a unit volume; differential reflectivity->Characterizing the ratio of the spatial orientation and the long and short axes of the particles; cross-correlation coefficient->A measure representing the similarity of horizontally and vertically polarized pulses within a pulse volume; differential phase shift->A phase change caused by a water fall region representing the same motion state for horizontally polarized waves and vertically polarized waves; differential phase shift rate->Representing the horizontal and vertical directionsRadial derivative of the differential phase shift of the pulse; the radial velocity V represents the velocity of the particles in the same direction as the radar, the direction towards the radar is negative, and the direction away from the radar is positive;
the preprocessing of radar variables comprises:
by horizontal polarization reflectivity factor Z and differential phase shiftCalculating a horizontal polarization reflectance factor standard deviation SD (Z) and a differential phase shift standard deviation SD (++>)；
Differential phase shift rateConverting into logarithmic expression form to obtain +.>；
For the horizontal polarization reflectivity factor Z, differential reflectivityDifferential phase shift->And cross-correlation coefficient->Radial smoothing to obtain->、/>、/> and />；/>Horizontal polarization reflectivity factor representing radial smoothing, +.>Differential reflectivity representing radial smoothing, +.>Differential phase shift representing radial smoothing, +.>Representing cross-correlation coefficients of the radial smoothing process;
for the smoothed horizontal polarization reflectivity factorAnd differential reflectivity->Performing attenuation correction to obtain horizontal polarization reflectivity factor +.>And differential reflectivity->The correction amount is composed of the smoothed +.>To determine.
Further, the standard deviation SD (Z) of the horizontal polarization reflectivity factor and the standard deviation SD of the differential phase shift are equal to each other) The calculation formulas of (a) are respectively as follows:
wherein ,for the calculation range in radial direction, +.> and />Are 2 x>Horizontal polarization reflectivity factor Z and differential phase shift +.>Is a mean value of (c).
Further, the method comprises the steps of,expressed as:
radial smoothing is carried out on the horizontal polarization reflectivity factor Z by using a first average window; for differential reflectivityDifferential phase shift->And cross-correlation coefficient->Radial smoothing is respectively carried out by using a second average window; wherein the width of the second average window is greater than the width of the first average window.
Further, the calculation formula of the correction amount is as follows:
will correct the amount and />Respectively with the horizontal polarization reflectivity factor after the smoothing treatment->And differential reflectivity after smoothing +.>Adding to obtain horizontal polarization reflectivity factor after precipitation attenuation correction>And differential reflectivity->。
Further, the formula for calculating the trusted probability is as follows:
wherein ,、/>、/>、/>、/>、/>respectively the horizontal polarization reflectivity factor after attenuation correction>Differential reflectivity after attenuation correction>Cross-correlation coefficient of radial smoothing>Differential phase shift->Standard deviation of the horizontal polarization reflectivity factor SD (Z) and standard deviation of the differential phase shift SD (+)>) Is a trusted probability of (1); />Differential phase shift representing radial smoothing, +.>A fixed parameter representing the differential phase shift versus the horizontal polarization reflectivity factor Z when calculating the confidence probability of the horizontal polarization reflectivity factor, snr is the signal to noise ratio, +.>A fixed signal-to-noise ratio parameter representing the probability of calculating the horizontal polarization reflectivity factor>For the degree of radar beam blocking, +.>A fixed parameter representing differential reflectivity of a differential phase-shift pair when calculating a trusted probability of differential reflectivity, < +.>Representing differential reflectivity +.>Relative to the variation of elevation and azimuth, +.>Representing differential reflectivity +.>Fixed parameters with respect to elevation and azimuth +.>A first fixed parameter representing the difference in cross-correlation coefficients when calculating the probability of confidence +.>A signal-to-noise ratio fixed parameter for differential reflectivity representing the probability of calculating the differential reflectivity>Represents the horizontal polarization reflectivity factor after attenuation correction,representing differential reflectivity after attenuation correction, < ->A second fixed parameter representing the difference in cross-correlation coefficients when calculating the probability of confidence +.>A fixed signal-to-noise ratio parameter representing the probability of calculating the cross-correlation coefficient>Representing differential phase shiftFor the variation of elevation and azimuth, +.>A fixed parameter representing the differential phase shift difference when calculating the confidence probability of the differential phase shift ratio,/for>A signal-to-noise ratio fixed parameter for differential phase shift representing the probability of calculating the differential phase shift>Representing differential phase shift +.>And the horizontal polarization reflectivity factor after attenuation correction +.>For the variation of elevation and azimuth, +.>A signal-to-noise ratio fixed parameter for the standard deviation of the horizontal polarization reflectivity factor representing the calculated probability of confidence in the standard deviation of the horizontal polarization reflectivity factor,/>Is the width of the unidirectional 3 dB antenna pattern, < >> and />Is the elevation and azimuth of the antenna.
Further, the step 2 includes:
using the attenuated corrected horizontal polarization reflectivity factorDifferential reflectivity after attenuation correction>And radial smoothing cross-correlation coefficient +>The radar scan data of (2) determining the top and bottom of the melt layer;
if the radar echo meets the following conditions, identifying the height of the radar echo as the height of a melting layer; wherein the top height of the melting layer is marked as Ht, and the bottom height of the melting layer is marked as Hb;
cross correlation coefficient of radial smoothingThe horizontal polarization reflectivity factor after attenuation correction is between 0.90 and 0.97>Between 30 dBZ and 47 dBZ, with differential reflectivity after attenuation correction +.>Between 0.8 dB and 2.5 dB.
Further, the step 3 includes:
the aggregate values of all radar echoes to be identified are calculated respectively by the following additive aggregation formula:
wherein ,then indicate the likelihood associated with the ith radar return wave,/th radar return wave>Is a membership function, and characterizes the distribution of the jth radar variable of the ith radar echo; />Is the weight value assigned to the j-th variable of the i-th radar echo,is the confidence probability assigned to the j-th variable.
Further, the radar returns include ground clutter, including clutter due to anomalous propagation, biological returns, dry snow, wet snow, crystals, aragonite, heavy droplets, light and medium rain, heavy rain, rain and hail mixtures;
the step 4 comprises the following steps:
determining tilt ranges Rbb, rb, rt and Rtt according to geometric projections of intersection points of top height Ht and bottom height Hb of the melting layer and radar beams with radar emission elevation angles of 0 °, 0.5 ° and 1 °, respectively;
when 0 is<R<When the radar echo is ground object echo, biological echo, big drops, small rain, medium rain, heavy rain, rain and hail mixture; wherein R is the radial distance of the radar echo;
when (when)<R</>When the radar echo is ground object echo, biological echo, wet snow, aragonite, big drops, light rain and medium rain, heavy rain, rain and hail mixture;
when (when)<R</>The radar echo is ground object echo, biological echo, dry snow, wet snow, aragonite, big drops, rain and hail mixture;
when (when)<R</>The radar echo is ground object echo, biological echo, dry snow, wet snow, crystal, aragonite, rain and hail mixture;
when R is>When the radar echo is a mixture of dry snow, crystals, aragonite, rain and hail;
and sequencing each aggregate value of all radar echoes to be identified from high to low, wherein the echo type represented by the maximum aggregate value is the classification result.
Further, the method further comprises the following steps: by setting a hard threshold condition to suppress misclassification of all radar returns to be identified:
when the radial velocity V is more than 1 m/s, the radar echo to be identified cannot be judged as the ground object echo;
when the cross-correlation coefficient>0.97, the radar echo to be identified cannot be judged as a biological echo;
when the reflectivity is differential>2 dB, the radar echo to be identified cannot be judged as dry snow;
when the horizontal polarization reflectivity factor Z is less than 20 dBZ, the radar echo to be identified cannot be judged to be wet snow;
when the reflectivity is differential<At 0 dB, the radar echo to be identified cannot be determined as wet snow;
when the horizontal polarization reflectivity factor Z is more than 40 dBZ, the radar echo to be identified cannot be judged as a crystal;
when the horizontal polarization reflectivity factor Z <10 dBZ or the horizontal polarization reflectivity factor Z >60 dBZ, the radar echo to be identified cannot be judged to be aragonite;
when the reflectivity is differential<f2 (Z) -0.3, the radar echo to be identified cannot be judged to be a big drop;；
when the horizontal polarization reflectivity factor Z is more than 50 dBZ, the radar echo to be identified cannot be judged to be light rain or medium rain;
when the horizontal polarization reflectivity factor Z is less than 30 dBZ, the radar echo to be identified cannot be judged to be heavy rain;
when the horizontal polarization reflectivity factor Z <40 dBZ, the radar echo to be identified cannot be determined as a rain and hail mixture.
Due to the adoption of the technical scheme, the invention has the following advantages:
based on the traditional fuzzy logic algorithm, a trusted probability and a hard threshold are added. The credible probability characterizes possible influence of radar measurement errors, provides greater flexibility for algorithm optimization, and the hard threshold can reduce the number of obvious error class assignment.
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 apparent that the drawings in the following description are only some embodiments described in the embodiments of the present invention, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a diagram of a trapezoidal membership function according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the geometry of a radar beam relative to a ablation layer according to an embodiment of the present invention;
FIGS. 3 (a) to 3 (f) are respectively elevation 7.5℃based data PPI diagrams of an embodiment of the present invention;
FIG. 4 is a graph of a temperature profile and a melting layer cross-over plot obtained from sounding data in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a particle phase recognition result according to an embodiment of the present invention;
FIGS. 6 (a) to 6 (f) are respectively views of dual polarized radar-based data PPI with an elevation angle of 0.5 DEG according to an embodiment of the present invention;
FIGS. 7 (a) and 7 (b) are schematic diagrams showing the results of the particle phase identification and the results of the particle phase identification of HCA products in the United states, respectively, according to embodiments of the present invention;
fig. 8 is a schematic diagram showing an application of a particle phase recognition algorithm in a far-reaching radar terminal system according to an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and examples, wherein it is apparent that the examples described are only some, but not all, of the examples of the present invention. All other embodiments obtained by those skilled in the art are intended to fall within the scope of the embodiments of the present invention.
The particle phase state recognition algorithm provided by the invention is mainly based on X-band dual-polarization parameters, adopts a fuzzy logic principle to recognize the particle phase state, and mainly distinguishes 10 radar echoes: (1) clutter including clutter due to abnormal propagation, (2) biological echoes, (3) dry snow, (4) wet snow, (5) crystals, (6) aragonite, (7) large drops, which refer to rain water with a size distribution prone to large rain drops, (8) light and medium rain, (9) heavy rain, and (10) a mixture of rain and hail. The particle phase recognition results are inaccurate due to errors that may exist in radar measurements and antenna beam broadening at longer distances. In order to solve the problems of radar measurement errors and beam broadening, a trusted probability and a hard threshold are added on the basis of a traditional fuzzy logic algorithm. The credible probability characterizes possible influence of radar measurement errors, provides greater flexibility for algorithm optimization, and the hard threshold can reduce the number of obvious error class assignment.
The invention provides an embodiment of a phase state identification method of weather radar particles based on X-band dual polarization, which mainly comprises the following steps:
s1, 6 radar variables are input: (1) a horizontal polarization reflectivity factor Z; (2) Differential reflectanceThe method comprises the steps of carrying out a first treatment on the surface of the (3) Cross-correlation coefficient->The method comprises the steps of carrying out a first treatment on the surface of the (4) Differential phase shift->The method comprises the steps of carrying out a first treatment on the surface of the (5) Differential phase shift rate->The method comprises the steps of carrying out a first treatment on the surface of the (6) radial velocity V. By means of a horizontal polarization reflectivity factor Z and a differential phase shift +.>Calculating the standard deviation SD (Z) of the horizontal polarization reflectivity factor and the standard deviation SD of the differential phase shift). SD (Z) and SD (>) The calculation formula of (2) is as follows:
wherein ,is defined as the calculation range in radial direction (present method +.>The value is 2, and can be adjusted according to actual conditions); and />Are 2 x>Within the radial distance range, the horizontal polarization reflectivity factor Z and differential phase shift within the radial distance range are calculated>Is a mean value of (c).
Due to differential phase shift rateThe parameter range of (2) is large, therefore, the parameter +.>The definition formula is as follows:
s2, quality control is carried out on the input data. Before running the classification program, the horizontal polarization reflectivity factor Z and the differential reflectivity Z are required to be calculated DR Differential phase shiftAnd cross-correlation coefficient->Radial smoothing is performed. An average window of 1km was used for the horizontal polarization reflectivity factor Z, for differential reflectivity +.>Differential phase shift->And cross-correlation coefficient->Radial smoothing was performed using windows of 2km, respectively. In addition, the horizontal polarization reflectance factor Z and the differential reflectance are to be taken as +.>Performing attenuation correction, wherein the correction amount is differential phase shift processed by radial smoothing>To determine, the correction amount is formulated as follows:
finally, the correction quantity and the horizontal polarization reflectivity factor after the smoothing treatment are carried outAnd differential reflectivity after smoothing +.>Adding to obtain horizontal polarization reflectivity factor after precipitation attenuation correction>And differential reflectivity->。
S3, calculating the credibility. The quality of radar echo classification is significantly affected by the accuracy of radar measurements, which have certain deviations and noise. In the aggregation rule, the confidence probability F takes into account the influence of the measurement error. The calculation formula of the six parameter credibility probabilities is as follows:
wherein ,,/>,/>,/>,/>，,/>. snr is signal to noise ratio, < >>For the degree of beam blocking (this parameter can be estimated from the azimuth and elevation digital elevation map),>is the width of the unidirectional 3 dB antenna pattern, < >> and />Is the elevation and azimuth of the antenna.
In the formulas (8) to (10),
wherein ,、/>、/>、/>、/>、/>respectively the horizontal polarization reflectivity factor after attenuation correction>Differential reflectivity after attenuation correction>Cross-correlation coefficient of radial smoothing>Differential phase shift->Standard deviation of the horizontal polarization reflectivity factor SD (Z) and standard deviation of the differential phase shift SD (+)>) Is a trusted probability of (1); />Differential phase shift representing radial smoothing, +.>A fixed parameter representing the differential phase shift versus the horizontal polarization reflectivity factor Z when calculating the confidence probability of the horizontal polarization reflectivity factor, snr is the signal to noise ratio, +.>A fixed signal-to-noise ratio parameter representing the probability of calculating the horizontal polarization reflectivity factor>For the degree of radar beam blocking, +.>A fixed parameter representing differential reflectivity of a differential phase-shift pair when calculating a trusted probability of differential reflectivity, < +.>Representing differential reflectivity +.>Relative to the variation of elevation and azimuth, +.>Representing differential reflectivity +.>Fixed parameters with respect to elevation and azimuth +.>A first fixed parameter representing the difference in cross-correlation coefficients when calculating the probability of confidence +.>A signal-to-noise ratio fixed parameter for differential reflectivity representing the probability of calculating the differential reflectivity>Represents the horizontal polarization reflectivity factor after attenuation correction,indicating after the attenuation correctionDifferential reflectivity, +.>A second fixed parameter representing the difference in cross-correlation coefficients when calculating the probability of confidence +.>A fixed signal-to-noise ratio parameter representing the probability of calculating the cross-correlation coefficient>Representing differential phase shiftFor the variation of elevation and azimuth, +.>A fixed parameter representing the differential phase shift difference when calculating the confidence probability of the differential phase shift ratio,/for>A signal-to-noise ratio fixed parameter for differential phase shift representing the probability of calculating the differential phase shift>Representing differential phase shift +.>And the horizontal polarization reflectivity factor after attenuation correction +.>For the variation of elevation and azimuth, +.>A signal-to-noise ratio fixed parameter for the standard deviation of the horizontal polarization reflectivity factor representing the calculated probability of confidence in the standard deviation of the horizontal polarization reflectivity factor,/>Is the width of the unidirectional 3 dB antenna pattern, < >> and />Is the elevation and azimuth of the antenna.
S4, calculating the height of the melting layer. Using radar body scan data (horizontal polarization reflectivity factor after attenuation correctionDifferential reflectivity after attenuation correction>And radial smoothing cross-correlation coefficient +>) To determine the top and bottom of the melt layer. If the cross-correlation coefficient->Between 0.90 and 0.97, the horizontal polarization reflectivity factor +.>Between 30 dBZ and 47 dBZ with differential reflectivity +.>Between 0.8 dB and 2.5 dB, the height at which the radar echo satisfies the above condition is identified as the melt level. The top height of the melt layer is labeled Ht and the bottom height of the melt layer is labeled Hb.
S5, calculating an aggregation value. The 6 variables involved in the aggregation operation are respectively: horizontal polarization reflectivity factor after attenuation correctionDifferential reflectivity after attenuation correction>Cross-correlation coefficient of radial smoothing>、/>Standard deviation of the horizontal polarization reflectivity factor SD (Z) and standard deviation of the differential phase shift SD (+)>). The method adopts the following addition polymerization formula:
the method mainly distinguishes 10 radar echoes: (1) Ground clutter, including clutter due to anomalous propagation; (2) a biological scatterer; (3) dry snow; (4) wet snow; (5) a crystal; (6) aragonite; (7) Large drops, which refer to rain water, the size distribution of which tends to be large drops; (8) light and medium rain; (9) heavy rain; (10) a mixture of rain and hail. For the 10 radar echoes, an aggregate value is correspondingly calculated(i=1, 2, ·,10, the same applies below), and each aggregate value +.>The likelihood associated with each echo is indicated. />Is a membership function, the distribution of the j-th variable characterizing the i-th radar echo (j=1, 2, (6, the same applies below); />A weight value between 0 and 1 being assigned to the j-th variable of the i-th radar echo; />Is the confidence probability assigned to the j-th variable.
As shown in FIG. 1, the abscissa X represents radar parameter values and the ordinate P (X) represents membership of radar echoesFunction values. In the present embodiment, four parameters are used，/>，/> and />To describe a trapezoidal function. As shown in Table 1, membership functions for 10 radar echoes and 6 parameters are given.
TABLE 1 membership functions of 10 radar returns
Differential reflectance and />In the membership functions of (2), function->-/> and />-/>Determination of the parameters in Table 1->-And is given by: />
Wherein the unit of radar reflectivity Z is dBZ.
The basic parameters of the weights W are listed in table 2.
TABLE 2 weight matrix
S6, echo classification. As shown in fig. 2, is the geometry of the radar beam relative to the ablation layer. Wherein the abscissa represents radial distance and the ordinate represents height. Two black lines represent the top and bottom of the melt layer, respectively. Three black curves represent radar beams with radar transmit elevation angles of 0 °, 0.5 ° and 1 °. The four black dot dashed lines represent the determination of the tilt ranges Rbb, rb, rt and Rtt from the geometrical projections of the melt layer top and bottom heights Ht and Hb intersecting radar beams at 0 °, 0.5 ° and 1 ° radar emission elevation angles.
Fig. 2 is a diagram of the geometry of a radar beam relative to a ablation layer. Radar beam axes with radar transmit elevation angles of 0 °, 0.5 ° and 1 ° are plotted with a black thick solid line. The black dot dashed lines represent the tilt ranges Rbb, rb, rt and Rtt, and the two horizontal, thick solid lines represent the heights of the bottom and top of the molten layer, respectively. Corresponds to the geometric projection from the radar beam onto the substantially sloped ablation layer. The heights are shown in table 3, allowing the following radar echoes to occur over five tilt range intervals:
TABLE 3 Classification criteria
Based on the classification standard of table 3, each aggregate value a of the 10 radar echoes is ranked from high to low, and the echo type represented by the maximum aggregate value is the classification result.
S7, in order to check whether the result is reasonable, a set of hard thresholds is used to reduce the number of obvious error category assignments. For example, if Z is 40 dBZ, identifying hail is misclassified. In this case, the algorithm accepts the radar echo class with the next highest aggregate value. As shown in table 4, an empirical "hard threshold" is specified for suppressing significant errors.
TABLE 4 empirical "hard threshold" for suppression of obvious false designations "
The method can identify important precipitation categories (hail, heavy rain, and attribute of the melting layer) and simultaneously reduce obvious misclassification to the greatest extent. Improvements have been made to achieve this goal.
The first place of improvement was the probability of trust, which can quantify the data quality of each radar variable. Beam filling non-uniformity, attenuation, statistical errors, partial beam blocking, and noise all affect the quality of the classification scheme. In the fuzzy logic scheme, the influence of all the factors on radar measurement is measured by using a trapezoidal function and additional weights, and data with poor quality are endowed with lower credibility probability, and data with higher quality are endowed with higher credibility probability.
The second place of improvement is to add a hard threshold, which mainly plays a role of 'leak detection and deficiency repair', and reduces the number of obvious error category assignment.
The following example uses data from the Chengdu long-range YW-X3-B model X-band dual-polarization Doppler weather radar, data time 2019, 10 months, 24 days 13:59 (Beijing time). As shown in fig. 3 (a) to 3 (f), the base data PPI is at an elevation angle of 7.5 °. From the slave、/> and />In the PPI plot of (c), a significant melting layer can be observed.
As shown in fig. 4, the temperature profile and the melting layer alignment chart are the probe data, the abscissa indicates the temperature, and the ordinate indicates the height. Two thick solid lines represent the top and bottom heights of the melt layer, respectively. The data obtained in the vicinity of the Wenjiang observation station at 2019, 10, 24, 20:00 (Beijing) are shown as thin black solid lines indicating temperature. From the figure, the top of the melting layer is intersected with a temperature profile line of 0 ℃ to verify the effectiveness of the melting layer identification algorithm. As shown in fig. 5, the particle phase recognition result is shown.
The results of this process were compared to the HCA product of NOAA in the United states. This time was used with dual polarized radar data from the city of oklahoma, usa, time 2015, month 16, day 22:42 (universal time). As shown in fig. 6 (a) to 6 (f), the elevation angle is 0.5 ° base data PPI.
As shown in fig. 7 (a) and 7 (b), the particle phase recognition result obtained using this algorithm is substantially identical to that of the us HCA product.
The method has been applied to the double-line polarized Doppler weather radar (YW-X1-B, YW-X3-B/BM, YLD6-D and other types of radar) with the X-band of the Chengdu long-range. Fig. 8 is an application display interface of the particle phase recognition algorithm in the adult long range radar terminal system. Through a large number of case tests, the algorithm obtains better particle phase recognition effect for the case with obvious melting layer.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The method for identifying the phase state of the weather radar particles based on the X-band double polarization is characterized by comprising the following steps:
step 1: preprocessing radar variables, and calculating the credibility of the radar variables based on the radar variables obtained by preprocessing;
step 2: calculating the height of the melting layer according to the radar variable;
step 3: respectively calculating aggregation values corresponding to all radar variables obtained by preprocessing;
step 4: based on the aggregate values, radar returns are classified.
2. The method of claim 1, wherein the radar variable comprises: horizontal polarization reflectance factor Z, differential reflectanceCross-correlation coefficient->Differential phase shift->Differential phase shift->Radial velocity V; the horizontal polarization reflectivity factor Z represents the sum of 6 times of the diameter of precipitation particles in a unit volume; differential reflectivity->Characterizing the ratio of the spatial orientation and the long and short axes of the particles; cross-correlation coefficient->A measure representing the similarity of horizontally and vertically polarized pulses within a pulse volume; differential phase shift->A phase change caused by a water fall region representing the same motion state for horizontally polarized waves and vertically polarized waves; differential phase shift rate->Representing horizontal and vertical pulse differencesRadial derivative of the fractional phase shift; the radial velocity V represents the velocity of the particles in the same direction as the radar, the direction towards the radar is negative, and the direction away from the radar is positive;
the preprocessing of radar variables comprises:
by horizontal polarization reflectivity factor Z and differential phase shiftCalculating a horizontal polarization reflectance factor standard deviation SD (Z) and a differential phase shift standard deviation SD (++>)；
Differential phase shift rateConverting into logarithmic expression form to obtain +.>；
For the horizontal polarization reflectivity factor Z, differential reflectivityDifferential phase shift->And cross-correlation coefficient->Radial smoothing to obtain->、/>、/> and />；/>Horizontal polarization reflectivity factor representing radial smoothing, +.>Differential reflectivity representing radial smoothing, +.>Differential phase shift representing radial smoothing, +.>Representing cross-correlation coefficients of the radial smoothing process;
for the smoothed horizontal polarization reflectivity factorAnd differential reflectivity->Performing attenuation correction to obtain horizontal polarization reflectivity factor +.>And differential reflectivity->The correction amount is composed of the smoothed +.>To determine.
3. The method according to claim 2, wherein the horizontal polarization reflectivity factor standard deviation SD (Z) and differential phase shift standard deviation SD #) The calculation formulas of (a) are respectively as follows:
wherein ,for the calculation range in radial direction, +.> and />Are 2 x>Horizontal polarization reflectivity factor Z and differential phase shift +.>Is a mean value of (c).
4. The method of claim 2, wherein the step of determining the position of the substrate comprises,expressed as:
radial smoothing of the horizontal polarization reflectivity factor Z using a first averaging windowAnd (3) managing; for differential reflectivityDifferential phase shift->And cross-correlation coefficient->Radial smoothing is respectively carried out by using a second average window; wherein the width of the second average window is greater than the width of the first average window.
5. The method according to claim 2, wherein the calculation formula of the correction amount is:
will correct the amount and />Respectively with the horizontal polarization reflectivity factor after the smoothing treatment->And differential reflectivity after smoothing +.>Adding to obtain horizontal polarization reflectivity factor after precipitation attenuation correction>And differential reflectivity/>。
6. The method of claim 5, wherein the formula for calculating the probability of trustworthiness thereof is:
wherein ,、/>、/>、/>、/>、/>respectively, the horizontal polarization reflectivity factors after attenuation correctionDifferential reflectivity after attenuation correction>Cross-correlation coefficient of radial smoothing>Differential phase shift->Standard deviation of the horizontal polarization reflectivity factor SD (Z) and standard deviation of the differential phase shift SD (+)>) Is a trusted probability of (1); />Differential phase shift representing radial smoothing, +.>A fixed parameter representing the differential phase shift versus the horizontal polarization reflectivity factor Z when calculating the confidence probability of the horizontal polarization reflectivity factor, snr is the signal to noise ratio, +.>A fixed signal-to-noise ratio parameter representing the probability of calculating the horizontal polarization reflectivity factor>For the degree of radar beam blocking, +.>A fixed parameter representing differential reflectivity of a differential phase-shift pair when calculating a trusted probability of differential reflectivity, < +.>Representing differential reflectivity +.>Relative to the variation of elevation and azimuth, +.>Representing differential reflectivity +.>Fixed parameters with respect to elevation and azimuth +.>A first fixed parameter representing the difference in cross-correlation coefficients when calculating the probability of confidence +.>A signal-to-noise ratio fixed parameter for differential reflectivity representing the probability of calculating the differential reflectivity>Represents the horizontal polarization reflectivity factor after attenuation correction, < ->Representing differential reflectivity after attenuation correction, < ->A second fixed parameter representing the difference in cross-correlation coefficients when calculating the probability of confidence +.>A fixed signal-to-noise ratio parameter representing the probability of calculating the cross-correlation coefficient>Representing differential phase shift +.>For the variation of elevation and azimuth, +.>A fixed parameter representing the differential phase shift difference when calculating the confidence probability of the differential phase shift ratio,/for>Representing a signal-to-noise ratio fixed parameter for the differential phase shift rate when calculating the confidence probability for the differential phase shift rate,representing differential phase shift +.>And the horizontal polarization reflectivity factor after attenuation correction +.>For the variation of elevation and azimuth, +.>A signal-to-noise ratio fixed parameter for the standard deviation of the horizontal polarization reflectivity factor representing the calculated probability of confidence in the standard deviation of the horizontal polarization reflectivity factor,/>Is the width of the unidirectional 3 dB antenna pattern, < >> and />Is the elevation and azimuth of the antenna.
7. The method according to claim 1, wherein the step 2 comprises:
using the attenuated corrected horizontal polarization reflectivity factorDifferential reflectivity after attenuation correction>And radial smoothing cross-correlation coefficient +>The radar scan data of (2) determining the top and bottom of the melt layer;
if the radar echo meets the following conditions, identifying the height of the radar echo as the height of a melting layer; wherein the top height of the melting layer is marked as Ht, and the bottom height of the melting layer is marked as Hb;
cross correlation coefficient of radial smoothingThe horizontal polarization reflectivity factor after attenuation correction is between 0.90 and 0.97Between 30 dBZ and 47 dBZ, with differential reflectivity after attenuation correction +.>Between 0.8 dB and 2.5 dB.
8. The method according to claim 1, wherein the step 3 comprises:
the aggregate values of all radar echoes to be identified are calculated respectively by the following additive aggregation formula:
wherein ,then indicate the likelihood associated with the ith radar return wave,/th radar return wave>Is a membership function, and characterizes the distribution of the jth radar variable of the ith radar echo; />Is the weight value assigned to the j-th variable of the i-th radar echo,/for the radar echo>Is the confidence probability assigned to the j-th variable.
9. The method of claim 1, wherein the radar returns comprise clutter including clutter due to abnormal propagation, biological returns, dry snow, wet snow, crystals, aragonite, big drops, light and medium rain, heavy rain, a mixture of rain and hail;
the step 4 comprises the following steps:
determining tilt ranges Rbb, rb, rt and Rtt according to geometric projections of intersection points of top height Ht and bottom height Hb of the melting layer and radar beams with radar emission elevation angles of 0 °, 0.5 ° and 1 °, respectively;
when 0 is<R<When the radar echo is ground object echo, biological echo, big drops, small rain, medium rain, heavy rain, rain and hail mixture; wherein R is the radial distance of the radar echo;
when (when)<R</>When the radar echo is ground object echo, biological echo, wet snow, aragonite, big drops, light rain and medium rain, heavy rain, rain and hail mixture;
when (when)<R</>The radar echo is ground object echo, biological echo, dry snow, wet snow, aragonite, big drops, rain and hail mixture;
when (when)<R</>The radar echo is ground object echo, biological echo, dry snow, wet snow, crystal, aragonite, rain and hail mixture;
when R is>When the radar echo is a mixture of dry snow, crystals, aragonite, rain and hail;
and sequencing each aggregate value of all radar echoes to be identified from high to low, wherein the echo type represented by the maximum aggregate value is the classification result.
10. The method as recited in claim 9, further comprising: by setting a hard threshold condition to suppress misclassification of all radar returns to be identified:
when the radial velocity V is more than 1 m/s, the radar echo to be identified cannot be judged as the ground object echo;
when the cross-correlation coefficient>0.97, the radar echo to be identified cannot be judged as a biological echo;
when the reflectivity is differential>2 dB, the radar echo to be identified cannot be judged as dry snow;
when the horizontal polarization reflectivity factor Z is less than 20 dBZ, the radar echo to be identified cannot be judged to be wet snow;
when the reflectivity is differential<At 0 dB, the radar echo to be identified cannot be determined as wet snow;
when the horizontal polarization reflectivity factor Z is more than 40 dBZ, the radar echo to be identified cannot be judged as a crystal;
when the horizontal polarization reflectivity factor Z <10 dBZ or the horizontal polarization reflectivity factor Z >60 dBZ, the radar echo to be identified cannot be judged to be aragonite;
when the reflectivity is differential<f2 (Z) -0.3, the radar echo to be identified cannot be judged to be a big drop; ；
when the horizontal polarization reflectivity factor Z is more than 50 dBZ, the radar echo to be identified cannot be judged to be light rain or medium rain;
when the horizontal polarization reflectivity factor Z is less than 30 dBZ, the radar echo to be identified cannot be judged to be heavy rain;
when the horizontal polarization reflectivity factor Z <40 dBZ, the radar echo to be identified cannot be determined as a rain and hail mixture.
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