CN113406644A - Weather radar data quality control method, device and equipment - Google Patents

Weather radar data quality control method, device and equipment Download PDF

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CN113406644A
CN113406644A CN202110885643.9A CN202110885643A CN113406644A CN 113406644 A CN113406644 A CN 113406644A CN 202110885643 A CN202110885643 A CN 202110885643A CN 113406644 A CN113406644 A CN 113406644A
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dual
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polarization radar
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CN113406644B (en
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张�林
李峰
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CMA Meteorological Observation Centre
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
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Abstract

The embodiment of the disclosure discloses a method, a device and equipment for processing weather radar data, wherein the method comprises the following steps: acquiring dual-polarization radar data; processing the dual-polarization radar data according to a dual-polarization radar data quality control algorithm to obtain a dual-polarization radar labeling result; determining a first initial radar data processing model, taking the dual-polarization radar data at a first moment as input, taking the dual-polarization radar labeling result at the first moment as output, and training the first initial weather data processing model to obtain a dual-polarization radar data processing model; and acquiring real-time Doppler radar data, and processing the real-time Doppler radar data according to the dual-polarization radar data processing model to acquire a Doppler radar real-time identification result. This technical scheme can improve the recognition effect to the clutter, and the precipitation echo and the clutter are distinguished to the accuracy of being convenient for, have reduced the misjudgement rate to the precipitation echo.

Description

Weather radar data quality control method, device and equipment
Technical Field
The disclosure relates to the field of atmospheric detection and atmospheric remote sensing, in particular to a method, a device and equipment for controlling the data quality of a weather radar.
Background
Since the birth of radar, people begin to use the radar to research precipitation detection and measurement, and weather radars with wider application comprise a Doppler radar and a dual-polarization radar. Compared with a Doppler radar, the dual-polarization radar data acquired by the dual-polarization radar has more measurement information (characteristic parameters such as differential reflectivity, differential phase, correlation coefficient and the like), a plurality of polarization quantities containing raindrop spectrum information can be provided, and the plurality of polarization quantities can better represent micro physical characteristics of precipitation, so that the dual-polarization radar can accurately distinguish precipitation echoes and clutters, and accuracy of meteorological detection and forecasting is improved. Due to the influence of the above factors, the dual-polarization radar becomes the mainstream of the current weather radar. However, although the number of doppler radars is small compared to the number of dual polarization radars, doppler radars are still an indispensable part of weather prediction systems at present. Because the recognition rate of the Doppler radar for clutter such as ground objects, super refraction, electromagnetic interference, clear sky and the like is not high, the rainfall echo and the clutter are difficult to distinguish, and the misjudgment rate of the Doppler radar for the rainfall echo is high, so that the method has important research significance on how to accurately recognize the clutter.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device and equipment for controlling the data quality of a weather radar.
In a first aspect, an embodiment of the present disclosure provides a weather radar data quality control method, including:
acquiring dual-polarization radar data collected by a dual-polarization radar;
processing the dual-polarization radar data according to a dual-polarization radar data quality control algorithm to obtain a dual-polarization radar labeling result;
determining a first initial radar data processing model, taking the dual-polarization radar data at a first moment as input, taking the dual-polarization radar labeling result at the first moment as output, and training the first initial weather data processing model to obtain a dual-polarization radar data processing model;
and acquiring real-time Doppler radar data acquired by the Doppler radar in real time, and processing the real-time Doppler radar data according to the dual-polarization radar data processing model to acquire a Doppler radar real-time identification result, wherein the distance between the Doppler radar and the dual-polarization radar is smaller than or equal to a radar distance threshold value.
Further, before determining a first initial radar data processing model, taking the dual-polarization radar data at the first moment as input, taking the dual-polarization radar labeling result at the first moment as output, and training the first initial weather data processing model to obtain the dual-polarization radar data processing model, the method further comprises:
acquiring the accuracy of the labeling result of the dual-polarization radar according to a dual-polarization quality control inspection algorithm;
performing error correction processing on the dual-polarization radar labeling result with the accuracy rate smaller than or equal to the accuracy rate threshold value according to an error correction processing algorithm to obtain the dual-polarization radar labeling result after error correction processing;
and determining the dual-polarization radar labeling result at the first moment in the dual-polarization radar labeling result after error correction processing and the dual-polarization radar labeling result with the accuracy rate larger than the accuracy rate threshold value.
Further, before acquiring real-time doppler radar data acquired by the doppler radar in real time and processing the real-time doppler radar data according to the dual-polarization radar data processing model to acquire a doppler radar real-time identification result, the method further includes:
processing the dual-polarization radar data at the second moment according to the dual-polarization radar data processing model to obtain a dual-polarization radar identification result;
acquiring the similarity of the identification result of the dual-polarization radar and the identification result of the dual-polarization radar marking result at the second moment;
the real-time Doppler radar data of Doppler radar real-time collection is obtained, and the real-time Doppler radar data is processed according to the dual-polarization radar data processing model to obtain the Doppler radar real-time identification result, and the method comprises the following steps:
and when the similarity of the identification result is greater than or equal to the similarity threshold of the identification result, acquiring real-time Doppler radar data, and processing the real-time Doppler radar data according to the dual-polarization radar data processing model to acquire the real-time Doppler radar identification result.
Further, the method further comprises:
acquiring Doppler radar data collected by a Doppler radar;
determining a second initial radar data processing model, taking Doppler radar data at a third moment as input, and training the second initial weather data processing model according to a dual-polarization radar labeling result at the third moment to obtain the Doppler radar data processing model;
and processing the real-time Doppler radar data according to the Doppler radar data processing model to obtain a target Doppler real-time weather identification result.
Further, the method for acquiring the labeling result of the dual-polarization radar by processing the dual-polarization radar data according to the dual-polarization radar data quality control algorithm comprises the following steps:
obtaining cross-correlation coefficient rho between horizontally polarized radar returns and vertically polarized radar returns according to dual-polarized radar dataHV；
According to rhocor＝ρhv×(1+1/100.1SNR) Obtaining a noise reduction cross-correlation coefficient rhocorWherein SNR is a signal ratio parameter measured by the dual-polarization radar;
and acquiring a dual-polarization radar labeling result according to the noise reduction cross correlation coefficient.
Further, the method further comprises:
obtaining differential reflectivity factor Z of precipitation system according to dual-polarization radar dataDR；
When cross-correlation coefficient ρcorGreater than 0.9, according to
Figure BDA0003194053920000021
Obtaining a differential reflectance factor horizontal texture ZDRTexture, and is based on
Figure BDA0003194053920000031
Obtaining correlation coefficient horizontal texture rhoHVTexture of which N isATo be an index value identifying the orientation of the window, NRIs an index value that is the windowed distance;
obtaining a dual-polarization radar labeling result according to the noise reduction cross correlation coefficient, comprising:
and acquiring a dual-polarization radar labeling result according to the differential reflectivity factor horizontal texture and the correlation coefficient horizontal texture.
Further, the method further comprises:
acquiring a horizontal polarization reflectivity factor of the precipitation system according to the dual-polarization radar data;
and filling the precipitation echo cavity according to the horizontal polarization reflectivity factor on the dual-polarization radar labeling result.
Further, the method further comprises:
acquiring a horizontal polarization reflectivity factor Z of the precipitation system and an 18dBz echo top height ETOP of the precipitation system according to the number of the dual-polarization radars18dBzAnd 0dBz echo top height ETOP of precipitation system0dBzAnd the slope distance r from the storm monomer nucleus to the dual-polarization radar station in the precipitation systemstorm_coreAnd the range of the single body to the dual-polarization radar reaching station observed in the precipitation system;
when cross-correlation coefficient ρcorLess than or equal to 0.9 and cross-correlation coefficient rhocor18dBz echo top height ETOP of precipitation system18dBzAnd the horizontal polarization reflectivity factor Z of the precipitation system satisfies rhocor＜0.95∩(ETOP18dBzandZ is more than 45dBz) when the sum is more than 8.0km, determining that the precipitation system is hail;
when cross-correlation coefficient ρcorLess than or equal to 0.9, and cross-correlation coefficient rhocor0dBz echo top height ETOP of precipitation system0dBzAnd the slope distance r from the storm monomer nucleus to the dual-polarization radar station in the precipitation systemstorm_coreAnd the range of the slant of the single body to the dual-polarization radar station observed in the precipitation system meets rhocor＜0.95∩(ETOP0dBz＞9.0km∩range＞rstorm_core) And meanwhile, determining that the precipitation system is filled with the non-uniform wave beams.
In a second aspect, an embodiment of the present invention provides a weather radar data quality control processing apparatus, including:
a dual polarization radar data acquisition module configured to acquire dual polarization radar data acquired by a dual polarization radar;
the dual-polarization radar data processing module is configured to process dual-polarization radar data according to a dual-polarization radar data quality control algorithm and obtain a dual-polarization radar labeling result;
the dual-polarization model training module is configured to determine a first initial radar data processing model, take dual-polarization radar data at a first moment as input, take a dual-polarization radar labeling result as output, train the first initial weather data processing model and obtain a dual-polarization radar data processing model;
and the Doppler radar data processing module is configured to acquire real-time Doppler radar data acquired by the Doppler radar in real time and process the real-time Doppler radar data according to the dual-polarization radar data processing model so as to acquire a Doppler radar real-time identification result, and the distance between the Doppler radar and the dual-polarization radar is smaller than or equal to a radar distance threshold value.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement any one of the methods in the first aspect.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
the present disclosure learns dual polarization radar data collected by a dual polarization radar and dual polarization radar labeling results obtained by processing the dual polarization radar data according to a dual polarization radar data quality control algorithm using a first initial radar data processing model, the dual-polarization radar data processing model obtained after training can learn the dual-polarization radar data quality control algorithm, and then the dual-polarization radar data processing model is applied to the Doppler radar data collected by the Doppler radar, the radar detection radar can be accurately distinguished from the clutter, and the misjudgment rate of the real-time identification result of the doppler radar echo is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:
FIG. 1 shows a flow diagram of a method of weather radar data processing according to an embodiment of the present disclosure;
FIG. 2 illustrates a flow diagram of a method of weather radar data processing according to an embodiment of the present disclosure;
FIG. 3 shows a flow diagram of a method of weather radar data processing according to an embodiment of the present disclosure;
FIG. 4 illustrates a flow diagram of a method of weather radar data processing according to an embodiment of the present disclosure;
FIG. 5 shows a flow diagram of a method of weather radar data processing according to an embodiment of the present disclosure;
FIG. 6 shows a flow diagram of a method of weather radar data processing, according to an embodiment of the present disclosure;
FIG. 7 shows a flow diagram of a method of weather radar data processing, according to an embodiment of the present disclosure;
FIG. 8 shows a schematic block diagram of an air radar data processing apparatus according to an embodiment of the present disclosure;
FIG. 9 shows a schematic block diagram of an electronic device according to an embodiment of the present disclosure;
fig. 10 shows a schematic structural diagram of an electronic device for implementing a weather radar data method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.
In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, actions, components, parts, or combinations thereof, and do not preclude the possibility that one or more other features, numbers, steps, actions, components, parts, or combinations thereof are present or added.
It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Since the birth of radar, people begin to use the radar to research precipitation detection and measurement, and weather radars with wider application comprise a Doppler radar and a dual-polarization radar. Compared with a Doppler radar, the dual-polarization radar data acquired by the dual-polarization radar has more measurement information (characteristic parameters such as differential reflectivity, differential phase, correlation coefficient and the like), a plurality of polarization quantities containing raindrop spectrum information can be provided, and the plurality of polarization quantities can better represent the micro-physical characteristics of precipitation, so that the dual-polarization radar can accurately distinguish precipitation echoes, namely the phase states of cloud and rain targets, so as to determine the precipitation echoes and the non-precipitation echoes. The rainfall echoes mainly comprise convective rainfall echoes, layered cloud rainfall echoes, typhoon rainfall echoes and the like, and the non-rainfall echoes mainly comprise ground object/super-refraction clutter, noise/isolated point clutter, electromagnetic interference clutter, sea wave echoes, clear sky echoes and the like. By 6 months in 2021, 216 parts of weather radars are co-established in China, wherein 68 parts of dual-polarization radar upgrading sites are provided, a weather radar service network for monitoring large, medium and small-scale disastrous weather is formed, and the monitoring capability for structural evolution of large-scale and medium-scale weather systems is improved.
Doppler radar is still an indispensable part of weather prediction systems at present. Because the recognition rate of the Doppler radar for clutter such as ground objects, super refraction, electromagnetic interference, clear sky and the like is not high, the rainfall echo and the clutter are difficult to distinguish, and the misjudgment rate of the Doppler radar for the rainfall echo is high, so that the method has important research significance on how to accurately recognize the clutter.
In order to solve the above problems, the present disclosure learns dual polarization radar data collected by a dual polarization radar by using a first initial radar data processing model and dual polarization radar labeling results obtained by processing the dual polarization radar data according to a dual polarization radar data quality control algorithm, the dual-polarization radar data processing model obtained after training can learn the dual-polarization radar data quality control algorithm, and then the dual-polarization radar data processing model is applied to the Doppler radar data collected by the Doppler radar, the radar detection radar can be accurately distinguished from the clutter, and the misjudgment rate of the real-time identification result of the doppler radar echo is reduced.
The details of the embodiments of the present disclosure are described in detail below with reference to specific embodiments.
Fig. 1 shows a flowchart of a weather radar data processing method according to an embodiment of the present disclosure, and as shown in fig. 1, the method for inverting visibility in a fog area includes the following steps S101 to S104:
in step S101, dual polarization radar data collected by the dual polarization radar is acquired.
In step S102, the dual-polarization radar data is processed according to the dual-polarization radar data quality control algorithm, and a dual-polarization radar labeling result is obtained.
And determining precipitation echoes and non-precipitation echoes in the radar echoes according to the dual-polarization radar labeling result.
In step S103, a first initial radar data processing model is determined, the dual-polarization radar data at the first time is used as input, the dual-polarization radar labeling result at the first time is used as output, and the first initial weather data processing model is trained to obtain the dual-polarization radar data processing model.
The first initial radar data processing model may be a Linknet image semantic segmentation network model, and the Linknet image semantic segmentation network model may include a first convolution layer with a size of 7 × 7, a pooling layer with a size of 3 × 3, 4 encoders, 4 decoders, a first convolution layer with a size of 3 × 3, a second convolution layer with a size of 3 × 3, and a second deconvolution layer with a size of 2 ×.2.
In step S104, real-time doppler radar data acquired by the doppler radar in real time is acquired, and the real-time doppler radar data is processed according to the dual-polarization radar data processing model to acquire a doppler radar real-time identification result.
And determining precipitation echoes and non-precipitation echoes in the radar echoes according to the Doppler radar real-time identification result.
The distance between the Doppler radar and the dual-polarization radar is smaller than or equal to a radar distance threshold value. Specifically, considering that weather conditions faced by a weather radar with an excessively long distance may be greatly different due to the influence of geographical factors, it is possible to ensure that the weather conditions faced by the doppler radar and the dual-polarization radar are relatively similar by limiting the distance between the doppler radar and the dual-polarization radar. For example, the radar distance threshold may be 200 km.
The present disclosure learns dual polarization radar data collected by a dual polarization radar and dual polarization radar labeling results obtained by processing the dual polarization radar data according to a dual polarization radar data quality control algorithm using a first initial radar data processing model, the dual-polarization radar data processing model obtained after training can learn the dual-polarization radar data quality control algorithm, and then the dual-polarization radar data processing model is applied to the Doppler radar data collected by the Doppler radar, the radar detection radar can be accurately distinguished from the clutter, and the misjudgment rate of the real-time identification result of the doppler radar echo is reduced.
In an optional implementation manner of this embodiment, as shown in fig. 2, fig. 2 shows a flowchart of a weather radar data processing method according to an embodiment of the present disclosure, before step S103, the weather radar data processing method may further include the following steps:
in step S105, the accuracy of the labeling result of the dual polarization radar is obtained according to the dual polarization quality control verification algorithm.
In step S106, performing error reconnaissance processing on the dual-polarization radar labeling result with the accuracy rate less than or equal to the accuracy rate threshold according to an error reconnaissance processing algorithm to obtain an error reconnaissance processed dual-polarization radar labeling result.
In step S107, a dual-polarization radar labeling result at the first time is determined from the double-polarization radar labeling result after the error correction processing and the double-polarization radar labeling result with the accuracy greater than the accuracy threshold.
In the optional implementation mode, although the overall quality control effect of the dual-polarization radar labeling result is good, some examples with poor effects still exist, the examples are often concentrated at night of 9 months, due to the fact that in autumn the near-ground inverse temperature effect causes super refraction and large clear-sky echo area, the dual-polarization radar data quality control algorithm cannot effectively identify, and the examples with poor effects account for about 10% of all dual-polarization radar labeling results. Through the steps, the dual-polarization radar labeling results of which the accuracy is smaller than or equal to the accuracy threshold can be determined, the examples are subjected to error correction processing, the accuracy is over-critical, data which can be used for training are prevented from being lost, and the training speed is accelerated on the premise that the training reliability is not reduced.
In an optional implementation manner of this embodiment, fig. 3 shows a flowchart of a weather radar data processing method according to an embodiment of the present disclosure, as shown in fig. 3, before step S104, the weather radar data processing method may further include the following steps:
in step S108, the dual-polarization radar data at the second time is processed according to the dual-polarization radar data processing model to obtain a dual-polarization radar recognition result.
Wherein the dual-polarization radar data at the second moment may account for 20% of the total dual-polarization radar data. Correspondingly, the dual-polarization radar labeling result at the second time may also account for 20% of all the dual-polarization radar labeling results.
In step S109, the similarity between the dual polarization radar identification result and the dual polarization radar labeling result at the second time is obtained.
In step S104, real-time doppler radar data acquired by the doppler radar in real time is acquired, and the real-time doppler radar data is processed according to the dual-polarization radar data processing model to acquire a doppler radar real-time identification result, which can be implemented through step S1041:
in step S1041, when the similarity of the recognition result is greater than or equal to the similarity threshold of the recognition result, acquiring real-time doppler radar data, and processing the real-time doppler radar data according to the dual-polarization radar data processing model to acquire a real-time recognition result of the doppler radar.
In the optional implementation manner, the dual-polarization radar data processing model is used for processing the dual-polarization radar data at the second moment to obtain a dual-polarization radar recognition result, and the similarity between the dual-polarization radar recognition result and the dual-polarization radar marking result at the second moment is obtained, the similarity between the recognition result and the recognition result can reflect the accuracy of the trained dual-polarization radar data processing model in recognizing precipitation echoes and non-precipitation clutter, when the similarity between the recognition result and the recognition result is greater than or equal to the similarity threshold value of the recognition result, the accuracy of the trained dual-polarization radar data processing model in recognizing the precipitation echoes and the non-precipitation clutter is higher, at the moment, real-time Doppler radar data is obtained, and the real-time Doppler radar data is processed according to the dual-polarization radar data processing model to obtain a Doppler radar real-time recognition result, the real-time identification result of the Doppler radar can be ensured to have higher accuracy for identifying the precipitation echo and the non-precipitation clutter.
In an optional implementation manner of this embodiment, fig. 4 is a flowchart illustrating a weather radar data processing method according to an embodiment of the present disclosure, and as shown in fig. 4, the weather radar data processing method may further include the following steps:
in step S110, doppler radar data acquired by the doppler radar is acquired.
In step S111, a second initial radar data processing model is determined, doppler radar data at a third time is used as input, a dual-polarization radar labeling result at the third time is used as output, and the second initial weather data processing model is trained to obtain the doppler radar data processing model.
And the second initial radar data processing model can also be a Linknet image semantic segmentation network model.
In step S112, the real-time doppler radar data is processed according to the doppler radar data processing model to obtain a target doppler real-time weather identification result.
In the optional implementation mode, the Doppler radar data acquired by the Doppler radar is processed by the second initial radar data processing model and the dual-polarization radar labeling result acquired by the dual-polarization radar data is processed according to the dual-polarization radar data quality control algorithm, so that the dual-polarization radar data processing model obtained after training can learn how to obtain the dual-polarization radar labeling result according to the Doppler radar data, and then the Doppler radar data processing model is applied to the Doppler radar data acquired by the Doppler radar, so that the recognition effect of the real-time recognition result of the Doppler radar on ground objects/super-refraction clutter, electromagnetic interference clutter and noise/isolated point clutter is improved, and the misjudgment rate of the real-time recognition result of the target radar on precipitation echoes is reduced.
In an optional implementation manner of this embodiment, fig. 5 shows a flowchart of a weather radar data processing method according to an embodiment of the present disclosure, and as shown in fig. 5, step S102 in the weather radar data processing method may be implemented as the following steps:
in step S1021, a cross-correlation coefficient ρ between a horizontally polarized radar return and a vertically polarized radar return is acquired from dual-polarized radar dataHV。
In step S1022, according to ρcor＝ρhv×(1+1/100.1SNR) ObtainTaking noise reduction cross correlation coefficient rhocorWherein SNR is a signal ratio parameter measured by the dual-polarization radar;
in step S1023, a dual polarization radar labeling result is obtained according to the noise reduction cross-correlation coefficient.
In this optional implementation, since the signal-to-noise ratio of the dual-polarization radar is often unstable, the obtained cross-correlation coefficient is easily affected by the signal-to-noise ratio and is smaller than a normal value, and a certain error exists. By obtaining the noise reduction cross correlation coefficient and obtaining the dual-polarization radar labeling result according to the noise reduction cross correlation coefficient, misjudgment on partial precipitation echoes caused by errors of the cross correlation coefficient can be reduced.
In an optional implementation manner of this embodiment, fig. 6 is a flowchart illustrating a weather radar data processing method according to an embodiment of the present disclosure, and as shown in fig. 6, before step S1023, the weather radar data processing method may further include:
in step S113, a differential reflectivity factor Z of the precipitation system is obtained according to the dual-polarization radar dataDR；
In step S114, when the cross-correlation coefficient ρ is zerocorGreater than 0.9, according to
Figure BDA0003194053920000081
Obtaining a differential reflectance factor horizontal texture ZDRTexture, and is based on
Figure BDA0003194053920000082
Obtaining correlation coefficient horizontal texture rhoHV_Texture。
Wherein N isATo be an index value identifying the orientation of the window, NRIs an index value that is the distance windowed.
In step S1023, a dual-polarization radar labeling result is obtained according to the noise reduction cross-correlation coefficient, which can be implemented by the following steps:
in step S1123, a dual-polarization radar labeling result is obtained according to the differential reflectivity factor horizontal texture and the correlation coefficient horizontal texture.
Wherein the texture ρ is horizontal according to the correlation coefficientHVTexture can distinguish precipitation echo from non-precipitation echo, and particularly, the Texture structure of the precipitation echo is uniform, and the correlation coefficient horizontal Texture rho of the Texture is horizontalHVTexture value is small, but the non-precipitation echo Texture structure is coarse, its correlation coefficient horizontal Texture ρHVThe value of _ Texture is large. The horizontal texture Z may then be based on the differential reflectivity factorDRTexture filters residual clutter.
In this alternative implementation, the horizontal texture ρ is scaled by the number of relationsHVTexture and differential reflectance factor horizontal Texture ZDRTexture identifies precipitation echoes and non-precipitation echoes, so that a dual-polarization radar labeling result is obtained, and the effective identification rate of the precipitation echoes and the non-precipitation echoes can be improved.
In an optional implementation manner of this embodiment, fig. 7 is a flowchart illustrating a weather radar data processing method according to an embodiment of the present disclosure, and as shown in fig. 7, before step S103, the weather radar data processing method may further include the following steps:
in step S115, a horizontal polarization reflectivity factor of the precipitation system is obtained from the dual polarization radar data.
In step S116, filling precipitation echo cavities in the labeling result of the dual-polarization radar according to the horizontal polarization reflectivity factor.
When the rainfall echo cavity filling is carried out on the dual-polarization radar labeling result according to the horizontal polarization reflectivity factor, a median method can be adopted. For example, in a window of 9 × 9 centered on the echo cavity point, if the number of effective echoes accounts for 70% or more of the total number of windows, the average value of the horizontal polarized reflectance factors of the effective echoes in the 9 × 9 window may be used instead of the value of the horizontal polarized reflectance factor of the echo cavity point, where the unit of the horizontal polarized reflectance factor is (mm6/m 3). Specifically, the reflectivity factor dBz value in the 9 × 9 window is converted into (mm6/m3) and then averaged, and finally the averaged (mm6/m3) value is converted into the reflectivity factor dBz value.
Since the correlation coefficient of most precipitation echoes is greater than 0.95 and the correlation coefficient of only few precipitation echoes is less than 0.7, some precipitation echo points may be misjudged as non-precipitation echoes to form precipitation echo cavities.
In the optional implementation mode, the horizontal polarization reflectivity factor of the precipitation system is obtained according to the dual-polarization radar data, and precipitation echo holes are filled in the labeling result of the dual-polarization radar according to the horizontal polarization reflectivity factor, so that the probability that the precipitation echo points are judged as non-precipitation echoes by mistake to form the precipitation echo holes can be reduced, and the effective identification rate of the precipitation echo and the non-precipitation echo is improved.
In an optional implementation manner of this embodiment, the weather radar data processing method may further include the following steps:
acquiring a horizontal polarization reflectivity factor Z of a precipitation system and an 18dBz echo top height ETOP of the precipitation system according to the dual-polarization radar data18dBzAnd 0dBz echo top height ETOP of precipitation system0dBzAnd the slope distance r from the storm monomer nucleus to the dual-polarization radar station in the precipitation systemstorm_coreAnd the range of the single body to the dual-polarization radar station observed in the precipitation system.
When cross-correlation coefficient ρcorLess than or equal to 0.9 and cross-correlation coefficient rhocor18dBz echo top height ETOP of precipitation system18dBzAnd the horizontal polarization reflectivity factor Z of the precipitation system satisfies rhocor＜0.95∩(ETOP18dBzAndz > 8.0km and >45dBz), determining that the precipitation system is hail.
When cross-correlation coefficient ρcorLess than or equal to 0.9, and cross-correlation coefficient rhocor0dBz echo top height ETOP of precipitation system0dBzAnd the slope distance r from the storm monomer nucleus to the dual-polarization radar station in the precipitation systemstorm_coreAnd the range of the slant of the single body to the dual-polarization radar station observed in the precipitation system meets rhocor＜0.95∩(ETOP0dBz＞9.0km∩range＞rstorm_core) And meanwhile, determining that the precipitation system is filled with the non-uniform wave beams.
Among the radar echoes, the correlation coefficient value of the water echo is large and is more than 0.95, and the correlation coefficient of the non-precipitation echo is small and is less than 0.7. When the correlation coefficient of the precipitation echo is less than 0.95 or even lower, the possibility of non-uniform beam filling in the observation process of hail, convection monomer and ice-water mixture is considered to be higher. When the value of the correlation coefficient decreases with a large gradient, or due to a change in the gradient, the cross-correlation coefficient p is reduced within a pulse periodHVThis phenomenon is called non-uniform beam filling. When the horizontal and vertical beam resolutions of the dual-polarization radar are both 1 degree, in the observation process of convective rainfall, if hail or a convection monomer exists in a beam irradiation body and the convection monomer is not filled with a 1 degree beam, non-uniform beam filling is easy to occur, and the longer the distance is, the more obvious the non-uniform beam filling phenomenon is along with the broadening effect of the radar beam, and the cross correlation coefficient rho isHV。
In this alternative implementation, due to hail, convective singlets and non-uniform beam filling typically occur during strong convective precipitation with high echo intensity and echo top height. When the correlation coefficient in a distance bank is less than 0.95, the corresponding reflectivity factor value and echo top height ETOP (18dBz) and ETOP (0dBz) values of the distance bank are checked. When the phase relation number in the distance bin is less than 0.95, the reflectivity factor value is higher than 45dBz, and the 18dBz echo top height value is higher than 8km, the precipitation system is identified as hail. When high reflectivity factor values (Z >45dBz) continuously appear in the radial direction and the length exceeds 1km, the precipitation system is considered as a storm monomer core and is far away from the storm monomer core, and when the echo top height of 0dBz is more than 9km, the non-uniform wave beam is identified as being filled. Thereby improving the accuracy of identifying hail and non-uniform beam congestion.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.
Fig. 8 shows a schematic block diagram of a weather radar data processing apparatus according to an embodiment of the present disclosure. The air radar data processing device can be realized by software, hardware or a combination of the two to become part or all of the electronic equipment. As shown in fig. 8, the air radar data processing apparatus includes:
a dual polarization radar data acquisition module 201 configured to acquire dual polarization radar data acquired by the dual polarization radar.
The dual-polarization radar data processing module 202 is configured to process dual-polarization radar data according to a dual-polarization radar data quality control algorithm to obtain a dual-polarization radar labeling result;
and the dual-polarization model training module 203 is configured to determine a first initial radar data processing model, take the dual-polarization radar data at the first moment as input, take the dual-polarization radar labeling result at the first moment as output, train the first initial weather data processing model, and obtain the dual-polarization radar data processing model.
And the Doppler radar data processing module 204 is configured to acquire real-time Doppler radar data acquired by the Doppler radar in real time and process the real-time Doppler radar data according to the dual-polarization radar data processing model to acquire a Doppler radar real-time identification result, wherein the distance between the Doppler radar and the dual-polarization radar is smaller than or equal to a radar distance threshold value.
The technical scheme provided by the embodiment of the disclosure is that through utilizing a first initial radar data processing model to learn dual-polarization radar data collected by a dual-polarization radar and processing the dual-polarization radar data according to a dual-polarization radar data quality control algorithm to obtain a dual-polarization radar labeling result, the dual-polarization radar data processing model obtained after training can learn the dual-polarization radar data quality control algorithm, and then the dual-polarization radar data processing model is applied to the Doppler radar data collected by the Doppler radar, the radar detection radar can be accurately distinguished from the clutter, and the misjudgment rate of the real-time identification result of the doppler radar echo is reduced.
The present disclosure also discloses an electronic device, fig. 9 shows a schematic structural block diagram of an electronic device according to an embodiment of the present disclosure, as shown in fig. 9, the electronic device 300 includes a memory 301 and a processor 302; wherein the content of the first and second substances,
the memory 301 is used to store one or more computer instructions, which are executed by the processor 302 to implement any of the methods of the embodiments of the present disclosure.
Fig. 10 shows a schematic structural diagram of an electronic device for implementing a weather radar data method according to an embodiment of the present disclosure.
As shown in fig. 10, electronic device 400 includes a processing unit 401, which may be implemented as a CPU, GPU, FPGA, NPU, or other processing unit. The processing unit 401 may execute various processes in the embodiment of any one of the above-described methods of the present disclosure according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing unit 401, the ROM402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to embodiments of the present disclosure, any of the methods described above with reference to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing any of the methods of the embodiments of the present disclosure. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.
As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims (10)

1. A weather radar data processing method is characterized by comprising the following steps:
acquiring dual-polarization radar data collected by a dual-polarization radar;
processing the dual-polarization radar data according to a dual-polarization radar data quality control algorithm to obtain a dual-polarization radar labeling result;
determining a first initial radar data processing model, taking dual-polarization radar data at a first moment as input, taking a dual-polarization radar labeling result at the first moment as output, and training the first initial weather data processing model to obtain a dual-polarization radar data processing model;
and acquiring real-time Doppler radar data acquired by a Doppler radar in real time, and processing the real-time Doppler radar data according to the dual-polarization radar data processing model to acquire a Doppler radar real-time identification result, wherein the distance between the Doppler radar and the dual-polarization radar is smaller than or equal to a radar distance threshold value.
2. The weather radar data processing method of claim 1, wherein the determining a first initial radar data processing model, the receiving dual-polarization radar data at a first time as input, the receiving dual-polarization radar tagging result at the first time as output, and the training the first initial weather data processing model further comprises:
acquiring the accuracy of the labeling result of the dual-polarization radar according to a dual-polarization data quality control algorithm;
performing error correction processing on the dual-polarization radar labeling result with the accuracy rate smaller than or equal to the accuracy rate threshold value according to an error correction processing algorithm to obtain the dual-polarization radar labeling result after error correction processing;
and determining the dual-polarization radar labeling result at the first moment in the dual-polarization radar labeling result after error reconnaissance processing and the dual-polarization radar labeling result with the accuracy rate larger than the accuracy rate threshold value.
3. The weather radar data quality control method of claim 1, wherein before the obtaining real-time Doppler radar data collected by a Doppler radar in real time and processing the real-time Doppler radar data according to the dual-polarization radar data processing model to obtain a Doppler radar real-time identification result, the method further comprises:
processing the dual-polarization radar data at the second moment according to the dual-polarization radar data processing model to obtain a dual-polarization radar identification result;
acquiring the similarity of the identification result of the dual-polarization radar and the identification result of the dual-polarization radar marking result at the second moment;
the real-time Doppler radar data acquired by the Doppler radar in real time is acquired, and the real-time Doppler radar data is processed according to the dual-polarization radar data processing model to acquire a Doppler radar real-time identification result, and the method comprises the following steps:
and when the similarity of the identification result is greater than or equal to the similarity threshold of the identification result, acquiring the real-time Doppler radar data, and processing the real-time Doppler radar data according to the dual-polarization radar data processing model to acquire the real-time identification result of the Doppler radar.
4. The weather radar data processing method of any one of claims 1-3, wherein the method further comprises:
acquiring Doppler radar data acquired by the Doppler radar;
determining a second initial radar data processing model, taking Doppler radar data at a third moment as input, taking a dual-polarization radar labeling result at the third moment as output, and training the second initial weather data processing model to obtain a Doppler radar data processing model;
and processing the real-time Doppler radar data according to the Doppler radar data processing model to obtain a target Doppler real-time weather identification result.
5. The weather radar data processing method of any one of claims 1 to 3, wherein the processing the dual-polarization radar data according to a dual-polarization radar data quality control algorithm to obtain dual-polarization radar labeling results comprises:
obtaining a cross-correlation coefficient rho between a horizontally polarized radar return and a vertically polarized radar return according to the dual-polarized radar dataHV；
According to rhocor＝ρhv×(1+1/100.1SNR) Obtaining a noise reduction cross-correlation coefficient rhocorWherein SNR is a signal ratio parameter measured by the dual-polarization radar;
and acquiring a data quality control result of the dual-polarization radar according to the noise reduction cross correlation coefficient.
6. The weather radar data processing method of claim 5, wherein the method further comprises:
obtaining a differential reflectivity factor Z of a precipitation system according to the dual-polarization radar dataDR；
When the cross-correlation coefficient pcorGreater than 0.9, according to
Figure FDA0003194053910000021
Obtaining a differential inverseIndex factor horizontal texture ZDRTexture, and is based on
Figure FDA0003194053910000022
Obtaining correlation coefficient horizontal texture rhoHVTexture of which N isATo be an index value identifying the orientation of the window, NRIs an index value that is the windowed distance;
the obtaining of the dual-polarization radar data quality control result according to the denoising cross correlation coefficient includes:
and acquiring the data quality control result of the dual-polarization radar according to the differential reflectivity factor horizontal texture and the correlation coefficient horizontal texture.
7. The weather radar data processing method of claim 6, wherein the method further comprises:
acquiring a horizontal polarization reflectivity factor of a precipitation system according to the dual-polarization radar data;
and filling the void of the precipitation echo according to the horizontal polarization reflectivity factor and the labeling result of the dual-polarization radar.
8. The weather radar data processing method of claim 6, wherein the method further comprises:
acquiring a horizontal polarization reflectivity factor Z of the precipitation system and an 18dBz echo top height ETOP of the precipitation system according to the dual-polarization radar data18dBzAnd 0dBz echo top height ETOP of precipitation system0dBzAnd the slope distance r from the storm monomer core in the precipitation system to the dual-polarization radar arrival stationstorm_coreAnd the range of the slant of the single body observed in the precipitation system to the dual-polarization radar arrival station;
when the cross-correlation coefficient pcorLess than or equal to 0.9 and the cross-correlation coefficient pcor18dBz echo top height ETOP of the precipitation system18dBzAnd the horizontal polarization reflectivity factor Z of the precipitation system satisfies rhocor＜0.95∩(ETOP18dBzAndz > 8.0km & ltn & gt 45dBz), determining that the precipitation system is hail;
when the cross-correlation coefficient pcorLess than or equal to 0.9, and the cross-correlation coefficient pcor0dBz echo top height ETOP of the precipitation system0dBzAnd the slope distance r from the storm monomer core in the precipitation system to the dual-polarization radar arrival stationstorm_coreAnd the range of the slant range from the single body to the dual-polarization radar station, observed in the precipitation system, meets rhocor＜0.95∩(ETOP0dBz＞9.0km∩range＞rstorm_core) And determining that the precipitation system is filled with non-uniform wave beams.
9. A weather radar data processing apparatus, comprising:
a dual polarization radar data acquisition module configured to acquire dual polarization radar data acquired by a dual polarization radar;
the dual-polarization radar data processing module is configured to process the dual-polarization radar data according to a dual-polarization radar data quality control algorithm and obtain a dual-polarization radar labeling result;
the dual-polarization model training module is configured to determine a first initial radar data processing model, take dual-polarization radar data at a first moment as input, take a dual-polarization radar labeling result at the first moment as output, train the first initial weather data processing model and obtain a dual-polarization radar data processing model;
and the Doppler radar data processing module is configured to acquire real-time Doppler radar data acquired by a Doppler radar in real time and process the real-time Doppler radar data according to the dual-polarization radar data processing model to acquire a Doppler radar real-time identification result, wherein the distance between the Doppler radar and the dual-polarization radar is smaller than or equal to a radar distance threshold value.
10. An electronic device comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method of any of claims 1-8.
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