Weather radar echo type identification method and device, medium and equipment

Abstract

The invention discloses a weather radar echo type identification method and device, a medium and equipment. According to the method, the preset polarization radar observation data is acquired, the distribution characteristics of the ocean clutters are accurately calculated by using a statistical method, then the membership function is constructed, and a quantitative basis is provided for a fuzzy logic algorithm. And processing through a fuzzy logic algorithm to obtain a membership degree of each pixel belonging to a preset echo category, and finally determining the echo category according to the membership degree. The problem that the echo type of the weather radar cannot be accurately identified in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the field of weather radar echo type recognition, and in particular to a weather radar echo type recognition method, device, medium and equipment. Background Art

[0002] Weather radar is an indispensable tool in meteorological observation. It detects various particles in the atmosphere, such as clouds, precipitation particles, and wildfire plume particles, by emitting and receiving electromagnetic waves. It can not only monitor weather conditions in real time, but also provide meteorological warning information. It is widely used in the fields of wildfire plume monitoring and warning, forest disaster prevention and mitigation, etc. With the development of technology, dual-polarization radar has gradually become the mainstream. In addition to reflectivity and radial wind speed, dual-polarization radar can also provide observations such as differential reflectivity, correlation coefficient, and differential phase, which provides a solid data foundation for distinguishing different types of particles (such as rain, snow, hail, and non-meteorological particles).

[0003] Many weather radars are deployed along China's coastal areas. When observing at low elevation angles, sea clutter (i.e., ocean echoes) often affects the accuracy of radar observations. Sea clutter has the following characteristics: (1) The time of occurrence is uncertain; (2) It usually appears near low elevation angles (0.5° to 1.5°) and is more common within 100 kilometers of the radar station; (3) For large areas of sea clutter, the average intensity is weak, but the individual peak intensity is sometimes extremely high; (4) Sea clutter has certain Doppler radial velocity characteristics.

[0004] In order to identify sea clutter, some literatures have proposed different solutions. For example, Steiner et al. used the decision tree method to study a series of reflectivity parameters and found that the vertical extension of radar echoes, the horizontal change of the reflectivity factor field, and the vertical gradient of reflectivity are the most effective ways to identify sea clutter. However, the ocean clutter identification method based on polarization radar observation is still worthy of further study.

[0005] Although existing technologies can identify sea clutter to a certain extent, these methods have some shortcomings:

[0006] Limited recognition accuracy: Under complex meteorological conditions, existing methods have low recognition accuracy for sea clutter and are prone to misjudgment.

[0007] High computational complexity: Some methods are computationally intensive and difficult to process large amounts of radar data in real time.

[0008] Poor adaptability: Existing methods have poor adaptability to radar data from different regions and it is difficult to maintain stable recognition effects in different geographical environments.

[0009] These problems result in the inability of existing technologies to accurately identify the echo type of weather radar. Summary of the invention

[0010] The present invention provides a method, device, medium and equipment for identifying the echo type of a weather radar, so as to solve the problem that the echo type of a weather radar cannot be accurately identified in the prior art.

[0011] In a first aspect, the present application provides a method for identifying a weather radar echo type, comprising:

[0012] Obtain preset polarization radar observation data;

[0013] Calculating the distribution characteristics of the ocean echo of the polarization radar observation data according to the polarization radar observation data and a statistical method;

[0014] constructing a membership function according to the distribution characteristics of the ocean echo;

[0015] Processing the polarization radar observation data according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to a preset echo category;

[0016] According to the membership degree of each pixel belonging to a preset echo category, each echo category of the polarization radar observation data is determined.

[0017] This application obtains preset polarimetric radar observation data to ensure the representativeness and timeliness of the data, providing a solid foundation for subsequent analysis. Then, statistical methods are used to calculate the ocean clutter distribution characteristics of the polarimetric radar observation data, providing a scientific basis for constructing the membership function. The construction of the membership function enables the characteristic fuzziness of ocean clutter to be quantified, laying the foundation for the application of fuzzy logic algorithms. The polarimetric radar observation data is processed by combining the fuzzy logic algorithm with the membership function to obtain the membership degree of each pixel belonging to the preset echo category. This process effectively handles the uncertainty and ambiguity of the data and improves the accuracy and robustness of the recognition. Finally, each echo category is determined according to the membership degree to ensure the scientificity and accuracy of the classification. This application solves the problem that the echo type of weather radar cannot be accurately identified in the prior art.

[0018] As a preferred embodiment of the first aspect, after determining the respective echo categories of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category, the method further comprises:

[0019] Get underlying surface type information;

[0020] According to the underlying surface type information, each echo that does not conform to the underlying surface type in each echo category of the polarization radar observation data is eliminated.

[0021] In this preferred embodiment, after determining each echo category of the polarization radar observation data, the present application further obtains the underlying surface type information, and removes the echo that does not conform to the underlying surface type based on this information. This process significantly improves the accuracy and reliability of echo identification. Specifically, by obtaining the underlying surface type information, we can distinguish different surface features, such as ocean, land, city, etc. In combination with this information, those unreasonable echo types on a specific underlying surface can be more accurately identified and removed, for example, ocean clutter that appears on land can be removed. This step not only reduces misjudgment, but also further purifies the radar data and improves the purity and quality of the data.

[0022] As a preferred embodiment of the first aspect, the obtaining of preset polarization radar observation data is specifically:

[0023] Obtaining initial polarization radar observation data for a preset time;

[0024] The preset polarization radar observation data is obtained by screening from the initial polarization radar observation data according to the time, position, intensity and Doppler characteristics of the ocean echo.

[0025] In this preferred embodiment, the present application obtains high-quality preset polarization radar observation data by obtaining the initial polarization radar observation data of the preset time, and screening according to the time, position, intensity and Doppler characteristics of the ocean echo. This process first ensures the timeliness and representativeness of the data. Through the limitation of the preset time, the collected data can reflect the meteorological characteristics in a specific time period, providing an accurate time background for subsequent analysis. Then, through the screening step, the characteristic parameters of the ocean echo are used to accurately extract the observation data related to the ocean echo from a large amount of initial data. This screening mechanism effectively removes irrelevant data, reduces data noise, and improves the purity and relevance of the data. The screened data not only reduces the amount of calculation for subsequent processing, but also improves the efficiency and accuracy of data processing. Therefore, this processing step of the present invention significantly improves the quality of polarization radar observation data, provides a solid data foundation for subsequent echo type identification and classification, and thus improves the application effect and reliability of weather radar in meteorological monitoring and early warning.

[0026] As a preferred embodiment of the first aspect, the distribution characteristics of the ocean echo of the polarization radar observation data are calculated according to the polarization radar observation data and a statistical method, and a membership function is constructed according to the distribution characteristics of the ocean echo, specifically:

[0027] Calculate the one-dimensional probability density distributions of the differential phase standard deviation, differential reflectivity, radial velocity, and signal-to-noise ratio standard deviation of the polarization radar observation data according to a statistical method;

[0028] The calculation formula of the differential phase standard deviation is:

[0029]

[0030] In the formula, σ X is the standard deviation of the differential phase, σ is the standard deviation, X represents the differential phase KDP or signal-to-noise ratio SNR of the polarimetric radar observation, represents the mean value; N represents the number of standard deviation statistical samples;

[0031] A membership function is constructed according to each one-dimensional probability density distribution.

[0032] The construction of the membership function according to each one-dimensional probability density distribution is specifically as follows:

[0033] The membership function is:

[0034]

[0035] Where P(X) is the membership function value, X1 is 5% of the one-dimensional probability density distribution of the standard deviation of the differential phase, X2 is 25% of the one-dimensional probability density distribution of the differential reflectivity, X3 is 75% of the one-dimensional probability density distribution of the radial velocity, and X4 is 95% of the one-dimensional probability density distribution of the standard deviation of the signal-to-noise ratio.

[0036] In this preferred embodiment, the present application calculates the ocean echo distribution characteristics of polarization radar observation data by statistical methods, and constructs a membership function based on these characteristics, which significantly improves the accuracy and reliability of ocean echo identification. Specifically, first, the one-dimensional probability density distribution of the standard deviation of the differential phase, differential reflectivity, radial velocity, and signal-to-noise ratio of the polarization radar observation data is calculated using statistical methods. These distribution characteristics provide a scientific basis for the quantitative analysis of ocean echoes, so that the characteristic fuzziness of ocean echoes can be quantified. By calculating the standard deviation of the differential phase, the characteristic description of the ocean echo is further refined, and the distinguishability of the characteristics is improved. Then, according to these one-dimensional probability density distributions, a membership function is constructed, and the construction of the membership function enables the fuzzy logic algorithm to more effectively handle the uncertainty and fuzziness of the data, thereby improving the accuracy and robustness of identification. Therefore, the present invention not only improves the accuracy of ocean echo identification through this series of scientific and reasonable steps, but also provides a solid foundation for the subsequent echo type classification, significantly improving the application effect and reliability of weather radar in marine meteorological monitoring.

[0037] As a preferred embodiment of the first aspect, the polarization radar observation data is processed according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to a preset echo category, specifically:

[0038] Processing the polarization radar observation data according to a fuzzy logic algorithm, a preset equal weight principle and the membership function to obtain a membership degree of each pixel of the polarization radar observation data belonging to a preset echo category;

[0039] The preset echo categories include meteorological echo, ground clutter, biological echo and ocean clutter.

[0040] In this preferred embodiment, the present application processes polarimetric radar observation data by applying fuzzy logic algorithm and membership function, combined with the preset equal weight principle, so as to calculate the membership of each pixel belonging to the preset echo category. This process first uses fuzzy logic algorithm to process the uncertainty and fuzziness of data, so that the algorithm can process complex meteorological data more flexibly. The preset equal weight principle ensures that when calculating the membership, the contribution of each characteristic parameter to the final result is equal, avoiding the excessive influence of a certain characteristic parameter, and improving the fairness and reliability of the result. Through the membership function, the characteristics of polarimetric radar observation data are converted into specific membership values, so that the classification of each pixel belonging to meteorological echo, ground clutter, biological echo and ocean clutter is more clear and scientific. Finally, this method not only improves the accuracy of echo type recognition, but also enhances the robustness of the algorithm, so that weather radar can accurately identify and classify echo types under different meteorological conditions, providing higher quality data support for meteorological monitoring and early warning.

[0041] In a second aspect, the present application provides a device for identifying the type of weather radar echo. The device for identifying the type of weather radar echo includes an acquisition module, a calculation module, a construction module, and an identification module;

[0042] The acquisition module is used to acquire preset polarization radar observation data;

[0043] The calculation module is used to calculate the distribution characteristics of the ocean echo of the polarization radar observation data according to the polarization radar observation data and statistical methods;

[0044] The construction module is used to construct a membership function according to the distribution characteristics of the ocean echo;

[0045] The identification module is used to process the polarization radar observation data according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to a preset echo category;

[0046] According to the membership degree of each pixel belonging to a preset echo category, each echo category of the polarization radar observation data is determined.

[0047] This device uses four modules to divide the work and coordinate work to more accurately identify the echo signal of the weather radar. This application obtains the preset polarization radar observation data to ensure the representativeness and timeliness of the data, providing a solid foundation for subsequent analysis. Then, the distribution characteristics of ocean clutter of the polarization radar observation data are calculated using statistical methods, providing a scientific basis for constructing the membership function. The construction of the membership function enables the characteristic fuzziness of ocean clutter to be quantified, laying the foundation for the application of fuzzy logic algorithms. The polarization radar observation data is processed by combining the fuzzy logic algorithm with the membership function to obtain the membership degree of each pixel belonging to the preset echo category. This process effectively handles the uncertainty and ambiguity of the data and improves the accuracy and robustness of the recognition. Finally, each echo category is determined according to the membership degree, ensuring the scientificity and accuracy of the classification. This application solves the problem that the echo type of the weather radar cannot be accurately identified in the prior art.

[0048] As a preferred embodiment of the second aspect, after determining the respective echo categories of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category, the method further comprises:

[0049] Get underlying surface type information;

[0050] According to the underlying surface type information, each echo that does not conform to the underlying surface type in each echo category of the polarization radar observation data is eliminated.

[0051] In this preferred embodiment, after determining each echo category of the polarization radar observation data, the present application further obtains the underlying surface type information, and removes the echo that does not conform to the underlying surface type based on this information. This process significantly improves the accuracy and reliability of echo identification. Specifically, by obtaining the underlying surface type information, we can distinguish different surface features, such as ocean, land, city, etc. In combination with this information, those unreasonable echo types on a specific underlying surface can be more accurately identified and removed, for example, ocean clutter that appears on land can be removed. This step not only reduces misjudgment, but also further purifies the radar data and improves the purity and quality of the data.

[0052] In a third aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a method for identifying a weather radar echo type as described. The beneficial effect is the same as the method for identifying a weather radar echo type provided in the first aspect of the present application.

[0053] In a fourth aspect, the present application provides a terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the weather radar echo type identification methods described in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 : A flow chart of an embodiment of a method for identifying a weather radar echo type provided in the present application;

[0055] Figure 2 : A structural schematic diagram of an embodiment of elevation angle observation provided by the present application;

[0056] Figure 3 : A structural schematic diagram of an embodiment of the membership function of ocean clutter provided by the present application;

[0057] Figure 4 : A structural schematic diagram of an embodiment of the polarization radar ocean clutter identification result provided by the present application;

[0058] Figure 5 : A structural schematic diagram of an embodiment of a device for identifying weather radar echo types provided in the present application. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1

[0061] Please refer to Figure 1 , which is a method for identifying weather radar echo types provided by an embodiment of the present invention.

[0062] In this embodiment, the process of the method for identifying the weather radar echo type in the present application is described in detail through steps S01-S05.

[0063] S01: Obtain preset polarization radar observation data.

[0064] As a preferred embodiment of embodiment 1, the obtaining of preset polarization radar observation data is specifically:

[0065] Acquire initial polarization radar observation data for a preset time, where the preset time is 30 days;

[0066] The preset polarization radar observation data is obtained by screening from the initial polarization radar observation data according to the time, position, intensity and Doppler characteristics of the ocean echo.

[0067] More specifically, the steps of collecting and organizing ocean clutter examples are as follows: screening ocean clutter examples based on the temporal, spatial distribution, intensity and other characteristics of ocean clutter. Figure 2 The following is an example of ocean clutter observed by the Zhanjiang polarization radar at 01:30 on December 5, 2019. The ocean clutter is mainly distributed on the east side of the radar, with an average reflectivity intensity between 0-30dBZ. Figure 2 This is the observation of 0.5° elevation angle by Zhanjiang polarization radar at 01:30 (UTC) on December 5, 2019. (a) is the reflectivity; (b) is the differential reflectivity; (c) is the radial velocity; and (d) is the correlation coefficient.

[0068] The radar station observation data described in this embodiment comes from the Guangdong Province business observation system, which has 11 S-band polarization radars in the province.

[0069] In this preferred embodiment, the present application obtains high-quality preset polarization radar observation data by obtaining the initial polarization radar observation data of the preset time, and screening according to the time, position, intensity and Doppler characteristics of the ocean echo. This process first ensures the timeliness and representativeness of the data. Through the limitation of the preset time, the collected data can reflect the meteorological characteristics in a specific time period, providing an accurate time background for subsequent analysis. Then, through the screening step, the characteristic parameters of the ocean echo are used to accurately extract the observation data related to the ocean echo from a large amount of initial data. This screening mechanism effectively removes irrelevant data, reduces data noise, and improves the purity and relevance of the data. The screened data not only reduces the amount of calculation for subsequent processing, but also improves the efficiency and accuracy of data processing. Therefore, this processing step of the present invention significantly improves the quality of polarization radar observation data, provides a solid data foundation for subsequent echo type identification and classification, and thus improves the application effect and reliability of weather radar in meteorological monitoring and early warning.

[0070] S02: Calculate the distribution characteristics of the ocean echo of the polarization radar observation data based on the polarization radar observation data and statistical methods.

[0071] As a preferred embodiment of the first embodiment, the distribution characteristics of the ocean echo of the polarization radar observation data are calculated based on the polarization radar observation data and a statistical method, specifically:

[0072] Calculate the one-dimensional probability density distributions of the differential phase standard deviation, differential reflectivity, radial velocity, and signal-to-noise ratio standard deviation of the polarization radar observation data according to a statistical method;

[0073] The calculation formula of the differential phase standard deviation is:

[0074]

[0075] In the formula, σ X is the standard deviation of the differential phase, σ is the standard deviation, X represents the differential phase KDP or signal-to-noise ratio SNR of the polarimetric radar observation, Represents the average value; N represents the number of standard deviation statistical samples; in this case, 8 consecutive distance libraries of radar radial observation are taken.

[0076] In this preferred embodiment, the present application calculates the ocean echo distribution characteristics of polarization radar observation data by statistical methods, and constructs a membership function based on these characteristics, which significantly improves the accuracy and reliability of ocean echo identification. Specifically, first, the one-dimensional probability density distribution of the standard deviation of the differential phase, differential reflectivity, radial velocity, and signal-to-noise ratio of the polarization radar observation data is calculated using statistical methods. These distribution characteristics provide a scientific basis for the quantitative analysis of ocean echoes, so that the characteristic fuzziness of ocean echoes can be quantified. By calculating the standard deviation of the differential phase, the characteristic description of the ocean echo is further refined, and the distinguishability of the characteristics is improved. Then, according to these one-dimensional probability density distributions, a membership function is constructed, and the construction of the membership function enables the fuzzy logic algorithm to more effectively handle the uncertainty and fuzziness of the data, thereby improving the accuracy and robustness of identification. Therefore, the present invention not only improves the accuracy of ocean echo identification through this series of scientific and reasonable steps, but also provides a solid foundation for the subsequent echo type classification, significantly improving the application effect and reliability of weather radar in marine meteorological monitoring.

[0077] S03: Constructing a membership function according to the distribution characteristics of the ocean echo.

[0078] As a preferred embodiment of the first embodiment, the membership function is constructed according to the distribution characteristics of the ocean echo, specifically:

[0079] The membership function is the core of the fuzzy logic method and is used to measure the fuzziness of the ocean clutter characteristics. In this case, a trapezoidal function is used to represent the membership function value. The membership function is:

[0080]

[0081] Where P(X) is the membership function value, X1 is 5% of the one-dimensional probability density distribution of the standard deviation of the differential phase, X2 is 25% of the one-dimensional probability density distribution of the differential reflectivity, X3 is 75% of the one-dimensional probability density distribution of the radial velocity, and X4 is 95% of the one-dimensional probability density distribution of the standard deviation of the signal-to-noise ratio.

[0082] The steps of statistical membership function are as follows: the probability density distribution of polarization radar observations of several ocean clutter cases is statistically analyzed; and the threshold of membership function is given by 5%, 25%, 75% and 95% quantile values. The results are shown in Figure 3 As shown, Figure 3 (a) is the standard deviation of the differential phase; (b) is the differential reflectivity; (c) is the radial velocity; and (d) is the standard deviation of the signal-to-noise ratio.

[0083] S04: Processing the polarization radar observation data according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to a preset echo category.

[0084] As a preferred embodiment of the first embodiment, the polarization radar observation data is processed according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to the preset echo category, specifically:

[0085] The equal weight principle is used to infer the membership of each pixel to the specified echo category, and finally the echo category (meteorological echo, ground clutter, biological echo and ocean clutter) is determined and output based on the inference result.

[0086] In this preferred embodiment, the present application processes polarimetric radar observation data by applying fuzzy logic algorithm and membership function, combined with the preset equal weight principle, so as to calculate the membership of each pixel belonging to the preset echo category. This process first uses fuzzy logic algorithm to process the uncertainty and fuzziness of data, so that the algorithm can process complex meteorological data more flexibly. The preset equal weight principle ensures that when calculating the membership, the contribution of each characteristic parameter to the final result is equal, avoiding the excessive influence of a certain characteristic parameter, and improving the fairness and reliability of the result. Through the membership function, the characteristics of polarimetric radar observation data are converted into specific membership values, so that the classification of each pixel belonging to meteorological echo, ground clutter, biological echo and ocean clutter is more clear and scientific. Finally, this method not only improves the accuracy of echo type recognition, but also enhances the robustness of the algorithm, so that weather radar can accurately identify and classify echo types under different meteorological conditions, providing higher quality data support for meteorological monitoring and early warning.

[0087] S05: Determine each echo category of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category.

[0088] As a preferred embodiment of the first embodiment, the determining of each echo category of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category is specifically:

[0089] The ocean clutter recognition algorithm based on fuzzy logic effectively distinguishes ocean clutter, weather echoes, ground object echoes and biological echoes. The results are as follows: Figure 4 At an elevation angle of 0.5°, a total of 14,937 pixels were identified as ocean clutter. Figure 4 These are: the ocean clutter identification results of Zhanjiang polarization radar at 01:30 on December 5, 2019 (UTC), SC: ocean clutter; GC: ground clutter; BS: biological echo; ME: meteorological echo.

[0090] As a preferred embodiment of the first embodiment, after determining each echo category of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category, the method further includes:

[0091] Get underlying surface type information;

[0092] According to the underlying surface type information, each echo that does not conform to the underlying surface type in each echo category of the polarization radar observation data is eliminated.

[0093] In this preferred embodiment, after determining each echo category of the polarization radar observation data, the present application further obtains the underlying surface type information, and removes the echo that does not conform to the underlying surface type based on this information. This process significantly improves the accuracy and reliability of echo identification. Specifically, by obtaining the underlying surface type information, we can distinguish different surface features, such as ocean, land, city, etc. In combination with this information, those unreasonable echo types on a specific underlying surface can be more accurately identified and removed, for example, ocean clutter that appears on land can be removed. This step not only reduces misjudgment, but also further purifies the radar data and improves the purity and quality of the data.

[0094] This application obtains preset polarimetric radar observation data to ensure the representativeness and timeliness of the data, providing a solid foundation for subsequent analysis. Then, statistical methods are used to calculate the ocean clutter distribution characteristics of the polarimetric radar observation data, providing a scientific basis for constructing the membership function. The construction of the membership function enables the characteristic fuzziness of ocean clutter to be quantified, laying the foundation for the application of fuzzy logic algorithms. The polarimetric radar observation data is processed by combining the fuzzy logic algorithm with the membership function to obtain the membership degree of each pixel belonging to the preset echo category. This process effectively handles the uncertainty and ambiguity of the data and improves the accuracy and robustness of the recognition. Finally, each echo category is determined according to the membership degree to ensure the scientificity and accuracy of the classification. This application solves the problem that the echo type of weather radar cannot be accurately identified in the prior art.

[0095] Embodiment 2

[0096] Please refer to Figure 5, which is a weather radar echo type identification device provided in an embodiment of the present application.

[0097] In this embodiment, the device for identifying the type of weather radar echo includes an acquisition module 10 , a calculation module 20 , a construction module 30 and an identification module 40 .

[0098] The acquisition module 10 is used to acquire preset polarization radar observation data.

[0099] As a preferred embodiment of the second embodiment, the obtaining of preset polarization radar observation data is specifically:

[0100] Acquire initial polarization radar observation data for a preset time, where the preset time is 30 days;

[0101] The preset polarization radar observation data is obtained by screening from the initial polarization radar observation data according to the time, position, intensity and Doppler characteristics of the ocean echo.

[0102] More specifically, the steps of collecting and organizing ocean clutter examples are as follows: screening ocean clutter examples based on the temporal, spatial distribution, intensity and other characteristics of ocean clutter. Figure 2 The following is an example of ocean clutter observed by the Zhanjiang polarization radar at 01:30 on December 5, 2019. The ocean clutter is mainly distributed on the east side of the radar, with an average reflectivity intensity between 0-30dBZ. Figure 2 This is the observation of 0.5° elevation angle by Zhanjiang polarization radar at 01:30 (UTC) on December 5, 2019. (a) is the reflectivity; (b) is the differential reflectivity; (c) is the radial velocity; and (d) is the correlation coefficient.

[0103] The radar station observation data described in this embodiment comes from the Guangdong Province business observation system, which has 11 S-band polarization radars in the province.

[0104] In this preferred embodiment, the present application obtains high-quality preset polarization radar observation data by obtaining the initial polarization radar observation data of the preset time, and screening according to the time, position, intensity and Doppler characteristics of the ocean echo. This process first ensures the timeliness and representativeness of the data. Through the limitation of the preset time, the collected data can reflect the meteorological characteristics in a specific time period, providing an accurate time background for subsequent analysis. Then, through the screening step, the characteristic parameters of the ocean echo are used to accurately extract the observation data related to the ocean echo from a large amount of initial data. This screening mechanism effectively removes irrelevant data, reduces data noise, and improves the purity and relevance of the data. The screened data not only reduces the amount of calculation for subsequent processing, but also improves the efficiency and accuracy of data processing. Therefore, this processing step of the present invention significantly improves the quality of polarization radar observation data, provides a solid data foundation for subsequent echo type identification and classification, and thus improves the application effect and reliability of weather radar in meteorological monitoring and early warning.

[0105] The calculation module 20 is used to calculate the distribution characteristics of the ocean echo of the polarization radar observation data according to the polarization radar observation data and statistical methods.

[0106] As a preferred embodiment of the second embodiment, the distribution characteristics of the ocean echo of the polarization radar observation data are calculated based on the polarization radar observation data and statistical methods, specifically:

[0107] Calculate the one-dimensional probability density distributions of the differential phase standard deviation, differential reflectivity, radial velocity, and signal-to-noise ratio standard deviation of the polarization radar observation data according to a statistical method;

[0108] The calculation formula of the differential phase standard deviation is:

[0109]

[0110] In the formula, σ X is the standard deviation of the differential phase, σ is the standard deviation, X represents the differential phase KDP or signal-to-noise ratio SNR of the polarimetric radar observation, Represents the average value; N represents the number of standard deviation statistical samples; in this case, 8 consecutive distance libraries of radar radial observation are taken.

[0111] In this preferred embodiment, the present application calculates the ocean echo distribution characteristics of polarization radar observation data by statistical methods, and constructs a membership function based on these characteristics, which significantly improves the accuracy and reliability of ocean echo identification. Specifically, first, the one-dimensional probability density distribution of the standard deviation of the differential phase, differential reflectivity, radial velocity, and signal-to-noise ratio of the polarization radar observation data is calculated using statistical methods. These distribution characteristics provide a scientific basis for the quantitative analysis of ocean echoes, so that the characteristic fuzziness of ocean echoes can be quantified. By calculating the standard deviation of the differential phase, the characteristic description of the ocean echo is further refined, and the distinguishability of the characteristics is improved. Then, according to these one-dimensional probability density distributions, a membership function is constructed, and the construction of the membership function enables the fuzzy logic algorithm to more effectively handle the uncertainty and fuzziness of the data, thereby improving the accuracy and robustness of identification. Therefore, the present invention not only improves the accuracy of ocean echo identification through this series of scientific and reasonable steps, but also provides a solid foundation for the subsequent echo type classification, significantly improving the application effect and reliability of weather radar in marine meteorological monitoring.

[0112] The construction module 30 is used to construct a membership function according to the distribution characteristics of the ocean echo.

[0113] As a preferred embodiment of the second embodiment, the membership function is constructed according to the distribution characteristics of the ocean echo, specifically:

[0114] The membership function is the core of the fuzzy logic method and is used to measure the fuzziness of the ocean clutter characteristics. In this case, a trapezoidal function is used to represent the membership function value. The membership function is:

[0115]

[0116] Where P(X) is the membership function value, X1 is 5% of the one-dimensional probability density distribution of the standard deviation of the differential phase, X2 is 25% of the one-dimensional probability density distribution of the differential reflectivity, X3 is 75% of the one-dimensional probability density distribution of the radial velocity, and X4 is 95% of the one-dimensional probability density distribution of the standard deviation of the signal-to-noise ratio.

[0117] The steps of statistical membership function are as follows: the probability density distribution of polarization radar observations of several ocean clutter cases is statistically analyzed; and the threshold of membership function is given by 5%, 25%, 75% and 95% quantile values. The results are shown in Figure 3 As shown, Figure 3 (a) is the standard deviation of the differential phase; (b) is the differential reflectivity; (c) is the radial velocity; and (d) is the standard deviation of the signal-to-noise ratio.

[0118] The identification module 40 is used to process the polarization radar observation data according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to a preset echo category.

[0119] As a preferred embodiment of the second embodiment, the polarization radar observation data is processed according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to the preset echo category, specifically:

[0120] The equal weight principle is used to infer the membership of each pixel to the specified echo category, and finally the echo category (meteorological echo, ground clutter, biological echo and ocean clutter) is determined and output based on the inference result.

[0121] In this preferred embodiment, the present application processes polarimetric radar observation data by applying fuzzy logic algorithm and membership function, combined with the preset equal weight principle, so as to calculate the membership of each pixel belonging to the preset echo category. This process first uses fuzzy logic algorithm to process the uncertainty and fuzziness of data, so that the algorithm can process complex meteorological data more flexibly. The preset equal weight principle ensures that when calculating the membership, the contribution of each characteristic parameter to the final result is equal, avoiding the excessive influence of a certain characteristic parameter, and improving the fairness and reliability of the result. Through the membership function, the characteristics of polarimetric radar observation data are converted into specific membership values, so that the classification of each pixel belonging to meteorological echo, ground clutter, biological echo and ocean clutter is more clear and scientific. Finally, this method not only improves the accuracy of echo type recognition, but also enhances the robustness of the algorithm, so that weather radar can accurately identify and classify echo types under different meteorological conditions, providing higher quality data support for meteorological monitoring and early warning.

[0122] The identification module 40 is further configured to determine each echo category of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category.

[0123] As a preferred embodiment of the second embodiment, the determination of each echo category of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category is specifically as follows:

[0124] The ocean clutter recognition algorithm based on fuzzy logic effectively distinguishes ocean clutter, weather echoes, ground object echoes and biological echoes. The results are as follows: Figure 4 At an elevation angle of 0.5°, a total of 14,937 pixels were identified as ocean clutter. Figure 4 These are: the ocean clutter identification results of Zhanjiang polarization radar at 01:30 on December 5, 2019 (UTC), SC: ocean clutter; GC: ground clutter; BS: biological echo; ME: meteorological echo.

[0125] As a preferred embodiment of the second embodiment, after determining each echo category of the polarization radar observation data according to the membership degree of each pixel belonging to a preset echo category, it also includes:

[0126] Get underlying surface type information;

[0127] According to the underlying surface type information, each echo that does not conform to the underlying surface type in each echo category of the polarization radar observation data is eliminated.

[0128] In this preferred embodiment, after determining each echo category of the polarization radar observation data, the present application further obtains the underlying surface type information, and removes the echo that does not conform to the underlying surface type based on this information. This process significantly improves the accuracy and reliability of echo identification. Specifically, by obtaining the underlying surface type information, we can distinguish different surface features, such as ocean, land, city, etc. In combination with this information, those unreasonable echo types on a specific underlying surface can be more accurately identified and removed, for example, ocean clutter that appears on land can be removed. This step not only reduces misjudgment, but also further purifies the radar data and improves the purity and quality of the data.

[0129] This device uses four modules to divide the work and coordinate work to more accurately identify the echo signal of the weather radar. This application obtains the preset polarization radar observation data to ensure the representativeness and timeliness of the data, providing a solid foundation for subsequent analysis. Then, the distribution characteristics of ocean clutter of the polarization radar observation data are calculated using statistical methods, providing a scientific basis for constructing the membership function. The construction of the membership function enables the characteristic fuzziness of ocean clutter to be quantified, laying the foundation for the application of fuzzy logic algorithms. The polarization radar observation data is processed by combining the fuzzy logic algorithm with the membership function to obtain the membership degree of each pixel belonging to the preset echo category. This process effectively handles the uncertainty and ambiguity of the data and improves the accuracy and robustness of the recognition. Finally, each echo category is determined according to the membership degree, ensuring the scientificity and accuracy of the classification. This application solves the problem that the echo type of the weather radar cannot be accurately identified in the prior art.

[0130] Embodiment three:

[0131] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for identifying a weather radar echo type;

[0132] Wherein, the weather radar echo type identification method, if implemented in the form of a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0133] Embodiment 4

[0134] The present application provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, any one of the weather radar echo type identification methods described in Example 1 is implemented.

[0135] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for identifying weather radar echo types, characterized in that: include: Obtain preset polarization radar observation data; Calculating the distribution characteristics of the ocean echo of the polarization radar observation data according to the polarization radar observation data and a statistical method; constructing a membership function according to the distribution characteristics of the ocean echo; Processing the polarization radar observation data according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to a preset echo category; According to the membership degree of each pixel belonging to a preset echo category, each echo category of the polarization radar observation data is determined.

2. The method for identifying the type of weather radar echo according to claim 1, characterized in that: After determining the echo categories of the polarization radar observation data according to the membership degree of each pixel belonging to the preset echo category, the method further includes: Get underlying surface type information; According to the underlying surface type information, each echo that does not conform to the underlying surface type in each echo category of the polarization radar observation data is eliminated.

3. The method for identifying the type of weather radar echo according to claim 1, characterized in that: The obtaining of preset polarization radar observation data is specifically as follows: Obtaining initial polarization radar observation data for a preset time; The preset polarization radar observation data is obtained by screening from the initial polarization radar observation data according to the time, position, intensity and Doppler characteristics of the ocean echo.

4. The method for identifying the type of weather radar echo according to claim 1, characterized in that: The distribution characteristics of the ocean echo of the polarization radar observation data are calculated based on the polarization radar observation data and the statistical method, and the membership function is constructed based on the distribution characteristics of the ocean echo, which is specifically: Calculate the one-dimensional probability density distributions of the differential phase standard deviation, differential reflectivity, radial velocity, and signal-to-noise ratio standard deviation of the polarization radar observation data according to a statistical method; The calculation formula of the differential phase standard deviation is: In the formula, σ X is the standard deviation of the differential phase, σ is the standard deviation, X represents the differential phase KDP or signal-to-noise ratio SNR of the polarimetric radar observation, represents the mean value; N represents the number of standard deviation statistical samples; A membership function is constructed according to each one-dimensional probability density distribution.

5. The method for identifying the weather radar echo type according to claim 4, characterized in that: The construction of the membership function according to each one-dimensional probability density distribution is specifically as follows: The membership function is: Where P(X) is the membership function value, X1 is 5% of the one-dimensional probability density distribution of the standard deviation of the differential phase, X2 is 25% of the one-dimensional probability density distribution of the differential reflectivity, X3 is 75% of the one-dimensional probability density distribution of the radial velocity, and X4 is 95% of the one-dimensional probability density distribution of the standard deviation of the signal-to-noise ratio.

6. The method for identifying the type of weather radar echo according to claim 1, characterized in that: The polarization radar observation data is processed according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to the preset echo category, specifically: Processing the polarization radar observation data according to a fuzzy logic algorithm, a preset equal weight principle and the membership function to obtain a membership degree of each pixel of the polarization radar observation data belonging to a preset echo category; The preset echo categories include meteorological echo, ground clutter, biological echo and ocean clutter.

7. A device for identifying the type of weather radar echo, characterized in that: include: Acquisition module, calculation module, construction module and recognition module; The acquisition module is used to acquire preset polarization radar observation data; The calculation module is used to calculate the distribution characteristics of the ocean echo of the polarization radar observation data according to the polarization radar observation data and statistical methods; The construction module is used to construct a membership function according to the distribution characteristics of the ocean echo; The identification module is used to process the polarization radar observation data according to the fuzzy logic algorithm and the membership function to obtain the membership degree of each pixel of the polarization radar observation data belonging to a preset echo category; According to the membership degree of each pixel belonging to a preset echo category, each echo category of the polarization radar observation data is determined.

8. The device for identifying the type of weather radar echo according to claim 7, characterized in that: After determining the echo categories of the polarization radar observation data according to the membership degree of each pixel belonging to the preset echo category, the method further includes: Get underlying surface type information; According to the underlying surface type information, each echo that does not conform to the underlying surface type in each echo category of the polarization radar observation data is eliminated.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for identifying the weather radar echo type according to any one of claims 1 to 6.

10. A terminal device, characterized in that: The invention comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for identifying the weather radar echo type according to any one of claims 1 to 6 is implemented.

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