Precipitation particle identification method and device based on Kmeans clustering

A kmeans clustering and precipitation particle technology, applied in the field of data processing, can solve the problem of inaccurate identification of precipitation particles, and achieve the effect of avoiding phase folding, improving accuracy and ensuring continuity.

Active Publication Date: 2022-01-14
SUN YAT SEN UNIV
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Problems solved by technology

[0003] The purpose of the present invention is to provide a method for identifying precipitation particles based on Kmeans clustering to solve the problem of inaccurate identification of precipitation particles containing noise in the prior art

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  • Precipitation particle identification method and device based on Kmeans clustering
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  • Precipitation particle identification method and device based on Kmeans clustering

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[0047]The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0048] It should be understood that the step numbers used herein are only for convenience of description, and are not intended to limit the execution order of the steps.

[0049] It should be understood that the terminology used in the description of the present invention is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in this specification and the appended claims, the singular forms "a", "an" ...

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Abstract

The invention discloses a precipitation particle identification method and device based on Kmeans clustering. The method comprises the steps: obtaining the polarization variables of radar observation data, wherein the polarization variables comprise a horizontal reflectivity factor, a differential reflectivity factor and a differential propagation phase shift; reconstructing a ratio differential propagation phase shift according to a physical constraint relationship between the horizontal reflectivity factor and the differential reflectivity factor, and determining reconstructed observation data according to the horizontal reflectivity factor, the differential reflectivity factor and the reconstructed ratio differential propagation phase shift; carrying out precipitation particle identification on the reconstructed observation data by adopting a fuzzy classification algorithm to obtain a first identification result; and carrying out secondary classification on the first identification result by adopting a Kmeans clustering method to obtain a second identification result. According to the invention, through the reconstruction ratio differential propagation phase shift and the adoption of the Kmeans clustering method for secondary classification, the accuracy of precipitation particle identification is improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method and device for identifying precipitation particles based on Kmeans clustering. Background technique [0002] The main task of dual-polarization weather radar is to carry out quantitative precipitation estimation. The relationship between different precipitation particles and radar observation variables is different, such as horizontal reflectivity factor, differential reflectivity factor factor, differential phase shift and differential propagation phase shift, etc. , identifying different precipitation particles can better estimate quantitative precipitation. The current mainstream precipitation particle recognition is based on fuzzy logic classification methods. These methods are mainly based on the classification of precipitation particles based on a single radar library, without considering the surrounding radar. When the input radar observations contain nois...

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Application Information

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IPC IPC(8): G01S7/41G01S7/02G01S13/95G06K9/62
CPCG01S7/41G01S7/024G01S13/95G06F18/23213Y02A90/10
Inventor 陈生刘陈帅
Owner SUN YAT SEN UNIV
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