Cloud precipitation refined inversion method based on cloud radar

Abstract

The invention discloses a cloud precipitation refined inversion method based on a cloud radar. The method comprises the steps of extracting cloud radar data, firstly, performing quality control on thecloud radar data, then automatically identifying the height of a cloud precipitation melting layer by utilizing an independently designed melting layer detection algorithm; and then carrying out classification and identification on hydrometeor particles in the cloud precipitation by adopting the steps of fuzzification, rule judgment, phase state classified limitation check, maximum integration method integration, defuzzification and the like, carrying out refined inversion on the phase state and distribution of the hydrometeor particles, and finally outputting a refined inversion result of the cloud precipitation. Refined inversion of the hydrometeor particles in the cloud precipitation and automatic identification of the height of the melting layer are realized. According to the method,the phase state and distribution of the hydrometeor particles in the cloud precipitation can be inversed only by means of the cloud radar data, and the identification and inversion rate of the hydrometeor particles in the cloud precipitation is improved. Meanwhile, particle phase state limitation check is added on the basis of a common fuzzy logic method, and the identification accuracy of the phase state of the hydrometeor particles is improved.

Description

technical field [0001] The present application relates to the field of atmospheric science and technology, in particular to a cloud radar-based fine cloud precipitation inversion method. Background technique [0002] The cloud precipitation system has its basic general characteristics and laws, but at the same time it has a strong temporal and spatial variability. Analyzing the particle changes inside the cloud precipitation system is the basic way to study the cloud system structure and its evolution characteristics. At present, observations of the internal structure, particle phase, and size of cloud precipitation are mainly carried out by aircraft observation, which can provide changes in the internal water content, particle concentration, particle spectrum, and particle phase state of cloud precipitation. However, because aircraft observations are restricted by factors such as meteorological conditions and airspace, continuous observation data of the precipitation proces...

AI Technical Summary

Problems solved by technology

Due to the complex characteristics of hydrometeor particles in cloud precipitation, the radar polarization parameter information corresponding to different hydrometeor particles is not absolutely exclusive, but overlaps to some extent, so the decision tree based on "rigid" boundary conditions and Boolean logic The identification method is not suitable for the classification of hydrometeor particles in cloud precipitation

However, the decision tree identification method requires that the data used do not allow measurement errors, which is difficult to achieve in practice.

Statistical decision identification method is another method that can solve the problem of hydrometeor classification, but it is difficult to establish statistical models based on different hydrometeors

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