Dual-polarization radar precipitation optimization inversion method

Abstract

Based on dual-polarization radar data, raindrop spectrum data and rain gauge data, a neural network and a Logistic multiple regression method are utilized to realize the adaptive optimization inversion of the dual-polarization radar precipitation, and the precipitation inversion formula can be optimally selected to obtain high-precision radar precipitation data, so that the method can be well applied to disastrous weather monitoring and early warning forecasting. Besides, the method is good in adaptability, parameter setting does not need to be changed according to the weather process, and dual-polarization radar precipitation optimization inversion can be achieved through raindrop spectrum data, dual-polarization radar observation data and rain gauge data in the radar coverage range. Moreover, the method is less affected by rainfall system examples and data measurement precision, can be applied to any detection position of the ground-based radar, and has high rainfall estimation precision and good universality and robustness.

Description

technical field [0001] The invention relates to the technical field of data processing and application of weather radar, more precisely, relates to a ground-based dual-polarization radar adaptive optimization method for retrieving ground precipitation. Background technique [0002] The main principle of radar quantitative precipitation measurement is to establish the relationship between the radar reflectivity factor Z and the rainfall R, that is, the Z-R relationship, based on a certain droplet spectrum distribution assumption. The Z-R relationship of quantitative measurement of precipitation by conventional single-polarization radar is greatly affected by the phase state of precipitation particles and particle droplet spectrum, and the accuracy of precipitation inversion is often limited. Dual polarization radar can provide more echo parameters than conventional Doppler weather radar, such as differential reflectivity Z DR , Differential phase Φ DP , Differential phase s...

AI Technical Summary

Problems solved by technology

However, radar is affected by many factors in actual operation, and the data accuracy is often limited. Observation errors can lead to large precipitation estimation errors, and different radar precipitation inversion formulas have different performances in different precipitation types. How to optimize The selection of precipitation retrieval formula is still an important challenge in the application of dual polarization radar precipitation retrieval

[0003] Compared with the single precipitation formula, the performance of the polarized radar pr

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