Wind Field Retrieval Based On Dual-polarimetric Radar Detection

Wind field information plays a vital role in aerospace security,air pollution prevention,weather monitoring,disaster warning,wind power generation and etc.At present,under rainy conditions,the instrument for wind detection in large scale with high precision is mainly the Doppler radar,and it is to reflect the wind field indirectly by detecting the movement of raindrops.Plenty of raindrops in a radar unit make the relationship between the Doppler data and raindrops of different sizes complicated.Besides,strong inertia of raindrops leads to the difference between the raindrops’ velocity and the background wind velocity.So,it is a challenging scientific issue to accurately retrieve the wind velocity with the radar observations modulated by the background wind.This thesis tries to find out the physical relationship among Doppler velocity,raindrops’ velocity and background wind velocity based on dual-polarimetric radar,and to retrieve the background wind velocity effectively with the motion modulation mechanism of raindrops.Firstly,a retrieval model of raindrops’ characteristic size based on dual-polarimetric radar variables is established,and the physical feature representation of raindrops corresponding to Doppler velocity is realized.By establishing a radar echo model under rainy conditions,analyzing the relationship between raindrops’ scattering characteristics and Doppler data,the raindrops’ characteristic size is defined.Based on simulation with the T-matrix method,the relationship between the raindrops’ characteristic size and the dual-polarimetric radar variables is analyzed,and a raindrops’ characteristic size retrieval model that can adapt to different detection conditions is constructed with the support vector machine.The evaluation results show that the model can achieve high-precision retrieval of raindrops’ characteristic size,and the root mean square error in 3 precipitation events is 0.6236 mm.Then,a wind velocity correction scheme based on the motion equation of characteristic raindrops is proposed,which can be used to correct the wind velocity inversion error caused by the raindrop inertia in the traditional wind field inversion algorithm.The terminal falling velocity and the motion velocity of characteristic raindrops are calculated respectively based on the raindrops characteristic size and the Doppler velocity,and the wind velocity after correction is calculated with the characteristic raindrops’ motion equation.The feasibility and effectiveness of the scheme have been verified under uniform wind field and complex wind shear settings.Compared with the other two existing schemes,this scheme reduces the root mean square error of background wind velocity inversion by more than 80%.Finally,a complex wind field retrieval algorithm under rainy conditions based on the variational method is proposed,which can be used to retrieve complex wind field under rainy conditions by introducing the basic physical model of the rain field.Based on the anelastic continuum equation of the wind field and the rain field convection-diffusion equation,the equation between the background wind velocity and radar data is derived.Based on this equation,the cost function is constructed and the variational method is applied to obtain the optimal solution of the wind velocity.This algorithm does not need to make uniform or linear assumptions about the background wind field,and it can solve the problem of refined inversion of small-scale or uneven wind field velocity under rainy conditions.The raindrops’ characteristic size retrieval model based on the dual-polarimetric radar variables and the wind velocity correction scheme based on the motion equation of characteristic raindrops proposed in this thesis have both been verified by experiments,and show excellent performance;and the complex wind field retrieval algorithm based on the variational method has significance on both theory and application,which has a wide range of application prospects for wind velocity derivation under rainy conditions.