【摘要】：云內(nèi)降水粒子合理的分類具有重要的應用價值,其不僅可以提高定量降水的精確測量,而且能為人工影響天氣的運行決策和評估提供重要的參考依據(jù)。論文利用雙極化氣象雷達對降水粒子分類技術(shù)進行研究,主要工作內(nèi)容如下:1、研究了氣象回波和非氣象回波的微物理特性,并重點分析了氣象回波中各降水類型在雙極化氣象雷達中的極化特性。在對氣象回波和非氣象回波的微物理特性研究中,主要對氣象回波中降水粒子的尺寸、形狀、取向等方面進行研究分析,對非氣象回波中的生物回波和地雜波的強度和徑向速度進行了研究分析。通過對雙極化氣象雷達的極化參量研究來解釋氣象回波中各降水粒子的極化特性。2、針對雙極化氣象雷達降水分類研究中各極化參量隸屬函數(shù)的建立往往采用經(jīng)驗值,不能準確對降水粒子分類的問題,提出一種基于T-S(Takagi-Sugeno)模型的FNN(Fuzzy Neural Network)有導師監(jiān)督降水粒子分類方法。該方法結(jié)合模糊邏輯思想和神經(jīng)網(wǎng)絡(luò)學習訓練思想,建立了一種自適應的修正隸屬函數(shù)參數(shù)的模糊神經(jīng)網(wǎng)絡(luò)。首先對雙極化氣象雷達接收的極化參量進行模糊化、規(guī)則計算、退模糊處理。其次,利用FNN誤差反饋的學習特點對模糊化過程中的不同降水類型各極化參量隸屬函數(shù)參數(shù)計算,并重新建立新的隸屬函數(shù),保證了降水粒子分類精度。通過對S波段、C波段、X波段雙極化氣象雷達實測數(shù)據(jù)的處理結(jié)果證明了該方法的有效性。3、針對存在地雜波情況下的降水粒子分類問題,提出一種基于FNN-CM(Fuzzy Neural Network-C Mean)的無導師監(jiān)督降水粒子分類方法。該方法首先利用FNN對晴空模式下地雜波訓練學習,計算得到地雜波各極化參量的隸屬函數(shù)參數(shù),并利用其對降雨模式下的地雜波進行抑制。其次,對雜波抑制后的降水粒子進行分類研究。通過計算每種降水類型的聚類中心和每個降水粒子隸屬于每種降水類型的隸屬度來構(gòu)造降水粒子隸屬度的代價函數(shù)。當降水粒子代價函數(shù)滿足條件時,對計算得到的模糊隸屬度矩陣進行退模糊處理,得到每個降水粒子的類型。該方法可以有效消除地雜波對降水粒子分類精度的影響。通過對C波段、S波段的雙極化氣象雷達實測數(shù)據(jù)的處理結(jié)果證明了該方法的有效性。
[Abstract]:The reasonable classification of precipitation particles in the cloud has important application value. It can not only improve the accurate measurement of quantitative precipitation, but also provide an important reference for artificial weather decision making and evaluation. In this paper, the precipitation particle classification technology is studied by using dual-polarization weather radar. The main work is as follows: 1. The microphysical characteristics of meteorological echo and non-meteorological echo are studied. The polarization characteristics of different precipitation types in meteorological echo in dual polarization weather radar are analyzed. In the study of microphysical characteristics of meteorological echo and non-meteorological echo, the size, shape and orientation of precipitation particles in meteorological echo are studied and analyzed. The intensity and radial velocity of biological echo and ground clutter in non-meteorological echo are studied and analyzed. The polarization characteristics of the precipitation particles in the meteorological echo are explained by studying the polarization parameters of the dual-polarization weather radar. The membership function of each polarization parameter in the precipitation classification research of the dual-polarization meteorological radar is usually established by using the empirical value. In order to solve the problem of precipitation particle classification, a new FNN (Fuzzy Neural Network) supervised precipitation particle classification method based on T-S (Takagi-Sugeno) model is proposed. This method combines the idea of fuzzy logic and the learning and training idea of neural network to establish an adaptive fuzzy neural network which modifies the parameters of membership function. Firstly, the polarization parameters received by dual polarimetric weather radar are fuzzy, regular calculation and deblurring processing are carried out. Secondly, the membership function parameters of different precipitation types and polarization parameters are calculated by using the learning characteristics of FNN error feedback, and a new membership function is established to ensure the precision of precipitation particle classification. The processing results of S band C band and X band dual polarization meteorological radar data show that the method is effective. 3. Aiming at the precipitation particle classification problem in the presence of ground clutter. An unsupervised precipitation particle classification method based on FNN-CM (Fuzzy Neural Network-C Mean is proposed. The method firstly uses FNN to train and study ground clutter in clear sky mode, and calculates the membership function parameters of each polarization parameter of ground clutter, and uses it to suppress ground clutter under rainfall mode. Secondly, the precipitation particles after clutter suppression are classified. The cost function of each precipitation particle membership degree is constructed by calculating the cluster center of each precipitation type and the membership degree of each precipitation particle belonging to each precipitation type. When the cost function of precipitation particle satisfies the condition, the fuzzy membership matrix obtained by the calculation is de-fuzzy, and the type of each precipitation particle is obtained. This method can effectively eliminate the influence of ground clutter on the accuracy of precipitation particle classification. The validity of the proposed method is proved by processing the measured data of the C-band / S-band dual-polarization weather radar.
【學位授予單位】：中國民航大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P412.25

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相關(guān)期刊論文 前8條

1 黃鈺;阮征;郭學良;何暉;嵇磊;;垂直探測雷達對北京地區(qū)夏季降水分類統(tǒng)計[J];高原氣象;2016年03期

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