融合K-means和熵权法的高鲁棒性大气边界层高度估计方法  

A Highly Robust Atmospheric Boundary Layer Height Estimation Method Combining K-means and Entropy Weight Method

作　　者：刘振兴 常建华[1,2] 李红旭[4] 孟园园[1] 周妹 戴腾飞 Liu Zhenxing;Chang Jianhua;Li Hongxu;Meng Yuanyuan;Zhou Mei;Dai Tengfei(School of Electronics&Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Department of Information Technology,Taizhou Polytechnic College,Taizhou 225300,Jiangsu,China;School of Electronic Information Engineering,Wuxi University,Wuxi 214105,Jiangsu,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,江苏南京210044 [2]南京信息工程大学江苏省大气环境与装备技术协同创新中心,江苏南京210044 [3]泰州职业技术学院信息技术学院,江苏泰州225300 [4]无锡学院电子信息工程学院,江苏无锡214105

出　　处：《光学学报》2023年第12期236-246,共11页Acta Optica Sinica

基　　金：国家自然科学基金(61875089,62175114);江苏高校“青蓝工程”资助项目(苏教师函[2020]10号);泰州市科技支撑计划社会发展项目(TSZ202132);泰州职业技术学院院级重点科研项目(TZYKYZD-19-5)。

摘　　要：针对常用激光雷达边界层高度估计方法在云层或悬浮气溶胶层等复杂大气结构下会产生误判的问题,提出一种融合K-means和熵权法的高鲁棒性大气边界层高度估计方法。选取美国大气辐射测量项目南部大平原站点的微脉冲激光雷达数据,将K-means算法和熵权法应用于多种条件下的边界层高度估计,从初始参数选取和距离计算两个方面提升基于聚类分析的边界层高度的估计性能。实验结果表明:与常用激光雷达边界层高度估计方法相比,所提方法具有较强的抗干扰能力,能更好地追踪复杂大气结构下的边界层高度日变化过程;在晴朗无云天气和复杂大气结构下,其边界层高度的估计值与无线电探空仪边界层高度的测量值基本一致,相关系数分别为0.9718和0.9175。所提方法具有较高的鲁棒性,可以可靠地估计多种条件下的大气边界层高度。Objective The atmospheric boundary layer is the lowest layer of the troposphere,which is directly influenced by the surface.The atmospheric boundary layer height(ABLH)is an important parameter of the atmospheric boundary layer,whose value ranges from several hundred meters to thousands of meters.It plays an important role in analyzing the heat radiation transmission process in the boundary layer,acquiring the air pollution status,and formulating pollution control strategies.Lidar is an active remote sensing tool,which has high spatial and temporal resolutions and can continuously and automatically measure ABLH.The methods of estimating ABLH based on lidar data mainly include the threshold method,the gradient method,the wavelet covariance transform method,and the variance method.However,these methods are only suitable for specific meteorological conditions,and the interference of clouds or a suspended aerosol layer can easily lead to the misjudgment of ABLH.A highly robust ABLH estimation method combining Kmeans and entropy weight method,i.e.,EKmeans,is proposed to solve the problem of erroneous detection by commonly used lidarbased ABLH estimation methods under complex atmospheric structures.The proposed method improves the performance of ABLH estimation based on cluster analysis in terms of initial parameter selection and distance calculation.Compared with commonly used lidarbased ABLH estimation methods,the proposed method has a strong antiinterference ability.It can well track the diurnal variation process of the boundary layer under complex atmospheric structures.Under clear sky and cloudy weather or a suspended aerosol layer structure,the ABLH estimated by the proposed method is basically consistent with that measured by a radiosonde,and the correlation coefficient is 0.9718 and 0.9175,respectively.The proposed method has high robustness and can reliably estimate ABLH under different conditions.Methods The proposed method integrates Kmeans and entropy weight method to improve the ABLH estimation performance

关 键 词：遥感 激光雷达 大气边界层高度 复杂大气结构 聚类 

分 类 号：P407.5[天文地球—大气科学及气象学]