M-估计法广义变分同化FY-3B/IRAS通道亮温

采用FY-3B/IRAS亮温资料进行广义变分同化研究。广义变分同化结合了经典变分同化和稳健M-估计两者的优点。区别于经典变分同化依赖于先前的质量控制并要求误差服从高斯分布,把M-估计法耦合到经典变分同化框架中,得到广义变分同化,其弱化了同化前的质量控制和误差服从高斯分布这两个条件。目标能量泛函包含M-估计以保证对离群值具有稳健性,从而能够得到较好的同化结果。对比变分同化前后的FNL资料湿度与GDAS湿度相关系数作为同化结果检验评价。具体操作过程在FNL作为背景场的基础上分别采用经典和M-估计不同的权重因子变分同化FY3B/IRAS资料,把得到的分析场与GDAS进行相关性比较,由于湿度具有较强的非高斯性,文中首先评估了安徽省13个站GPS/PWV和积分相关湿度廓线得到大气可降水量(即GDAS/PWV和FNL/PWV资料)的相关性,进一步基于信息熵自由度思想进行了近一个月IRAS 20个通道对分析场的影响贡献率诊断研究。     

基于高光谱大气红外探测器模拟亮温的稳健变分反演研究

经典变分反演法是基于观测误差服从高斯分布的假定,对偏离均值较大的离群值较敏感。当实际观测数据包含离群值观测误差呈现非高斯分布时,如果采用经典反演法进行变分反演就会产生大的偏差,甚至导致变分反演的失败。使用经典变分反演法首先需要进行质量控制,剔除所谓的离群值,但有相当部分的离群值包含了一些亮点,如天气现象。如果对其“视而不见”,则对很多重要的信息就无法把握。基于此,研究采用稳健变分反演的思想同化这些离群值,主要思想是把M-估计法（L2、Huber、Fair和Cauchy一估计）的权重函数耦合到经典变分反演中,在每次变分反演极小化迭代过程中重新估计观测项对经典变分反演目标泛函的贡献率。采用高光谱大气红外探测器（AtmosphericInfraRedSounder,AIRS）的通道模拟亮温进行理想试验,结果表明：采用Huber一估计进行稳健变分反演对温度和湿度反演具有较好的效果;采用Cauchy-估计得到的效果反而更差,这是由Cauchy等分布函数固有的缺陷所决定的。因此,稳健变分反演观测误差非高斯分布是可行的,但依赖M-估计法权重函数的选取。     

GPS坐标时间序列时变振幅周期信号的Huber M估计

GPS时间序列周期信号的精准提取对趋势的估计具有重要的影响。相较于传统的常数振幅周期信号模型,已有研究表明GPS时间序列周期信号的振幅是随时间变化的。实际的GPS时间序列存在异常值,且在提取周期信号过程中会产生新的异常值。针对以上两点,该文提出了一种基于Huber函数M估计(HM)的GPS坐标时间序列时变振幅周期信号估计方法:采用关于时间的多项式函数来建立时变振幅模型,由HM方法及交替方向乘子法求解。通过模拟数据及实际GPS站点数据将HM方法与小波分解方法、奇异谱分析方法和滑动最小二乘方法进行比较,结果表明HM方法在估计精度上要优于其他3种方法,弥补了已有方法在时变振幅情形下会吸收噪声以及噪声较强时对周期信号提取能力较弱的不足。     

Estimation of background levels of contaminants

Samples from hazardous waste site investigations frequently come from two or more statistical populations. Assessment of background levels of contaminants can be a significant problem. This problem is being investigated at the U.S. Environmental Protection Agency's Environmental Monitoring Systems Laboratory in Las Vegas. This paper describes a statistical approach for assessing background levels from a dataset. The elevated values that may be associated with a plume or contaminated area of the site are separated from lower values that are assumed to represent background levels. It would be desirable to separate the two populations either spatially by Kriging the data or chronologically by a time series analysis, provided an adequate number of samples were properly collected in space and/or time. Unfortunately, quite often the data are too few in number or too improperly designed to support either spatial or time series analysis. Regulations typically call for nothing more than the mean and standard deviation of the background distribution. This paper provides a robust probabilistic approach for gaining this information from poorly collected data that are not suitable for above-mentioned alternative approaches. We assume that the site has some areas unaffected by the industrial activity, and that a subset of the given sample is from this clean part of the site. We can think of this multivariate data set as coming from two or more populations: the background population, and the contaminated populations (with varying degrees of contamination). Using robust M-estimators, we develop a procedure to classify the sample into component populations. We derive robust simultaneous confidence ellipsoids to establish background contamination levels. Some simulated as well as real examples from Superfund site investigations are included to illustrate these procedures. The method presented here is quite general and is suitable for many geological and biological applications.     

改进权重的迭代最近点算法在点云配准中的应用

针对传统迭代最近点算法不具备抗差性的难题，利用迭代最近点算法配准残差的分布规律，综合M估计及选权迭代思想，提出改进权重的迭代最近点配准算法。根据每个点对配准计算出对应的初始权重，然后在附加点对权重的基础上使用选权迭代法计算出满足条件的权重，以达到抵御粗差的目的。结果表明，选权迭代过程能合理改善三维空间转换参数计算的结果，提出的改进算法较适合含粗差点的点云数据的配准。