Calculating the Climatology and Anomalies of Surface Cloud Radiative Effect Using Cloud Property Histograms and Cloud Radiative Kernels

Cloud radiative kernels (CRK) built with radiative transfer models have been widely used to analyze the cloud radiative effect on top of atmosphere (TOA) fluxes, and it is expected that the CRKs would also be useful in the analyses of surface radiative fluxes, which determines the regional surface temperature change and variability. In this study, CRKs at the surface and TOA were built using the Rapid Radiative Transfer Model (RRTM). Longwave cloud radiative effect (CRE) at the surface is primarily driven by cloud base properties, while TOA CRE is primarily decided by cloud top properties. For this reason, the standard version of surface CRK is a function of latitude, longitude, month, cloud optical thickness (τ) and cloud base pressure (CBP), and the TOA CRK is a function of latitude, longitude, month, τ and cloud top pressure (CTP). Considering that the cloud property histograms provided by climate models are functions of CTP instead of CBP at present, the surface CRKs on CBP-τ histograms were converted to CTP-τ fields using the statistical relationship between CTP, CBP and τ obtained from collocated CloudSat and MODIS observations. For both climate model outputs and satellites observations, the climatology of surface CRE and cloud-induced surface radiative anomalies calculated with the surface CRKs and cloud property histograms are well correlated with those calculated from surface radiative fluxes. The cloud-induced surface radiative anomalies reproduced by surface CRKs and MODIS cloud property histograms are not affected by spurious trends that appear in Clouds and the Earth's Radiant Energy System (CERES) surface irradiances products.     

顾及一次差分数据结构特征的钟差预报模型

星载原子钟在运行过程中会受到恶劣空间环境与设备老化等因素的影响, 使得卫星钟差数据中经常存在异常值, 其中AO(additive outlier)类异常值是钟差序列中常见的一类异常值。结合最大期望算法与自回归滑动平均(autoregressive moving average, ARMA)模型, 提出一种AO类异常值探测算法。该算法可以准确探测孤立AO类异常值与成片AO类异常值, 有效克服了其他算法经常出现的淹没与掩盖现象。在成功探测钟差序列AO类异常值的同时, 该算法可以估计得到精确的ARMA模型, 进而能准确地进行卫星钟差预报。利用仿真数据与北斗卫星钟差实测数据进行计算分析, 结果表明, 所提算法可以精确探测出钟差序列AO类异常值, 并且具有很好的卫星钟差预报效果。     

多特征差分核支持向量机遥感影像变化检测方法

讨论了利用遥感影像光谱、纹理等多种特征信息的多核函数组合方式,给出了多特征空间差分核函数的构建方法,设计了多特征差分核支持向量机变化检测算法,该算法能够实现联合类别样本加权和遥感影像多种变化类别信息的直接检测。实验结果表明,该算法综合利用多种特征信息,检测精度明显高于传统方法,有利于提取小样本的变化信息,避免了以往检测方法需要确定变化阈值的复杂性和不确定性。     

球坐标系下谱元法三维地震波场模拟

谱元法已成为区域性乃至大陆性尺度地震波场模拟的重要工具.对于区域或大陆尺度层析成像而言,地球曲率不可忽略,此时模拟地震波传播采用球坐标系更为合适.本文从球坐标系下弹性波动方程弱形式出发,基于球坐标系变分原理给出了球坐标系下求解三维地震波方程的谱元法.另一方面,计算Fréchet敏感核是进行全波形反演的关键,本文借助伴随原理,推导了全波走时层析成像三维Fréchet敏感核表达式.为了验证球坐标系下谱元法的精度,我们将数值模拟结果与normal mode方法得到的解析解在1-D PREM模型下进行了对比.同时,我们将此方法应用到华北克拉通区域,以期获得地球内部结构精确成像.基于3-D全球径向各向异性地幔模型S362ANI和3-D地壳模型Crust1.0,我们建立了华北克拉通初始3-D背景模型,并将数值模拟结果与实际观测台站记录波形资料进行对比分析,利用互相关方法提取走时残差,最后给出了Fréchet敏感核在3-D空间中的分布,这些工作为下一步开展球坐标系下三维大尺度全波形反演奠定了基础.

     

A Simple and Robust Lognormal Estimator

In an open pit mine, the selection of blocks for mill feed necessitates the use of a conditionally unbiased estimator not only to maximize profits, but also to predict precisely the grades at the mill. Estimation of blocks usually is done using a series of blasthole assays on a regular grid. In many instances, the blasthole grades show a lognormal-like distribution. This study examines an estimator based on the hypothesis of bilognormality between the true block grade and the estimate obtained using the blastholes. The properties of the estimator are established and the estimator is proven to be conditionally unbiased. It is almost as precise as the lognormal kriging estimator when the points are multilognormal. However, it is more precise than lognormal krigings when only univariate lognormality is present or when the distribution is not exactly lognormal. The estimator also is shown to be robust to errors in the specifications of the variogram model or of the expectation of Z. Contrary to lognormal krigings, the estimator does only a slight correction to the original estimate obtained using the blastholes assays.     

多核多类关联向量机的高分辨率影像目标检测

从高分辨率遥感图像数据中准确检测多类目标的任务对于检测速度和模型训练时间提出了较高的要求.文章提出了一种MKL_mRVM方法:该方法采用基于快速边缘似然最大算法直接计算mRVM分类器的决策函数,避免了传统RVM重复计算目标函数Hessian矩阵的过程,并且因为不需要构造一系列两类分类器,缩短了多类模型的训练时间;同时,将多个基础核引入多类模型,训练过程中采用交叉验证方法确定基础核权重,在随机分出的确认集上检验分类器的精度,选取使得分类模型精度最高的值作为权重的优化结果.实验结果表明,该方法能够在保持解的稀疏性的前提下,有效地缩短模型训练时间.     

基于小波核双重加权SVM模型的蓝藻水华识别与变化检测

以淀山湖为研究区域,利用Landsat系列遥感影像,提出了归化蓝藻指数的构建方法,结合波段组合和Gabor滤波器构建多特征空间,并基于变精度粗糙集和灰色关联决策相结合的方法进行特征空间的优化。在此基础上,采用小波核双重加权SVM分类模型,得到研究区蓝藻水华空间分布格局的识别与检测结果。以误差矩阵证明此分类模型能够较准确地识别出蓝藻水华覆盖区,满足环境调查的要求。研究成果为淀山湖蓝藻水华的防治和水生生态系统的保护提供了科学依据。     

Detecting and Enumerating New Building Structures Utilizing Very‐High Resolution Imaged Data and Image Processing

Abstract

Information on the number and type of new building structures is required by urban and transportation planners and the real estate industry. The goal of this paper is to explore the potential of high resolution imagery for meeting public and private sector demands for information on new buildings. The value of 1 m, 5 m, and 10 m panchromatic and 1 m color scanned aerial photography images acquired in 1997 and 1998 for a study area within the City of San Diego, California is assessed for general change detection and building enumeration. Both semi‐automated and interactive change‐detection approaches are evaluated. We demonstrate that interactive, visual‐based approaches appear to be the most accurate (within 1% of actual count) and efficient approach for generating information on the number of new buildings associated with single family residential land use. More automated approaches to detecting and enumerating image microfeatures may be useful as enhancements for visual‐based assessments and may be practical in areas composed mostly of large buildings associated with commercial and industrial land use. The highest accuracy for automated approaches was an undercounting of 11% for residential buildings and overcounting of 20% for those associated with commercial and industrial land use.     

基于核密度分析与关联规则的天水盆地黄土滑坡规律研究

滑坡灾害是天水盆地最严重的地质灾害.本文基于已有的灾害数据,利用GIS叠加分析的结果作为滑坡灾害点的属性特征,与灾害点的核密度分析结果建立关联规则,找出高风险滑坡区域与相关属性特征的关联关系.结果表明:天水盆地的滑坡灾害与降水条件、地层岩性以及地形坡度有较强的关联关系.     

Identifying spatial concentrations of surnames

Surnames (family names) have been overlooked as a valuable source of spatially referenced population data. Presented here is a methodology, based on kernel density estimation, which is used to identify the areas of Great Britain where any surname is most concentrated. This not only indicates a surname's geographic origin in the country but also its current spatial extent and spatial relationship with other surnames and place names. We argue that such analysis can provide baseline and change measures, and an empirical basis to change forecasting. Such analysis offers valuable insights into national, regional and local changes in population structure, and testimony to the relevance of GIScience to population genetics, historical geography and genealogy.