从MODIS资料提取土壤湿度信息的主成分分析方法

沿袭了遥感地物分类中K-L变换思想, 以归一化处理后的遥感数据, 结合地面土壤湿度观测数据, 应用主成分分析方法, 采用MODIS不同红外波段数据, 通过相关关系计算, 在监测结果中融合MODIS具有250 m分辨率的第2波段数据, 建立了青海省多维特征空间土壤湿度监测模型。模型的建立克服了热惯量法监测干旱需多时相遥感数据的缺陷, 经初步检验, 此模型可以在一定精度范围之内监测表层土壤湿度, 具有业务应用潜力。     

Principal Component Analysis of precipitation and rainfall regionalization in Spain

Summary A T-mode Principal Component Analysis (PCA) based on a network including 68 pluviometric gauges and their 12 mean monthly amounts of rainfall is attempted in order to describe the main patterns governing precipitation in Spain. The procedure is applied to a 12 × 12 intermonth covariance matrix; the unrotated components and two additional solutions deduced after varimax and oblimin rotations are presented and discussed. In all cases component scores are computed and their spatial distribution is discussed. Two regionalizations of Spanish rainfall are then obtained and compared in terms of group homogeneity. There follows a discussion concerning the main pluviometric characteristics of each region deduced from the best division.With 7 Figures     

甘肃省玉米产量的主成分分析

应用自然正交函数方法将甘肃省玉米产量的趋势产量项(Yt)和气候产量项(Yw) 进行展开,展开后的Yt、Yw第一特征向量都表明了Yt、Yw总趋势的一致性,其大值中心表明产量变率较大。通过对各主成分主要时间系数与各站Yt、Yw进行相关分布分析,筛选出全省玉米产量的八个站点,这些站点基本上代表了本省不同类型的农业气候区域的玉米产量水平和生育状况。     

Temporal variations in surface ozone and its precursors and meteorological effects at an urban site in China

Continuous measurements of ozone and its precursors including NO, NO₂, and CO at an urban site (32°03′N, 118°44′E) in Nanjing, China during the period from January 2000 to February 2003 are presented. The effects of local meteorological conditions and distant transports associated with seasonal changed Asian monsoons on the temporal variations of O₃ and its precursors are studied by statistical, backward trajectory, and episode analyses. The diurnal variation in O₃ shows high concentrations during daytime and low concentrations during late night and early morning, while the precursors show high concentrations during night and early morning and low concentrations during daytime. The diurnal variations in air pollutants are closely related to those in local meteorological conditions. Both temperature and wind speed have significant positive correlations with O₃ and significant negative correlations with the precursors. Relative humidity has a significant negative correlation with O₃ and significant positive correlations with the precursors. The seasonal variation in O₃ shows low concentrations in late autumn and winter and high concentrations in late spring and early summer, while the precursors show high concentrations in late autumn and winter and low concentrations in summer. Local mobile and stationary sources make a great contribution to the precursors, but distant transports also play a very important role in the seasonal variations of the air pollutants. The distant transport associated with the southeastern maritime monsoon contributes substantially to the O₃ because the originally clean maritime air mass is polluted when passing over the highly industrialized and urbanized areas in the Yangtze River Delta. The high frequency of this type of air mass in summer causes the fact that a common seasonal characteristic of surface O₃ in East Asia, summer minimum, is not observed at this site. The distant transports associated with the northern continental monsoons that dominate in autumn and winter are related to the high concentrations of the precursors in these two seasons. This study can contribute to a better understanding of the O₃ pollution in vast inland of China affected by meteorological conditions and the rapid urbanization and industrialization.     

新疆玛纳斯灌区水面蒸发量主成分分析

对新疆玛纳斯灌区水面蒸发气象影响因子进行了主成分分析.第一主成分代表空气冷热状况;第二主成分代表空气动力条件和日照条件;第三主成分代表相对湿度.前3个主成分累计贡献率已达97.7%,故提取3个主成分已能满足要求.经过检验,利用2个主成分建立二元一次回归方程,并与应用所有气象影响因子建立的多元线性回归方程相比较,结果显示,主成.分分析方法建立的回归方程的偏回归系数均通过t检验,达到极显著水平,多元线性回归方程虽拟合效果稍优于主成分分析方法,但偏回归系数b均未通过t检验,系数显著性水平不如主成分分析法.     

新疆地区多时相MODIS-NDVI数据主成分分析

利用EOS／MODIS的250m卫星遥感数据,对新疆2010年全年36个旬的数据,用最大值合成方法将每3旬NDVI数据合成每月NDVI数据,对每月NDVI数据进行主成分分析,提取了不同主成分分量,分析了不同主分量累积方差。结果表明,主成分变换的前4个主分量包含了大部分信息：第一主分量反映了NDVI在全年的空间分布和累积值。第二、三、四主分量反映的是冬夏、春夏、春秋等季节性的变化情况。     

Analysis of annual cyclic variations in total ozone column over Indian region

Annual Cyclic Variations (ACV) in the Total Ozone Column (TOC) were estimated in latitudinally averaged Multi Sensor Reanalysis (MSR) monthly mean TOC time-series data-set from Jan 1979 to Dec 2008 for Indian region. The TOC contents over any latitude is controlled by the photochemistry and dynamics present in different regions of the stratosphere and troposphere, correlation between ACV in TOC, and ACV in other climatic and dynamical factors—(i) Solar Insolation on a horizontal surface at the top of the atmosphere (ETSI); (ii) Zonal Wind at 30 hPa pressure level (ZW); (iii) Meridional Wind at 30 hPa pressure level (MW); and (iv) Air Temperature at 30 hPa pressure level (AT)—were taken into account to understand their role in the annual cyclic variability present in the TOC over Indian region. Contributions of ACV present in these climatic and dynamical factors to the ACV in TOC were ascertained by performing a multiple linear regression analysis by taking ACV in ETSI, ACV in ZW and ACV in AT as independent variables (co-variates) for ACV in TOC. It is concluded that in the tropical part of Indian region ACV in TOC is largely controlled by the photochemistry; whereas in the subtropical part of the region, the dynamics present in the stratosphere mainly decides ACV in TOC.     

非线性主成分分析在中国四季降水异常分布中的应用

利用中国160站逐月降水资料,运用一种基于前馈型人工神经网络的非线性主成分分析方法(nonlinear principal component analysis,NLPCA)研究了中国近50 a四季降水异常分布的非线性特征.结果表明,NLPCA有能力表示出更一般的低维结构特征.四季降水的异常分布都具有一定的非线性相关空间结构,其中春夏季节非线性较强,秋冬季节稍弱;四季降水距平的一维NLPCA近似在非线性主成分取极端相反位相时,对应的空间分布型表现出明显的不对称性.四季降水异常的一维NLPCA近似都比传统一维PCA的近似逼真,且存在季节变化.     

基于主分量神经网络的降水集成预报方法研究

运用人工神经网络与主分量分析(PCA)相结合的方法,对同一降水预报量的各种数值预报产品进行集成预报研究.结果表明:主分量人工神经网络方法所构造的集成预报模型,不仅对历史样本的拟合精度好于个各子预报产品,独立样本的实验预报结果也显示出更好的预报准确率及稳定性.业务应用前景良好.     

基于主成分分析的两次雷电过程特性对比

雷电发生时涉及的宏观环境参量多且复杂,考虑不损失大量信息的前提下,基于主成分分析法对两次雷电天气过程进行特性对比,凝练雷电天气潜势预报和临近预报的着眼点,有助于提高其预报准确率。利用闪电、雷达和天气背景资料选取2011—2013年6—8月西宁雷电活动样本,提取同时次探空资料中26个环境参数作主成分分析,深入分析2012年7月29日(过程Ⅰ)和2013年8月26日(过程Ⅱ)两次雷电天气过程的演变和环境条件。结果表明:大气动力和大气温湿类参数与西宁雷电活动关系最为密切,闪电强度累积概率显示动力类较温湿类偏强,平均负地闪强度偏大-1.91 kA,平均正地闪强度偏大1.92 kA。过程Ⅰ表现为西风槽型雷电天气,受大气温湿类参数影响较大;而过程Ⅱ属于副高边缘型雷电天气,明显受大气动力参数影响。两次过程的闪电活动均以负地闪为主,都有明显的日变化规律,且呈单峰型分布。过程Ⅰ在闪电频次上占优势,但在强度上明显弱于过程Ⅱ的。     

基于主成分分析的人工智能台风路径预报模型

利用主成分分析可以从具有随机噪声干扰的气象场提取主要信号特征,排除随机干扰的能力,论文以1980～2010年共31年6～9月西行进入南海海域的台风样本为基础,综合考虑台风移动路径的气候持续因子和数值预报产品动力预报因子,采用主成分分析的特征提取与逐步回归计算相结合的预报因子信息数据挖掘技术,以进化计算的遗传算法,生成期望输出相同的多个神经网络个体,建立了一种新的非线性人工智能集合预报模型,进行了分月台风路径预报模型的预报建模研究。在预报建模样本、独立预报样本相同的情况下,分别采用人工智能集合预报方法和气候持续法进行了预报试验,试验对比结果表明,前者较后者在6、7、8和9月份台风路径预报中,平均绝对误差分别下降了7.4%、4.8%、12.4%、17.0%。另外,论文进一步在初选预报因子和样本个例相同的情况下,通过比较新模型与直接采用主成分分析方法选因子并分别运用逐步回归和遗传—神经网络集合预报模型进行计算的预报精度差异表明,前者具有更高的预报精度,其原因是该方法挖掘利用了全部备选预报因子的有用预报信息,而且遗传—神经网络集合预报模型的是由多个神经网络个体预报结果合成,集合模型的各个神经网络个体的网络结构,是通过遗传算法的优化计算确定的,因此,该集合预报模型的泛化能力显著提高,在实际天气预报中具有较好的实用性和推广价值。     

平流层下部气温年际异常的旋转主成分分析

利用旋转主成分方法分析了北半球冬季平流层下部月平均气温距平，得到的4个主要的空间分布型，分别表示中纬、低纬的纬向对称异常、太平洋北部暖中心和极区冷中心的异常。还指出中纬异常的时间演变最单调，整个冬季有持续性，年际变化呈现明显的准两年振荡周期(QBD)。     

基于主分量的神经网络水位预报模型应用研究

根据气象和水文资料，以上游面雨量、水位值为预报因子，以西江流域的梧州水位为预报量，发现预报因子与预报量有很好的相关性。采用人工神经网络与主分量分析相结合的方法，建立了梧州水位的预报模型。结果表明，该预报模型对历史样本拟合精度高，试报效果及预报稳定性明显好于传统的神经网络预报模型，可在预报业务中应用。     

Interannual variability and temporal variations of total column ozone in Visakhapatnam from ground based observations

Total column ozone (TCO) measured by the portable, handheld, multichannel Sun photometer (ozonometer-Microtops-II) at Visakhapatnam (17.43°N, 83.14°E; 51 m above mean sea level), India, which is a tropical urban station has been analysed in this study. The results showed the estimate of the annual and seasonal trends of TCO from the period February 2005 to December 2008. The linear regression technique was applied in Microtops II data to study the trend of TCO during 2005?C2008. It also provides statistics of mean and variability on temporal scale. The results showed increasing trend at Visakhapatnam an average of 1.88 DU per year (or 0.61% per year). However the maximum deviation in diurnal variability of TCO were 28 DU (9.19%) while the minimum deviation were ?36 DU (?11.8%) respectively, which is one of the unique result of this study. The range of the seasonal change represents 9.2% of the mean TCO value with maximum/minimum during March/December.     

华北夏季降水地域特征的旋转主分量研究及其相关分析

用旋转主分量(RPC)方法,研究华北降水的空间分布特征.给出了华北降水空间分布的RPC的前10个模态.它们概括了华北地区降水的基本特征及历史上的大旱和大涝年的地域分布.每一种降水模态的时间系数都表现出了长周期的年代际变化的规律.华北发生大旱与大涝的最可能的分布是模态3、4与模态10.如1965、1968和1983年的大旱;1963和1973年的大涝年等.分析了各个模态与太平洋副热带高压强度、东亚环流指数、前冬赤道东太平洋海温、以及江淮流域降水的相关表明:它们之间的相关都有各自的最佳模态.如对赤道东太平洋海温与太平洋副热带高压强度而言,最佳模态的特征是:当海温呈El Nino型和西太副高偏强时,华北降水的最可能分布是东部沿海为强的干旱区,其余地区降水皆偏多.与江淮流域降水和EAP流型的相关则主要以1954、1980及1991年为代表的第1和第9模态为主要形态.它们与华北降水有极好的反相关.     

Synoptic patterns in relation to ozone concentrations in west-central Taiwan

Summary Recent studies have shown that regional orography and synoptic patterns greatly affect ozone concentrations. The orography of certain locations is unfavorable for pollutant dispersion, and specific synoptic patterns in these locations lead to serious air pollution episodes. Frequent ozone episodes (defined as hourly ozone ≥ 120 ppbv) in west-central Taiwan during recent years have generated much concern. High ozone days (defined as hourly ozone ≥ 80 ppbv) are also occurring more frequently. High ozone days occur mainly during autumn, but there has been no clear linear relationship demonstrated between any single meteorological variable and pollutant concentration. In this study, statistical data from 1997–1999 has shown that high ozone levels occur with two types of synoptic patterns. The first type consists of a continental anticyclone emanating from mainland China which is swept towards Taiwan. The second is a tropical low pressure system moving northwards closer to Taiwan. West-central Taiwan is located in the lee of the Central Ranges (altitude of 2500 ∼ 3500 m). These synoptic patterns are unfavorable for pollutant dispersion and cause high ozone days. Received October 14, 2000/Revised January 5, 2001     

The Space and Time Features of Global SST Anomalies Studied by Complex Principal Component Analysis

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北半球对流层厚度的时空变化特征

利用1948—2010年NCEP/NCAR逐月位势高度再分析资料、美国国家海洋局提供的1948—2010年逐月海温再分析资料,分别定义了1 000—500 hPa和500—200 hPa厚度,利用EOF、SVD等方法研究了北半球对流层厚度时空演变特征及其与大气环流和海面温度的关系。结果表明,冬季平均厚度EOF第一模态均具有北太平洋及附近高纬度亚洲大陆地区与北美大陆高纬地区反位相变化的特点,而夏季第一模态则是北半球范围内较一致的位相分布;冬、夏季平均厚度EOF第二模态均突出体现了欧亚大陆及附近地区与北半球其他地区反位相变化的特点;冬、夏季厚度场的变化形势与大气环流及海面温度具有密切联系。