1981-2020年安顺市大雾天气的气候特征分析

利用1981-2020年安顺市6个国家气象站的逐日能见度资料和同期地面逐小时降水资料,运用统计分析、Morlet小波分析、Mann-Kendall非参数统计检验以及Pettitt突变检验,对安顺市大雾天气的气候特征进行分析,结果表明：1981-2020年安顺市总共出现大雾1111d,年平均大雾日数为27.8d;大雾日数的季节变化呈冬季＞春季(秋季)＞夏季,其中冬季大雾出现的频率占全年的44%;大雾主要出现在1月,其出现的频率占全年的18%,其次是2月和12月。近40年安顺市出现大雾最多的站点是西秀区,其次是关岭县;夏季大雾的高发区是紫云县,西秀区是大雾的低发区,春、秋、冬三季,大雾的高发区是关岭县和西秀区。安顺市大雾日数存在明显的准5a、准12a和准20a的周期震荡,其中准20a为第一主周期,具有明显的稳定性和全域性。1981-1996年期间安顺市的大雾日数总体呈减少趋势,且1986-1990年期间减少趋势显著;1996年以后安顺市大雾日数总体呈增加趋势,且2004年以后增加趋势显著;安顺市大雾日数在2003年发生突变,且突变特征显著,突变后的大雾日数较突变前增加了157%。     

Probabilistic impedance of foundation: Impact of the seismic design on uncertain soils

In the linear seismic design of buildings, the (deterministic) substructure method is a customary and efficient approach. However, the existence of spatial variability in the parameters of the mechanical model of the soil, as well as parametric errors, calls for the use of probabilistic approaches in order to provide a reliable design of the structure. The construction of probabilistic models of the soil impedance matrix provides a natural path to such approaches within the context of the substructure method. Two main techniques are described in this paper: a parametric one, typically using the stochastic finite element method, and a nonparametric one, which was introduced more recently. The latter is explored more specifically, and the possibilities it offers in terms of seismic design are presented. In particular, it is shown that it allows for the estimation of quantiles of the quantities of interest, rather than confidence intervals, which lead to highly conservative design. Copyright © 2008 John Wiley & Sons, Ltd.     

Variogram Model Selection via Nonparametric Derivative Estimation

Before optimal linear prediction can be performed on spatial data sets, the variogram is usually estimated at various lags and a parametric model is fitted to those estimates. Apart from possible a priori knowledge about the process and the user's subjectivity, there is no standard methodology for choosing among valid variogram models like the spherical or the exponential ones. This paper discusses the nonparametric estimation of the variogram and its derivative, based on the spectral representation of positive definite functions. The use of the estimated derivative to help choose among valid parametric variogram models is presented. Once a model is selected, its parameters can be estimated—for example, by generalized least squares. A small simulation study is performed that demonstrates the usefulness of estimating the derivative to help model selection and illustrates the issue of aliasing. MATLAB software for nonparametric variogram derivative estimation is available at http://www-math.mit.edu/~gorsich/derivative.html. An application to the Walker Lake data set is also presented.     

45a来塔里木河流域气温、降水变化及其对积雪面积的影响

对塔里木河流域19个台站45 a(1958-2002年)的气温、降水序列进行非参数检验,查明其变化趋势及特征,在此基础上,对近20 a(1982-2001年)流域的积雪面积(SCA,%)变化进行趋势与相关分析.结果表明:流域的气温和降水均在20世纪80年代中期发生了阶段式的跳跃增长,气温和降水增加的主要季节分别为冬季和夏季.流域总体的积雪面积呈缓慢增加态势,其中北区和西区增加较为稳定,而南区相对不稳定.在垂直方向上,海拔<2 500 m的区域积雪面积表现缓慢增加,而海拔≥2 500 m的区域则减少.相比较,低海拔区域更易受降水影响,而高海拔区域更易受气温影响.海拔2 500~5 000 m的高度带是对气候变化较为敏感的区域.20世纪90年代与80年代相比,降雪和融雪的速度都更快.积雪与冷季降水呈正相关,但与冷季气温没有明显的相关关系.     

THE NONPARAMETRIC ANALYSIS OF VARIANCE AND MULTIPLE COMPARISONS PROCEDURES IN GEOGRAPHY*

Geographers employing the parametric F statistic in analyses of variance should consider that it can be highly sensitive to departures from normality. The Friedman and Kruskal-Wallis tests offer geographers nonparametric alternatives to the analysis of variance, for k dependent and independent samples respectively. Associated with the tests are little known methods for examining ordered alternatives and making multiple comparisons between samples, thereby permitting in-depth analysis of sample data. Both tests and these latter procedures are illustrated by examples drawn from population and urban retailing geography.     

Evaluation of typhoon‐induced rainfall using nonparametric Monte Carlo simulation and locally weighted polynomial regression

Typhoons in Korea are the major causes of natural disasters in the Korean peninsula. In this study, rainfall generated by typhoons was quantitatively analysed using various statistical methods. First, the frequency analysis of rainfall induced by typhoons was carried out to calculate the design rainfall. Second, the frequency analysis of simulated rainfall derived by nonparametric Monte Carlo simulation (NMCS) was performed to evaluate the uncertainty of rainfall caused by typhoons. Third, the regression relationship between the physical characteristic factors of typhoons and rainfall was established by locally weighted polynomial regression (LWPR), and the characteristic factors of typhoons were simulated. The simulated characteristic factors were then used to estimate rainfall and to calculate the design rainfall by typhoons. Comparative analyses of design rainfalls as estimated using various statistical methods were performed. The LWPR showed good performance in terms of reproducing typhoon characteristics. Therefore, the combined NMCS and LWPR method suggested in this study can be used as a supplementary technique for assessing extreme rainfall with climate change and reflected variability. Copyright © 2010 John Wiley & Sons, Ltd.     

Influences of local hydroclimatology and teleconnections on Florida's precipitation and temperature variability

Understanding the influences of local hydroclimatology and two large-scale oceanic-atmospheric oscillations (i.e., Atlantic Multidecadal Oscillation (AMO) and El Niño-Southern Oscillation (ENSO)) on seasonal precipitation (P) and temperature (T) relationships for a tropical region (i.e., Florida) is the focus of this study. The warm and cool phases of AMO and ENSO are initially identified using sea surface temperatures (SSTs). The associations of SSTs and regional minimum, maximum and average surface air temperatures (SATs) with precipitation are then evaluated. The seasonal variations in P-SATs and P-SSTs associations considering AMO and ENSO phases for sites in (1) two soil temperature regimes (i.e., thermic and hyperthermic); (2) urban and non-urban regions; and (3) regions with and without water bodies, are analysed using two monthly datasets. The analyses are carried out using trend tests, two association measures, nonparametric and parametric statistical hypothesis tests and kernel density estimates. Decreasing (increasing) trend in precipitation (SATs) is noted in the recent multi-decadal period (1985–2019) compared to the previous one (1950–1984) indicating a progression towards warmer and drier climatic conditions across Florida. Spatially and temporally non-uniform variations in the associations of precipitation with SATs and SSTs are noted. Strong positive (weak negative) P–T associations are noted during the wet (dry) season for both AMO phases and El Niño, while significant (positive) P–T associations are observed across southern Florida during La Niña in the dry season. The seasonal influences are predominant in governing the P–T relationship over the regions with and without water bodies; however, considerable variations between El Niño and La Niña are noted during the dry season. The climate variability influences on P–T correlations for hyperthermic and thermic soil zones are found to be insignificant (significant) during the wet (dry) season. Nonparametric clustering is performed to identify the spatial clusters exhibiting homogeneous P–T relationships considering seasonal and climate variability influences.     

NWFE结合纹理特征的SVM土地覆被分类方法研究

为提高土地覆被分类精度,采用非参数权重特征提取(nonparametric weighted feature extraction,NWFE)结合纹理特征的支持向量机(support vector machines,SVM)的分类法,对新疆玛纳斯河流域绿洲区2006年的土地覆被进行分类,并将该方法与主成分分析(principal component analysis,PCA)结合纹理特征的SVM分类、原始波段结合纹理特征的SVM分类进行对比。结果表明,NWFE结合纹理特征的SVM分类结果优于其他2种分类结果,不仅反映了土地覆被分布的整体情况,而且使不同土地覆被类型得到较好的区分,总体分类精度达89.17%。     

Kernel bandwidth selection for a first order nonparametric streamflow simulation model

A new approach for streamflow simulation using nonparametric methods was described in a recent publication (Sharma et al. 1997). Use of nonparametric methods has the advantage that they avoid the issue of selecting a probability distribution and can represent nonlinear features, such as asymmetry and bimodality that hitherto were difficult to represent, in the probability structure of hydrologic variables such as streamflow and precipitation. The nonparametric method used was kernel density estimation, which requires the selection of bandwidth (smoothing) parameters. This study documents some of the tests that were conduced to evaluate the performance of bandwidth estimation methods for kernel density estimation. Issues related to selection of optimal smoothing parameters for kernel density estimation with small samples (200 or fewer data points) are examined. Both reference to a Gaussian density and data based specifications are applied to estimate bandwidths for samples from bivariate normal mixture densities. The three data based methods studied are Maximum Likelihood Cross Validation (MLCV), Least Square Cross Validation (LSCV) and Biased Cross Validation (BCV2). Modifications for estimating optimal local bandwidths using MLCV and LSCV are also examined. We found that the use of local bandwidths does not necessarily improve the density estimate with small samples. Of the global bandwidth estimators compared, we found that MLCV and LSCV are better because they show lower variability and higher accuracy while Biased Cross Validation suffers from multiple optimal bandwidths for samples from strongly bimodal densities. These results, of particular interest in stochastic hydrology where small samples are common, may have importance in other applications of nonparametric density estimation methods with similar sample sizes and distribution shapes. Received: November 12, 1997     

Bounding the required sample size for geologic site characterization

The proposed objective of limited sample geologic site characterization is to minimize the chance of unknown and unexpected extremes. This problem proves to be extremely difficult when the data are spatially correlated. A generalization of the classical one-sided nonparametric tolerance interval, based upon the statistical concept of associated random variables, establishes a rigorous, almost distribution-free, tool for computing the minimum required sample size for site characterization. An upper bound on the required number of samples follows from a heuristic measure for the quantity of information in a spatially dependent sample; the measure presented is the equivalent number of uncorrelated samples and is calculated using an estimated variogram. An empirical check of the upper and lower bounds, using more than 2 million simulations and seven real data sets produces a heuristic rule for quantifying the required number of samples.This paper was presented at Emerging Concepts, MGUS-87 Conference, Redwood City, California, 13–15 April 1987.