地震作用下串、并联工程系统中结构失效相关性的近似处理

“工程系统”指的是工程项目所包括的主要工程结构的集合。当工程项目具有不止一个主要结构时,各结构单独优化的所组成的集合不一定是全局最优的工程系统。工程系统全局优化过程中非常重要的一环就是该系统失效概率的计算。这时必须考虑而又非常困难的问题就是系统中各个结构之间失效相关性的问题。我们认为,在自然灾害作用下各结构之间的失效相关性主要决定于自然灾害的程度,强度愈高相关性就愈大。据此,给出了这个问题的近似处     

Estimation of regionalized phenomena by geostatistical methods: lake acidity on the Canadian Shield

 This paper describes a geostatistical technique based on conditional simulations to assess confidence intervals of local estimates of lake pH values on the Canadian Shield. This geostatistical approach has been developed to deal with the estimation of phenomena with a spatial autocorrelation structure among observations. It uses the autocorrelation structure to derive minimum-variance unbiased estimates for points that have not been measured, or to estimate average values for new surfaces. A survey for lake water chemistry has been conducted by the Ministère de l'Environnement du Québec between 1986 and 1990, to assess surface water quality and delineate the areas affected by acid precipitation on the southern Canadian Shield in Québec. The spatial structure of lake pH was modeled using two nested spherical variogram models, with ranges of 20 km and 250 km, accounting respectively for 20% and 55% of the spatial variation, plus a random component accounting for 25%. The pH data have been used to construct a number of geostatistical simulations that produce plausible realizations of a given random function model, while 'honoring' the experimental values (i.e., the real data points are among the simulated data), and that correspond to the same underlying variogram model. Post-processing of a large number of these simulations, that are equally likely to occur, enables the estimation of mean pH values, the proportion of affected lakes (lakes with pH≤5.5), and the potential error of these parameters within small regions (100 km×100 km). The method provides a procedure to establish whether acid rain control programs will succeed in reducing acidity in surface waters, allowing one to consider small areas with particular physiographic features rather than large drainage basins with several sources of heterogeneity. This judgment on the reduction of surface water acidity will be possible only if the amount of uncertainty in the estimation of mean pH is properly quantified. Received: 3 March 1997 · Accepted: 16 November 1998     

The Nu Expression for Probabilistic Data Integration

The general problem of data integration is expressed as that of combining probability distributions conditioned to each individual datum or data event into a posterior probability for the unknown conditioned jointly to all data. Any such combination of information requires taking into account data interaction for the specific event being assessed. The nu expression provides an exact analytical representation of such a combination. This representation allows a clear and useful separation of the two components of any data integration algorithm: individual data information content and data interaction, the latter being different from data dependence. Any estimation workflow that fails to address data interaction is not only suboptimal, but may result in severe bias. The nu expression reduces the possibly very complex joint data interaction to a single multiplicative correction parameter ν ₀, difficult to evaluate but whose exact analytical expression is given; availability of such an expression provides avenues for its determination or approximation. The case ν ₀=1 is more comprehensive than data conditional independence; it delivers a preliminary robust approximation in presence of actual data interaction. An experiment where the exact results are known allows the results of the ν ₀=1 approximation to be checked against the traditional estimators based on assumption of data independence.     

Modeling distribution of temporal sampling errors in area-time-averaged rainfall estimates

In this paper, the authors examine models of probability distributions for sampling error in rainfall estimates obtained from discrete satellite sampling in time based on 5 years of 15-min radar rainfall data in the central United States. The sampling errors considered include all combinations of 3, 6, 12, or 24 h sampling of rainfall over 32, 64, 128, 256, or 512 km square domains, and 1, 5, or 30 day rainfall accumulations. Results of this study reveal that the sampling error distribution depends strongly on the rain rate; hence the conditional distribution of sampling error is more informative than its marginal distribution. The distribution of sampling error conditional on rain rate is strongly affected by the sampling interval. At sampling intervals of 3 or 6 h, the logistic distribution appears to fit the conditional sampling error quite well, while the shifted-gamma, shifted-weibull, shifted-lognormal, and normal distributions fit poorly. At sampling intervals of 12 or 24 h, the shifted-gamma, shifted-weibull, or shifted-lognormal distribution fit the conditional sampling error better than the logistics or normal distribution. These results are vital to understanding the accuracy of satellite rainfall products, for performing validation assessment of these products, and for analyzing the effects of rainfall-related errors in hydrological models.     

一次暴雨过程降水强度“双峰”结构成因分析

利用LAPS再分析高分辨率资料,对2008年6月9—11日浙、赣、皖地区一次具有"双峰"结构的暴雨过程进行了诊断分析。结果表明:中低层切变线和低空急流是此次暴雨过程的直接影响系统,为暴雨形成提供了良好的动力抬升机制。暴雨过程中暴雨中心区域水汽一直比较充沛且变化较小。对不稳定机制的分析表明,10日03时降水第一峰值可能是由低层对流不稳定和中层条件性对称不稳定机制的共同作用下形成,而条件性对称不稳定可能是10日09时降水出现第二峰值的主要成因,而绝对动量距平调整,则是条件性对称不稳定形成的可能机制。     

2013年2月大气环流和天气分析

综合应用常规观测资料、再分析资料、多普勒雷达探测资料等多源数据,分析了2013年2月发生在长江中下游地区一次高架雷暴过程的形成机制。结果表明,此类高架雷暴过程发生在冷锋后部对流稳定的环境大气中,其主要形成过程为：首先气块在地形强迫和锋面抬升作用下产生垂直运动,由于对流稳定环境大气的抑制,气块只能到达较低的中性浮力高度并产生与环境大气之间的绝对地转动量差（ΔM）,在ΔM调整机制作用下自低层抬升的气块将产生沿等熵面的惯性加速度从而加强了倾斜运动,当暖湿气流沿锋面爬升至约700进一步加强,并在此过程中产生对称不稳定并通过反馈机制促进倾斜运动的进一步发展。在地形强迫和锋面抬升的基础上,ΔM调整与对称不稳定的共同作用使得冷锋后部锋区上空产生旺盛的倾斜运动,倾斜运动的持续发展使对流得到组织化,产生带状的对流云和降水带,最终形成了雷暴等强对流天气。总之,在对流稳定的环境大气条件下,由于地形强迫和锋面抬升、ΔM调整、辐合切变线和高低空急流耦合以及对称不稳定等机制的联合作用激发了倾斜对流运动的强烈发展,使倾斜对流有效位能大量累积并释放,最终形成了此次冬季高架雷暴天气过程。     

Conditional Independence Test for Weights-of-Evidence Modeling

Weights-of-evidence modeling is a GIS-based technique for relating a point pattern for locations of discrete events with several map layers. In general, the map layers are binary or ternary. Weights for presence, absence or missing data are added to a prior logit. Updating with two or more map layers is allowed only if the map layers are approximately conditionally independent of the point pattern. The final product is a map of posterior probabilities of occurrence of the discrete event within a small unit cell. This paper contains formal proof that conditional independence of map layers implies that T, the sum of the posterior probabilities weighted according to unit cell area, is equal to n, being the total number of discrete events. This result is used in the overall or omnibus test for conditional independence. In practical applications, T generally exceeds n, indicating a possible lack of conditional independence. Estimation of the standard deviation of T allows performance of a one-tailed test to check whether or not T-n is significantly greater than zero. This new test is exact and simpler to use than other tests including the Kolmogorov-Smirnov test and various chi-squared tests adapted from discrete multivariate statistics.     

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.     

Transitive Geostatistics for Stepwise Modeling Across Boundaries between Rock Regions

A single intrinsic stationary random field may not account for transitional heterogeneity and abrupt dissimilarity of geological properties across boundaries between rock type regions. This paper proposes the stepwise construction of transitive covariance models for modeling continuous properties correlated across boundaries of multiple disjoint physical domains such as rock type bodies. Modeling in geology is usually simplified by splitting the geological space into rock type geo-domains (e.g., strata, sedimentary facies, soil series, diagenetic regions and alteration zones). Due to the limitations of simultaneous solutions, a simplification is to model each domain independently at the cost of losing the conditioning of properties across domains. This paper proposes to organize the modeling process in a triangular array which follows events in the geological time domain; for example, the younger formations are at the top of the pyramid and the older formation at the base. The estimation may go from top to base by assuming that younger events have perturbed older formations. Geology shows the scars of events that cumulate in rock formations before they are finally eroded. In some cases, older formations may be parent material for younger formations. The continuous property within each geo-domain has a conditional covariance in the main diagonal of the array which may belong to a specific event in the geological time. This sequence leads to transitive estimation and simulations in the physical space. If a simultaneous solution is sought (i.e., the future and past are correlated both ways), the complex covariance functions can be constructed stepwise from conditional spectra.     

A comparative study of nonlinear Markov chain models for conditional simulation of multinomial classes from regular samples

Simulating fields of categorical geospatial variables from samples is crucial for many purposes, such as spatial uncertainty assessment of natural resources distributions. However, effectively simulating complex categorical variables (i.e., multinomial classes) is difficult because of their nonlinearity and complex interclass relationships. The existing pure Markov chain approach for simulating multinomial classes has an apparent deficiency—underestimation of small classes, which largely impacts the usefulness of the approach. The Markov chain random field (MCRF) theory recently proposed supports theoretically sound multi-dimensional Markov chain models. This paper conducts a comparative study between a MCRF model and the previous Markov chain model for simulating multinomial classes to demonstrate that the MCRF model effectively solves the small-class underestimation problem. Simulated results show that the MCRF model fairly produces all classes, generates simulated patterns imitative of the original, and effectively reproduces input transiograms in realizations. Occurrence probability maps are estimated to visualize the spatial uncertainty associated with each class and the optimal prediction map. It is concluded that the MCRF model provides a practically efficient estimator for simulating multinomial classes from grid samples.