The Feasibility of Constraining Dark Energy Using LAMOST Redshift Survey

We consider using future redshift surveys with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) to constrain the equation of state of dark energy ω. We analyze the Alcock & Paczyriski (AP) effect imprinted on the two-point correlation function of galaxies in redshift space. The Fisher matrix analysis is applied to estimate the expected error bounds of ω0 and ωa from galaxy redshift surveys, ω0 and ωa being the two parameters in the equation of state parametrization ω(z) = ω0 + ωaz/(1 + z). Strong degeneracies between ω0 and ωa are found. The direction of the degeneracy in ω0 - ωa plane, however, rotates counter-clockwise as the redshift increases. LAMOST can potentially contribute in the redshift range up to 0.5. In combination with other high redshift surveys, such as the proposed Kilo-Aperture Optical Spectrograph project (KAOS), the joint constraint derived from galaxy surveys at different redshift ranges is likely to efficiently break the degeneracy of ω0 and ωa. We do not anticipate that the nature of dark energy can be well constrained with LAMOST alone, but it may help to reduce the error bounds expected from other observations, such as the Supernova/Acceleration Probe (SNAP).     

断层形变短临丛集性前兆研究

断层形变短临前兆表现形变是多样的，丛集性高频波动是其一种重要表现形式。探索了这种前兆信息的提取思路和方法，认为信息丛集度是描述这种前兆信息较理想的特征量     

基于深层土壤数据的多维分形成矿异常识别研究 ——以铜陵矿集区Cu元素为例

以铜陵矿集区Cu元素为例,开展基于深层土壤数据的多维分形成矿异常识别研究。结果表明,在土壤采样密度相对较低、元素含量空间分布差异不大的情况下,多维分形克里格插值法较普通克里格插值法对于成矿异常的识别具有极大的优势。对于铜陵矿集区这类矿床开采、开发程度已较高,表层土壤元素分布主要受控于外源污染的老矿集区,基于深层土壤样品数据的多维分形克里格方法可以有效地进行成矿异常识别,服务于老矿集区的深部、外围隐伏矿床找矿。而对于空白研究区,无论是基于深层土壤数据还是表层土壤数据,多维分形克里格方法应同样有效。多维分形方法下土壤元素成矿预测的异常下限值确定尚无标准,文中采用元素含量 矿床数目累积频率的计算方法,基于该方法提取的成矿异常区域较好地识别出了绝大多数已知矿床,同时识别出了值得作进一步异常查证的空白异常区域。     

广义多维云模型及在空间聚类中的应用

传统的空间聚类方法难于脱离"硬划分"的束缚,且不能有效描述空间对象的复杂特征.一维云模型无法准确反映现实世界的多属性特征.简单的数据融合丢失了空间对象的必要信息.标准二维云模型克服了一维云的不足,但是在模拟复杂地理现象的非齐性和非对称性方面显得捉襟见肘.基于以上考虑,提出了广义多维云模型,以分段特性来体现空间对象的综合特征,并推导出模型的数学表达式.在实证研究的基础上,从空间聚类的隶属程度空间分布特征、与模糊C均值的对比研究及与住宅地价的耦合分析三个视角,详实解读了聚类结果.分析发现,广义多维云模型更能体现空间对象的综合特征,空间聚类结果能够反映出空间分布的潜在信息.更为准确的实现了复杂情况下的空间划分.该模型在刻画地理现象中更为合理,但由于地理实体的空间作用极其复杂.建立模型是一项既具体又充满挑战的任务.     

A latent class MDS model with spatial constraints for non-stationary spatial covariance estimation

Multidimensional scaling (MDS) has played an important role in non-stationary spatial covariance structure estimation and in analyzing the spatiotemporal processes underlying environmental studies. A combined cluster-MDS model, including geographical spatial constraints, has been previously proposed by the authors to address the estimation problem in oversampled domains in a least squares framework. In this paper is formulated a general latent class model with spatial constraints that, in a maximum likelihood framework, allows to partition the sample stations into classes and simultaneously to represent the cluster centers in a low-dimensional space, while the stations and clusters retain their spatial relationships. A model selection strategy is proposed to determine the number of latent classes and the dimensionality of the problem. Real and artificial data sets are analyzed to test the performance of the model.     

Temporal and spatial circulation patterns in the East Frisian Wadden Sea

This work deals with the analysis of simulations carried out with a primitive equation numerical model for the region of the East Frisian Wadden Sea. The model, with 200-m resolution, is forced by wind, air–sea heat, and water fluxes and river runoff and is nested in a German Bight 1-km-resolution numerical model, the latter providing tidal forcing for the fine resolution model. The analysis of numerical simulations is focused both on responses due to moderate conditions, as well as to extreme events, such as the storm surge Britta, for which the model demonstrates very good skills. The question addressed in this paper is how well the model output can be compressed with the help of empirical orthogonal function analysis. It is demonstrated that, for the short-time periods of the order of a spring–neap cycle, only a few modes are necessary to almost fully represent the circulation. This is just an illustration that the circulation in this region is subject to the dominating tidal forcing, creating clear and relatively simple response patterns. However, for longer periods of about several months, wind forcing is also very important, and correspondingly, the circulation patterns become much more complex. Possible applications of the results in hindcasting and forecasting of hydrodynamics and sediment dynamics in the coastal zone are considered.     

Fuzzy model identification based on cluster estimation for reservoir inflow forecasting

Fuzzy theory appears to be extremely effective at handling dynamic, non‐linear and noisy data, especially when the underlying physical relationships are not fully understood. Since hydrologists are still uncertain about many of the aspects of the physical processes in the watershed, fuzzy theory has proved to be a very attractive tool enabling them to investigate such problems. The effectiveness of the fuzzy model lies in the identification of the antecedent membership function (MF), which is generally addressed through a fuzzy clustering approach. Most of the applications of fuzzy computing in hydrology seem to have selected the clustering algorithm quite arbitrarily. However, it is apparent that, as the antecedent parameters are based solely on the identified clusters, the method used for clustering should certainly have an impact on the overall performance of the model. This paper presents the results of a study conducted to investigate the impact of choice of clustering algorithm on the overall performance of a fuzzy‐based hydrologic model. The research is illustrated through a case study of developing a Takagi–Sugeno fuzzy model for reservoir inflow forecasting in the Narmada basin, India. The model was developed using two popular clustering techniques, namely Gustafson–Kessel (GK) and subtractive clustering (SC), and was extensively evaluated for performance based on various statistical indices. The results show that the model performance is comparable at a 1 h lead forecast. However, it is observed that the GK approach results in a better performance than the SC approach in computing forecasts at higher lead times. The analysis suggest that the GK method clusters the input space based on the actual pattern, since it uses a membership‐grade weighted‐distance measure as the measure of closeness, whereas the SC method classifies the input space more logically according to the magnitude of flow available in the data set. Copyright © 2007 John Wiley & Sons, Ltd.     

基于K-means聚类距离准则的R树结点分配算法研究

对于空间数据库,R树索引是非常有效的空间索引。本文针对R树的结点分配算法存在的不足,提出了一种新的结点分配算法—基于K-means聚类距离最小的R树结点分配算法。研究结果表明,新的分配算法比原始的算法,产生的虚结点的最小约束矩形具有更少的空白区域,较明显地提高了空间查询的效率。     

近40年内蒙古候平均气温变化趋势

基于1964—2003年内蒙古有关台站的逐日气温数据, 对前20年 (1964—1983年) 和后20年 (1984—2003年) 的候平均气温中值进行了对比, 并利用k-means方法进行了候平均气温变化趋势的聚类分析, 旨在阐明内蒙古气温变化的时空特点和规律。研究结果表明:内蒙古全境年平均气温普遍上升, 没有下降的现象。不同地区、不同季节气温变化格局不同, 北部变暖比南部更为明显, 冬季和夏末秋初变暖出现得更频繁。绝大部分地区大多数候平均气温都有所上升, 而且候平均气温变化存在纬向地带性。大多数台站四季里都有一些显著升温的候, 显著升温在冬季发生得更为频繁。同时, 有少数几个候出现气温下降的现象, 但未达到显著下降的程度。内蒙古绝大部分地区候最高、最低气温都有所升高, 但二者的差值在缩小。候最低气温出现时间有所提前, 候最高气温出现时间有提前也有推迟, 但总体上不同地区候最高、最低气温出现时间更加同步。因此, 候最高、最低气温的时空均一性有所增强。候最高气温出现时间与候最低气温出现时间的间隔有扩大迹象。总之, 全球气候变暖降低了气温的季节和地区差异。     

气象科技期刊2001—2007年主要评价指标分析

为客观定量地了解目前气象科技期刊的学术水平现状,促进气象科技期刊的发展,依据学术期刊综合评价指标,对2001—2007年气象科技期刊的影响因子、5年影响因子、即年指标、被引半衰期、Web即年下载率以及网络传播趋势等进行统计对比分析,得出了相关结论。