基于K-均值聚类方法的大气环流模式IAP AGCM4.1对西北太平洋热带气旋的模拟评估

IAP AGCM4.1（Institute of Atmospheric Physics Atmospheric General Circulation Model, version 4.1）是中国科学院大气物理研究所自主研发的大气环流模式,也是中科院地球系统模式CAS-ESM1（Chinese Academy of Sciences Earth System Model, version 1）的大气分量模式。本文利用极端气候分析软件TECA（Toolkit for Extreme Climate Analysis）,对IAP AGCM4.1模拟的1979～2012年西北太平洋热带气旋（TC）进行了识别与评估。结果表明IAP AGCM4.1模拟的TC空间分布、路径走向与生成源地与观测基本一致,但模拟的TC个数有所低估,仅为观测的36%。基于K-均值聚类方法的分类评估显示,这种低估主要体现在模式对于西北行转向类和西行类TC没有模拟能力。对于近海西—西北行类、西转向类和东转向类TC,模式模拟的个数可分别达到观测的39%,48%和85%,模拟的季节变化与观测的相关系数在0.89～0.91之间,周期误差在1～2天。就TC路径而言,模式对于近海西—西北行类和东转向类TC模拟效果较好,质心经度误差、质心纬度误差和经纬向标准差的模拟误差分别为1%～5%、4%～16%和5～15%。此外,环流合成分析表明模式很好地再现了东转向类TC发生、发展期间环境流场的演变以及副热带高压的变化情况,模拟的副热带高压强度和面积指数与观测的相关系数可达0.89。模式对西北行转向类和西行类TC模拟能力较差的原因可能与模式对副热带高压的模拟偏差有关。     

Heavy minerals clustering provenance analysis of Kong analysis in application of 2 Member in Kongnan area

The main task of provenance analysis is to determine the source of sediments and the position of parent rocks. Provenance analysis may find out the relationship between erosion districts and sediment zone, between the uplift and the depression in the process of basin development. The authors use the method of heavy mineral clustering analysis and estimate the provenance direction of Huanghua Depression in the Paleogene Kong 2 Member. Research shows that there were five provenance areas of Kong 2 Member in Kongnan area. They are western （Shenusi）, northwestern （Cangzhou）, eastern （Ganhuatun）, northeastern and southeast- ern. The main provenance areas were northwestern and western, while the southern provenance could not be ruled out. And these areas are consistent with the known provenance areas.     

矢量量化编码的码书设计研究

以矢量量化技术在图像压缩领域的应用作为研究目标，详细阐述了矢量量化码书生成技术，总结分析了现有典型的LBG算法，并针对LBG算法的不足，提出了改进的覆盖聚类算法，减少了计算复杂度，缩短了程序运行时间。通过理论推导和具体实现，证明了改进方法的可行性和有效性。     

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.