银杏叶化学组分群动态FTIR指纹图谱研究

比较了相同产地不同月份与相同月份不同产地银杏叶的FTIR图谱,研究了银杏叶化学组分群随气候、季节、地域等的差异而呈现出的动态分布规律。结果表明,从4月份到10月份,银杏叶内化学组分群呈现动态分布与变化,其变化在6月16日,9月1日,10月4日3个时段。5月份所采集的不同产地银杏叶的化学组分群分布基本一致。这在银杏叶药材种植质量管理规范(GAP)基地的建设、确保银杏叶原料的稳定性以及其后各类相关指纹图谱的研究及制定等方面具有重要意义。     

2009年11月冰冻雨雪事件10—30d延伸期稳定分量提取及分析

针对2009年11月冰冻雨雪事件,通过经验正交函数分解等方法提取10—30 d稳定分量,并第一次运用计算贡献率和相似系数判定等手段把稳定分量进一步客观地分离成气候态稳定分量和异常型稳定分量.将气候态稳定分量与低通滤波分量合成气候背景场.研究发现:气候背景场环流形势持续时间较长,随时间变化缓慢,主要提供了事件发生的气候背景,指示大尺度环流调配置和调整;气候背景场空间活动范围较大,能较好地刻画永久半永久性大气活动中心变化情况,且在垂直各层有稳定一致的配置关系;异常型稳定分量重点体现了环流相对异常特征,与2009年11月冰冻雨雪事件有较好的对应关系.在气候背景场提供确定的环流大背景下,异常型稳定分量表征了对应天气系统的相对强弱变化.     

基于复杂网络的北半球遥相关 年代际变化特征研究

基于北半球中高纬度环流系统关联网络, 研究了冬季北半球各种遥相关年代际尺度的变化特征及其与气候突变的可能联系等. 研究结果表明, NAO, EUPA和WP 等遥相关的作用中心在1970年代中后期和1990年代初期均对应有显著的跃变过程, 且主要空间模态也发生一定的变化. 结合网络顶点度, 定义直接体现遥相关作用强弱的指数并分析其随时间的变化:1980年代以前以PNA, EUPA和WP三种模态的共同作用为主导, 1980年代以后NAO的作用显著增强, 1987年以后AO的作用显著增强, 其他模态的作用则相对较弱. 因此, 多种遥相关年代际尺度的调整可能是1970年代中后期和1990年代初期气候突变的重要表现形式之一.     

陇中黄土高原自然植被下垫面陆面过程及其参数对降水波动的气候响应

作为气候敏感区和生态脆弱区,黄土高原地区的陆面物理过程受气候波动影响十分明显.目前,虽然对黄土高原特定气候条件下的陆面物理过程有了一定认识,但对其随气候波动的动态变化规律认识却非常有限.本文利用黄土高原陆面过程观测试验在陇中黄土高原榆中SACOL站5年多的观测资料,系统分析了该地区自然植被下垫面陆面水分收支和能量平衡及地表反照率、土壤热传导率和粗糙度长度等陆面过程参数对气候波动的响应规律,发现陇中黄土高原自然植被下垫面的陆面过程特征及其参数对降水的气候波动十分敏感,不仅对降水量波动响应显著,而且与降水性质也密切相关.土壤湿度并不完全随年总降水量波动,而是随有效降水量增加而增大;陆面水分收支和土壤水分收支也随有效降水量波动变化明显;近地层垂直感热平流通量随年总降水量增加而减弱,地表能量不平衡差额却随年总降水量增加而增大.夏半年平均反照率随有效降水量增加而减小,但全年平均地表反照率却受冬季积雪时数影响十分明显,而且随积雪时数增加而增大;土壤热传导率和粗糙度长度均随年有效降水量增加而增加.并且,降水量越小,陆面过程参数对降水波动的响应就越敏感.不过,陆面过程参数基本在荒漠气候平均值和农田气候平均值之间波动,并没有突破气候约束,能够用拟合关系来表示它们对降水因子波动的响应规律,这种动态参数化关系可用来改善以往数值模拟或微气象分析时由于使用非动态陆面过程参数所带来的误差.     

基于非线性相似度量方法研究中国季节划分

利用中国气象局1961—2008年752个站点的候平均气温、气压、相对湿度和降水量资料,运用候平均气温划分方法、非线性相似度量方法和季节内离差最优分割方法,对中国的四季进行了划分,并研究了中国四季起始时间的空间分布.用多要素构造了描述气候状态的变量,以气候状态在不同季节间的转折变化为切入点,并与传统季节划分方法进行了对比研究,验证了非线性相似度量方法的可行性,得到了以下研究结果:一是多要素比单一的温度要素更全面贴切地描述气候状态的变化,划分结果更体现出大气环流形势和大气活动中心等的季节变化;二是从气候状态转折角度考虑季节划分是可行的,揭示了季节转换时大气系统的调整和转折变化,体现了大气系统在季节内保持相对平稳的特征,在季节间发生较大的调整的观测事实. 关键词： 季节划分 非线性相似度量方法 季节内离差最优分割法     

基于物候特征的植物光谱识别方法研究

光谱分析对于植物识别特征与机理研究具有重要意义,长期以来在植被光谱的叶绿素吸收特征、水分吸收特征、红边效应、光谱波形参数提取、波形转换、以及植被叶面结构和化学组份对光谱的影响方面进行了大量研究,而在植物因季节变化引起的叶面结构、叶绿素、叶面积指数变化而产生的光谱变化研究较少。通过开展对不同生活型、叶面结构与大小、物候特征的11类植物季节生长过程的地物光谱观测,提取植物的归一化植被指数NDVI、包络线去除后的绿波段最大光谱吸收深度、红波段最大光谱吸收深度参数,对该参数时间过程曲线的均值、变幅、斜率进行分析,研究植物生长期和成熟期特征参数的植物光谱可分性研究。提出了植被光谱区分度的参数运算方法,利用该参数进行植被识别能力分析。结果表明：植物生长过程的光谱特征比成熟期的光谱特征更容易区分。在相同参数对比中,生长期植物区分度比成熟期区分度高出三个点。在植物生长过程中,总体上植被的季节变幅区分度>斜率区分度>均值区分度,而对于NDVI参数季节变化,植物季节斜率区分度最大。所以利用植物的季节NDVI斜率、绿波段最大光谱吸收深度的季节变幅、红波段最大光谱吸收深度的季节变幅进行植物的识别效果最佳。     

试用临界慢化原理探讨气候突变

近年来, 临界慢化现象在揭示复杂动力系统是否趋向于临界性灾变方面展示了重要潜力. 本文基于临界慢化的理念, 着重研究不同噪声影响下, 气候突变前兆信号出现的时间; 对含有不同噪声的气温观测资料、太平洋年代际振荡指数以及阿留申低压强度资料进行处理, 研究突变的前兆信号. 以20世纪70年代末80年代初这次气候突变为例, 分别计算了表征临界慢化现象的方差和自相关系数. 结果表明, 临界慢化现象出现在具有不同信噪比的资料中, 这表明临界慢化现象可能是气候突变发生前的一个前兆信号, 并且噪声对气候突变前兆信号的检测结果影响较小, 从而论证了临界慢化现象检测突变前兆信号的可靠性, 为该方法在实际观测资料中的广泛应用提供了实验基础.     

用特征矩阵法计算光子晶体的带隙结构

讨论了利用特征矩阵计算一维光子晶体的光子带隙结构的方法.利用此方法计算了不同介电常量、不同几何结构以及掺杂的晶体结构的带隙特征,并推广至介质介电常量ε随入射光波频率ω变化的情况. 关键词： 光子带隙 特征矩阵 缺陷态     

基于贝叶斯理论的全球海温异常对500 hPa 温度场的影响分析

本文利用经验正交函数(EOF) 将海表温度(SST) 距平场进行分解, 得到一组相互正交的模态构成重构空间, 然后在该空间中展开500 hPa温度场, 进一步借助贝叶斯分析方法定义各个模态对温度场的影响指数, 并研究指数随不同海温分布型(模态) 的变化特征. 结果发现SST场在4-6月份对500 hPa温度场的影响较大, 且气候发生转变后, 不同海温分布型对温度场的影响不同.     

红外伪装涂料发射率的季节性要求

为了研究伪装涂料8～14 μm波段的平均发射率在不同季节的取值范围,结合太阳辐射,植被温度以及气温的一日变化,建立目标及单一植被背景在晴朗天气情况下不同季节的一日辐射温度变化模型.以合肥地区为例,分别以春分、夏至、秋分、冬至四个节气的日气候状况代表春夏秋冬四季的气候特征,采集该地区在四个节气近五年的日值气侯数据,对覆盖不同发射率伪装涂料的目标以及植被背景在四个节气中从6时到22时的辐射温度变化进行模拟.获得目标和植被背景的日辐射温度变化数据,计算不同覆盖发射率值的伪装涂层时的目标和背景的欧氏距离和伪装效率.利用欧氏距离和伪装效率对目标的伪装效果进行分析.当欧氏距离最小时目标与植被背景红外特征最接近,春夏秋冬欧氏距离最小时的发射率值依次为0.58、0.33、0.41、0.86;以参考目标与背景的辐射温度差控制在4K以内为实现隐身的标准,春夏秋冬伪装效率最高时的发射率值依次为0.6、0.3、0.4、0.9.     

A preliminary investigation on the topology of Chinese and climate networks

Complex networks have been studied across many fields of science in recent years. In this paper, we give a brief introduction of networks, then follow the original works by Tsonis et al (2004, 2006) starting with data of the surface temperature from 160 Chinese weather observations to investigate the topology of Chinese climate networks. Results show that the Chinese climate network exhibits a characteristic of regular, almost fully connected networks, which means that most nodes in this case have the same number of links, and so-called super nodes with a very large number of links do not exist there. In other words, though former results show that nodes in the extratropical region provide a property of scale-free networks, they still have other different local fine structures inside. We also detect the community of the Chinese climate network by using a Bayesian technique; the effective number of communities of the Chinese climate network is about four in this network. More importantly, this technique approaches results in divisions which have connections with physics and dynamics; the division into communities may highlight the aspects of the dynamics of climate variability.     

Complex networks in climate dynamics

Complex network theory provides a powerful framework to statistically investigate the topology of local and non-local statistical interrelationships, i.e. teleconnections, in the climate system. Climate networks constructed from the same global climatological data set using the linear Pearson correlation coefficient or the nonlinear mutual information as a measure of dynamical similarity between regions, are compared systematically on local, mesoscopic and global topological scales. A high degree of similarity is observed on the local and mesoscopic topological scales for surface air temperature fields taken from AOGCM and reanalysis data sets. We find larger differences on the global scale, particularly in the betweenness centrality field. The global scale view on climate networks obtained using mutual information offers promising new perspectives for detecting network structures based on nonlinear physical processes in the climate system.     

Synchronizability of network ensembles with prescribed statistical properties

It has been shown that synchronizability of a network is determined by the local structure rather than the global properties. With the same global properties, networks may have very different synchronizability. In this paper, we numerically studied, through the spectral properties, the synchronizability of ensembles of networks with prescribed statistical properties. Given a degree sequence, it is found that the eigenvalues and eigenratios characterizing network synchronizability have well-defined distributions, and statistically, the networks with extremely poor synchronizability are rare. Moreover, we compared the synchronizability of three network ensembles that have the same nodes and average degree. Our work reveals that the synchronizability of a network can be significantly affected by the local pattern of connections, and the homogeneity of degree can greatly enhance network synchronizability for networks of a random nature.     

Analysis of Air Mean Temperature Anomalies by Using Horizontal Visibility Graphs

The last decades have been successively warmer at the Earth’s surface. An increasing interest in climate variability is appearing, and many research works have investigated the main effects on different climate variables. Some of them apply complex networks approaches to explore the spatial relation between distinct grid points or stations. In this work, the authors investigate whether topological properties change over several years. To this aim, we explore the application of the horizontal visibility graph (HVG) approach which maps a time series into a complex network. Data used in this study include a 60-year period of daily mean temperature anomalies in several stations over the Iberian Peninsula (Spain). Average degree, degree distribution exponent, and global clustering coefficient were analyzed. Interestingly, results show that they agree on a lack of significant trends, unlike annual mean values of anomalies, which present a characteristic upward trend. The main conclusions obtained are that complex networks structures and nonlinear features, such as weak correlations, appear not to be affected by rising temperatures derived from global climate conditions. Furthermore, different locations present a similar behavior and the intrinsic nature of these signals seems to be well described by network parameters.     

Exploring maps with greedy navigators

During the last decade of network research focusing on structural and dynamical properties of networks, the role of network users has been more or less underestimated from the bird's-eye view of global perspective. In this era of global positioning system equipped smartphones, however, a user's ability to access local geometric information and find efficient pathways on networks plays a crucial role, rather than the globally optimal pathways. We present a simple greedy spatial navigation strategy as a probe to explore spatial networks. These greedy navigators use directional information in every move they take, without being trapped in a dead end based on their memory about previous routes. We suggest that the centralities measures have to be modified to incorporate the navigators' behavior, and present the intriguing effect of navigators' greediness where removing some edges may actually enhance the routing efficiency, which is reminiscent of Braess's paradox. In addition, using samples of road structures in large cities around the world, it is shown that the navigability measure we define reflects unique structural properties, which are not easy to predict from other topological characteristics. In this respect, we believe that our routing scheme significantly moves the routing problem on networks one step closer to reality, incorporating the inevitable incompleteness of navigators' information.     

不同耦合下混沌神经元网络的同步

采用Hindmarsh-Rose神经元动力学模型, 对二维点阵上的神经元网络的同步进行了研究. 为了解不同耦合对网络同步的影响, 提出了一般反馈耦合、分层反馈耦合和分层局域平均场反馈耦合三种方案.研究表明:在耦合强度较小的近邻耦合下, 一般反馈耦合不能使网络达到完全同步, 而分层反馈耦合和分层局域平均场反馈耦合可以使网络出现局部同步和全局同步. 不同形式的耦合会导致网络出现不同的斑图, 随着耦合强度的增大, 网络从不同步到同步的过程也不相同, 一般反馈耦合和分层反馈耦合网络是突然出现全局同步, 同步之前网络出现非周期性的相干斑图; 对于分层局域平均场反馈耦合网络, 同层神经元之间先出现从簇放电同步到同步的转变, 形成靶波, 然后同步区由中心向外逐渐扩大, 最终达到网络的全局同步. 这些结果表明, 只有适当的耦合才能实现信号的无损耗的传递. 此外我们发现分层局域平均场反馈耦合可以促进网络的同步.     

花簇分形无标度网络中节点影响力的区分度

大量研究表明分形尺度特性广泛存在于真实复杂系统中, 且分形结构显著影响网络上的传播动力学行为. 虽然复杂网络的节点传播影响力吸引了越来越多学者的关注, 但依旧缺乏针对分形网络结构的节点影响力的系统研究. 鉴于此, 本文基于花簇分形网络模型, 研究了分形无标度结构上的节点传播影响力. 首先, 对比了不同分形维数下的节点影响力, 结果表明, 当分形维数很小时, 节点影响力的区分度几乎不随节点度变化, 很难区分不同节点的传播影响力, 而随着分形维数的增大, 从全局和局域角度都能很容易识别网络中的超级传播源. 其次, 通过对原分形网络进行不同程度的随机重连来分析网络噪声对节点影响力区分度的影响, 发现在低维分形网络上, 加入网络噪声之后能够容易区分不同节点的影响力, 而在无穷维超分形网络中, 加入网络噪声之后能够区分中间度节点的影响力, 但从全局和局域角度都很难识别中心节点的影响力. 所得结论进一步补充、深化了基于花簇分形网络的节点影响力研究, 研究结果对实际病毒传播的预警控制提供了一定的理论借鉴.     

A new local-world evolving network model

In some real complex networks, only a few nodes can obtain the global information about the entire networks, but most of the nodes own only local connections therefore own only local information of the networks. A new local-world evolving network model is proposed in this paper. In the model, not all the nodes obtain local network information, which is different from the local world network model proposed by Li and Chen (LC model). In the LC model, each node has only the local connections therefore owns only local information about the entire networks. Theoretical analysis and numerical simulation show that adjusting the ratio of the number of nodes obtaining the global information of the network to the total number of nodes can effectively control the valuing range for the power-law exponent of the new network. Therefore, if the topological structure of a complex network, especially its exponent of power-law degree distribution, needs controlling, we just add or take away a few nodes which own the global information of the network.     

Network analysis of time series under the constraint of fixed nearest neighbors

In this paper, we carried out network analysis for typical time series, such as periodic signals, chaotic maps, Gaussian white noise, and fractal Brownian motions. By reconstructing the phase space for a given time series, we can generate a network under the constraint of fixed nearest neighbors. The mapped networks are then analyzed from both the statistical properties, such as degree distribution, clustering coefficient, betweenness, etc, as well as the local topological structures, i.e., network motifs. It is shown that time series of different nature can be distinguished from these two aspects of the constructed networks.     

Symbolic synchronization and the detection of global properties of coupled dynamics from local information

We study coupled dynamics on networks using symbolic dynamics. The symbolic dynamics is defined by dividing the state space into a small number of regions (typically 2), and considering the relative frequencies of the transitions between those regions. It turns out that the global qualitative properties of the coupled dynamics can be classified into three different phases based on the synchronization of the variables and the homogeneity of the symbolic dynamics. Of particular interest is the homogeneous unsynchronized phase, where the coupled dynamics is in a chaotic unsynchronized state, but exhibits qualitative similar symbolic dynamics at all the nodes in the network. We refer to this dynamical behavior as symbolic synchronization. In this phase, the local symbolic dynamics of any arbitrarily selected node reflects global properties of the coupled dynamics, such as qualitative behavior of the largest Lyapunov exponent and phase synchronization. This phase depends mainly on the network architecture, and only to a smaller extent on the local chaotic dynamical function. We present results for two model dynamics, iterations of the one-dimensional logistic map and the two-dimensional Henon map, as local dynamical function.