基于二分图的温度网络拓扑性质研究  

Topological analysis of temperature networks using bipartite graph model

作　　者：周磊[1] 支蓉[1,2] 冯爱霞[1] 龚志强[2] 

机构地区：[1]扬州大学物理科学与技术学院,扬州225002 [2]国家气候中心,中国气象局诊断预测室,北京100081

出　　处：《物理学报》2010年第9期6689-6696,共8页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:40930952;40875040;40705031和40705036);国家重点基础研究发展计划(批准号:2006CB400503)资助的课题~~

摘　　要：利用中国地区435个台站1961—2002年逐日平均温度序列,将温度变化发生在9天时间尺度上的特征编码在网络中,通过研究二分图温度网络(BGT网络)中节点与项目的关系,揭示出9天时间尺度上温度变化的特征及其在空间上的拓扑统计性质.网络中各节点RRRD,RrDD,eeed,DRRD,DDRR等所代表的温度波动模态在网络中异常频发,对9天尺度温度变化的预报有一定的指导意义.统计网络的节点度分布,集群系数等拓扑结构特征量,发现BGT网络服从正态分布特征.BGT网络项目内节点度的多样性大体上表现为江南和华南地区偏少,华东和华中地区偏多,华北地区偏少,东北部分地区偏多的四极型分布特征;且这种区域特征与文献[15]中根据温度波动网络(FT网络)划分的复杂区域存在一定的异同性,两种网络从不同的角度共同揭示了区域内温度变化的背景信息.因此,二分图温度网络的构建为从时间和空间尺度相结合的角度研究温度变化的特性和规律,提供了一条可能的有效途径.Using the daily main observational temperature data of 435 stations in China from 1961 to 2002,temperature change on 9-day scale has been compiled in network. Research on the connection between nodes and items in bipartite graph temperature （BGT） network,reveals the temperature change on 9-day scale and the topological statistics in the space. The nodes of RRRD,RrDD,eeed,DRRD and DDRR have remarkably high degree,which is helpful to predict the temperature change on 9-day scale. Calculation of the topological parameters of this network,including degree distribution and clustering coefficients,shows the normal school character of the bipartite graph model temperature network. The distribution of nodes’degree diversity in each item presents quadruple type character,and has similar characteristics as the complex regions defined by the fluctuant temperature network（FT network）,displaying the background information of temperature change in the region by this two kind of network respectively. Thus,temperature network modeled by bipartite graph model present a possible and available approach to research the characteristic and rule about temperature change from the combination of the time and space scale.

关 键 词：二分图温度网络 气候系统 拓扑结构 

分 类 号：O157.5[理学—数学]