气候噪声和气候系统的分维

根据相空间嵌入定理，按照Grassberger和Procaccia提出的计算分数维的方法，利用近百年来南、北半球地面气温资料，估算了气候吸引子的分数维，计算结果表明:气候吸引子的分数维南半球为3.3～3.7，北半球为3.2～3.7。它提供了气候吸引子的自相似结构的基本信息，表明模似气候系统最少需要4个独立变量。另外，还讨论了气候噪声对估算维数的影响。     

青海南部冬季积雪和雪灾变化的特征

利用1961-2005年青海南部牧区气象台站观测的气温、降水、积雪资料,用气候诊断方法分析了该地区积雪等气候要素的年代际演变特征以及雪灾变化的成因。结果表明：20世纪60-90年代冬季青海南部牧区中雪和大雪出现的站次以及雪灾出现的站次有逐步增多的趋势,降雪量和地表平均积雪量每10 a分别增加1. 253 mm和8.246 cm,单站积雪量在海拔4100 m左右的高度上增加比较明显,其变化是由气候的年代际波动引起的。     

疏勒河绿洲系统气候变化的特征分析

概述了疏勒河流域绿洲系统的基本气候特点及近5O年来的气候演变特征,分析了疏勒河绿洲上游产流区和中下游绿洲开发区的主要气候要素的年际、年代际变化规律。指出在有限水资源条件下,中游人工绿洲的过度开发可能导致下游自然生态环境的恶化。     

气侯层次和分数维

本文应用非线性理论分析了多层次的气候系统,指出分数维是气候系统结构的特征,是气候系统中尺度变换后的不变量。分数维可用于平滑气候资料,滤掉尺度较小的振动,形成较高的气候层次。平滑前后的气候层次结构已不相同。低层次气候变化的规律很不确定,在高层次上看就比较确定了。高、低层次之间存在非线性的相互作用。以上是气候研究中的一些基本问题,本文用我国的气候资料对此作了初步的探讨。     

北京城市下垫面的非均匀性度量研究

城市下垫面非均匀性度量研究对于改进中尺度数值模式、提高城市气象预报准确率具有重要意义.基于分形布朗运动理论和Landsat-TM卫星遥感图像,估算了2011年北京典型下垫面类型个例和整个空间区域的分数维,由此来度量北京城市下垫面的非均匀性特征.研究结果显示:分数维能有效度量北京城市下垫面的非均匀程度,典型下垫面类型的分数维高低顺序为商业区 >大型居民区 >城市绿地 >农田>林地 >水体;北京六环内区域及郊区城镇地表非均匀特征明显,分数维一般在2.50以上,北京城市中心(二环内)存在一个分数维相对低值区,二环—四环之间区域分数维普遍较高,在2.70以上,五环以外分数维则不断降低,反映出北京城市下垫面非均匀性从中心向外呈低—高—低的空间分布;下垫面类型中建筑用地、绿地、未利用地、农田、林地和水体的平均分数维分别为2.71、2.62、2.55、2.38、2.30和2.28,分数维值的高低反映了城市下垫面类型的非均匀程度大小,对定量描述城市边界层参数的复杂特征具有参考意义.     

热带纬向平均臭氧层动力学系统的重建

将ＴＯＭＳ测量的５°Ｎ纬向平均Ｏ３总量逐日序列作相空间拓展,使Ｏ３层的行为特征可用相轨迹刻划。首先计算Ｏ３层系统吸引子的分数维,而后把相点作为动力学系统的离散特解集合,反演出描述系统状态随时间演化的数学模型中的客观控制参数值,建立起反映热带Ｏ３层短期演化特征的非线性动力学系统。     

民勤绿洲1971-2010年沙尘暴特征及影响因素分析

根据1971～2010年民勤近40a气象资料、民勤周边县市沙尘暴资料,详细分析了民勤绿洲沙尘暴变化特征及影响因素,结果表明：①绿洲以局地性沙尘暴为主,但区域性沙尘暴无论是持续时间、伴随的大风强度和成灾性均大于局地性沙尘暴。②一日中局地性和区域性沙尘暴主要出现在下午和傍晚;月际变化除3月份外其它月份局地性均较区域性沙尘暴多,6～8月份偏多明显,各季局地性均比区域性沙尘暴多,夏季偏多明显;年际局地和区域性沙尘暴均为显著减少趋势,年代际为明显减少趋势。③干旱气候、丰富的沙源是民勤多沙尘暴的自然原因,气候的变化和下垫面状况的改善使沙尘暴减少。分析表明气候变暖使大风日数减少、风力减小,从而影响局地和区域性沙尘暴减少,气候变暖使日照时数增长、蒸发增大,从而影响区域性沙尘暴减少,而对局地性沙尘暴的减少影响不大。     

卫星云图模式识别小波分形特征量分析

本文采用小波分析中的小波包函数对卫星云图进行分解和小波包系数重构,并结合分形分数维计算,提取卫星云图的分数维,分析不同类型的云系分数维的主要特征,来实现卫星云图的模式识别.     

近50年新疆温度降水配置演变及其尺度特征

用小波多分辨分析方法研究近20年新疆出现的高温多雨型气候的时间尺度特征及其演变趋势。新疆近55年温度和降水的小波功率谱分析显示,二者在年际尺度上都有2—4和6—8 a的显著周期分量,在年代际尺度上有准16 a周期;但它们的时间演变和时间平均谱都存在差异,导致温度和降水配置演变比较复杂,呈现非平稳性。正交小波分解证实,温度和降水年际变化的高频部分具有显著的负相关,除个别几年外几乎都是高温少雨或低温多雨配置;在年际变化的低频部分,即6—8 a尺度部分,高温少雨/低温多雨及高温多雨/低温少雨配置交替出现,55a平均而言二者相关性不显著。在年代际以上尺度,二者的能量主要集中在约50 a以上尺度部分,16—32 a尺度部分方差贡献很小。在降水和温度时间序列中去除趋势后发现,50 a以上尺度部分具有稳定的高温多雨/低温少雨配置。因此,近20年新疆高温多雨型气候的出现主要是二者50—60 a尺度成分的正位相和线性增加趋势部分叠加形成的,其中降水主要是年代际尺度成分的贡献,温度主要是线性增暖趋势即全球变暖的影响结果。     

北太平洋风暴轴的气候特征及其变化的初步研究

本文使用ECMWF再分析网格点资料(ERA-40),分析了不同季节和不同高度层上北太平洋风暴轴(天气尺度瞬变扰动活动)的气候特征及其时间演变规律。分析表明,气候平均而言,北太平洋风暴轴冬季强且偏西南,夏季弱且偏东北。在近45 a里,冬季及夏季风暴轴在整个对流层、尤其是对流层中上层具有整体一致的年际和年代际变化特征。在各个高度层上冬季风暴轴于1985年前后一致性地发生了由弱至强的年代际跃变;夏季风暴轴发生年代际由弱至强跃变的时间在各个高度层上略有不同,对流层中高层的跃变时间与北太平洋大气海洋系统1970年代的跃变时间一致。在冬季风暴轴活动偏强年里,中纬度天气尺度瞬变扰动增强的同时,位置也有所北抬,反之亦然。夏季风暴轴活动偏强(弱)年则主要表现为瞬变扰动在气候平均位置上的增强(减弱)。     

Multi-Fractal Analysis of Daily Air Temperature Time Series in Coastal Areas of China

In this article, the Multi-Fractal Detrended Fluctuation Analysis(MF-DFA) method is adopted to study the temperature, i. e., the maximum temperature(Tmax), mean temperature(Tavg) and minimum(Tmin) air temperature,multifractal characteristics and their formation mechanism, in the typical temperature zones in the coastal regions in Guangdong, Jiangsu and Liaoning Provinces. Following are some terms and concepts used in the present study.Multifractality is defined as a term that characterizes the complexity and self-similarity of objects, and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution. Fractality strength denotes the fluctuation range of the data set, and long-range correlation(LRC) measures the stability of the climate system and the trend of climate change in the future. In this research, it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences. Furthermore, the research also proves that the Tavg, Tmaxand Tminof the above three provinces are highly multifractal. The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning temperature series multifractality of Guangdong temperature series multifractality of Jiangsu, and the corresponding long-range correlations follow the same order. It reveals that the most stable temperature series is that of Liaoning, followed by the temperature series of Guangdong, and the most unstable one is that of Jiangsu.Liaoning has the most stable climate system, and it will thus be less responsive to the future climate warming. The stability of the climate system in Jiangsu is the weakest, and its temperature fluctuation will continue to increase in the future, which will probably result in the meteorological disasters of high temperature and heat wave there. Guangdong possesses the strongest degree of multifractal strength, which indicates that its internal temperature series fluctuation is the largest among the three regions. The Tmaxmultifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavgand Tminmultifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmaxfluctuation than Liaoning does, while it has a smaller fluctuation of Tavgand Tminthan Liaoning does. Guangdong and Liaoning both show the strongest Tminmultifractal strength, followed by Tavgmultifractal strength, and the weakest Tmax multifractal strength. However, Jiangsu has the strongest Tmax, followed by Tavg, and the weakest Tmin. The research findings show that these phenomena are closely related to solar radiation, monsoon strength, topography and some other factors. In addition, the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation, in which the long-range correlation influence of temperature series itself plays the dominant role. With the backdrop of global climate change, this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes, and thus the present study will be significant for the environmental protection of coastal areas.     

Multi-Fractal Analy is of Daily Air Temperature Time Series in Coastal Areas of China

In this article, the Multi-Fractal Detrended Fluctuation Analysis (MF-DFA) method is adopted to study the temperature, i. e., the maximum temperature (Tmax), mean temperature (Tavg) and minimum (Tmin) air temperature, multifractal characteristics and their formation mechanism, in the typical temperature zones in the coastal regions in Guangdong, Jiangsu and Liaoning Provinces. Following are some terms and concepts used in the present study. Multifractality is defined as a term that characterizes the complexity and self-similarity of objects, and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution. Fractality strength denotes the fluctuation range of the data set, and long-range correlation (LRC) measures the stability of the climate system and the trend of climate change in the future. In this research, it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences. Furthermore, the research also proves that the Tavg, Tmax and Tmin of the above three provinces are highly multifractal. The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning > temperature series multifractality of Guangdong > temperature series multifractality of Jiangsu, and the corresponding long-range correlations follow the same order. It reveals that the most stable temperature series is that of Liaoning, followed by the temperature series of Guangdong, and the most unstable one is that of Jiangsu. Liaoning has the most stable climate system, and it will thus be less responsive to the future climate warming. The stability of the climate system in Jiangsu is the weakest, and its temperature fluctuation will continue to increase in the future, which will probably result in the meteorological disasters of high temperature and heat wave there. Guangdong possesses the strongest degree of multifractal strength, which indicates that its internal temperature series fluctuation is the largest among the three regions. The Tmax multifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavg and Tmin multifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmax fluctuation than Liaoning does, while it has a smaller fluctuation of Tavg and Tmin than Liaoning does. Guangdong and Liaoning both show the strongest Tmin multifractal strength, followed by Tavg multifractal strength, and the weakest Tmax multifractal strength. However, Jiangsu has the strongest Tmax, followed by Tavg, and the weakest Tmin. The research findings show that these phenomena are closely related to solar radiation, monsoon strength, topography and some other factors. In addition, the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation, in which the long-range correlation influence of temperature series itself plays the dominant role. With the backdrop of global climate change, this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes, and thus the present study will be significant for the environmental protection of coastal areas.     

从时间序列中提取维数信息

作者介绍了分形和分维的概念、性质和意义，重点讨论了从一维时间序列中提取维数信息的方法，并给出了一些在天气和气候中的应用。     

热带气旋复杂程度的分形维数表征

应用2006-2010年69个热带气旋1 295个时次的红外云图等资料,提取了1 295个TC的外缘线,用圆规法计算了这些外缘线的分形维数。将这1 295个分形维数自小到大排列,按等频数规则,将1 295个数分为5类,分别记为A、B、C、D、E类。A、B、C、D、E类分形维数的均值分别为1.21、1.26、1.29、1.33、1.40。然后寻找与这5个均值最接近的样本。这5个样本的红外云图和TBB等值线分布图显示:随着分形维数的加大,边缘线的非光滑程度逐渐加大,图形与准圆形的偏离程度逐渐加大,TC空间结构的复杂程度也逐渐加大。说明外缘线的分形维数可以在一定程度上定量表征TC的复杂程度。     

从时间序列中提取维数信息

作者介绍了分形和分维的概念、性质和意义，重点讨论了从一维序列中提取维数信息的，并给出了一些在天气了气候中的应用。     

FY-3A水汽资料在江淮梅雨强降水系统湿动力诊断中的应用

中国卫星FY-3A水汽产品为大气水汽混合比,对大气各层水汽条件具有精细化描述能力。将卫星水汽混合比空间分布与区域流场对应叠加,进行综合量化分析,是目前卫星监测反演数据研究分析与业务应用的形式之一。由于卫星数据与流场数据为非同源数据,具有各自的坐标系,综合分析需要将坐标系统一,卫星坐标系随时间变化,因此向固定的流场坐标系转化。整个数据预处理过程,包括卫星数据插值至标准等压面层,卫星轨道网格转为经纬等距标准网格;双权重法异常数据剔除;平滑滤除高分辨率"噪音";十折交叉相关检验确认卫星资料精细化特征保持良好。对流场分析首先选择关键系统,江淮梅雨强降水系统主要为浅薄低涡和辐合线,近5 a的统计显示3/5的低涡活跃在江南,2/3的辐合线活跃在淮北。诊断分析合成的江淮梅雨强降水典型系统,获得辐合线系统纬向湿度锋区更强,低涡系统湿舌经向度大,显示更多干湿气团混合。进而将预处理后的FY-3A水汽分布与强降水系统低层流场对应时刻综合比对,结果显示:与流场气旋性辐合区对应的卫星湿区对强降水落区具有精细化指示性。同时FY-3A湿度产品计算的大气低层(1 000~850 h Pa)可降水量、以及降水区对应的假相当位温高能区及高能锋区,均与降水强度呈正比关系。将卫星水汽资料诊断方法应用于近海海域,可估测系统强降水落区以及降水强度,有利于改善海上缺乏降水观测站的问题。     

1951—2010年大连市气温变化特征

利用1951—2010年大连市气温资料,采用气候趋势系数和气候倾向率、Mann-Kendal1突变分析等方法对年和季平均气温、最高最低气温变化特征进行了分析和突变检验。结果表明：大连市年和季平均气温呈上升趋势,进入21世纪,升温趋势有所减缓;大连市年平均气温的增温速率为0.33/10 a,明显高于近50 a中国平均增温...