The Physical Significance of the Synthetic Running Correlation Coefficient and Its Applications in Oceanic and Atmospheric Studies

In order to study the temporal variations of correlations between two time series, a running correlation coefficient (RCC) could be used. An RCC is calculated for a given time window, and the window is then moved sequentially through time. The current calculation method for RCCs is based on the general definition of the Pearson product-moment correlation coefficient, calculated with the data within the time window, which we call the local running correlation coefficient (LRCC). The LRCC is calculated via the two anomalies corresponding to the two local means, meanwhile, the local means also vary. It is cleared up that the LRCC reflects only the correlation between the two anomalies within the time window but fails to exhibit the contributions of the two varying means. To address this problem, two unchanged means obtained from all available data are adopted to calculate an RCC, which is called the synthetic running correlation coefficient (SRCC). When the anomaly variations are dominant, the two RCCs are similar. However, when the variations of the means are dominant, the difference between the two RCCs becomes obvious. The SRCC reflects the correlations of both the anomaly variations and the variations of the means. Therefore, the SRCCs from different time points are intercomparable. A criterion for the superiority of the RCC algorithm is that the average value of the RCC should be close to the global correlation coefficient calculated using all data. The SRCC always meets this criterion, while the LRCC sometimes fails. Therefore, the SRCC is better than the LRCC for running correlations. We suggest using the SRCC to calculate the RCCs.     

Transition Periods Between Sea Ice Concentration and Sea Surface Air Temperature in the Arctic Revealed by an Abnormal Running Correlation

This study used the synthetic running correlation coefficient calculation method to calculate the running correlation coefficients between the daily sea ice concentration(SIC) and sea surface air temperature(SSAT) in the Beaufort-Chukchi-East Siberian-Laptev Sea(BCEL Sea), Kara Sea and southern Chukchi Sea, with an aim to understand and measure the seasonally occurring changes in the Arctic climate system. The similarities and differences among these three regions were also discussed. There are periods in spring and autumn when the changes in SIC and SSAT are not synchronized, which is a result of the seasonally occurring variation in the climate system. These periods are referred to as transition periods. Spring transition periods can be found in all three regions, and the start and end dates of these periods have advancing trends. The multiyear average duration of the spring transition periods in the BCEL Sea, Kara Sea and southern Chukchi Sea is 74 days, 57 days and 34 days, respectively. In autumn, transition periods exist in only the southern Chukchi Sea, with a multiyear average duration of only 16 days. Moreover, in the Kara Sea, positive correlation events can be found in some years, which are caused by weather time scale processes.     

台风期间PWV与降雨量的相关性分析

基于2015~2017年台湾地区“苏迪罗”、“鲇鱼”、“海棠”等3次台风事件,利用地基GPS数据反演得到大气可降雨量（PWV）,初步分析台风期间PWV与降雨量的关系,并利用交叉小波和小波相干进一步分析PWV与降雨量的时空变化规律。结果表明,台风发生期间,PWV总体呈先上升后下降的趋势,波动性剧烈;降雨发生时,PWV一定发生剧烈变化;在研究时域内,PWV与降雨量存在很强的正相关关系,相关系数达到0.7,PWV超前降雨量变化,PWV变化后的0~3 h发生强降雨。研究PWV与降雨量的时空变化特征可为短时降雨预报提供参考。     

基于SAR和AIS的角度最近邻数据关联方法

提升海上态势感知能力是构建智慧海洋的重要环节。针对目前海上目标研究单源传感器存在感知盲区,多源传感器数据关联易受杂波干扰、在密集区表现不佳等问题,本文基于合成孔径雷达(SAR)和船舶自动识别系统(AIS)数据,提出一种抗干扰性强的角度最近邻数据关联方法,充分利用SAR与AIS船舶目标的空间角度关系,提高船舶目标在密集区域点迹关联的准确性。首先,对AIS数据进行时空滤波,实现数据粗关联,构建关联分析的数据候选集;然后,从时空数据的空间关系角度出发,在灰狼优化和匈牙利算法的启发下,利用点迹对特征向量矩阵进行运算,实现对多源空间数据的优化关联;最后结合数据几何关系对结果进行置信度评估。本文选取5幅SAR影像与AIS数据进行实验,并基于SAR影像数据及船舶轨迹点分布密度设计仿真实验,结果表明,本文所提出的角度最近邻数据关联方法,在密集分布情况下,关联精度为传统NN、GNN算法的3.62和4.61倍,运行时间为1.69 s,相较于NN算法仅增长1.36 s,仅占GNN运行时间的0.49%,在运行时间增长不大的情况下具有更强的抗干扰能力,在密集区域仍能取得较好的关联效果。     

基于完备经验模态分解自相关消噪技术的GNSS高精度动态变形监测研究

为了更好地消除混杂在变形序列中的噪声,利用完备经验模态分解（CEEMD）将形变信号自适应分解为不同尺度的振动模态。针对分解分量中信号和噪声区分标准不唯一的问题,构造一种CEEMD与自相关分析相结合的去噪算法,实现有效信号和随机信号的分离。将该算法应用在仿真实验和GNSS变形监测实测数据处理中,并与传统的小波去噪方法进行比较。结果表明,该算法避免了小波基选择带来的影响。     

日长年际信号提取及其与大气相关性分析

为对日长年际变化进行更细致分析,利用标准Morlet小波变换方法,从日长变化序列中识别和提取出6个主要的年际信号,周期分别为2.3 a（2.4 a）、3.3 a、3.7 a、4.8 a、6.1 a和8.1 a,并基于信号的时域提取结果,计算获得相应信号的平均振幅,依次为0.08 ms、0.05 ms、0.05 ms、0.07 ms、0.10 ms和0.07 ms。仿照提取出的6个日长年际信号,对大气角动量序列中相应的信号进行提取,并对两者进行相关性分析。结果显示,大气和日长对应的4个高频年际信号（2.3 a（2.4 a）、3.3 a、3.7 a和4.8 a信号）存在强相关,相关系数分别为0.99、0.93、0.99和0.91,且大气的激发贡献率依次为99.7%、63.1%、94.7%和69.3%,表明日长2.3 a（2.4 a）和3.7 a信号几乎完全可由大气解释,而大气也为其余2个日长信号的主要激发源;大气6.0 a、8.5 a信号与日长对应的6.1 a、8.1 a信号不相关或弱相关,相关系数分别为-0.11和-0.56。     

一种基于特征子空间的多用户盲分离算法

提出了一种基于特征子空间的多用户盲分离算法。算法首先估计出信号源的导向矢量,然后再利用ESB算法进行波束形成,解决了当存在多个用户时权值收敛于单一用户的问题,可以在不知道信号先验知识的情况下对来自不同方向上的独立信号进行有效的分离。算法不需要进行繁琐的Gram—Schmidt正交化处理,并且在盲分离信号的基础上还可以估计出信号的波达方向。计算机仿真结果表明,分离出来的信号与源信号的相关系数均大于0．99,证实了算法的有效性。     

基于EWT-分量阈值与SVD的微震信号降噪方法

针对微震信号采集过程中存在大量不同频率的干扰信号,导致信号初至拾取难度大的问题,提出一种经验小波变换(EWT)结合分量阈值重构规则及奇异值分解(SVD)技术对微震信号进行降噪的方法.该方法利用EWT自适应分解和抗模态混叠的特点分解微震信号,得到各分量信号.对于高信噪比信号,选取相关系数大于0.3且方差贡献率大于15％的...     

福建省地表温度与植被覆盖度的相关性分析

地表温度（Land surface Tenperature, LST）和植被覆盖度（Fractional Vegetation Coverage, FVC)是生态环境变化的重要指标因子,研究两者的时空变化及相互关系对评价区域生态环境建设、改善区域生态环境具有重要意义。本文以福建省为研究区域,利用2001-2015年MODIS 11A2 LST和13Q1 NDVI数据,在时序数据重构的基础上对福建省LST时空变化及LST与FVC的相互关系进行分析。结果表明：①2001-2015年福建省LST总体呈轻微下降趋势,尤其是2010年之后其LST明显降低。LST与FVC的空间分布具有较好的负相关一致性：在FVC较高的区域,LST值较低;在FVC较低的区域,LST较高。② LST与FVC、DEM和纬度均成负相关关系,且负相关性在一年之中随着月份的变化而呈规律性增加或降低。夏季FVC对LST的负相关性最大为0.7,冬季FVC对LST的负相关性降低为0.4。③LST随着FVC增加而降低的趋势呈现分段线性关系,存在“FVC拐点”。“FVC拐点”前后随着FVC增加LST的降低速率在夏季 “先慢后快”,而在冬季则“先快后慢”。春秋两季,LST随着FVC增加而降低的速率在“FVC拐点”前后差异变小。在夏季,当FVC大于0.4时,FVC每增加0.1可降低LST约0.77 °C,降温效果大约是FVC小于0.4时的2倍。因此如果要有效地降低夏季地表高温,要使地表植被覆盖大于40%,才能较好的发挥植被的降温的作用。④在1-8月份,FVC对LST的负相关作用存在滞后性,FVC变化对滞后一个月的LST时空分布影响更大。研究成果对福建省生态环境建设与评估具有一定的意义,对于发挥植被对区域高温抑制作用提供了重要的参考依据。     

HHT-EEMD用于IGS站高程时间序列分析

利用经验模态分解（EMD）和整体经验模态分解（EEMD）方法,将BJFS站2000~2015年高程时间序列进行分解,发现其不仅存在1 a、0.5 a、0.25 a、2个月、1个月以及长周期等周期项,还存在以往方法很难探测出来的近似2 a周期信号。与EMD分解结果对比,整体经验模态分解可以明显减弱模式混叠现象。对各分量进行Hilbert 变换（HHT）,得到时间-频率-能量的Hilbert 频谱图。结果表明,年周期和2 a周期变化是高程运动的主要贡献项。利用小波变换方法对比验证EEMD的分解结果表明,与小波分析相比,EEMD重构信号与高程序列差异的RMS更小,证明了HHT-EEMD方法在数据资料分析过程中的有效性。对环境负载及GRACE负载造成的测站位移进行功率谱分析得出,环境负载确实会造成IGS站高程时间序列的1 a、0.5 a以及季节性运动,GRACE负载还验证了2 a信号的存在。     

一种考虑PMX与PMY之间相关性的极移预报新方法

提出采用多元回归模型（MAR）与最小二乘（LS）组合进行极移预报。该模型考虑极移PMX和PMY的LS拟合残差之间的相关性,采用PMX残差和PMY残差一起构建预报模型进行残差预报。通过与LS+AR预报结果的对比表明,LS+MAR模型的预报结果更优。此外,通过与EOP_PCC预报结果的对比也说明,LS+MAR模型的短期极移预报精度能够达到国际先进水平。     

Correlation and path analysis of morphological and weight traits in marine gastropod Glossaulax reiniana

Using correlation and path analysis, the genetic correlation between weight traits and morphological traits was determined in the marine gastropod Glossaulax reiniana. A total of 100 G. reiniana individuals from a wild population were used. Shell width （X1）, shell height （X2）, umbo-callus height （X3）, body width （X4）, operculum length （X5）, operculum width （X6）, body weight （Y1） and soft-tissue weight （Y2） were measured, and the correlation coefficient matrix calculated. Morphological traits were used as independent variables and weight traits as dependent variables for path coefficient analysis. Path coefficients, correlation indices and determination coefficients were also determined. Results indicate that the correlation coefficients associated with each morphological and weight trait were all highly significant （P〈0.01）. After deleting redundant independent variables, the following optimum multiple regression equations were obtained using stepwise multiple regression analysis： Y1=-29.317＋0.362X2＋0.349X4＋ 1.190）（5 for body weight; and Y2=-17.292＋0.166X1＋0.171X2＋0.703X5, for soft-tissue weight. Operculum height had the highest positive direct correlation with both body weight and soft-tissue weight, which was in accordance with the test results obtained from determinate coefficient analysis. The indication of high genetic correlations between weight traits and morphological traits will provide valuable information for G. reiniana breeding programs.     

基于改进的经验模态分解和SSA联合算法的GPS坐标时间序列重构

基于ICEEMDAN算法无需先验信息即可准确分离和提取低频信号与趋势信息的特性,以及SSA具有较好的信号重构优势,提出基于ICEEMDAN和SSA的联合重构方法。该方法将弱周期信号利用ICEEMDAN方法进行提取与重构,可弥补SSA方法中当弱周期信号对应的Hankel矩阵的奇异值和噪声Hankel矩阵的奇异值接近时容易被噪声掩盖而难以提取的不足。通过模拟实验和真实站点数据验证该算法分解和重构精度,并与奇异谱分析法、小波分解法、滑动最小二乘法进行比较。实验结果表明,ICEEMDAN-SSA联合算法相对于已有方法具有更好的重构精度。     

Correlation analysis of X-band sea clutter in complex domain

The correlation analysis of sea clutter data in a complex domain is conducted in this study. Specific to X-band sea clutter, the statistical characteristics of the complex correlation, particularly the phase characteristics which are closely related to the phase difference of the sea clutter and the Doppler properties, are analyzed in detail based on the experimental data, recorded by the Mc Master University IPIX radar in 1993. That the phase term of the complex correlation presents linear change means that there exists the linearity of phase differences between different time intervals in the X-band sea clutter. This investigation explores the regularities about the effect of wind on the complex correlation with similar patterns for different polarization modes. The regularities indicate that the wind direction can be inferred from the distribution pattern of the complex correlation. Moreover, a model describing the relationships between the statistics of the complex correlation and wind parameters is proposed. The application for target detection based on the differences of characteristics of complex correlations between the sea clutter and the target are also investigated and the proposed features have been confirmed. The principle of the method is fundamental for broader future applications.     

基于Spearman秩相关系数的PWV与PM2.5相关性研究

在讨论大气可降水量(PWV)与细颗粒物(PM 2.5)之间的相关性时,传统方法未能很好地顾及连续数据中包含的非雾霾天气信息的影响,为此本文提出2个数据选取标准——时间标准和空气质量指数(AQI)等级标准,用于获取雾霾期间对应的PWV和PM 2.5序列。为解决数据筛选后不连续的问题,引入一种非参数性质的Spearman秩相关系数ρ,在北京市2014~2016年雾霾多发期,分析不同AQI等级对应时段的非连续等距的PWV和PM 2.5序列的相关性可知,筛选后3 a的ρ在第1、4季度的均值分别为0.6613和0.6280,整体均值为0.6447,表明雾霾天气下PWV和PM 2.5序列具有单调正向的相关性,而传统分析方法(未筛选)下两者的相关系数均较小,表明在对数据进行选取后的分析结果更具针对性和准确性。     

川西高原植被特征及其气候变化的相关分析

利用常规气象资料及NOAA-AVHRR的归一化植被指数（NDVI）资料和趋势系数、皮尔逊相关、Morlet小波分析等统计诊断方法,分析了1982年1月-2002年12月川西高原植被和气候因子（气温和降水）的变化特征及其相关关系和周期特征。结果表明：川西高原地区植被覆盖良好,大部分区域植被覆盖增加,局部退化（高原南部和东部）;气温总体呈增加趋势,降水量总体少变,局部有所减少;NDVI与气温和降水有一定相关性,其中与气温的相关性比与降水相关性大;NDVI周期约为5和10年左右,与降水和气温周期相同。川西高原地区植被及气候特征的分析为川西高原旅游和经济的发展规划提供依据,为研究川西高原的生态、气候资源提供参考。     

一种去除地震背景噪音的新方法

结合希尔伯特-黄变换方法,根据信号背景噪音时间延续性假设,提出了一种自适应地震信号去噪算法。利用该算法实现了台站地震监测信号的去噪分析。分析表明：1）该去噪算法能根据背景信号特性有效去除信号的低频干扰;2）该算法可自适应完成信号分解去噪,计算效率高,无时间分辨率和频率分辨率问题;3）该算法处理高频干扰存在缺陷,有待进一步完善。     

基于标准时频变换的准勒夫波信号识别

利用意大利2个台站记录的2004-12-26苏门答腊Mw9.0大地震激发的面波分析准勒夫(Quasi-Love)波,同时尝试使用标准时频变换(NTFT)方法探测Quasi-Love波,结果发现,根据NTFT中频率随时间的变化可探测Quasi-Love波.另外,基于NTFT的时频谱提出估计信号相关性的相似度法,仿真实验表...     

Debris flows monitoring and localization using infrasonic signals

Infrasonic waves(frequency ≤ 20 Hz) are generated during the formation and movement of debris flows, traveling in air with a speed far higher than that of the debris-flow movement. Infrasound monitoring and localization of infrasonic waves can serve as warning properties for debris-flows. Based on the characteristics of infrasonic signals, this study presents a three-point array of infrasound sensors as time-synchronous multiple sensors for acquiring signals. In the meantime, the signals are sorted by mutual correlation of signals to figure out their latency, and by means of array coordinating to locate the sound source to realize the monitoring and positioning of a debris-flows hazard. The method has been in situ tested and has been proven to be accurate in monitoring debris-flow occurrences and determining their positions, which is particularly effective for pre-event warning of debris-flow hazards.     

一种新的基于模糊分析观测信息的局地化方法

地球表层系统是一个极其复杂的巨系统,为了更精确地表达地球表层系统各种过程的动态演进,解决数据同化系统观测误差的估计与处理已经成为地球科学领域备受关注的问题之一。在地球科学系统数值模拟中,一般采用集合数据同化来探讨地学变量预报时的各种误差。集合类卡尔曼滤波通常会由于集合数过小而带来欠采样、协方差低估、滤波发散和远距离虚假相关等问题。针对背景误差协方差被低估问题,局地分析方法（Local Analysis, LA）在一定程度上能起到抑制作用,但无法彻底解决背景误差协方差的虚假相关问题。因此,本文在集合卡尔曼滤波的算法框架下提出了一种与模糊逻辑控制算法相耦合的局地化分析方法（Fuzzy Analysis, FA）。在强非线性Lorenz-96模型中,对不同模型误差下的LA和FA方法进行了性能优劣方面的探讨,并比较分析了2种方法在集合数、观测数和观测位置、放大因子以及强迫参数变化时的同化性能。实验采用均方根误差作为算法评判依据,同时用功率谱密度（Power Spectral Density, PSD）更直接地对2种算法性能优劣作出了评价。结果表明：在完美模型下,FA相对于LA降低了17.5%的均方根误差（Root Mean Square Error, RMSE）;随着模型误差增大,RMSE减小的百分比和减小幅度都在降低;在严重模型误差下,FA降低了8.6%的RMSE。总体而言,新算法FA的有效性和鲁棒性都得到了验证,并且在EnKF同化基础下有效改进了传统的局地化分析方案,优化了观测误差处理,为今后的数据同化研究提供了一个较为全面的观测误差研究平台。