Auto-correlation analysis of wave heights in the Bay of Bengal

Time series observations of significant wave heights in the Bay of Bengal were subjected to auto-correlation analysis to determine temporal variability scale. The analysis indicates an exponential fall of auto-correlation in the first few hours with a decorrelation time scale of about six hours. A similar figure was found earlier for ocean surface winds. The nature of variation of auto-correlation with time lags was also found to be similar for winds and wave heights     

Retrieval of vertical wind profiles during monsoon from satellite observed winds over the Indian ocean using complex EOF analysis

The aim of this paper is to study the feasibility of deriving vertical wind profiles from current satellite observations. With this aim, we carried out complex empirical orthogonal function (CEOF) analysis of a large number of radiosonde observations of wind profiles over the Indian Ocean during the monsoon months. It has been found that the first two CEOFs explain 67% of the total variance in wind fields. While the first principal component is well correlated with the winds at 850 mb (r = 0:80), the second one is highly correlated with winds at 200 mb (r = 0:89). This analysis formed the basis of a retrieval algorithm which ensures the retrieval of vertical profiles of winds using satellite tracked cloud motion vector winds. Under the assumption that accurate measurements of wind are available at the above mentioned levels, the r.m.s. error of retrieval of each component of wind is estimated to range between 2 ms^-1 and 6 ms^-1 at different levels, which is much less than the natural variance of winds at these levels. For a better visualization of retrieval, we have provided retrieved and true wind profiles side by side for four typical synoptic conditions during the monsoon season.     

风机基础结构有限元分析

本文利用ANSYS软件建立风机基础的有限元模型,将数值计算结果与常规设计进行对比,发现风机基础结构设计中可能存在的问题。研究金属环和基础截面尺寸对结构受力的影响,分析变形、应力分布、荷载分配、裂缝规律,并提出设计建议。     

Finite element analysis of deformation of pile groups for Xiangshui offshore wind power station

Xiangshui offshore wind power station is located in shore water near Chengang of Jiangsu province. As the pile groups of the wind turbines have complicated engineering characteristics, their design is one of the technical difficulties of the project. Deformation behaviors of the piles foundation of Xiangshui 2MW wind turbine generator system are computed and analyzed under static loads and cyclic wind loads. The results show that deformations of the foundation are allowable; and the deformations due to static loads are comparatively smaller than that under the containing cyclic wind loads condition. With the depth increasing, the horizontal displacement and its influence range decrease; while the maximal vertical settlement is steady and vertical influence range increases. Deformation characteristics of pile-soil system depend not only on the loads combination of the subject, but also on the soil conditions.     

基于非平稳时间序列分析的滑坡变形预测

滑坡的位移监测资料通常可用来预测滑坡的变形发展趋势,位移的发展反映了滑坡的变形过程.为了预测在现有条件持续情况下的滑坡变形趋势,将滑坡位移监测数据视为非平稳时间序列,应用时间序列分析方法,建立了滑坡变形趋势的预测模型.以三峡库区秭归县白水河滑坡为例,通过对变形预警区监测点位移实测时间序列的分析,取监测点ZG93和XD-04为代表,建立了时间序列预测模型,从第17个月开始向前做6步预测,分析预测曲线与实测曲线之间的关系,并计算预测误差,结果显示除个别数据点之外,预测误差均在±9%以内,曲线吻合较好,说明所建模型效果良好,从而为判断白水河滑坡未来的变形发展趋势提供了可靠的理论依据.     

On smoothing techniques for the removal of periodic noise of known period

This paper is concerned with the analysis of the linear modely(n)=X(n)+S(n)+(n) for the data sequencey(n) (n=1, 2, ..., N) whereX={x _IJ} is a knownJ × M matrix of full rankM. Here, the(n) are unknown vectors, which we wish to estimate for eachn; S(n) (n=1, 2, ..., N) is a periodic component (which we wish to estimate or remove) superimposed on the linear structureX(n); and(n) is an error vector which is specified as having zero expectation (with possible further properties). Such models commonly occur in geophysical data analysis.Modified from Technical Report No. 33, Computer Centre, The Australian National University.     

Assimilation of IRS-P4 (MSMR) meteorological data in the NCMRWF global data assimilation system

Oceansat-1 was successfully launched by India in 1999, with two payloads, namely Multi-frequency Scanning Microwave Radiometer (MSMR) and Ocean Color Monitor (OCM) to study the biological and physical parameters of the ocean. The MSMR sensor is configured as an eight-channel radiometer using four frequencies with dual polarization. The MSMR data at 75 km resolution from the Oceansat-I have been assimilated in the National Centre for Medium Range Weather Forecasting (NCMRWF) data assimilation forecast system. The operational analysis and forecast system at NCMRWF is based on a T80L18 global spectral model and Spectral Statistical Interpolation (SSI) scheme for data analysis. The impact of the MSMR data is seen globally, however it is significant over the oceanic region where conventional data are rare. The dry-nature of the control analyses have been removed by utilizing the MSMR data. Therefore, the total precipitable water data from MSMR has been identified as a very crucial parameter in this study. The impact of surface wind speed from MSMR is to increase easterlies over the tropical Indian Ocean. Shifting of the positions of westerly troughs and ridges in the south Indian Ocean has contributed to reduction of temperature to around 30‡S.     

基于时间序列分析法的岩溶泉水位预测

济南城区岩溶泉是当地主要的供水水源,查明泉水动态规律并科学合理的预测泉水位对于泉域岩溶水资源的开发利用和保护具有重要意义。本文首先应用时间序列分析法将趵突泉和黑虎泉自2012年5月2日至2018年10月31日的逐日水位数据分解为趋势项、周期项和随机项,分析其水位动态变化规律并建立水位预测模型,结果显示泉水位动态在该阶段无显著趋势性;但受降水的影响,泉水位动态变化呈现两个主要的周期,多年性变化（3.2年）和季节性变化;同时由于受到各种无规律干扰因素的影响,泉水位动态呈现随机波动的随机项。其次,利用2018年11月1日至2020年8月24日的逐日泉水位数据验证上述水位预测模型的预测精度,结果表明该模型运行合理,预测效果较好,具有一定的实用价值。最后利用该模型预测了2020年8月25日至2022年10月31日泉水位动态变化,为当地岩溶水资源开发和管理提供了依据。     

海上风电桩基础竖向承载力循环弱化简化分析

提出了一个考虑循环衰减作用的软黏土弹塑性损伤弱化模型,在此模型基础上建立了一个基于剪切位移法的单桩竖向承载力简化计算方法,并利用大型有限元软件进行了不同循环荷载水平下单桩竖向承载力的计算。为了证明简化方法的准确性与有效性,将计算结果与有限元计算结果进行对比,二者的吻合度较高。先期循环荷载能引起单桩竖向承载力弱化,并且弱化程度随先期循环荷载水平的增加而增加。最后将简化方法应用于某海上风电场桩基础承载力的计算中,结果表明,提出的简化方法可应用于实际工程问题的分析。     

基于语义相似的水文时间序列相似性挖掘——以太湖流域大浦口站水位数据为例

利用数据挖掘技术从长期观测的数据序列中发现蕴藏的规律是当前研究热点之一。相似性挖掘是时间序列挖掘的基础,提出一种基于语义相似的水文时间序列相似性查询方法。首先利用小波变换将时间序列进行平滑处理,在此基础上进行极值点分段并符号化,每个符号代表一种语义模式,从而选取语义相似的子序列作为候选集,再将候选集中子序列通过动态时间弯曲距离进行精确匹配从而得到相似子序列(以太湖流域大浦口站水位数据为例),实验证明,该方法能够在大幅度降低时间复杂度的基础上较准确地查找出相似子序列。     

应用时间序列InSAR技术监测上海磁悬浮列车专线形变

新代高分辨军、短重访周期SAR卫星的发射运行,使得InSAR技术不仅能满足大范围地表沉降监测的数据需求,还可以监测到大型人工线状地物的形变,使得对短周期微小形变的监测和预警成为可能。本文以上海市在运营的磁悬浮列车专线为研究对象,利用时间序列分析方法对20112~F9月～2012年10月期间的15景TenaSAR-X数据进行了处理和分析,实验结果表明高分辨军SAR数据可以用于公共交通设施微小形变的监测,在公共安全领域具有广阔的应用前景。     

模糊时间序列分析方法在铜资源消费预测中的应用

通过对铜消费量肖费量的动态模糊数据时间序列分析模型。了模型的求参方法,并对进行了应用尝试。     

基于粒子群-高斯过程回归耦合算法的滑坡位移时序分析预测智能模型

高斯过程回归(GPR)学习机有着容易实现、超参数自适应获取及预测输出具有概率意义等优点。通常采用共轭梯度法获取GPR超参数,但其存在优化效果对初值依赖性太强,迭代次数难以确定,易陷入局部最优的缺点。改用粒子群优化 (PSO)算法进行最优超参数搜索,形成粒子群-高斯过程回归耦合算法(PSO-GPR)。将该算法引入三峡永久船闸高边坡、卧龙寺新滑坡、链子崖滑坡3个不同的典型滑坡变形时序分析中,对每个滑坡分别采用稳态核及一种新式神经网络（NN）、平方指数（SE）、有理二次型（RQ）3种单一核函数进行外推预报测试。工程应用表明,基于3种不同单一核函数的粒子群-高斯过程回归算法（PSO-GPR）均能完全适应不同滑坡时序分析,其中以NN核函数外推预测效果最佳,平均相对误差分别为6.37%、7.62%、1.07%,从而改善了在进行不同滑坡变形时序分析时采用单一核函数的核机器外推能力存在较大差异性的问题,提高了单一核函数对不同数据类型的兼容性     

南洞地下河月径流时间序列的混沌特征及预测

利用基于相空间重构技术、混沌识别与预测理论对1993-2013年南洞地下河月径流时间序列的非线性特征进行了分析,由所获得的延迟时间和最佳嵌入维数实现了月径流时间序列的相空间重构,运用饱和关联维数法和小数据量法计算出南洞地下河月径流时间序列的饱和关联维数和最大Lyapunov指数,并运用Volterra模型对南洞地下河月径流时间序列进行了多步预测研究。研究结果表明,南洞地下河月径流时间序列相空间重构的延迟时间和最佳嵌入维数分别为τ=5、m=8,饱和关联维数D和最大Lyapunov指数λ分别为4.63、0.748 9,从定性和定量的角度证明了南洞地下河月径流时间序列具有弱混沌特征。Volterra自适应滤波模型的预测结果能较好地表征南洞地下河月径流的变化趋势和规律,对18个月内的短期预测精度较高,模拟效果较好。