一种新的固体潮观测数据特征量提取方法

将经验模态分解方法应用于固体潮观测数据的处理,通过对模态分量从频域上以及从固体潮调和分析结果上与原始观测数据进行对比证明：经验模态分解可以将固体潮观测数据完整地分离成5种特征量：潮汐观测高频信号、半日波信号、周日波信号、潮汐观测低频信号和观测数据的长趋势变化。     

基于经验模态分解与神经网络的信号预测

利用经验模态分解在处理非线性、非平稳信号以及人工神经网络可以较好地处理非线性问题的优点,通过经验模态分解把加入噪声的仿真信号分解成几个本征模态函数分量和一个趋势项,在分解过程中采用两种方法处理端点效应问题,结果表明两种方法都能很好的解决端点问题,然后对每个分量分别运用径向基函数神经网络进行预测,并重构出最后的预测结果。与不经EMD处理直接运用神经网络进行预测及真实数据进行对比,结果表明,相对于直接预测,该方法具有更好的预测效果。     

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。     

一种削弱信噪混叠的EMD降噪方法

针对经验模态分解(empirical mode decomposition,EMD)降噪过程中存在信号与噪声模态混叠及直接将分界本征模态函数(intrinsic mode function,IMF)分量归入高频噪声造成真实信号被“湮没”等问题,提出一种改进的EMD降噪方法。该方法将经EMD得到的第2个IMF分量至分界IMF分量进行重构,对重构信号进行下一次EMD,获取其中的“真实”信号,多次重复此操作,最后将所有获得的低频信号累加,从而达到降噪的目的。利用2种模拟数据和1种GPS实测高程数据进行实验,模拟数据与实测数据采用不同的评价指标评价降噪效果,结果表明,改进的EMD方法较传统EMD方法降噪效果更佳,验证了该方法的可靠性。     

SSA-VMD与小波分解结合的GNSS坐标时序降噪方法

利用麻雀搜索算法(sparrow search algorithm, SSA)优化变分模态分解(VMD),然后结合小波分解(WD),提出一种GNSS坐标时间序列降噪方法IVMD-WD。利用仿真信号和10个基准站的实测数据进行GNSS坐标时间序列降噪实验。结果表明,IVMD-WD方法的降噪效果优于经验模态分解(EMD)、集合经验模态分解(EEMD)和WD,能够更加有效地剔除GNSS坐标时间序列中的噪声。     

结合CEEMDAN的改进小波阈值地下水温观测数据去噪方法

提出一种结合自适应噪声完备集合经验模态分解方法(CEEMDAN)的改进小波阈值降噪算法,用于地下水温观测数据的去噪。在利用该方法与传统的去噪方法分别对仿真信号进行降噪后发现,该去噪方法性能更优,并且在对实际采集到的含有噪声及异常突变的地下水温数据进行处理方面,也展现出比传统单一滤波手段更好的效果。     

基于HHT的地震信号自动去噪算法

根据希尔伯特-黄模态分解的特点,结合STA/LTA算法自动识别信号模态与噪音模态,提出了基于HHT的模态分解-STA/LTA的地震信号自动去噪算法.该方法不需分析信号的频谱特征,可在时域自动实现信号的高通、低通、带通滤波.对江苏如东海 M s2.9、江苏盐城滨海 M s3.8、江苏句容 M s3.7等地震信号作自动去噪处理,证明了该方法的可行性.     

基于EMD方法的SAR海浪参数反演

【目的】采用经验模态分解(EMD)方法,基于SAR图像反演海浪参数。【方法】以东北太平洋两幅RADARSAT-2 SAR图像为例,利用谱方法和C波段地球物理模式函数(CMOD5)反演风场,在此基础上,对SAR图像进行EMD分解,得到5个波段的固有模态,由固有模态波长,结合波浪理论和风浪谱,得出研究海域波浪的主波周期和有效波高。【结果与结论】与浮标实测数据对比,反演主波周期偏差为1.9%和13.2%,有效波高偏差为12.2%和12.8%。SAR图像分解与海浪理论结合,为探测反演远海波浪信息提供了新方法。     

长基线时间序列季节项提取和重构方法分析

运用经验模态分解（EMD）、集合经验模态分解（EEMD）和完备总体经验模态分解（CEEMD）3种方法对原始基线时间序列进行分解,得到各自时间序列的本征函数模态分量（IMF）、相关系数及周期性强度,进而确定其季节项分量,同时通过比较季节项和原始基线时间序列的叠加功率谱图优选分解方法。结果表明,CEEMD方法对基线时间序列季节项提取和重构效果最佳。     

VP型倾斜仪自动信号判别与调零实现

基于VP型垂直摆倾斜仪的观测原理及数据存储模式,应用经验模态分解(EMD)、STA/LTA能量比及最小二乘拟合联合算法,通过C++与MATLAB编程,优化了倾斜仪的自动化观测,实现了异常信号自动判别与自动调零,提高了数据获取与应用质量,并通过实例验证了该方法的有效性。     

基于多源数据的青藏高原雪深重建

青藏高原地形复杂,积雪时空分布异质性较强且大部分地区积雪较薄,而被动微波遥感因其空间分辨率低以及雪深反演中的不确定性,极大地限制了其反演青藏高原雪深的精度。本文尝试将多源遥感数据以及与积雪模型（SnowModel）相结合,来重建更高质量的青藏高原雪深数据。首先,利用MODIS积雪面积比例产品,根据构建的积雪衰减曲线以及经验的融合规则对低分辨率被动微波雪深进行了降尺度;然后,结合MODIS/被动微波融合雪深数据和SnowModel对研究区进行雪深数据同化实验;最后,利用地面站实测雪深数据对MODIS/被动微波融合雪深以及同化输出雪深的精度进行了分析和对比。结果表明,基于数据同化方法得到的雪深数据更接近地面观测雪深值,通过均方根误差以及相关系数的对比,同化雪深结果优于MODIS/被动微波融合雪深结果。     

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains,China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R~2=0.55 vs.R~2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.     

Effect of snow-cover duration on plant species diversity of alpine meadows on the eastern Qinghai-Tibetan Plateau

Although snow cover plays an important role in structuring plant diversity in the alpine zone, there are few studies on the relationship between snow cover and species diversity of alpine meadows on the eastern Qinghai-Tibetan Plateau. To assess the effect of snow cover on plant species diversity of alpine meadows, we used ten parallel transects of 60 m × 1 m for this study and described the changes in species diversity and composition associated with snow depth. With the division of snow depth into six classes, the highest species richness （S） and species diversity （H′） occurred with an intermediate snow depth, i.e., class Ⅲ and class Ⅳ, showing a unimodal curve with the increase in snow depth. The relationship between snow depth and plant diversity （both richness and Shannon index） could be depicted by quadratic equations. There was no evident relationship between diversity （both S and H′） and soil water content, which implied that other more important factors influenced species diversity. The patterns of diversity found in our study were largely attributed to freeze-thaw alteration, length of growing season and disturbances of livestock grazing. Furthermore, snow depth affected species composition, as evaluated by the Sorensen＇s index of similarity. In addition, almost all species limited to one snow depth class were found only in class Ⅲand class Ⅳ, indicating that intermediate snow depth was suitable for the survival and growth of many alpine species.     

Snow cover influences the thermal regime of active layer in Urumqi River Source,Tianshan Mountains,China

Snow cover is characterized by the high albedo, low thermal conductivity, and notable heat transition during phase changes. Thus, snow cover significantly affects the ground thermal regime. A comparison of the snow cover in high latitudes or high-altitude snowy mountain regions indicates that the eastern Tianshan Mountains (China) show a characteristically thin snow cover (snow depth below 15 cm) with remarkable temporal variability. Based on snow depth, heat flux, and ground temperature from 2014 to 2015 in the Urumqi River source, the spatialtemporal characteristics of snow cover and snow cover influences on the thermal conditions of active layer in the permafrost area were analyzed. During the autumn (Sept. - Oct.), thin and discontinuous snow cover can noticeably accelerate the exothermic process of the ground, producing a cooling effect on the shallow soil. During the winter (Nov. - Mar.), it is inferred that the effective thermal insulation starts with snow depth exceeding 10 cm during early winter. However, the snow depth in this area is generally below 15 cm, and the resulting snow-induced thermal insulation during the winter is very limited. Due to common heavy snowfalls in the spring (Apr. to May), the monthly mean snow thickness in April reached to 15 cm and remained until mid-May. Snow cover during the spring significantly retarded the ground warming. Broadly, snow cover in the study area exerts a cooling effect on the active layer and plays a positive role in the development and preservation of permafrost.     

1979-2010年青藏高原积雪深度时空变化遥感分析

积雪深度是表征积雪特征的重要参数,也是区域气候变化最敏感的响应因子之一。利用1979-2010年逐日中国雪深长时间序列数据集,采用GIS空间分析和地统计方法,分析了青藏高原积雪深度的时空变化规律及异常空间分布特征。结果表明：近32年来,青藏高原雪深呈显著增加趋势,增加速率为0.26 cm/10a,其中,昆仑高寒荒漠地带雪深增加最为明显,增加速率达0.73 cm/10a;20世纪80年代至90年代青藏高原雪深呈逐步增加趋势,21世纪初变化平稳;青藏高原4个季节雪深变化均呈现为上升趋势,尤以冬季增加最为明显,增加速率达0.57 cm/10a。青藏高原东南、西部和南部为雪深分布高值区;逐像元回归分析表明,高原雪深呈增加趋势的像元数占全区像元总数的67.1%,其中有91.3%为轻度和中度增加,主要分布在高原北部和西部;最大雪深变化基本维持在-0.1~0.1 cm/a（45.47%）之间,在昆仑北翼山地、柴达木山地、羌塘高寒地带南部等局部地区最大雪深有增加趋势,主要是轻度增加,面积比例为36.66%。果洛那曲高寒地带、青南高寒地带和羌塘高寒地带为青藏高原积雪深度异常变化敏感区。     

基于三维粒子系统的雪灾模拟与实时绘制方法

利用遥感影像反演积雪的范围与雪深是当前的主流方法之一,但是由于卫星影像获取的时效性约束,存在难以表示积雪动态信息、模型通用性弱、对遥感资料质量依赖程度高等问题。三维粒子系统作为一种优秀的不规则模糊物体模拟方法,可以大大提高自然现象三维场景的真实感。本文提出了一种基于三维例子系统的雪灾模拟与实时绘制方法,将降雪的物理属性及几何属性转化为雪粒子属性,并以物理引擎为基础,建立基于三维粒子系统的积雪模型和融雪模型,模拟降雪过程并计算积雪范围和积雪深度,真实再现雪灾场景和发展趋势,方法简单、可操作且约束较少。实验表明,该方法的模拟结果与实际灾情具有较强的一致性,算法具有可靠性,并能再现三维雪灾场景,具有实时性和逼真性。     

利用GPS的SNR观测值进行雪深监测研究

在分析多路径与信噪比关系的基础上,给出基于SNR观测值的GPS-MR技术探测雪深的基本原理。利用板块监测PBO网P360站2013年174 d～2014年151 d的GPS实测数据进行算例分析,其结果与实测雪深数据吻合较好,相关系数大于0.97。     

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial(the ground profile) and time series analysis in the extremely snowy winter of 2012–2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011–2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive(warmer) effect of snow cover on the ground temperature(GT) on the daily basis, the highest difference between GT and daily mean air temperature(DGAT) is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm.     

东北典型林区雪深反演算法的验证与分析

积雪对自然环境和人类活动都有极其重要的影响。积雪参数（雪面积、雪深和雪水当量）反演对水文模型和气候变化研究有着实际的意义。然而,目前森林区的雪深遥感反演精度一直有待于进一步提高。东北地区是我国最大的天然林区和重要的季节性积雪区之一,本文利用FY3B卫星微波成像仪（MWRI）L1级亮温数据和L2级雪水当量数据,以及东北典型林区野外实测雪深数据,对Chang算法、NASA 96算法和FY3B雪深业务化反演算法进行了验证与分析。结果表明：在东北典型林区的雪深反演中,Chang算法和NASA 96算法反演的雪深波动都比较大,当森林覆盖度f≤0.6时,NASA 96算法表现比较好,均方根误差值在3种算法中较小,但当f >0.6时,NASA 96算法失真严重。当考虑纯森林像元（f=1）时,Chang算法低估了雪深47%。当f≤0.3时,FY3B业务化算法始终优于Chang算法。整体上,FY3B业务化算法相对稳定,具有较高的精度。     

多源信息结合的雪灾交通风险评估研究

本研究将气象观测信息与网络信息相结合,提出了一种多源信息结合的雪灾交通风险评估方法：利用长时间气象观测数据分析雪灾的致灾强度时空特征,计算不同年遇型雪灾致灾强度;对门户网站、高速公路网站中节假日道路拥堵的新闻报道进行信息挖掘,获取高速公路暴露度;采用风险矩阵进行雪灾交通风险评估。将该方法应用于河北省,研究结果如下： ① 近5年来全省降雪有所下降,但长时间尺度来看,各地降雪呈波动变化;积雪深度高值区分布在张家口、承德、石家庄地区,但各年代间会有所变化;降雪次数高值区基本固定,在张家口康保、沽源、崇礼以及承德丰宁西北方向。② 暴露度级别高的路段是连接北京与上海、广州、哈尔滨等城市的高速公路,以及重要省市级联络线。③ 受致灾强度与暴露度的综合影响,河北省雪灾高风险路段集中在京港澳高速（石安G4）、京昆高速G5、京承高速G45、长深高速G25、张承高速G95等。这些路段必须做好雪灾风险防范措施。