基于IGS超级跟踪站的近实时GPS气象研究及应用

目前国内许多城市开始将CORS综合应用网服务于气象部门,为给地基GPS气象研究人员提供一个真实的研究环境,本文基于IGS超级跟踪站网络搭建了一个近实时GPS气象系统,并结合实测数据验证了该系统的有效性。基于该平台可以系统地研究GPS遥感水汽、层析水汽三维分布、数值天气预报同化GPS水汽以及利用电离层分布进行空间天气预报等理论。     

碳酸盐岩溶洞型储层反演方法

常规声波反演的方法原理和反演技术以层状介质为基础,其研究目标多是层状储层。碳酸盐岩溶洞型储层具有非规则形态、非均匀散布的特征,常规声波反演技术有其不适应之处。笔者研究的测井约束多重反演技术,解决了非层状、非均匀储层的地震反演问题,得出反映碳酸盐岩溶洞储层信息的波阻抗数据体,提取了突出溶洞型储集体低速特征的差异波阻抗,为寻找碳酸盐岩溶洞型油气藏提供了可靠的依据。     

RESEARCH OF THE INHOMOGENEITY TEST OF CLIMATOLOGICAL DATA SERIES IN CHINA*

Some achievements on the inhomogeneity test of climatological data series and some correlative conclusions were described,from which we concluded that it was very necessary to test the homogeneity of the Chinese climatological data series.Many techniques on this field developed by foreign experts are suitable for Chinese climatological series,and the main factors for the inhomogeneity of the Chinese climatological data are the relocation of stations and the change of instruments.     

地球物理反问题中的随机反演理论

本文从最大后验概率密度观点出发，在数据噪音向量和待求模型向量为具有零均值的独立高斯随机过程的假设前提下，建立起了随机反演的非线性系统方程；给出了模型方差估计的函数表达式，并在文章最后，证明了反演解的稀疏性，即解释了随机反演的输出解的高分辨率特征。文章在最小二乘反演方法的基础上，发展并完善了随机反演方法的理论基础；揭示了随机反演方法与最小二乘反演方法之间的本质区别；阐述了随机反演方法的优越性，并指出了其广阔的应用前景。     

四川雅安地质灾害时空预警试验区初步研究

借鉴美国旧金山湾和香港地区的经验,提出了地质灾害监测预警试验区建设和研究思路。经过近两年的工作,初步建成了四川雅安地质灾害监测预警试验区。取得的阶段性成果主要有:(1)根据现场考察和试验区地质灾害数据库,统计研究了降雨型滑坡的几何特征;(2)建成了由20台遥测雨量计构成的降雨观测网,取得了2002年4月～2003年8月的降雨观测数据;(3)结合历史降雨资料分析,初步研究了雅安试验区的年、日、小时和十分钟最大降雨特征;(4)研制了斜坡岩土体含水量野外监测仪,取得了桑树坡试验点2003年4月～8月的实时监测数据;(5)自上而下分4层研究了斜坡岩土层含水量变化,发现了岩土层含水量变化对降雨过程的滞后性;(6)基于区域地质灾害评价预警的递进分析理论与方法(AMFP),利用地质灾害因子分析结果,分别计算了雅安试验区地质灾害"发育度"和"潜势度";(7)利用2003年8月23～25日的过程降雨观测资料,对雅安试验区在该降雨过程中发生的地质灾害事件进行了时空预警反演模拟研究,计算出的地质灾害"危险度"分布比较符合实际,"危险度"可以作为预警指数使用。     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

高光谱影像概率分类向量机分类方法研究

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。     

自动气象观测数据的质量控制

精细化气候变化分析及气候预测服务亟待发展,对高分辨率优质量的气候资料需求不断增长。本文利用缺测率、对比差值、空间一致性、粗差率、一致率5个指标对2009—2014年浙江省2 209个自动站温度数据进行评估分析,运用开放式可调整的评分标准,选取数据质量稳定可靠的站点,满足气候研究与业务的发展需求。共取得91个一级可用站,499个二级可用站,363个三级可用站,一级站主要分布于宁波和嘉兴地区,二级和三级站在各地都有较均匀的分布。在全省气候统计分析中,可以根据需求利用可用站。此外,有可用自动站参与的温度分布图完美呈现了地形对温度的影响,也使城市化发展突显出来,精细化的气候分析能更有效的为高山地区耕种制度及城市建设与规划提供科学依据。     

中国大陆流域分区TRMM降水质量评价

根据中国境内2 257个气象站点1998-2013年逐日降水资料,结合流域分区,采用探测准确性、相关系数以及相对误差等指标,对热带降水测量（TRMM）降水精度和一致性进行系统评价。结果表明:① TRMM日降水准确性从东南沿海向西北内陆递减;② 气象站点年均降水日数显著大于TRMM年均降水日数;③ 西北片区以外气象站点降水量和TRMM降水量在月尺度和年尺度上均具有较好的相关关系;④ 各流域年均TRMM面降水量均高于气象站点面降水量,且TRMM面降水量相对误差雨季较小,枯季较大;⑤ 各流域TRMM面降水量与气象站点面降水量演变趋势基本一致,南方各流域年降水量均呈减少趋势,北方各流域年降水量均呈增加趋势,全国尺度上年降水量呈微弱的减少趋势。     

基于改进K-SVD字典学习方法的地震数据去噪

为实现更好的地震数据去噪技术,笔者引入一种新的算法:快速迭代收缩阀值法(FISTA),通过FISTA和K-奇异值分解(K-SVD)不断迭代更新K-SVD字典,利用更新得到的K-SVD字典对地震数据进行稀疏表示,去除稀疏系数中较小的数值,使数据中的随机噪声得到压制。对层状模型合成地震记录,Marmousi模型合成地震记录以及实际地震数据进行对比实验,得出FISTA算法较OMP算法能更好地提高地震数据的信噪比,同时有效地保护了反射信号。