应用Kriging方法研究格尔木河流域地下水位动态观测网的优化配置

地下水动态是水文地质研究的一个重要方面，如何获满足一定精度要求全面而合理的地下水位动态资料具有现实经济效益。本文研究格尔木河流域地下水位动态测网，通过选取理想的变差函，运有Ｋｒｉｇｉｎｇ方法进行地下水位的线性无偏最优估计和计算估计误差的标准差，结合给定的允许误差限评判地下水位动态观测网的配置是否合适，并提出调整现有观测网的方案     

超低仰角扫描改进高山雷达降水估测

利用2004年5月云南大暴雨过程的26次昆明雷达超低仰角对比观测体扫资料,分析了昆明雷达0°和0.5°仰角数据以及对比了定量估测降水的情况。结果表明：0°仰角探测到的降水分布范围更大;与0.5°仰角相比,通过定量计算,探测降水范围能提高约9.96%,估测的降水总量约为11.6%。从探测到的降水量和范围来看,0°仰角较0...     

Application of a cloud-texture analysis scheme to the cloud cluster structure recognition and rainfall estimation in a mesoscale rainstorm process

It is thought that satellite infrared (IR) images can aid the recognition of the structure of the cloud and aid the rainfall estimation. In this article, the authors explore the application of a classification method relevant to four texture features, viz. energy, entropy, inertial-quadrature and local calm, to the study of the structure of a cloud cluster displaying a typical meso-scale structure on infrared satellite images. The classification using the IR satellite images taken during 4–5 July 2003, a time when a meso-scale torrential rainstorm was occurring over the Yangtze River basin, illustrates that the detailed structure of the cloud cluster can be obviously seen by means of the neural network classification method relevant to textural features, and the relationship between the textural energy and rainfall indicates that the structural variation of a cloud cluster can be viewed as an exhibition of the convection intensity evolvement. These facts suggest that the scheme of following a classification method relevant to textural features applied to cloud structure studies is helpful for weather analysis and forecasting.     

NOAA RFE 2.0在西藏高原的验证

随着卫星探测技术的不断提高和资料处理方法的不断改进,出现了许多卫星遥感降水估算产品。每种产品都有优点及不足,且卫星间接式降水估算方法的精度也有限,但对地形复杂常规气象站台站稀少且分布极不均匀的大面积地区如西藏高原来说,卫星遥感不失为估算区域降水的有效方法之一。鉴于卫星遥感降水估算精度的局限性,每种产品需要利用地面观测数据来定量化降水估算误差,分析和评价这些资料的可用性。利用NOAA气候预测中心(CPC)研发的RFE 2.0降水估算产品,从西藏高原东南部到西北部不同气候区选取11个典型气象站2005—2006年6—9月的日降水量观测资料,验证了RFE 2.0降水估算产品在西藏高原的应用效果。结果表明,西藏高原的主要气候区RFE 2.0估算值与地面观测值之间的相关系数在0.45～0.86,平均为0.74;RFE 2.0估算的正确率POD (Probability Of Detection)一般大于73%,而空报率FAR(False Alarm Rate)为2%～12%;仅在喜马拉雅南麓地区估算精度相对较差。      

A Comparison of De-noising Methods for Differential Phase Shift and Associated Rainfall Estimation

Measured differential phase shift ΦDP is known to be a noisy unstable polarimetric radar variable, such that the quality of ΦDP data has direct impact on specific differential phase shift KDP estimation, and subsequently, the KDP-based rainfall estimation. Over the past decades, many ΦDP de-noising methods have been developed; however, the de-noising effects in these methods and their impact on KDP-based rainfall estimation lack comprehensive comparative analysis. In this study, simulated noisy ΦDP data were generated and de-noised by using several methods such as finite-impulse response(FIR), Kalman, wavelet,traditional mean, and median filters. The biases were compared between KDP from simulated and observedΦDP radial profiles after de-noising by these methods. The results suggest that the complicated FIR, Kalman,and wavelet methods have a better de-noising effect than the traditional methods. After ΦDP was de-noised,the accuracy of the KDP-based rainfall estimation increased significantly based on the analysis of three actual rainfall events. The improvement in estimation was more obvious when KDP was estimated with ΦDP de-noised by Kalman, FIR, and wavelet methods when the average rainfall was heavier than 5 mm h-1.However, the improved estimation was not significant when the precipitation intensity further increased to a rainfall rate beyond 10 mm h-1. The performance of wavelet analysis was found to be the most stable of these filters.     

临界值可变的抗差估计等价权函数

抗差估计等价权函数一般由正态分布统计量构造,其临界值(或称准则)一般由实际经验确定。首先分析了正态分布统计量和学生化残差统计量的区别,然后分别讨论了基于这两种统计量构造的等价权函数的区别。研究表明,利用学生化残差统计量构造的等价权函数以及顾及误差显著性水平确定的临界值,不仅考虑了观测误差的大小,而且还可以顾及了实际观测的图形强度和多余观测数,可以克服人为确定临界值可能带来的参数估计的有效性和抗差性方面的风险。     

Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania

Field surveys are often a primary source of aboveground biomass (AGB) data, but plot-based estimates of parameters related to AGB are often not sufficiently precise, particularly not in tropical countries. Remotely sensed data may complement field data and thus help to increase the precision of estimates and circumvent some of the problems with missing sample observations in inaccessible areas. Here, we report the results of a study conducted in a 15,867 km² area in the dry miombo woodlands of Tanzania, to quantify the contribution of existing canopy height and biomass maps to improving the precision of canopy height and AGB estimates locally. A local and a global height map and three global biomass maps, and a probability sample of 513 inventory plots were subject to analysis. Model-assisted sampling estimators were used to estimate mean height and AGB across the study area using the original maps and then with the maps calibrated with local inventory plots. Large systematic map errors – positive or negative – were found for all the maps, with systematic errors as great as 60–70 %. The maps contributed nothing or even negatively to the precision of mean height and mean AGB estimates. However, after being calibrated locally, the maps contributed substantially to increasing the precision of both mean height and mean AGB estimates, with relative efficiencies (variance of the field-based estimates relative to the variance of the map-assisted estimates) of 1.3–2.7 for the overall estimates. The study, although focused on a relatively small area of dry tropical forests, illustrates the potential strengths and weaknesses of existing global forest height and biomass maps based on remotely sensed data and universal prediction models. Our results suggest that the use of regional or local inventory data for calibration can substantially increase the precision of map-based estimates and their applications in assessing forest carbon stocks for emission reduction programs and policy and financial decisions.     

基于变化向量分析（CVA）的土地利用／覆盖变化动态监测（Ⅱ）—变化类型的确定方法

以光谱直接比较为基础的变化向量分析法是一种非常有效的土地利用／覆盖变化动态监测方法，在双窗口变步长阈值搜寻方法确定变化和非变化像元的基础上，提出了参考图像分类并结合变化向量方向余弦最小距离分类的变化类型确定方法，同时应用该方法在北京市海淀区进行了实验研究，得到了较为理想的结果。变化类型的判断精度达到70％以上，显示了新方法的优越性和技术可行性。     

一种顾及电子元件延迟的超宽带室内定位方法

针对超宽带定位系统中存在多种误差影响定位精度的问题,本文全面分析了超宽带室内定位的误差源,给出了超宽带室内定位基本函数模型,提出了将电子元件延迟这一系统差与三维坐标同时作为参数进行估计,并通过高斯-牛顿迭代算法得到定位结果的方法。试验结果表明,该方法能有效消除位置系统偏差,两组静态试验均以全站仪测量结果作为真值,点位误差分别为4.1、2.3 cm,相较于已有方法分别提高了64%和87%,而动态试验结果更接近于参考轨迹,最大误差小于10 cm。     

变形信号与噪声的分离方法及其应用

在对变形体进行监测的时候会得到一系列不同时间的观测结果,比如对滑坡的位移监测,得到一系列的坐标,通过与首期观测数据的比较可获得变形量,然而测量中不可避免的含有误差,如何将变形信息和观测噪声分离是一个需要解决的问题。本文在变形是一个连续过程的假设下,以给定的验前方差为约束条件,通过三次连续逼近样条函数建立变形曲线,达到变形信号与噪声分离的目的,文中通过对某滑坡多年监测数据的分析验证了该方法的可行性。