GIS中3维空间圆曲线的不确定性εσ模型

随着GIS向3维领域的不断发展,对3维空间元素的不确定性研究日趋重要.基于不确定性理论及概率统计理论,研究GIS中3维空间圆曲线的不确定性模型.首先建立3维空间圆曲线上任意点坐标的微分关系式,然后得到圆曲线上任意点坐标的方差, 进而获得3维空间圆曲线的εσ模型.实际算例表明,3维空间圆曲线误差分布呈现出"两端大,中间小"的趋势.     

A Regional Scale Investigation of Climatological Tropical Convection and Precipitation in the Amazon Basin

This study constructs a regional scale climatology of tropical convection and precipitation from more than 15 years of monthly outgoing longwave radiation (OLR) and precipitation data on 2.5°× 2.5° latitude-longitude grid to examine the spatial and temporal patterns and variability of convection and precipitation in the Amazon Basin. A linear regression analysis also detects if any trends exist in the two datasets. The region of study extends from 15°N to 25°S and 30° to 80°W that encompass the Amazon Basin and surrounding fringe areas for the period from January 1979 through December 1995 for the OLR data and up to 1996 for the precipitation dataset. The basin-average mean monthly and seasonal climatology serve as a ‘baseline’ reference for comparison with the full time series of basin-average monthly OLR and precipitation to illustrate the interannual variability and identify anomalous periods of wet and dry conditions. A linear trend analysis of OLR data found small negative values across the Amazon Basin indicating a slight increase in convective activity over the period of study. The analysis of the precipitation time series, however, shows no coincidental increase in precipitation as would be expected with an increase in convective activity. Portions of Rondônia and Mato Grosso, areas that have undergone extensive deforestation, illustrate no trend in precipitation as suggested by GCM simulation results. The only area featuring any large change in precipitation occurs in a small area in the northwestern region of South America where a large positive trend in precipitation exists.     

WATER-QUALITY MONITORING AND SPECIOUS DATA: THE EXAMPLE OF GEORGES CREEK,MARYLAND∗

Concurrent water data were collected by the U.S. Geological Survey, the U.S. Corps of Army Engineers, and the Maryland Department of Health and Mental Hygiene at the same site in order to describe present conditions and predict future environmental change in the Georges Creek basin in western Maryland. Evaluation of the data sets reveals measurement errors and weaknesses in sample design so that published complications contain significant errors. Unless these errors can be identified, policy based on such information may have unfortunate results.     

Predicting socioeconomic conditions from satellite sensor data in rural developing countries: A case study using female literacy in Assam,India

Social data from census and household surveys provide key information for monitoring the status of populations, but the data utility can be limited by temporal gaps between surveys. Recent studies have pointed to the potential for remotely sensed satellite sensor data to be used as proxies for social data. Such an approach could provide valuable information for the monitoring of populations between enumeration periods. Field observations in Assam, north-east India suggested that socioeconomic conditions could be related to patterns in the type and abundance of local land cover dynamics prompting the development of a more formal approach. This research tested if environmental data derived from remotely sensed satellite sensor data could be used to predict a socioeconomic outcome using a generalised autoregressive error (GAR_err) model. The proportion of female literacy from the 2001 Indian National Census was used as an indicator of socioeconomic conditions. A significant positive correlation was found with woodland and a significant negative correlation with winter cropland (i.e., additional cropping beyond the normal cropping season). The dependence of female literacy on distance to nearest road was very small. The GAR_err model reduced residual spatial autocorrelation and revealed that the logistic regression model over-estimated the significance of the explanatory covariates. The results are promising, while also revealing the complexities of population–environment interactions in rural, developing world contexts. Further research should explore the prediction of socioeconomic conditions using fine spatial resolution satellite sensor data and methods that can account for such complexities.     

A comparative analysis of DEM‐based models to estimate the solar radiation in mountainous terrain

Daily solar radiation estimates of four up‐to‐date solar radiation models (Solar Analyst, r.sun, SRAD and Solei‐32), based on a digital elevation model (DEM), have been evaluated and compared in a Mediterranean environment characterized by a complex topography. The models' estimates were evaluated against 40 days of radiometric data collected in 14 stations. Analyzed sky conditions ranged from completely overcast conditions to clear skies. Additionally, the role of the spatial resolution of the DEM has been evaluated through the use of two different resolutions: 20 and 100 m. Results showed that, under clear‐sky conditions, the daily solar radiation variability in the study area may be reasonably estimated with mean bias errors under 10% and root mean square error values of around 15%. On the other hand, results proved that the reliability of the estimates substantially decreases under overcast conditions for some of the solar radiation models. Regarding the role of the DEM spatial resolution, results suggested that the reliability of the estimates for complex topography areas under clear‐sky conditions improves using a higher spatial resolution.     

The smoothness of HASM

To smooth noises inherent in uniformly sampled dataset, the smoothness of high accuracy surface modeling (HASM) was explored, and a smoothing method of HASM (HASM-SM) was developed based on a penalized least squares method. The optimal smoothing parameter of HASM-SM was automatically obtained by means of the generalized cross-validation (GCV) method. For an efficient smoothing computation, discrete cosine transform was employed to solve the system of HASM-SM and to estimate the minimum GCV score, simultaneously. Two examples including a numerical test and a real-world example were employed to compare the smoothing ability of HASM-SM with that of GCV thin plate smoothing spline (TPS) and kriging. The numerical test indicated that the minimum GCV HASM-SM is averagely more accurate than TPS and kriging for noisy surface smoothing. The real-world example of smoothing a lidar-derived Digital Elevation Model (DEM) showed that HASM-SM has an obvious smoothing effect, which is on a par with TPS. In conclusion, HASM-SM provides an efficient tool for filtering noises in grid-based surfaces like remote sensing–derived images and DEMs.     

Map errors that could account for deviations from a uniform intensity of land change

Intensity Analysis is a mathematical framework that compares a uniform intensity to observed intensities of temporal changes among categories. Our article summarizes Intensity Analysis and presents a new method to compute the minimum hypothetical error in the data that could account for each observed deviation from a uniform intensity. A larger hypothetical error gives stronger evidence against a hypothesis that a change is uniform. The method produces results for five groups of measurements, which are organized into three levels of analysis: interval, category, and transition. The method applies generally to analysis of changes among categories during time intervals, because the input is a standard contingency table for each time interval. We illustrate the method with a case study concerning change during three time intervals among four land categories in northeastern Massachusetts, USA. Modelers can perform the analysis using our computer program, which is free.     

雷达测雨误差及其对淮河流域径流模拟的影响

将雷达测雨数据与分布式水文模型相耦合进行径流过程模拟,分析雷达测雨误差及其径流过程模拟效果,研究雷达测雨误差对径流过程模拟的影响效应.在对淮河流域气象中心业务化的5种淮河流域雷达测雨数据进行误差分析的基础上,采用雷达测雨数据驱动HEC-HMS水文模型,模拟分析淮河息县水文站以上流域2007年7月1-10日强降雨集中期的径流过程.结果表明:利用雷达测雨数据的径流模拟结果与实测资料的模拟结果基本吻合,各种雷达测雨数据误差经过HEC-HMS水文模型传递后,误差明显减小.联合校准法对应的模拟效果最好,过程流量相对误差NBs'和洪峰流量相对误差Z'分别为-20.2%和-13.3%.     

Accuracy of the half-power bandwidth method with a third-order correction for estimating damping in multi-DOF systems

A third-order correction was recently suggested to improve the accuracy of the half-power bandwidth method in estimating the damping of single DOF systems.This paper analyzes the accuracy of the half-power bandwidth method with the third-order correction in damping estimation for multi-DOF linear systems.Damping ratios in a two-DOF linear system are estimated using its displacement and acceleration frequency response curves,respectively.A wide range of important parameters that characterize the shape of these response curves are taken into account.Results show that the third-order correction may greatly improve the accuracy of the half-power bandwidth method in estimating damping in a two-DOF system.In spite of this,the half-power bandwidth method may significantly overestimate the damping ratios of two-DOF systems in some cases.     

利用F10.7和Mg II构建太阳极紫外辐射长时间序列

利用SOHO/SEM在1996-2008年的太阳EUV观测数据,比较和评估F10.7和Mg II作为EUV代理参数的代表性,不能支持Viereck等关于Mg II是比F10.7更好的代理参数的结论.通过比较对两种参数的多种回归计算结果,确立双因子极大似然估计方法构建EUV计算模式,通过模型计算结果与SEM观测数据比较,表明该模型能够很好地重建EUV数据系列.利用该模式,构建了1978年11月以来的太阳极紫外辐射数据序列.