中国大范围雷暴大风事件(Derechos)研究:时空分布、环境背景和对流系统形态特征

基于2002—2019年高空、地面常规观测资料,卫星云图,部分新一代天气雷达单站资料以及2009年以后的中国雷达拼图资料,采用个例筛选与统计、动态合成分析、层次聚类和雷达图分析等方法,对发生在中国的大范围雷暴大风事件(Derechos)的时空分布、环境背景和对流系统形态特征进行了分析.结果表明:(1)Derechos事...     

交通地理信息系统中的时空动态分段模型研究

动态分段技术是交通地理信息系统(GIS-T)中一项重要的线性要素动态显示与分析技术,该文提出了一种时空动态分段模型,使动态分段系统的组成成分融入时态因素,并将属性的时态信息作为属性信息表的字段存储,通过时态地理信息系统技术完成物理段的时态变化。利用统一建模语言(UML)的类图设计方式开发了物理模型,描述了时空动态分段模型中对象之间的关系,并定义了必要的属性和操作;同时基于线性参照系统(LRS)表达空间实体的方法,概括了模型中所涉及到的主要拓扑关系。实验表明,时空动态分段模型弥补其他模型将时空参考分为时间参考和空间参考所产生的不足,而且更明确地将多重属性和物理实体有机结合起来。     

Atmospheric and Surface Water Pollution Interpretation in the Gdańsk Beltway Impact Range by the Use of Multivariate Analysis

The present study deals with the application of the hierarchical cluster analysis and non‐parametric tests in order to interpret the Gdańsk Beltway impact range. The data set represents concentration values for major inorganic ions (Na⁺, NH, K⁺, Mg²⁺, Ca²⁺, F⁻, Cl⁻, NO, and SO) as well as electrolytic conductivity and pH measured in various water samples [precipitation, throughfall water, road runoff, and surface water (drainage ditches, surface water reservoirs, and spring water)] collected in the vicinity of the beltway. Several similarity groups were discovered both in the objects and in the variables modes according to the water sample. In the majority of cases clear anthropogenic (fertilizers usage and transport, road salting in winter) and semi‐natural (sea salt aerosols, erosion of construction materials) impacts were discovered. Spatial variation was discovered for road runoff samples and samples collected from surface water reservoirs and springs. Surprisingly no clear seasonal variability was discovered for precipitation chemistry, while some evidences for existing of summer and winter specific chemical profile was discovered for road runoff samples. In general, limited range of the Gdańsk Beltway impact was proven.     

Land cover classification by an artificial neural network with ancillary information

Abstract

Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary information on soil type and prior knowledge of class occurrence the accuracy of an artificial neural network classification was increased by 29–93 to 77–37 per cent. An artificial neural network can therefore be used generate a fuzzy classification output that may be used with other data sets in a GIS, which may not have been available to the producer of the classification, to increase the accuracy with which land cover may be classified.     

Multi‐scale spatiotemporal analyses of moose–vehicle collisions: a case study in northern Vermont

Moose–vehicle collisions (MVCs) pose a serious safety and environmental concern in many regions of Europe and North America. For example, in the state of Vermont, one‐third of all reported MVCs resulted in motorist injury or fatality while collisions have increased from two in 1982 to 164 in 2002. Our work used a MVC dataset from 1983 to 1999 in the Northeastern Highlands of Vermont (four major roads) to perform space, time and spatiotemporal analyses and guide future mitigation strategies. An adapted kernel density estimator was implemented for exploratory analyses to detect high density collision hotspots on roads. The kernel in space showed seven major density peaks which varied in magnitude and spread between roads. The kernel estimator in time for all roads showed an exponentially increasing trend with annual periodicity and a seasonal cyclic component, where the majority of collisions occurred from May to October. Spatiotemporal kernel estimation exhibited discontinuous density hotspots in time and space suggesting changing animal movement patterns across roads. We used an adapted Ripley's K‐function to test the hypothesis that MVCs clustering occurred at multiple scales in space, in time and in space–time combined. Statistically significant spatial clustering was evident on all roads at spatial scales from 2 to 10 km. A more consistent clustering in time occurred on all roads at a scale distance of 5 years. Similar to the kernel estimation, annual periodicity was also evident. Positive space–time clustering was present at small spatial (5 km) and temporal scales (2 years) indicating that where MVCs occur is also influenced by when they occur. In retrospect, using multiple road lengths, and the combined kernel estimation and Ripley's K‐function in time and space, provided a powerful methodology to study varying spatiotemporal patterns of wildlife collisions along roads. This can greatly assist transportation planners in identifying optimal mitigation strategies along specific roads, such as deciding on location and spatial length for permanent and expensive measures (e.g. crossing structures and associated fencing) versus less permanent and inexpensive structures (e.g. wildlife signage and reduced speed limits).     

中国东部高空颠簸时空分布特征及其与热带中东太平洋海温的关系

本文利用1979～2014年NCEP-DOE日平均再分析资料和中国区域2375份航空器空中颠簸报告资料,研究中国东部区域高空颠簸的时空分布特征及其与热带中东太平洋海温异常（简称“海温异常”;空间范围:5°S～5°N,120°～170°W）的关系以及产生这种关系的可能原因。结果表明:中国东部地区高空颠簸与东亚副热带西风急流之间存在显著时空相关关系,其原因是高空纬向风引起的垂直风切变是构成高空颠簸时空分布的主导因素。中国东部夏季高空颠簸与海温异常存在正相关关系;冬季呈现南北两个正负相关区:以30°N为界,北部区域存在显著的负相关,南部区存在显著的正相关,在30°N急流轴附近区域无显著相关关系。海温异常影响中国高空颠簸时空分布的可能原因是海温变化引起对流层高层温度出现异常,进而影响温度的经向梯度,导致东亚副热带西风急流强度和位置出现异常（夏季,急流轴南侧出现西风异常;冬季,急流轴北侧出现东风异常,南侧出现西风异常）。高空纬向风的变化导致纬向风的垂直梯度和经向梯度出现异常,最终影响高空颠簸的时空分布特征。对流层高层温度的异常变化可能是由与热带海温异常相关的平流层水汽变化所引起。     

1991~2010年中国小时暴雨时空变化格局及其与城镇化因子的空间相关分析

采用1991~2010 年小时降水数据对中国小时暴雨雨量和雨时进行研究。结果表明,在时间上,1991~2010年中国小时暴雨雨量和雨时的年累计值在波动中呈明显增加趋势。在空间上,小时暴雨雨量和雨时的高值区主要集中在中国黑龙江漠河—云南腾冲一线的东部地区,该界线以西则是低值地区,其中小时暴雨变化最为显著地区主要集中在中国东南沿海地区和西北内陆地区。中国白昼和夜晚的小时暴雨雨量和雨时在空间分布上也有类似的规律。在日变化的时间尺度上,小时暴雨雨量和雨时呈现出双峰现象,最高值均出现在17：00（北京时间,下同）,而最低值出现在12：00。同时选择表征城镇化发展水平的夜晚灯光指数、黑炭气溶胶、低能见度日数和细颗粒物（PM2.5）浓度4 个因子,分别与小时暴雨雨量和雨时做空间相关分析。在全国平均水平上,4 个空间相关系数均在波动中呈明显增加趋势;而在中国气候变化区划一级分区上,空间相关系数均呈增加趋势,且增加趋势最为明显的是II 分区和III 分区。     

1980-2020年山西省极端降水的研究

基于1980-2020年山西省109个气象观测站点的逐日降水资料,选取10个极端降水指数,采用气候倾向率、相关分析、因子分析、R/S预测方法等方法,对山西省极端降水进行了时空分布的研究,以期为山西省的气候变化、生态环境保护、防灾减灾、气象服务工作提供参考依据,结果表明：(1)从时间尺度来看,1980-2020年期间,山西省极端降水的强度和极值都有明显增加,连续干旱日数和连续湿日日数呈下降趋势,其余均表现出不同程度的增加,其中年总降水量增加幅度最明显;从空间尺度来看,年总降水量、降水强度、降水频率、极值均为从西北向东南逐渐增多,空间差异较明显;从各站点的空间分布来看,北部和中部地区的极端事件增加最显著,北部地区的干旱日数仍以增加趋势为主,连续湿日日数气候倾向率的空间差异较大,中部地区站点显著增加,南北部以减少趋势为主;(2)基于相关分析方法表明各极端降水指数(除干旱日数外)与年总降水量都有很好的相关关系,强降水量和极强降水量对年总降水量的贡献值呈现出增加趋势;采用因子分析方法提取了3个公共因子,方差贡献率累计达到了87％,可以看出极端降水强度和降水量指数在对极端降水方面影响较大;利用R/S分析法可以得到年总降水量、中雨日数、大雨日数、最大5日降水量这几个指数未来呈现弱减少趋势,而干旱日数仍为减少趋势,连续湿日日数为持续弱增加趋势。总体看来,山西省极端降水近年来呈现出增加趋势,在空间分布有明显差异。     

A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.     

The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context

Police databases hold a large amount of crime data that could be used to inform us about current and future crime trends and patterns. Predictive analysis aims to optimize the use of these data to anticipate criminal events. It utilizes specific statistical methods to predict the likelihood of new crime events at small spatiotemporal units of analysis. The aim of this study is to investigate the potential of applying predictive analysis in an urban context. To this end, the available crime data for three types of crime (home burglary, street robbery, and battery) are spatially aggregated to grids of 200 by 200 m and retrospectively analyzed. An ensemble model is applied, synthesizing the results of a logistic regression and neural network model, resulting in bi-weekly predictions for 2014, based on crime data from the previous three years. Temporally disaggregated (day versus night predictions) monthly predictions are also made. The quality of the predictions is evaluated based on the following criteria: direct hit rate (proportion of incidents correctly predicted), precision (proportion of correct predictions versus the total number of predictions), and prediction index (ratio of direct hit rate versus proportion of total area predicted as high risk). Results indicate that it is possible to attain functional predictions by applying predictive analysis to grid-level crime data. The monthly predictions with a distinction between day and night produce better results overall than the bi-weekly predictions, indicating that the temporal resolution can have an important impact on the prediction performance.