一次陕西关中强暴雨环境条件及中尺度系统分析

综合利用T213再分析资料和高时空分辨率观测资料包括地面区域逐时加密观测资料,对2007年8月8—9日陕西关中特大暴雨过程的环境条件和中尺度系统进行了分析。天气学分析表明:500 h Pa西太平洋副热带高压和青藏高原高压形成的高压坝在陕西中部断裂形成东北—西南向切变线、250 h Pa西风急流入口区右侧发散场和700 h Pa东西向切变线相互配合是特大暴雨形成的有利环境条件;低层风向快速变化使关中暴雨区低空水汽经历了减小—突然增加—快速减小的过程,关中周围水汽通过偏东气流输送至暴雨区为暴雨的发生提供了水汽和位势不稳定条件,而水汽的快速变化又形成关中暴雨的突发性和历时短而强的特征;高空反气旋涡度的发展形成强烈的"抽吸作用"、双圈垂直次级环流和强垂直上升运动及其两侧的弱下沉运动形成的不对称结构是暴雨形成的动力机制。强降水的中尺度特征分析显示:强暴雨是由一个中α尺度对流系统(MαCS)的发生发展产生的,MαCS又是由2个中β尺度对流系统(MβCS)合并发展而成,其内部对流单体的发展合并和独立加强形成岐山、礼泉和高陵3个大暴雨中心,这些对流单体的发展是由地面中尺度辐合系统产生的,强降水的强弱与地面中尺度辐合系统的强弱有很好的对应关系,地面中尺度辐合系统的形成和加强可能是强降水的触发机制和增幅原因之一。     

利用静止气象卫星监测初生对流的研究进展

本文主要对近年来利用静止气象卫星监测、分析和研究对流初生的国内外若干研究结果和进展给予了简要综述。主要涉及对流的判识、对流的追踪技术、初生对流的多光谱云顶特征、初生对流的判据和问题与展望等方面的内容。总体而言,利用静止气象卫星监测初生对流,以及发展的对流,是目前国内外研究和探索的热点之一。研究表明,通过精心挑选对对流敏感的光谱通道、通道组合和时间演变判据,可以对初生对流进行监测,可比地面雷达更早地预警降雨系统的发展,显示出在临近预报中的应用潜力。监测对流初生的算法流程大体上分为目标判识、目标追踪和初生对流判识三个部分。国际上主要的一些与对流初生相关的成熟算法各具特色,如RDT(Rapidly Developing Thunderstorms)算法在对流判识中强调了垂直形态的峰值检测,追踪对流时利用了速度外推并检测重叠面积。ForTraCC(Forecasting and Tracking the Evolution of Cloud Clusters)算法关注活跃对流,一定程度上考虑了对流合并和分裂的情况,具有外推预报功能。GOES-R(The Geostationary Operational Environmental Satellite R-Series Program)算法特点是多光谱的使用,利用多光谱判识技术进行对流云顶检测,是面向下一代静止气象卫星探测能力的算法。UWCI(The University of Wisconsin Convective Initiation)构思巧妙,所提出的box-average技术简单易行,适合软件工程化。随着卫星传感器和探测能力的发展,以及计算机技术的快速发展,更多卫星资料将用于联合判识对流。同时,更新的计算机视觉、计算机图像处理和模式识别技术也将用来解决追踪中的复杂问题,进一步改进初生对流的判识准确率。     

基于灰色定权聚类的江苏省工业节能减排评价研究

采用灰色定权聚类分析法,对江苏省近年来的工业节能减排情况进行评价.结果表明：2000年以及2009-2011年的节能减排效果较好,2002-2008年的节能减排效果一般,2001年的节能减排效果较差.另外,还结合各项指标对评价结果进行分析,并有针对性地提出有关江苏省节能减排措施的建议.     

基于MapReduce计算模型的气象资料处理调优试验

云计算技术使用分布式的计算技术实现了并行计算的计算能力和计算效率,解决了单机服务器计算能力低的问题。基于长序列历史资料所计算得出的气候标准值对于气象领域实时业务、准实时业务及科学研究中均具有重要的意义。由于长序列历史资料数据量大、运算逻辑较复杂,在传统单节点计算平台上进行整编计算耗时非常长。该文基于Hadoop分布式计算框架搭建了集群模式的云计算平台,以长序列历史资料作为源数据,基于MapReduce计算模型实现了部分整编算法,提高计算时效。同时,由于数据源本身具有文件个数多、单个文件小等特点,对数据源存储形式及数据文件大小进行改造,分别利用SequenceFile方式及文本文件合并方式对同一种场景进行计算时效对比测试,分别测试了10个文件合并、100个文件合并两种情况,使时效性得到了更大程度的提升。     

Automatic registration of optical imagery with 3D LiDAR data using statistical similarity

The development of robust and accurate methods for automatic registration of optical imagery and 3D LiDAR data continues to be a challenge for a variety of applications in photogrammetry, computer vision and remote sensing. This paper proposes a new approach for the registration of optical imagery with LiDAR data based on the theory of Mutual Information (MI), which exploits the statistical dependency between same- and multi-modal datasets to achieve accurate registration. The MI-based similarity measures quantify dependencies between aerial imagery, and both LiDAR intensity data and 3D point cloud data. The needs for specific physical feature correspondences, which are not always attainable in the registration of imagery with 3D point clouds, are avoided. Current methods for registering 2D imagery to 3D point clouds are first reviewed, after which the mutual MI approach is presented. Particular attention is given to adoption of the Normalised Combined Mutual Information (NCMI) approach as a means to produce a similarity measure that exploits the inherently registered LiDAR intensity and point cloud data so as to improve the robustness of registration between optical imagery and LiDAR data. The effectiveness of local versus global similarity measures is also investigated, as are the transformation models involved in the registration process. An experimental program conducted to evaluate MI-based methods for registering aerial imagery to LiDAR data is reported and the results obtained in two areas with differing terrain and land cover, and with aerial imagery of different resolution and LiDAR data with different point density are discussed. These results demonstrate the potential of the MI and especially the CMI methods for registration of imagery and 3D point clouds, and they highlight the feasibility and robustness of the presented MI-based approach to automated registration of multi-sensor, multi-temporal and multi-resolution remote sensing data for a wide range of applications.     

LiDAR点云数据的电力线3维重建

利用LiDAR数据进行电力设施提取与建模可以克服传统工程测量电力巡线工作量大,危险性高,效率低下等缺点,但现有的电力线提取研究主要集中在电力线的分离与提取,并且拟合的精度不高。针对此问题本文提出了一种精度较高的电力线拟合方法。首先,根据电力线两端悬挂、中间自然下垂的特点,求解电力线拟合的最佳几何模型;然后,通过电力线的走向和端点,建立电力线拟合的最佳平面坐标系;最后,采用基于二次多项式限制的最小二乘法拟合电力线,解算出最优参数,生成最终电力线模型。对真实数据的处理和精度评价表明,本文方法不仅能够实现电力线的快速3维重建,而且能够达到较高的拟合精度。     

Tetrolet域利用时空相关性的云图压缩感知重建

针对卫星云图数据量大,但传输通道和存储空间相对狭小的问题,本文基于Tetrolet变换,利用相邻时次云图的时空相关性,实现了一种高重构质量的卫星云图压缩感知方法。该方法将善于表达图像方向纹理及边缘信息的Tetrolet变换引入压缩感知的稀疏表示环节,从而很好地体现了卫星云图细节丰富、纹理结构复杂的特性;同时,考虑到卫星云图序列间的相关性,将时间相邻的卫星云图组成图像组,以中间时刻云图作为参考图像,计算其与相邻时次云图的差异,通过在参考图片及序列差异图片间合理分配采样率,获取测量数据,在压缩感知框架下,采用带平滑处理的投影Landweber算法,重构出相邻时次的图像组。实验结果表明,Tetrolet变换适用于卫星云图的稀疏表示,而且图像组时空相关性的利用可显著提高重构云图的视觉效果及客观评价指标;在采样率低于0.2时,红外1、水汽和可见光3个通道重构云图的峰值信噪比(PSNR)较传统方法平均提高了7.48 dB,13.51 dB和6.15 dB。由此可见,本文方法可以通过获取少数随机测量值,重构出高质量的卫星云图,不仅为云图数据的低比特率压缩提供了一种可行的解决方案,而且对于其他序列图像的压缩采样具有借鉴意义。     

The Characteristics of Atmospheric Ice Nuclei Measured at Different Altitudes in the Huangshan Mountains in Southeast China简

The concentration of ice nuclei （IN） and the relationship with aerosol particles were measured and analyzed using three 5-L mixing cloud chambers and a static diffusion cloud chamber at three altitudes in the Huangshan Mountains in Southeast China from May to September 2011.The results showed that the mean total number concentration of IN on the highest peak of the Huangshan Mountains at an activation temperature （Ta） of-20℃C was 16.6 L-1.When the supersaturation with respect to water （Sw） and with respect to ice （Si） were set to 5％,the average number concentrations of IN measured at an activation temperature of-20℃C by the static diffusion cloud chamber were 0.89 and 0.105 L-1,respectively.A comparison of the concentrations of IN at three different altitudes showed that the concentration of IN at the foot of the mountains was higher than at the peak.A further calculation of the correlation between IN and the concentrations of aerosol particles of different size ranges showed that the IN concentration was well correlated with the concentration of aerosol particles in the size range of 1.2-20 μtm.It was also found that the IN concentration varied with meteorological conditions,such as wind speed,with higher IN concentrations often observed on days with strong wind.An analysis of the backward trajectories of air masses showed that low IN concentrations were often related to air masses travelling along southwest pathways,while higher IN concentrations were usually related to those transported along northeast pathways.     

Statistical Characteristics of Convective Initiation in the Beijing-Tianjin Region Revealed by Six-Year Radar Data

Characteristics of convective initiation (CI) in the Beijing-Tianjin region during the warm season of2008-2013 are examined. A total of 38877 CI cases are identified by a thunderstorm identification, tracking, analysis, and nowcasting algorithm. CI cases are evaluated in the context of associated terrain, weather systems, and land cover properties. The spatial distribution of all CI cases shows that there are dense CI activities around the 200-m elevation, which means that convective storms are more easily triggered over foothills. From 1500-1800 to 0300-0600 BT (Beijing Time), the high-occurrence CI region tends to propagate southeastward (i.e., from mountains to plains, then to ocean). Among the four local weather systems, the Mongolian cold vortex has the highest CI frequency while the after-trough system has the lowest CI frequency. For the land cover relationships with CI, the urban land cover has the highest CI density and the forest-type land cover has the second highest CI density; these two types of land cover are more conducive to CI formation.