Analysis of mesoscale convective systems in Catalonia using meteorological radar for the period 1996–2000

The aim of this paper is to show a climatology of Mesoscale Convective Systems (MCS) in the NE of the Iberian Peninsula, on the basis of meteorological radar observations. Special attention was paid to those cases that have produced heavy rainfalls during the period 1996–2000. Identification of the MCS was undertaken using two procedures. Firstly, the precipitation structures at the lowest level were recognised by means of a 2D algorithm that distinguishes between convective and non-convective contribution. Secondly, the convective cells were identified using a 3D procedure quite similar to the SCIT (Storm Cell Identification and Tracking) algorithm that looks for the reflectivity cores in each radar volume. Finally, the convective cells (3D) were associated with the 2D structures (convective rainfall areas), in order to characterize the complete MCS. Once this methodology was presented the paper offers a proposal for classifying the precipitation systems, and particularly the MCS. 57 MCS structures were classified: 49% of them were identified as linearly well-organised systems, called TS (39%), LS (18%) and NS (43%). In addition to the classification, the following items were analysed for each MCS found: duration, season, time of day, area affected and direction of movement, and main radar parameters related with convection. The average features of those MCS show an area of about 25000 km², Z_max values of 47 dBz, an echotop of 12 km, the maximum frequency at 12 UTC and early afternoon and a displacement towards E-NE. The study was completed by analysing the field at surface, the presence of a mesoscale low near the system and the quasi-stationary features of three cases related with heavy rainfalls. Maximum rainfall (more then 200 mm in 6 h) was related with the presence of a cyclone in combination with the production of a convective train effect.     

Strategy and Scenario for Radioastron Space VLBI Observations

Space VLBI's highly dynamic geometry, ability to access the space radio telescope (SRT) only via distant communication links, very expensive mission cost, and scientific goals define the basic strategy and scenario for mission control and radio source observations. These are very different from those for ground-based VLBI. Space VLBI strategy must be based on the limitation of SRT repointings, periodic orbit determination before astronomical observations, preliminary preparation and checking of space and ground facilities, and recommended observing sequences and modes. A control and observation scenario is considered for an in-orbit-checkout period, and also for short (1-orbit – 1 week) and long (1 week and more) observation sessions. Examples and illustrations are given for the Radioastron space VLBI Project.     

基于系列斑点图像的视宁度估算精度方法研究

半高宽法和像运动法是估算大气视宁度的常用方法,两种方法的估算精度都受到望远镜跟踪误差、风或者机械振动等因素的影响,提高视宁度估算精度在高分辨成像、选址、台站视宁度监测等方面有重要意义.对于半高宽法,用二维高斯函数拟合长曝光点源单星星像并选取合适方向的半高宽估算视宁度,可以有效改善跟踪误差、风或者机械振动等因素的影响.对...     

利用数字图像相关方法实现高速相机序列影像运动目标的快速跟踪

以数字图像相关方法为核心设计了一套应用于高速相机序列影像运动目标的跟踪方案,该方案首先在基准窗口周围以局域搜索方法获取目标点的整像素位移值,然后联合使用梯度算法和最小二乘算法估计目标点的亚像素位移值,从而获得目标点的像素坐标,实现对目标点的自动跟踪。本文应用该技术方案处理了构筑物振动台实验的高速相机视频测量影像数据,与商用软件PhotoModeler Scanner 相比,跟踪结果具有同等精度,在计算速度上有优势。     

山东地震前兆台网地下流体数据跟踪分析

介绍了山东省地震前兆台网地下流体学科数据跟踪分析工作概况,从观测系统、自然环境、场地环境、人为干扰、地球物理事件、不明原因等方面分析了2015年1月至2017年6月跟踪到的非正常变化事件。通过数据跟踪分析,查明了山东区域前兆台网流体学科观测运行的制约因素,提出了合理化建议,以提高观测数据质量。并对荣成鲁32井水温观测数据出现的典型非正常变化事件进行了跟踪分析,判定其为地震前兆异常。     

基于高速摄影技术的风沙流输沙通量剖面的风洞实验测量

风沙研究者非常重视对输沙通量随高度变化特征的研究,并为寻找可靠的测量手段付出了不懈的努力。基于高速摄影技术获得的沙粒平均水平速度与沙粒数的垂直剖面,推导了较低风速下环境风洞内输沙通量的垂直剖面。结果表明：沙粒平均水平速度随高度呈幂函数增加,颗粒浓度随高度的算数平方根呈指数衰减。由颗粒平均水平速度剖面与浓度剖面的乘积可获得输沙通量剖面。所获得的输沙通量随高度变化曲线在距床面1~3 mm处均有一个明显的拐点,拐点上方输沙通量随高度呈指数衰减。在床面与拐点之间输沙通量没有明显的变化趋势,这可能是由于气流中颗粒间的碰撞以及颗粒与床面碰撞的影响。平均跃移高度和相对衰减系数是描述输沙通量随高度变化的两个重要参数,两者有着很好的相关性,表明了随着风速增加和沙粒粒径减小跃移颗粒可以达到更大的高度,随着风速减小与粒径增大,输沙通量迅速衰减。     

Metamorphism and microstructure of seasonal snow: Single layer tracking in Western Tianshan,China

Snowpack is a combination of several snow layers. Accordingly, snowpack natural metamorphism is composed of several stages. The aim of this study is to investigate the natural snow metamorphism at the snow layer unit. The field investigation was conducted at the Tianshan Station for Snow Cover and Avalanche Research, Chinese Academy of Sciences(43°16' N, 84°24' E, and 1,776 m a.s.l.), during the winter of 2010-2011. A complete metamorphic procedure and the corresponding microstructure of a target snow layer were tracked. The results indicate that: the ideal and complete metamorphic process and the corresponding predominant snow grain shape have 5 stages: 1) unstable kinetic metamorphism near the surface; 2) unstable kinetic metamorphism under pressure; 3) stable kinetic metamorphism; 4) equilibrium metamorphism; 5) wet snow metamorphism. Snow grain size sharply decreased in the surface stage, and then changed to continuously increase. Rapid increase of grain size occurred in the stable kinetic metamorphism and wet snow metamorphism stage. The characteristic length was introduced to represent the real sizes of depth hoar crystals. The snow grain circularity ratio had a variation of "rapid increase–slow decrease–slow increase", and the snow aggregations continuously increased with time. Snow density grew stepwise and remained steady from the stable kinetic to the equilibrium metamorphism stage. The differences in metamorphism extent and stages among snow layers, led to the characteristic layered structure of snowpack.     

基于雷达组合反射率拼图和深度学习的中尺度对流系统识别、追踪与分类方法

中尺度对流系统（Mesoscale Convective System,MCS）是很多对流性天气的主要致灾体,可导致严重的气象和水文灾害,如雷暴大风、冰雹、龙卷风和山洪。对MCS进行准确的识别和追踪,并根据追踪轨迹及获得的MCS特征实现MCS的分类,对灾害天气的分析和预报有重要意义。基于京津冀地区2010—2019年的雷达组合反射率拼图资料,分别使用支持向量机（SVM）、随机森林（RF）、极度梯度提升决策树（XGBoost）和深度神经网络（DNN）4种机器学习方法,研发了京津冀地区MCS的自动识别算法。使用时、空重叠追踪法对识别的MCS进行追踪匹配,得到包含强度、时间和空间信息的MCS追踪数据资料。在区分线状对流系统和非线状对流系统的基础上,进一步从经典的尾随层云（Trailing Stratiform,TS）、前导层云（Leading Stratiform,LS）和平行层云（Parallel Stratiform,PS）三类准线性MCS的概念模型和结构特征出发,根据追踪轨迹计算MCS的运动方向和MCS近似长轴两侧层状云和强对流云的面积占比,建立准线性MCS的分类算法。MCS的识别属于二分分类问题,以命中率（POD）、虚警率（FAR）、临界成功指数（CSI）和准确率（ACC）为评价指标,综合对比各项指标发现DNN模型较SVM、RF和XGBoost模型对MCS的识别效果更好。使用时、空重叠追踪法对DNN模型识别的MCS进行追踪,结合对两个追踪实例的分析,发现本研究所用的算法取得了很好的追踪结果,也进一步说明了深度学习方法识别MCS的准确性和优势。根据追踪轨迹计算某时刻MCS的运动方向,结合识别的层状云和强对流云的分布位置,准确实现了TS、LS和PS型准线性MCS的分类,为准线性MCS的生命史预测及其致灾天气特别是短时强降水的强度、位置和持续时间的客观预报提供了一种技术思路。      

Alternative measures of dispersion applied to flow in a convoluted channel

Steady flow in a convoluted channel is studied via Particle Tracking Velocimetry. The channel is constructed from a sequence of closed parallel cylindrical tubes welded together in plane which are then sliced down the lateral mid-plane and the lower complex is laterally shifted relative to the upper complex. Flow is induced in the lateral direction normal to the axis of the tubes. The a-time, T_a, finite-size Lyapunov exponent, λ_a, and the real-space self- and distinct-part of the intermediate scattering functions, G_s and G_d, and the pair density function, G_p, are computed from the data. Particle trajectories, velocity maps and streamlines show the channel has two prominent recirculation zones and a main flow region. The first passage time probability density function of tagged particles past a plane transverse to the mean flow illustrates how particles are delayed by recirculation zones. The delay caused by fluid element folding is manifested in single particle statistics such as the first passage time and the slowing increase in horizontal evolution of G_s. G_p describes initial particle distribution and allows areas in the flow domain trapping particles to be identified and visualized. G_d shows the evolution of the average separation of pairs of particles and when examined in a recirculation zone, it evolves little because of fluid element rotation. λ_a gives information on what transpires at a fixed scale and provides an estimate of the rate at which particles initially separated by a distance x separate to a distance ax as opposed to G_d which allows one to view changes over time. At small separations, λ_1.3 approaches a constant and for intermediate separations it scales as x^−0.8.     

The use of Bluetooth for analysing spatiotemporal dynamics of human movement at mass events: A case study of the Ghent Festivities

In this paper, proximity-based Bluetooth tracking is postulated as an efficient and effective methodology for analysing the complex spatiotemporal dynamics of visitor movements at mass events. A case study of the Ghent Festivities event (1.5 million visitors over 10 days) is described in detail and preliminary results are shown to give an indication of the added value of the methodology for stakeholders of the event. By covering 22 locations in the study area with Bluetooth scanners, we were able to extract 152,487 trajectories generated by 80,828 detected visitors. Apart from generating clear statistics such as visitor counts, the share of returning visitors, and visitor flow maps, the analyses also reveal the complex nature of this event by hinting at the existence of several mutually different visitor profiles. We conclude by arguing why Bluetooth tracking offers significant advantages for tracking mass event visitors with respect to other and more prominent technologies, and outline some of its remaining deficiencies.