Evaluation of Two Momentum Control Variable Schemes and Their Impact on the Variational Assimilation of Radar Wind Data:Case Study of a Squall Line

Different choices of control variables in variational assimilation can bring about different influences on the analyzed atmospheric state. Based on the WRF model＇s three-dimensional variational assimilation system, this study compares the be- havior of two momentum control variable options-streamfunction velocity potential （ψ-χ） and horizontal wind components （U-V）-in radar wind data assimilation for a squall line case that occurred in Jiangsu Province on 24 August 2014. The wind increment from the single observation test shows that the ψ-χ control variable scheme produces negative increments in the neighborhood around the observation point because streamfunction and velocity potential preserve integrals of velocity. On the contrary, the U-V control variable scheme objectively reflects the information of the observation itself. Furthermore, radial velocity data from 17 Doppler radars in eastern China are assimilated. As compared to the impact of conventional observation, the assimilation of radar radial velocity based on the U-V control variable scheme significantly improves the mesoscale dynamic field in the initial condition. The enhanced low-level jet stream, water vapor convergence and low-level wind shear result in better squall line forecasting. However, the ψ-χ control variable scheme generates a discontinuous wind field and unrealistic convergence/divergence in the analyzed field, which lead to a degraded precipitation forecast.

Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case

This paper examines how assimilating surface observations can improve the analysis and forecast ability of a four- dimensional Variational Doppler Radar Analysis System （VDRAS）. Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational （4DVAR） data assimilation system. A squall-line case observed during a field campaign is selected to investigate the performance of the technique. A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions. The surface-based cold pool, divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation. Three experiments--assimilating radar data only, assimilating radar data with surface data blended in a mesoscale background, and assimilating both radar and surface observations with a 4DVAR cost function--are conducted to examine the impact of the surface data assimilation. Independent surface and wind profiler observations are used for verification. The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations. It is also shown that the additional surface data can help improve the analysis and forecast at low levels. Surface and low-level features of the squall line-- including the surface warm inflow, cold pool, gust front, and low-level wind--are much closer to the observations after assimilating the surface data in VDRAS.

Power Line Monitoring Data Transmission Using Wireless Sensor Network

The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.

Research on the Algorithm of Extracting Ridgeand Valley Lines from Contour Data

In this paper, the authors put forward a brief and practical arithmetic for auto extracting terrain lines from digital terrain data after analyzing and comparing current arithmetics.This paper gives an experiment result that the ridge and valley extracted by the arithmetic is consistent with the experimental terrain.

Linear manifold clustering for high dimensional data based on line manifold searching and fusing

High dimensional data clustering,with the inherent sparsity of data and the existence of noise,is a serious challenge for clustering algorithms.A new linear manifold clustering method was proposed to address this problem.The basic idea was to search the line manifold clusters hidden in datasets,and then fuse some of the line manifold clusters to construct higher dimensional manifold clusters.The orthogonal distance and the tangent distance were considered together as the linear manifold distance metrics. Spatial neighbor information was fully utilized to construct the original line manifold and optimize line manifolds during the line manifold cluster searching procedure.The results obtained from experiments over real and synthetic data sets demonstrate the superiority of the proposed method over some competing clustering methods in terms of accuracy and computation time.The proposed method is able to obtain high clustering accuracy for various data sets with different sizes,manifold dimensions and noise ratios,which confirms the anti-noise capability and high clustering accuracy of the proposed method for high dimensional data.

Ethics Lines and Machine Learning: A Design and Simulation of an Association Rules Algorithm for Exploiting the Data

Data mining techniques offer great opportunities for developing ethics lines whose main aim is to ensure improvements and compliance with the values, conduct and commitments making up the code of ethics. The aim of this study is to suggest a process for exploiting the data generated by the data generated and collected from an ethics line by extracting rules of association and applying the Apriori algorithm. This makes it possible to identify anomalies and behaviour patterns requiring action to review, correct, promote or expand them, as appropriate.

One-End Data Method for Fault Position Estimate of Two-Parallel Transmission Lines

An accurate numerical algorithm for three-line fault involving different phases from each of two-parallel lines is presented. It is based on one-terminal voltage and current data. The loop and nodel equations comparing faulted phase to non-faulted phase of two-parallel lines are introduced in the fault location estimation modal, in which the faulted impedance of remote end is not involved. The effect of load flow and fault resistance on the accuracy of fault location are effectively eliminated, therefore an accurate algorithm of locating fault is derived. The algorithm is demonstrated by digital computer simulations and the results show that errors in locating fault are less than 1%.

Developing Blue Spots Model for Tennessee Using GIS, and Advanced Data Analytics: Literature Review

Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.

Data Logging and Control of a Remote Inverter Using LoRa and Power Line Communication

For decades, the power system was highly centralized. With the growing integration of distributed generations into the system, there is a necessity for bi-directional communication methods to monitor and control the remote assets. The primary objective of this paper is to develop a communication link for monitoring and controlling a grid-connected inverter in a remote location. Furthermore, the paper presents developments that have been incorporated to improve the communication link. The literature survey indicates that LoRa is superior compared to other technologies, but has some security and reliability issues. This paper also presents an encryption algorithm to improve the security of the LoRa link. Local data storage added to the system before transmitting data increases the system reliability. A display at the transmission end is added to improve the user-friendliness of the communication link. A Powerline Communication link is parallelly added to the LoRa link to improve the reliability. Finally, tests are conducted with an actual inverter and the results are presented. The tests show that the developed communication link has improved security and reliability, while its open nature makes it highly scalable and adaptable for employment in other smart grid applications.

气象大数据云平台算法集约化环境设计与应用

气象业务系统集约化发展和“云+端”业务技术体制改革是实现气象业务高质量发展的重要措施。2020年中国气象局提出构建以气象大数据云平台为云、气象业务系统为端的“云+端”业务技术体制,明确气象大数据云平台作为关键基础技术平台的定位。加工流水线作为气象算法的集约化环境,应用数算一体、高效任务调度、可视化流程编排、容器等技术,实现气象算法的统一管理与高效集约调度运行。2021年加工流水线业务运行,支撑全国202个业务系统的实时运行,业务系统性能提升1~10倍,集约化程度显著提高,对提升业务系统的运行效率、增强业务系统的协同性、加速“云+端”业务技术体制改革进程和推进气象业务集约发展发挥了重要支撑作用。

云原生数据湖服务平台的设计与实现

云原生数据湖已经成为数据管理和分析领域的研究热点,相关技术和应用也得到了广泛的关注和探索。数据湖部署存在着成本高、组件之间兼容性差等问题,存算不分离制约着数据湖平台延展性,缺乏完备的数据入湖体系容易引起数据湖沼泽的形成,导致用户无法从中提取数据价值。该文设计并实现了云原生数据湖服务平台,平台以Kubernetes为底层构建云原生环境,结合容器技术将数据湖组件镜像化,同时设计数据湖存算分离方案来提高数据湖平台的可扩展性和可移植性,并配合监控、组装生产线将镜像容器化,实现数据湖上云操作。并建立用户入湖作业与云原生计算引擎之间的桥梁,对入湖信息进行预处理,提供多类型作业以满足多元化入湖场景,以统一catalog的方式将数据写入数据湖中。实际运行结果表明,该平台既提高了数据湖平台的灵活性和可靠性,又确保了元数据和数据资产的规范性存储。

基于数据感知的气象算法调度框架设计与应用

气象产品加工任务往往由不同算法组合而成,其状态对数据源具有高依赖性。高效感知数据状态,并及时合理启动调度,对提升气象产品加工的时效性至关重要。设计并建设了基于数据感知技术的气象算法调度框架,对比测试结果分析表明:在实时分析数据源到报状态的基础上,数据感知调度实现数据到报即算法启动运行,任务调度延迟较定时调度显著降低,由3784 ms降至11 ms。数据感知调度作为气象大数据云平台加工流水线的核心能力,已在国省部署并实时运行。目前在国家级业务中支撑19种核心业务算法的高效调度,日调度次数约为6.67×10^(5),平均调度延迟为31 ms;在省级业务中支撑14种算法的高效调度,日调度次数约为8×10^(4),平均调度延迟为156 ms。此外,数据感知调度实现了气象业务上下游算法的无缝串联,可为解决气象业务间的脱节问题、提升气象业务间的协同性提供解决方案。

基于点云数据的大曲率工件表面法向计算方法研究

航空航天构件装配制孔时对垂直度有严格要求,采用机器人等自动设备制孔时,为保证垂直度需要对每一个制孔位置的法向进行在位测量。现有的工件表面法向测量方式难以适应大曲率工件表面的测量需求。通过线激光扫描,可以获得反映工件局部表面详细信息的点云数据,在此基础上提出一种大曲率工件表面法向计算方法。首先研究了通过主成分分析(PCA)对局部点云数据进行平面拟合获得曲面法向的方法,然后选取了若干典型曲率,通过仿真生成点云数据,并分析了法向拟合误差的变化规律。通过试验验证了上述方法的有效性,结果表明,当点云选择范围在12mm以下时,直径为50mm的圆柱试验件的法向拟合误差可以保证在0.219°以下。

基于SVM的台区线损异常数据治理方法

为降低台区异常数据造成的供电服务中线损率较高问题,引进支持向量机(support vector machine,SVM)技术,设计台区线损异常数据治理方法。使用传感器等相关设备,采集台区电力设备运行反馈数据;设定台区线损异常数据的聚类中心,辨识并聚类台区线损异常数据;应用VisuShrink技术,确定数据处理的阈值门限,使用小波阈值法,参照小波系数,重构异常信号,消除信号的背景噪声;引进SVM,设定训练样本集合,采用SVM中的机器学习算法,建立回归函数,辅助SVM的POS参数优化条件,控制空间粒子的粒距,通过对空间异常数据的重构,实现对台区线损异常数据的治理。以某地区大型电力企业为例,设计对比试验。试验结果表明,设计的治理方法在实际应用效果良好,实现将台区线损率稳定在5%以下。

基于激光点云技术的架空输电线路目标建模方法研究

文章研究基于架空输电线路三维激光点云数据,通过点云自动分类(杆塔、导地线、地面、植被等)实现不同地物提取,在此基础上建立不同塔型模型库并进行特征分析,实现杆塔和导地线的类别提取,并进行精准空间坐标(X、Y、H)提取,实现架空输电线路重要巡检对象的精准定位信息获取,并以此规划基于输电线路三维点云数据的架空输电线路全自动巡检航迹规划,为输电线路去人机全自动巡检技术的研究提供技术支撑。

基于HTML5的核电厂移动端离线数据同步电子工作包设计

为提高核电厂日常维护和管理的效率和安全性,提出一种基于HTML5技术的移动端离线数据同步电子工作包方案。基于对传统电子工作包在移动端离线使用时的局限性分析,详细阐述本系统的架构设计方案,并重点论述系统利用WebSocket等关键技术实现的离线存储功能和离线数据同步机制。对离线数据同步性能的测试结果显示,该离线数据同步机制具有良好的性能,可以在短时间内同步大量数据,并且具有良好的稳定性,操作人员可以更加方便快捷地访问离线数据同步电子工作包,从而提高工作效率和核电工作流程操作安全性。

室内移动机器人激光点云2D墙线检测实验

快速、准确地识别墙线环境元素是室内移动机器人导航功能的重要基础。该文针对传统霍夫变换方法易丢失信息和产生过多冗余线段问题,提出一种基于激光雷达点云数据空间特性的2D墙线直接提取算法。首先通过点云直通滤波技术去除原始三维点云数据中的地面点云干扰;然后将清洗后的点云投影到二维地平面上,转化为二值化图像后进行直线检测;再利用自定义的直线滤波增强算法,在减少冗余线段的同时保留更多有效墙线信息,从而快速生成更为吻合的建筑物墙线模型。仿真实验结果表明,新算法在不同复杂度场景下提取的线段数量整体平均降低50.8%,且处理时间小于100 ms,满足移动机器人导航过程中实时环境识别需求。该实验适合用作高校教学实验,有助于学生将理论知识与实践操作相结合,深入理解室内地图构建与环境感知技术。

铁路沿线气象灾害数据识别技术研究与应用

铁路是气象部门防灾减灾工作的重要服务领域。为简化和方便开展铁路沿线的气象灾害监测、预警和服务研究,基于气象灾害作用的主要气象要素,采用气象数据元数据信息和铁路路段的地理信息,利用搜索匹配和空间分析等技术,并结合专家经验,设计了一套数据清单的识别流程。以暴雨为例详细说明了数据识别的方法,并完成了原型系统软件功能设计和开发,采用Restful方式为专业技术人员提供铁路沿线有效的气象数据清单。以7·31北京暴雨过程对系统验证分析,识别结果符合预期,后续将为铁路气象防灾减灾工作提供有力的支持。

深度神经网络在线训练硬件加速器的数据量化综述

随着算法和数据的爆炸式增长,深度神经网络(Deep Neural Network, DNN)逐渐在实际应用中扮演愈发重要的角色。然而,真实场景中的数据与线下训练数据之间往往并不满足独立同分布假设,导致预训练DNN模型在实际应用中性能严重下降。所以,在资源供给相对有限的平台上进行DNN模型在线训练成为其有效应用的保证。为了满足真实场景对DNN模型质量与速度的多维度性能要求,如何在保证算法精度的同时显著降低计算复杂度是在此类应用中部署DNN的关键。数据量化是降低计算复杂度的主流优化技术之一,能够通过降低模型参数、中间值等数据的位宽来减少硬件加速器的资源耗费。因此,从软件和硬件两个方面对深度神经网络训练加速器中关于数据量化的研究进行总结。对国内外最新发表的相关文献进行归纳总结。首先,从软件的角度总结了不同的量化方法,包括简单映射数据量化和复杂映射数据量化;其次,从硬件的角度总结了DNN加速器对网络在线训练各计算步骤的量化支持;再次,阐述了数据量化对加速器设计的影响,包括存储单元和计算单元;最后,对本领域的研究进行总结,并展望了未来本领域的发展方向。文章提出的分类方法有助于对之前的DNN加速器在数据量化方面的工作进行分类。

Aeolus水平径向风速产品资料同化在台风预报中的应用研究

为评价星载激光测风雷达产品资料同化对台风预报的影响,以2020年台风“黑格比”和2019年台风“利奇马”为例,选用Aeolus卫星的水平径向风速产品,开展星载激光测风雷达资料同化及其对台风预报影响研究。首先基于ERA5再分析资料对Aeolus产品进行统计检验和偏差订正,并统计各高度层观测背景差的均方差作为观测误差用于同化试验。然后通过WRFDA(Weather Research and Forecast Model Data Assimilation)模式同化Aeolus产品并利用WRF(Weather Research and Forecast Model)模式开展预报试验。试验结果表明,Aeolus产品精度较高,统计所得各层观测误差在3~5 m s^(-1)范围且随高度的升高而增大,订正后资料满足三维变分同化无偏假设;在台风“黑格比”和“利奇马”期间同化该产品能使模式具备更加合理的环流形势,能够有效提高对台风“黑格比”和“利奇马”路径及强度的预报效果。