Integrating 3D Dynamic Meteorological Data and Algorithms into a Scalable Geospatial Framework

将 3D 动态气象数据和算法集成到可扩展的地理空间框架中

• 批准号: 9982299
• 负责人: Martin Ribarsky
• 金额: $ 190.21万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-05-01 至 2004-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9982299&HistoricalAwards=false
• 关键词: Integrating; 3D; Dynamic; Meteorological; Data

People, governments, manufacturers, airlines, and others rely more and more on accurate and timely weather forecasting. An increased population, especially in areas prone to flooding or severe weather, requires pinpoint weather warnings and longer range predictions. This project will help operational weather forecasters in making decisions about severe weather situations by developing tools to visualize and analyze large data sets, including real-time weather observations. Staff of the National Severe Storm Lab also will participate, especially in the evaluation and testing of visual analysis and decision-support tools and in the insertion of those tools in operational software for forecasters and weather researchers. These tools will provide a framework for the future that can be used not only by weather forecasters but also by researchers. The weather prediction and warning capabilities built on the methods proposed here may ultimately result in many billions of dollars saved in lost products, equipment, and time. Lives, too, can be saved and injuries reduced.Technically, the project will bring together, for the purposes of analysis and forecast decision-making, 3D time-dependent volumes from multiple overlapping Doppler radars, and simultaneous satellite information for the same and wider coverage areas. These will be combined with accurate terrain elevations, multiple image layers, maps, and other geospatial thematic data. This universal data collection will be organized for integrated visual analysis and made available for interactive navigation, exploration, and discovery by weather forecasters, researchers, and other users. These observational data will be displayed for the first time on accurate 3D terrain so that the correlation of landscape and weather can be revealed in detail, thus enabling new predictions of flood extents, inclusion of the effects of mountains on weather phenomena, and other new capabilities. To support fast, scalable visualization, a "geo-layered volume" will be introduced that will take advantage of the underlying terrain global quadtree organization and out-of-core paging structure. Hierarchical visual models will be developed within the geo-layered volume structure to produce several visual representation including volume rendering, isosurfaces, simple 3D time-dependent feature representations, and iconic annotations. A fast and scalable cluster-based feature analysis method will form a dynamic feature hierarchy also. This hierarchy will be used to produce levels of detail for the volume and isosurface visualization of the 3D time-dependent observational data. The visual analyses will be put in a decision-support framework. Since the analyses attach meaning and importance to the various observations, the observational data can be displayed in the right form for easy use. Important phenomena can be given visual forms that catch the eye, and more detail can be given to objects that the analysis says are important or to objects that the user looks at more closely.

人们、政府、制造商、航空公司和其他人越来越依赖准确及时的天气预报。人口的增加，特别是在容易发生洪水或恶劣天气的地区，需要精确的天气警报和更长范围的预测。该项目将通过开发可视化和分析大型数据集（包括实时天气观测）的工具，帮助业务天气预报员做出有关恶劣天气情况的决策。国家强风暴实验室的工作人员也将参加，特别是参加视觉分析和决策支持工具的评估和测试，并将这些工具插入预报员和天气研究人员的业务软件。这些工具将为未来提供一个框架，不仅可供天气预报员使用，也可供研究人员使用。建立在这里提出的方法上的天气预测和预警能力可能最终导致数十亿美元的产品，设备和时间损失。从技术上讲，该项目将汇集来自多个重叠多普勒雷达的3D时间相关体积以及相同和更广泛覆盖区域的同步卫星信息，以进行分析和预测决策。这些将与精确的地形高程、多个图像层、地图和其他地理空间专题数据相结合。这种通用数据收集将被组织用于集成的可视化分析，并可用于天气预报员，研究人员和其他用户的交互式导航，探索和发现。这些观测数据将首次在精确的3D地形上显示，以便详细揭示景观和天气的相关性，从而实现对洪水范围的新预测，包括山脉对天气现象的影响以及其他新功能。为了支持快速、可扩展的可视化，将引入一个“地理分层体积”，它将利用底层地形全局四叉树组织和核外分页结构。将在地质分层体积结构内开发分层视觉模型，以产生几种视觉表示，包括体积渲染、等值面、简单的3D时间相关特征表示和图标注释。一种快速、可扩展的基于聚类的特征分析方法也将形成一个动态的特征层次。该层次结构将用于生成三维时间相关观测数据的体积和等值面可视化的详细程度。视觉分析将被纳入决策支持框架。由于分析重视各种观察的意义和重要性，因此观察数据可以以正确的形式显示以方便使用。重要的现象可以被赋予吸引眼球的视觉形式，更多的细节可以被赋予分析认为重要的对象或用户更密切关注的对象。