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

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

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.