Abstract

The interactivity provided by rendering at the animation rate is the key to visualizing multivarite time varying volumetric data. We are developing a system for visualizing earth science data using a graphics supercomputer. We propose simple algorithms for real-time texture mapping and volume imaging using a graphics supercomputer. KEYIdORDS. Interactive, texture mapping, volume image, earth science. I N T R O D U C T I O N At the Space Science and Engineering Center we are concerned with the problem of helping earth scientists to visualize their huge data sets. A large weather model output data set may contain one billion points in a five-dimensional array, composed of a 100 by 100 horizontal grid, by 30 vertical levels, by 100 time steps, by 30 different physical variables. Remote sensing instruments such as satellites, radars and lidars produce similarly large data sets. During the last six years we have been developing software tools for managing and visualizing such data sets, as part of the Space Science and Engineering Center's Mancomputer Interactive Data Access System (MclDAS). These tools run on an IBM 4381 and produce animation sequences of multivariate three-dimensional images which are viewed on our large multiframe workstations. However, each image takes 10 to 30 seconds of CPU time to analyze and render, and another 30 seconds to load into the workstation frame store, so turnaround time for producing an animation sequence can be several hours. Figure 1 is a typical image produced by this system. We have applied this system to generate animations from at least twenty different model simulation and remote sensed data sources. Although our earth scientist collaborators are usually pleased with the results, they all express a desire to change the animations with quicker response. This is by far their (and our) primary request. Therefore we have begun developing a highly interactive workstation based on the Stellar GS-1000 graphics supercomputer. This system can produce three-dimensional images from model output data sets in real-time, giving the scientist control over the image generation with immediate feedback. This paper describes earth science data sets, the work we have done so far with the Stellar GS1000, and some thoughts on further development.

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