Visualizing oceanic and atmospheric flows with streamline splatting

Yinlong Sun; Erich Ess; David Sapirstein; Matthew Huber

doi:10.1117/12.643629

16 January 2006 Visualizing oceanic and atmospheric flows with streamline splatting

Yinlong Sun, Erich Ess, David Sapirstein, Matthew Huber

Proceedings Volume 6060, Visualization and Data Analysis 2006; 606002 (2006) https://doi.org/10.1117/12.643629
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

The investigation of the climate system is one of the most exciting areas of scientific research today. In the climate system, oceanic and atmospheric flows play a critical role. Because these flows are very complex in the span of temporal and spatial scales, effective computer visualization techniques are crucial to the analysis and understanding of the flows. However, the existing techniques and software are not sufficient to the demand of visualizing oceanic and atmospheric flows. In this paper, we use a new technique called streamline splatting to visualize 3D flows. This technique integrates streamline generation with the splatting method of volume rendering. It first generates segments of streamlines and then projects and splats the streamline segments onto the image plane. The projected streamline segments can be represented using a Hermite parametric model. Splatted curves are achieved by applying a Gaussian footprint function to the projected streamline segments and the results are blended together. Thus the user can see through a volumetric flow field and obtain a 3D representation view in one image. The proposed technique has been applied to visualizing oceanic and storm flows. This work has potential to be further developed into visualization software for regular PC workstations to help researchers explore and analyze climate flows.

Citation Download Citation

Yinlong Sun, Erich Ess, David Sapirstein, and Matthew Huber "Visualizing oceanic and atmospheric flows with streamline splatting", Proc. SPIE 6060, Visualization and Data Analysis 2006, 606002 (16 January 2006); https://doi.org/10.1117/12.643629

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