Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Sciences (ESS) are no exception. However, within ESS, specialized visualization requirements and data models, particularly for those data arising from numerical models, often make general purpose visualization packages difficult, if not impossible, to use effectively. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly-interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR’s utility in the study of a numerically- simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.

Figure 1. Solar convection is dominated by the formation of thermal downflow plumes in the surface layer. This image displays the enstrophy in a three-dimentional compressible starting plume driven by cooling at the top and descending (left to right) through a highly stratified (increasing density with depth) medium. ABSTRACTScientific visualization is routinely promoted as an indispensable component of the knowledge discovery process in a variety of scientific and engineering disciplines. However, our experiences with visualization at the National Center for Atmospheric Research (NCAR) differ somewhat from those described by many in the visualization community. Visualization at NCAR is used with great success to convey highly complex results to a wide variety of audiences, but the technology only rarely plays an active role in the day-to-day scientific discovery process. We believe that one reason for this is the mismatch between the size of the primary simulation data sets produced and the capabilities of the software and visual computing facilities generally available for their analysis. Here we describe preliminary results of our efforts to facilitate visual as well as non-visual analysis of terascale scientific data sets with the aim of realizing greater scientific return from such large scale computation efforts.

In this paper we present a hardware-assisted rendering technique coupled with a compression scheme for the interactive visual exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 45 millions voxels each time step) at interactive rates, permitting the visual exploration of large scientific data sets in both the temporal and spatial domain.

We present a scalable volume rendering technique that exploits lossy compression and low-cost commodity hardware to permit highly interactive exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 42 millions voxels each time step) at interactive rates. By clustering multiple PCs together we demonstrate the data-size scalability of our method. The frame rates achieved make possible the interactive exploration of data in the temporal, spatial, and transfer function domains. A comprehensive evaluation of our method based on experimental studies using data sets (up to 134 millions voxels per time step) from turbulence flow simulations is also presented.