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Seeing beyond 3-D
Posted By Jacob Berkowitz On December 28, 2011 @ 10:40 am In Brookhaven,Featured | No Comments
To give audiences a better feel for life on a fictional planet, movie director James Cameron made the film Avatar in 3-D and moviegoers donned 3-D glasses. For scientists trying to get a better grasp on what drives climate on Earth, 3-D isn’t good enough. Instead, they’re turning to new techniques to help them see the details and interconnections that ultimately shape the big picture.
“I’m interested in high-dimensional visualization, which is an extension from 3-D visualization into n-dimensional visualization,” says Klaus Mueller, director of the visual analytics and imaging lab in the Center for Visual Computing at Stony Brook University. “Three dimensions is a somewhat solved problem in computer visualizations; n-dimensions is the new frontier. There are all kinds of problems in terms of the user interface and how to make people understand what it means.”
A traditional graph with an x-and-y axis is a two-dimensional visualization, showing the relationship between two variables, or dimensions. An n-dimensional visualization involves showing the relationships between four or more variables.
Mueller is collaborating with the Brookhaven National Lab-led FASTER  (FAst-physics System TEstbed and Research) project to develop novel multi-dimensional visualization techniques and tools to help the FASTER researchers see how fine-level, fast physics processes, such as local aerosols and raindrops, shape global climate patterns.
For Mueller, the bigger question that connects all his research is how to effectively convey multi-dimensional information. He can’t give researchers the equivalent of n-dimensional glasses, so instead turns to how we see and interpret information.
We’re familiar with seeing four dimensions – 3-D plus time – and how to visually represent this – motion blur, for example, to show movement. Five dimensions and beyond, however, is a poorly explored territory in visualization yet one that’s crucial for dealing with complex simulations, such as climate. For example, in the FASTER models there are a dozen variables, or dimensions, that are linked to changes in atmospheric pressure.
One thing that’s clear: There’s a learning process in moving to n-dimensional viewing, Mueller says. With two-dimensional imaging sensors – our retinas – we’re able to see in 3-D. In fact, we teach ourselves to develop this fine-tuned spatial sense. When babies reach out to touch objects around them, they’re calibrating their depth and 3-D geometry perception.
“People are familiar with bar charts and pie charts and scatter plots for the display of two- and three-dimensional information,” says Mueller, who’s also an adjunct scientist with BNL’s Computational Science Center. “Anything else is a challenge for many people. This is the challenge for n-dimensional information visualization – to bring this to the masses.”
FASTER and better
The FASTER project is an ideal test bed for these new high-dimensional visualization tools, Mueller says. FASTER is itself an effort to explore how the computational modeling of rapid, small-scale fast physics processes, such as precipitation and cloud dynamics, fits into and shapes global climate models. It’s believed that errors in modeling these fast physics processes are responsible for major uncertainties in global climate model predictions.
“Virtually all of the fast-physics processes interact,” says Yangang Liu, the Brookhaven atmospheric scientist leading FASTER, “so once we get a handle on the individual processes, we want to see how they interact and how to evaluate these interactions. Data integration and visualization are an essential part of this analysis.”
Working with FASTER scientists and Stony Brook doctoral student Zhiyuan Zhang, Mueller has created a unique visualization system that links sophisticated multidimensional information displays with geographical context.
Recently presented at the Supercomputing 2011 conference in Seattle, “Visualization Support for Global Climate Modeling” is more than a way of presenting data. It’s also an interactive, Web-based visual research tool to help the FASTER researchers analyze their data and make links that might otherwise go unseen.
The tool uses a technique called illustrative parallel coordinates (IPC) to convey multidimensional data. Rather than a traditional graph based around x and y axes, an IPC visualization is a rectangular display that can incorporate a large multidimensional dataset in a way that’s aesthetically pleasing and easy to interpret – in a relatively small space.
IPC visualization is particularly effective at revealing central trends. It also includes transparent overlays to communicate hidden overlaps and can be changed to a network representation mode to show associations among various dimensions.
These multidimensional fast-physics climate factors are then linked to their geospatial context using Google Earth and its highly adaptable Application Programmers Interface.
What turns this n-dimensional, geographically referenced visualization into a powerful tool for insight is that researchers can rapidly add and adjust data through a simple-to-use interactive interface.
“You can interrogate the space,” Mueller says. “It’s the expert user who drives the analysis of the data. The user can choose either the locale and link to the high-dimensional data, or start from the data and link to geographic areas that it represents.”
The visualizations build on Mueller’s broad-based computational visualization research that extends from medical imaging to exploring the fundamentals of color perception and ultimately to how computers can interactively aid our visualization choices. For example, Mueller’s Magic Marker program is a step beyond Photoshop, not only providing color options but also suggesting the most effective color combinations for communicating scientific data.
For now, Mueller is adapting the Windows-based FASTER visualization package so researchers can access it online. It’s another link on the path toward giving even a bigger scientific audience the chance to see in N-D.
“Until now, model evaluations have largely relied on the so-called fried-egg approach and in static mode,” says Liu, referring to the splattered-egg appearance of traditional scatter plot graphs. “Despite their usefulness, such traditional approaches aren’t adequate when facing scientific problems with multiple variables and multiple space-time scales. I think the new visualization tools we’re developing with Dr. Mueller’s group will be gradually adapted by the climate science community in general.”
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 FASTER: http://www.deixismagazine.org/2010/11/in-climate-modeling-speed-matters/
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