interplay between biology, physics, and chemistry has driven the need for an interdisciplinary approach to data analysis.

Numerical models can now provide detailed three-dimensional views of the ocean. Such volumetric data are nearly impossible to analyze using traditional two-dimensional graphic techniques (see, e.g., Pool, 1992). The addition of the temporal dimension also requires animation tools to allow researchers to study model dynamics and evolution. Visualization tools play an important role in assessing model performance as well. For example, most model output has traditionally been discarded in an attempt to limit data volumes to manageable levels. However, specific events in model simulations often appear in just a few time steps, so that the ability to retain model output at every time step is useful for model diagnostics. The resulting large quantities of model output place a greater demand for sophisticated visualization techniques to search through the large volumes of data in an efficient manner that enables easy identification of the events.

CHALLENGES FOR VISUALIZATION

Visualization will continue to be important for oceanographic research as the ability to measure and model the ocean improves. Existing visualization tools, however, are inadequate for these tasks. Many deficiencies revolve around implementation problems and have been described in numerous NASA and other federal government reports (Botts, 1992; McCormick et al., 1987). For example, existing visualization packages are generally expensive and difficult to learn. Packages are usually not extensible, so that custom features cannot be added easily. Some tools cannot handle three-dimensional data sets or animations. One of the more difficult challenges is the ability to visualize evolving volumetric data, such as that produced by an ocean circulation model. It is very difficult to “see” into the interior of such volumes using present technology. Most commercially available packages that are designed for such volumetric data are capable of handling static images, such as automobiles. For many packages, visualizing three-dimensional systems that evolve over time is a difficult task. Such implementation deficiencies are slowly being addressed by the software vendors and developers.

The most troublesome aspect of existing visualization tools is that most of them break the link between the underlying data and the image on the screen. Although a researcher may be able to produce a sophisticated animation of the evolution of an ocean eddy, it is generally not easy to go from the animation on the computer screen back to the numbers that the various colors represent. As visualization is a tool to allow the detection of previously unknown relationships, it is still necessary to obtain quantitative information about the nature of the relationships. For example, if one notes a possible relationship between phytoplankton concentration and the strength of a density front in an eddy, it is desirable to examine the quantitative aspects of this relationship. Thus there must be techniques for excising subsets of the actual data for use in other analysis packages, such as statistical and plotting tools. Present visualization packages do not have probes or cursors that allow the user to examine the quantitative values of a three-dimensional image at specific locations,



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