Graphical interfaces and modern visualization methods provide a vehicle to help modelers and decision makers assimilate the massive data sets produced by leading edge numerical models. The human brain possesses a ``narrow bandwidth'' for processing raw numbers, but a surprisingly ``wide bandwidth'' for processing visual data. However, visualization is more than just a method for increasing the rate at which we absorb data. Visualization facilitates the discovery of new science by revealing hidden structures and behaviors in model output. As R. W. Hamming so aptly expresses it, ``The purpose of computing is insight, not numbers.'' It is in the areas of insight and understanding that visualization plays a central role.
Scientific visualization provides an
effective means for the presentation and communication of
results of numerical models. Creative uses of color, texture
and translucency allow subtle aspects of complicated, large
scale models to be displayed in a form that can be easily
understood. For example, two dimensional gridded field data
that might be typically displayed as line drawn contours,
can be visualized in a number of different ways, each
offering special advantages over traditional contouring
methods. The most common method for viewing the 2-D data is
to produce a raster image of the data. In this case, the
spatial dimensions of the model data are represented by the
computer screen and the third dimension is represented by
different colored pixels. Many visualization software
packages are capable of accepting such 
data sets and
producing a color raster map output. One additional tool in
most of these packages is the ability to interactively
manipulate the color palette. This allows the researcher
to make changes in the data representation akin to changing
the contour interval in an interactive and nearly
instantaneous manner. This activity often exposes important
structures and trends in data sets.
