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4 Interpretation and Presentation of Results

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.