Environmental data have inherent uncertainty which is often ignored in visualization. For example, meteorological stations measure wind with good accuracy, but winds are often averaged over minutes or hours. As another example, Doppler radars (wind profilers and ocean current radars) take thousands of samples and average the possibly spurious returns. Others, including time series data, have a wealth of uncertainty information that the traditional vector visualization methods such as using wind barbs and arrow glyphs simply ignore. We have developed new vector glyphs to visualize uncertain winds and ocean currents. Our approach is to include uncertainty in direction and magnitude, as well as the mean direction and length, in vector glyph plots. Our glyphs show the variation in uncertainty, and provide fair comparisons of data from instruments, models, and time averages of varying certainty. We use both qualitative and quantitative methods to compare our glyphs to traditional ones. Subjective comparison tests with experts (meteorologists and oceanographers) are provided, as well as objective tests (data ink manximization), where the information density of our new glyphs and traditional glyphs are compared. We have shown that visualizing data together with their uncertainty information enhances the understanding of the continuous range of data quality in environmental vector fields.
Recent efforts in visualization have concentrated on high volume data sets from numerical simulations and medical imaging. There is another large class of data, characterized by their spatial sparsity with noisy and possibly missing data points, that also need to be visualized. Two places where these type of data sets can be found are in oceanographic and atmospheric science studies. In such cases, it is not uncommon to have on the order on one percent of sampled data available within a space volume. Techniques that attempt to deal with the problem of filling in the holes range in complexity from simple linear interpolation to more sophisticated multiquadric and optimal interpolation techniques. These techniques will generally produce results that do not fully agree with each other. To avoid misleading the users, it is important to highlight these differences and make sure the users are aware of the idiosyncrasies of the different methods. This paper compares some of these interpolation techniques on sparse data sets and also discusses how other parameters such as confidence levels and drop-off rates may be incorporated into the visual display.