Uncertainty is inherent in scientific data. Quantification and interpretation of the uncertainty are essential to a holistic understanding of the data. Uncertainty representation is often overlooked in high-dimensional and multivariate data due to a lack of effective methods and a lack of guidance on appropriate existing methods. This project systematically investigates uncertainty visualization in high-dimensional data, within the application domain of meteorology. The interdisciplinary research team spans different areas such as visualization, atmospheric science, and statistics. The research team is investigating (1) quantifying uncertainty by using bootstrap mean confidence intervals, which relax assumptions about the underlying distribution; (2) designing a number of uncertainty visualization methods and implement these methods in an integrated user interface; and (3) employing both qualitative and quantitative user studies to evaluate the uncertainty visualization methods.
The resulting tools and methods are deployed to the Environmental Modeling Center. Although the primary application area of this project is meteorology, the principles learned have the potential to be applied to a variety of scientific fields, including hydrology, geospatial science, medical imaging, and biology. The research work of this project is integrated into Visualization and Meteorology classes at Mississippi State University.