Dr. Mirzargar presented a seminar on “Uncertainty Characterization, Visualization, and Validation of Ensemble Forecasts Using Data Depth”.
When computational models or predictive simulations are used, researchers, analysts and decision makers are not only interested in understanding the data but also interested in understanding the uncertainty present in the data as well. In such situations, using ensembles is a common approach to account for the uncertainty, explore the possible outcomes of a model, and validate the model outcomes. Visualization as an integral component of data-analysis task can significantly facilitate the communication of the characteristics of an ensemble including uncertainty information.
In this talk, I will introduce novel ensemble analysis and visualization paradigms based on the concept of data depth and the generalization of conventional univariate boxplots. Generalizations of boxplot provide an intuitive yet rigorous approach to studying variability and descriptive features of an ensemble. The nonparametric nature of this type of analysis makes it an advantageous approach to study uncertainty in various applications ranging from image analysis to fluid simulation to weather and climate modeling.