Comparative Visual Analysis of Transport Variability in Flow Ensembles
We propose a novel approach that enables a comparative visual exploration of the transport variability in ensembles of 2D flow fields. To reveal when and where divergences in transport occur, we first present a new approach to analyze the time-varying pairwise dissimilarities of ensemble trajectories, by using Gaussian Mixture Models (GMMs) to identify the distribution modes and the Mahalanobis distance to refine the dissimilarity measures. This enables drawing enhanced spaghetti plots, by using the color of the contour of each trajectory to encode the temporal evolution of the member, and the opacity for its representativeness relative to the ensemble behavior. To also allow a global view of the transport variability across selected sub-domains, we introduce a new graphical abstraction based on the visualization of miniaturized versions of the enhanced spaghetti plots in a small-multiples layout. To achieve this, we propose a new kind of downscaling that preserves the relevant trends in the transport behavior. We have designed a user interface comprising multiple linked views to visualize simultaneously global and local transport variations, as well as how similar the transport behavior of the ensemble members is.