Modality-Driven Classification and Visualization of Ensemble Variance

Abstract

Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. Finally, for the special application of evaluating the stability of bimodal regions, we develop local and regional metrics.

The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets

“…This is achieved by building a model; this model learns exemplars using the data characteristics from the training set. Then the model can be applied to unknown examples to predict their classes [AAB∗10, DIANS10, BGOJ16] (Task Requirement R7 and R8). For example, authors in [PDW∗14a] presented a classification of the specific domain intents of climate scientists, and underlying data facets, and then bridged the intents and facets with the visualizations tasks and designs through a classification scheme, and finally presented a tool called ‘SimilarityExplorer’ that implements this classification scheme.…”

Visual Analysis and Detection of Contrails in Aircraft Engine Simulations

“…Often statistical summaries such as mean, variance [62,93], modeling probability distributions [7,76], and clustering methods [25,26] are used to reduce the complexity of the ensemble data. Summary-based visualization techniques such as summary statistics [66], probabilistic features [70,72], color maps, contours, animation [17,71], contour boxplots [93], curve box plots [62], spaghetti plots [79], and glyph-based visualization [37,71] are used to display the overview and find relationships between ensemble members.…”

Visual Analysis and Detection of Contrails in Aircraft Engine Simulations

“…Often statistical summaries such as mean, variance [ 62 , 93 ], modeling probability distributions [ 7 , 76 ], and clustering methods [ 25 , 26 ] are used to reduce the complexity of the ensemble data. Summary-based visualization techniques such as summary statistics [ 66 ], probabilistic features [ 70 , 72 ], color maps, contours, animation [ 17 , 71 ], contour boxplots [ 93 ], curve box plots [ 62 ], spaghetti plots [ 79 ], and glyph-based visualization [ 37 , 71 ] are used to display the overview and find relationships between ensemble members.…”