Multi-Charts for Comparative 3D Ensemble Visualization

Abstract

Fig. 1. Multi-chart visualization of a temperature ensemble forecast from the ECMWF Ensemble Prediction System (EPS), ECMWF's operational ensemble weather forecast system [30]. The ensemble consists of 51 members of resolution 256 × 128 × 64 each. Each bar in the multi-chart is associated with a distinct 3D subdomain, and encodes the distribution of the ensemble members in this subdomain by means of a histogram. In addition, a few user-selected ensemble members are depicted using polylines. By means of brushing in the multi-chart view (indicated by yellow background color), the user has selected regions where the range over the ensemble members and thus the uncertainty is high. The selected regions are instantly emphasized in the 3D view.Abstract-A comparative visualization of multiple volume data sets is challenging due to the inherent occlusion effects, yet it is important to effectively reveal uncertainties, correlations and reliable trends in 3D ensemble fields. In this paper we present bidirectional linking of multi-charts and volume visualization as a means to analyze visually 3D scalar ensemble fields at the data level. Multi-charts are an extension of conventional bar and line charts: They linearize the 3D data points along a space-filling curve and draw them as multiple charts in the same plot area. The bar charts encode statistical information on ensemble members, such as histograms and probability densities, and line charts are overlayed to allow comparing members against the ensemble. Alternative linearizations based on histogram similarities or ensemble variation allow clustering of spatial locations depending on data distribution. Multi-charts organize the data at multiple scales to quickly provide overviews and enable users to select regions exhibiting interesting behavior interactively. They are further put into a spatial context by allowing the user to brush or query value intervals and specific distributions, and to simultaneously visualize the corresponding spatial points via volume rendering. By providing a picking mechanism in 3D and instantly highlighting the corresponding data points in the chart, the user can go back and forth between the abstract and the 3D view to focus the analysis.

Comparative visualization of vector field ensembles based on longest common subsequence

“…If observation data is available, it can estimate the predictive uncertainty [6], which can help identify potential outlier runs. Clustering is one of most powerful techniques used to extract the uncertainty or other ensemble behavior [20,3]. The histogram clustering method is used in an interactive tool named Multi-Charts [3].…”

“…Clustering is one of most powerful techniques used to extract the uncertainty or other ensemble behavior [20,3]. The histogram clustering method is used in an interactive tool named Multi-Charts [3]. It allows to explore the ensemble data at multiple levels (focus and context).…”

Interactive interaction plot

“…Sampling of parameter spaces has helped solve many high-dimensional domain problems, for example, in aircraft design [25], in fishery [4], and in engineering [3,28,30]. Demira and Westermann [9] and Demir et al [8] deal with 3D simulation results at a later stage of the design process. They focus on less simulation runs, but each run is very complex.…”

“…Ensemble simulation-multiple simulation runs for different sets of input (control) parameters-is often used to better understand the complex phenomena and a complex interplay between many parameters. Interactive visualization is a B Krešimir Matković matkovic@vrvis.at 1 VRVis Research Center, Vienna, Austria 2 Virginia Tech, Blacksburg, VA, USA 3 AVL-AST doo, Zagreb, Croatia proven technology for analysis of ensemble simulation data [8,14,21,22,24] and for studying systems in light of varying a set of controllable inputs and observed or simulated responses (outputs).…”

Multi-Granular Trend Detection for Time-Series Analysis

“…Finally, multi-dimensional ensembles may also be projected down to one-dimensional functions. Demir, Dick, and Westermann [9] combine histograms with local one-dimensional ensembles and brushing techniques to interactively explore a three-dimensional ensemble.…”