Abstract
Uncertainty analysis of a time-varying ensemble vector field is a challenging topic in geoscience. Due to the complex data structure, the uncertainty of a time-varying ensemble vector field is hard to quantify and analyze. Measuring the differences between pathlines is an effective way to compute the uncertainty. However, existing metrics are not accurate enough or are sensitive to outliers; thus, a comprehensive tool for the further analysis of the uncertainty of transport patterns is required. In this paper, we propose a novel framework for quantifying and analyzing the uncertainty of an ensemble vector field. Based on the classical edit distance on real sequence (EDR) method, a robust and accurate metric was proposed to measure the pathline uncertainty. Considering the spatial continuity, we computed the transport variance of the neighborhood of a location, and evaluated the uncertainty correlation between each location and its neighborhood by using the local Moran’s I. Based on the proposed uncertainty measurements, a visual analysis system called UP-Vis (uncertainty pathline visualization) was developed to interactively explore the uncertainty. It provides an overview of the uncertainty and supports detailed exploration of transport patterns at a selected location, and allows for the comparison of transport patterns between a location and its neighborhood. Through pathline clustering, the major trends of the ensemble pathline at a location were extracted. Moreover, a glyph was designed to intuitively display the transport direction and diverging degree of each cluster. For the uncertainty analysis of the neighborhood, a comparison view was designed to compare the transport patterns between a location and its neighborhood in detail. A synthetic data set and weather simulation data set were used in our experiments. The evaluation and case studies demonstrated that the proposed framework can measure the uncertainty effectively and help users to comprehensively explore uncertainty transport patterns.
Highlights
Uncertainty is inevitable in various geoscience-related domains, such as meteorology and computational fluid dynamics
In this paper, inspired by the work of Liu et al [22], we evaluate the effectiveness of adaptive EDR (AEDR) through comparing its ability to reveal uncertainty features with classical edit distance on real sequence (EDR), dynamic time warping (DTW), Edit distance with real penalty (ERP), and longest common subsequences (LCSS)
We used the Double-Gyre (DG) synthetic data set [41] to carry out the evaluation experiments, which is a commonly-used synthetic data set of a 2D vector field
Summary
Uncertainty is inevitable in various geoscience-related domains, such as meteorology and computational fluid dynamics. Taking advantage of the ever-increasing computational power available, it has become common to generate ensemble data which contain a collection of outputs generated from computer simulation models [1]. This makes it possible to intuitively analyze the uncertainty in simulations. Vector field data, such as wind flow or ocean current data, are commonly collected or simulated in geographic space. Understanding the uncertainty in a spatial vector field is very significant for domain experts to be able to draw reliable conclusions and make informed decisions. Using multi-view linkage techniques and interactions, domain experts can analyze uncertain behaviors and comprehensively explore the internal patterns of physical phenomena [5,6]
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