Visualizing Confidence Intervals for Critical Point Probabilities in 2D Scalar Field Ensembles

TL;DR

This paper introduces a method for visualizing confidence intervals of critical point probabilities in 2D scalar field ensembles, aiding analysis of uncertain data from chaotic systems.

Contribution

It presents a novel approach for computing and visualizing confidence intervals for critical point occurrence probabilities in ensemble data sets.

Findings

Effective visualization of confidence intervals enhances understanding of data variability.

Method outperforms existing critical point prediction techniques on synthetic data.

Applicable to climate research data, demonstrating real-world utility.

Abstract

An important task in visualization is the extraction and highlighting of dominant features in data to support users in their analysis process. Topological methods are a well-known means of identifying such features in deterministic fields. However, many real-world phenomena studied today are the result of a chaotic system that cannot be fully described by a single simulation. Instead, the variability of such systems is usually captured with ensemble simulations that produce a variety of possible outcomes of the simulated process. The topological analysis of such ensemble data sets and uncertain data, in general, is less well studied. In this work, we present an approach for the computation and visual representation of confidence intervals for the occurrence probabilities of critical points in ensemble data sets. We demonstrate the added value of our approach over existing methods for critical point prediction in uncertain data on a synthetic data set and show its applicability to a data set from climate research.