IEEE VIS 2024 Content: Visualizing Uncertainties in Ensemble Wildfire Forecast Simulations

Visualizing Uncertainties in Ensemble Wildfire Forecast Simulations

Jixian Li - University of Utah, Salt Lake City, United States

Timbwaoga A. J. Ouermi - Scientific Computing and Imaging Institute, Salk Lake City, United States

Chris R. Johnson - University of Utah, Salt Lake City, United States

Room: Bayshore VI

2024-10-14T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-14T12:30:00Z
Exemplar figure, described by caption below
We introduce our interactive interface for visualizing uncertainties of ensemble wildfire simulations. Our interface uses the contour boxplot to summarize the trend and variations of fire spreading patterns. Our interface also supports transfer-function-based color and opacity mapping for visualizing scalar functions from wildfire simulations, glyph- and streamline-based wind visualization, temporal events summary, contour band depths, spatial query for the fire arrival time (red sphere in the terrain shows the query point)
Abstract

Wildfire poses substantial risks to our health, environment, and economy. Studying wildfire is challenging due to its complex inter- action with the atmosphere dynamics and the terrain. Researchers have employed ensemble simulations to study the relationship be- tween variables and mitigate uncertainties in unpredictable initial conditions. However, many domain scientists are unaware of the advanced visualization tools available for conveying uncertainty. To bring some uncertainty visualization techniques, we build an interactive visualization system that utilizes a band-depth-based method that provides a statistical summary and visualization for fire front contours from the ensemble. We augment the visualiza- tion system with capabilities to study wildfires as a dynamic system. In this paper, We demonstrate how our system can support domain scientists in studying fire spread patterns, identifying outlier simu- lations, and navigating to interesting instances based on a summary of events.