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Visualizing Numerical Uncertainty in Climate Ensembles

abstract: The proper quantification and visualization of uncertainty requires a high level of domain knowledge. Despite this, few studies have collected and compared the roles, experiences and opinions of scientists in different types of uncertainty analysis. I address this gap by conducting two types of studies: 1) a domain characterization study with general questions for experts from various fields based on a recent literature review in ensemble analysis and visualization, and; 2) a long-term interview with domain experts focusing on specific problems and challenges in uncertainty analysis. From the domain characterization, I identified the most common metrics applied for uncertainty quantification and discussed the current visualization applications of these methods. Based on the interviews with domain experts, I characterized the background and intents of the experts when performing uncertainty analysis. This enables me to characterize domain needs that are currently underrepresented or unsupported in the literature. Finally, I developed a new framework for visualizing uncertainty in climate ensembles. / Dissertation/Thesis / Masters Thesis Computer Science 2016

Identiferoai:union.ndltd.org:asu.edu/item:40788
Date January 2016
ContributorsLiang, Xing (Author), Maciejewski, Ross (Advisor), Mascaro, Giuseppe (Committee member), Sarjoughian, Hessam (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format95 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved

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