Contrasting climate ensembles: A model-based visualization approach for analyzing extreme events

Robert Sisneros, Jian Huang, George Ostrouchov, Sean Ahern, B. David Semeraro

Research output: Contribution to journalConference articlepeer-review

Abstract

The use of increasingly sophisticated means to simulate and observe natural phenomena has led to the production of larger and more complex data. As the size and complexity of this data increases, the task of data analysis becomes more challenging. Determining complex relationships among variables requires new algorithm development. Addressing the challenge of handling large data necessitates that algorithm implementations target high performance computing platforms. In this work we present a technique that allows a user to study the interactions among multiple variables in the same spatial extents as the underlying data. The technique is implemented in an existing parallel analysis and visualization framework in order that it be applicable to the largest datasets. The foundation of our approach is to classify data points via inclusion in, or distance to, multivariate representations of relationships among a subset of the variables of a dataset. We abstract the space in which inclusion is calculated and through various space transformations we alleviate the necessity to consider variables' scales and distributions when making comparisons. We apply this approach to the problem of highlighting variations in climate model ensembles.

Original languageEnglish (US)
Pages (from-to)2347-2356
Number of pages10
JournalProcedia Computer Science
Volume18
DOIs
StatePublished - 2013
Event13th Annual International Conference on Computational Science, ICCS 2013 - Barcelona, Spain
Duration: Jun 5 2013Jun 7 2013

Keywords

  • Climate ensembles
  • Multivariate classification
  • Visualization

ASJC Scopus subject areas

  • General Computer Science

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