Abstract
Rapid increases in high performance computing are feeding the development of larger and more complex data sets in climate research, which sets the stage for so-called "big data" analysis challenges. However, conventional climate analysis techniques are inadequate in dealing with the complexities of today's data. In this paper, we describe and demonstrate a visual analytics system, called the Exploratory Data analysis ENvironment (EDEN), with specific application to the analysis of complex earth system simulation data sets. EDEN represents the type of interactive visual analysis tools that are necessary to transform data into insight, thereby improving critical comprehension of earth system processes. In addition to providing an overview of EDEN, we describe real-world studies using both point ensembles and global Community Land Model Version 4 (CLM4) simulations.
Original language | English |
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Pages (from-to) | 71-82 |
Number of pages | 12 |
Journal | Computers and Geosciences |
Volume | 61 |
DOIs | |
State | Published - Dec 2013 |
Funding
We wish to express our gratitude to the reviewers and editorial staff of Computers & Geosciences for their valuable feedback. We would also like to thank Jiafu Mao (ORNL) and Zhangshaun Hou (PNNL) for generating the global simulation data sets and parameter samples for the point simulations, respectively. This research is sponsored by the Office of Biological and Environmental Research; U.S. Department of Energy . The work was performed at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the Department of Energy , under Contract no. DE-AC05-00OR22725 . This research used resources of the Center for Computational Sciences at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract no. DE-AC0500OR22725 .
Funders | Funder number |
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U.S. Department of Energy | |
Office of Science | DE-AC05-00OR22725 |
Biological and Environmental Research |
Keywords
- Big data
- Climate
- Data intensive computing
- Data mining
- Multivariate
- Parallel coordinates
- Sensitivity analysis
- Statistical visualization
- Visualization