Data Exploration and Analysis of Ultra-Large Climate Data

Funding Program: 

The Visual Data Exploration and Analysis of Ultra-Large Climate Data project aims to develop, deploy, and apply parallel-capable visual data exploration and analysis software infrastructure to meet specific needs central to the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) climate science mission. The team includes climate, computational, and computer scientists from Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, Oak Ridge National Laboratory, University of California at Berkeley, and Los Alamos National Laboratory. The approach employs a set of science drivers, which reflect challenges in understanding regional-scale climate-change phenomena, as the basis for a coordinated effort that includes visualization of ultra-large data, statistical analysis, and feature detection/tracking techniques. Specific science case studies include cyclone tracking, atmospheric rivers, extreme event analysis, ecological "lifezone" multivariate tracking, and eddy detection. The aim is to deliver new capabilities needed by the climate science community to tackle problems of the scale required by Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) objectives. A major focus is on the comprehensive collection of near-term simulations that Oak Ridge National Laboratory, will conduct using the DOE-National Science Foundation (NSF) Community Climate System Model (CCSM) in support of DOE's contributions to AR5. The software will be delivered to the climate community via CDAT, a well-established software framework for climate data access and analysis. This approach ensures that the proposed technology advances meet specific DOE mission-critical climate science needs, and that the resulting technology will reach a large audience in the climate science community via deployment in a well-established and widely used software framework.

Project Term: 
Project Type: 
Laboratory Funded Research


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Research Highlights:

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