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Title: Web-based Visual Analytics for Extreme Scale Climate Science

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via new visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.
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  1. ORNL
  2. Lawrence Livermore National Laboratory (LLNL)
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OSTI Identifier:
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Resource Relation:
Conference: IEEE Big Data 2014, Washington DC, DC, USA, 20141027, 20141030
Research Org:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org:
SC USDOE - Office of Science (SC)
Country of Publication:
United States
visual analytics; climate science; Community Land Model; earth system model; web visualization; data mining