Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

Web-based Visual Analytics for Extreme Scale Climate Science

TitleWeb-based Visual Analytics for Extreme Scale Climate Science
Publication TypeConference Abstract or Presentation
Year of Publication2014
AuthorsSteed, C A., Evans K J., Harney J F., Jewell B C., Shipman G, Smith B E., Thornton P E., and Williams D N.
Conference Name2014 IEEE International Conference on Big Data
Conference LocationWashington, DC
Abstract / Summary

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, inefficient 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.

URLhttp://dx.doi.org/10.1109/BigData.2014.7004255
DOI10.1109/BigData.2014.7004255
Conference Name: 2014 IEEE International Conference on Big Data
Year of Publication: 2014
Publication Date: 10/2014

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, inefficient 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.

Conference Location: Washington, DC
DOI: 10.1109/BigData.2014.7004255
Citation:
Steed, CA, KJ Evans, JF Harney, BC Jewell, G Shipman, BE Smith, PE Thornton, and DN Williams.  2014.  "Web-based Visual Analytics for Extreme Scale Climate Science."  In 2014 IEEE International Conference on Big Data.  Presented at Washington, DC.  https://doi.org/10.1109/BigData.2014.7004255.