Interactive Correlation Analysis and Visualization of Climate Data
The relationship between our ability to analyze and extract insights from visualization of climate model output and the capability of the available resources to make those visualizations has reached a crisis point. The large volume of data currently produced by climate models is overwhelming the current, decades-old visualization workflow. The traditional methods for visualizing climate output also have not kept pace with changes in the types of grids used, the number of variables involved, and the number of different simulations performed with a climate model or the feature-richness of high-resolution simulations. This project has developed new and faster methods for visualization in order to get the most knowledge out of the new generation of high-resolution climate models. While traditional climate images will continue to be useful, there is need for new approaches to visualization and analysis of climate data if we are to gain all the insights available in ultra-large data sets produced by high-resolution model output and ensemble integrations of climate models such as those produced for the Coupled Model Intercomparison Project. Towards that end, we have developed new visualization techniques for performing correlation analysis. We have also introduced highly scalable, parallel rendering methods for visualizing large-scale 3D data. This project was done jointly with climate scientists and visualization researchers at Argonne National Laboratory and NCAR.
- Research Organization:
- Univ. of California, Davis, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- DOE Contract Number:
- SC0005334
- OSTI ID:
- 1325752
- Report Number(s):
- DOE-UCD-05334
- Resource Relation:
- Related Information: Jinrong Xie, Franz Sauer, Kwan-Liu Ma: Fast uncertainty-driven large-scale volume feature extraction on desktop PCs. In Proceedings of LDAV 2015, pp. 17-24.Jinrong Xie, Hongfeng Yu, and Kwan-Liu Ma. Visualizing Large 3D Geodesic Grid Data with Massively Distributed GPUs. In Proceedings of LDAV 2014, pp. 3-10.Yubo Zhang, Zhao Dong, and Kwan-Liu Ma. Real-time Volume Rendering in Dynamics Lighting Environments using Pre-computed Photo Mapping. IEEE Transactions on Visualization and Computer Graphics 19(8):1317-1330 (2013).Jinrong Xie, Hongfeng Yu, and Kwan-Liu Ma. Interactive Ray Casting of Geodesic Grids. Computer Graphics Forum 32(3):481-490 (EuroVis 2013). Yang Wang, Hongfeng Yu, and Kwan-Liu Ma. Scalable Parallel Feature Extraction and Tracking for Large Time-Varying 3D Volume Data. In Proceedings of EGPGV 2013, May 2013, pp. 55-62. Sedat Ozer, Jishang Wei, Deborah Silver, and Kwan-Liu Ma. Group Dynamics in Scientific Visualization. In Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), October 2012, pp. 97-104. Cheng-Kai Chen, Chaoli Wang, Kwan-Liu Ma, and Andrew Wittenberg. Static Correlation Visualization for Large Time-Varying Volume Data. In Proceedings of the IEEE Symposium on Large Data Analysis and Visualization (LDAV), October 2011, pp. 27-34.
- Country of Publication:
- United States
- Language:
- English
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