Interactive Correlation Analysis and Visualization of Climate Data

doi:10.2172/1325752

GSTDTAP > 地球科学

DOI	10.2172/1325752
报告编号	DOE-UCD--05334
来源ID	OSTI ID: 1325752
	Interactive Correlation Analysis and Visualization of Climate Data
	Ma, Kwan-Liu (ORCID:0000000180860366)
	2016-09-21
出版年	2016
页数	8
语种	英语
国家	美国
领域	地球科学
英文摘要	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.
英文关键词	Data Visualization and Analysis
URL	查看原文
来源平台	US Department of Energy (DOE)
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/7278
专题	地球科学
推荐引用方式 GB/T 7714	Ma, Kwan-Liu . Interactive Correlation Analysis and Visualization of Climate Data,2016.