Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots

被引:73
作者
Wang, Junpeng [1 ]
Liu, Xiaotong [1 ]
Shen, Han-Wei [1 ]
Lin, Guang [2 ]
机构
[1] Ohio State Univ, Columbus, OH 43210 USA
[2] Purdue Univ, W Lafayette, IN 47907 USA
基金
美国国家科学基金会;
关键词
Parallel coordinates plots; parameter analysis; multi-resolution climate ensembles; FRITSCH CONVECTIVE PARAMETERIZATION; VISUALIZATION; SPACE; SCHEME; MODEL; TOOL;
D O I
10.1109/TVCG.2016.2598830
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested Parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative data visualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.
引用
收藏
页码:81 / 90
页数:10
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