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PEViz: an in situ progressive visual analytics system for ocean ensemble data

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Abstract

Numerical simulation is crucial in scientific research. Visualizing simulation data helps scientists understand data and discover connections between data. However, the development of supercomputers brings larger amounts of simulation data, which poses huge challenges for visualization: (1) Storing the whole data used for visualization is hard under the limitation of I/O and storage, making visualization technology impractical; (2) the post-processing visual analysis mode wastes computation resources and reduces analysis efficiency. In this work, we present an in situ progressive visual analytics system, PEViz, which contains the back-end in situ processing and the interactive front-end interface. The in situ processes using real-time analysis and visualization reduce data storage, leading to a smaller data scale for visualization. The visual analytic interface has four aspect views: the parameters view, the statistics views of the ensemble, the one-moment view of all members, and the sequence view of a single member. While the simulation analytics programs are still running, the progressive analytic system allows scientists to explore data space, such as early analysis and interactively ending abnormal analysis. In addition, the system assists users in processing analytical tasks through real-time visualization and abstracting knowledge of the focused object. We present two case studies to evaluate the effectiveness of the system.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2019YFB1704200) and the National Natural Science Foundation of China (No. 62202446). We would like to thank full professor Zhu Jiang (The Institute of Atmospheric Physics, Chinese Academy of Sciences) and full professor Yan Changxiang(The Institute of Atmospheric Physics, Chinese Academy of Sciences) for helpful discussions.

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Authors and Affiliations

  1. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

    Yihan Zhang, Guan Li, Runpu Yue, Jun Liu & Guihua Shan

  2. University of Chinese Academy of Sciences, Beijing, China

    Yihan Zhang, Runpu Yue, Jun Liu & Guihua Shan

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  1. Yihan Zhang
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  2. Guan Li
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  3. Runpu Yue
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  4. Jun Liu
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  5. Guihua Shan
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Correspondence to Guihua Shan.

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Zhang, Y., Li, G., Yue, R. et al. PEViz: an in situ progressive visual analytics system for ocean ensemble data. J Vis 26, 423–440 (2023). https://doi.org/10.1007/s12650-022-00883-2

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