面向高分辨率海洋数据的可扩展可视化系统，支持交互式分析与快速预览。
A Scalable System for Visual Analysis of Ocean Data
- 基于ParaView构建，集成涡旋识别与盐度移动追踪模块
- 通过Cinema数据库缓解读写瓶颈，实现高效数据加载
- 适用于海洋学家研究复杂动态过程，尤其适合大规模数据集
海洋学家依赖可视化分析来解读模型模拟、识别事件和现象，并追踪动态海洋过程。由于海洋数据的动态性及多变量关系，其分辨率与复杂性持续提升，亟需可扩展且灵活的可视化工具以支持交互探索。我们提出pyParaOcean，一个专为海洋数据分析设计的可扩展、交互式可视化系统。该系统提供涡旋识别、盐度运动追踪等常用任务的专用模块，无缝集成于ParaView作为过滤器，兼具用户友好性与并行计算能力，同时利用ParaView丰富的通用可视化功能。通过创建存储在Cinema数据库中的辅助数据集，有效缓解了I/O与网络带宽瓶颈，支持快速生成概览可视化。我们以孟加拉湾（BoB）为例展示系统实用性，并通过扩展性研究评估系统效率。
原文摘要 · Abstract (English)
Oceanographers rely on visual analysis to interpret model simulations, identify events and phenomena, and track dynamic ocean processes. The ever increasing resolution and complexity of ocean data due to its dynamic nature and multivariate relationships demands a scalable and adaptable visualization tool for interactive exploration. We introduce pyParaOcean, a scalable and interactive visualization system designed specifically for ocean data analysis. pyParaOcean offers specialized modules for common oceanographic analysis tasks, including eddy identification and salinity movement tracking. These modules seamlessly integrate with ParaView as filters, ensuring a user-friendly and easy-to-use system while leveraging the parallelization capabilities of ParaView and a plethora of inbuilt general-purpose visualization functionalities. The creation of an auxiliary dataset stored as a Cinema database helps address I/O and network bandwidth bottlenecks while supporting the generation of quick overview visualizations. We present a case study on the Bay of Bengal (BoB) to demonstrate the utility of the system and scaling studies to evaluate the efficiency of the system.
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