STRIELAD -- A Scalable Toolkit for Real-time Interactive Exploration of Large Atmospheric Datasets
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912216537628672 |
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| author | Schneegans, Simon Neary, Lori Flatken, Markus Gerndt, Andreas |
| author_facet | Schneegans, Simon Neary, Lori Flatken, Markus Gerndt, Andreas |
| contents | Technological advances in high performance computing and maturing physical models allow scientists to simulate weather and climate evolutions with an increasing accuracy. While this improved accuracy allows us to explore complex dynamical interactions within such physical systems, inconceivable a few years ago, it also results in grand challenges regarding the data visualization and analytics process. We present STRIELAD, a scalable weather analytics toolkit, which allows for interactive exploration and real-time visualization of such large scale datasets. It combines parallel and distributed feature extraction using high-performance computing resources with smart level-of-detail rendering methods to assure interactivity during the complete analysis process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00033 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STRIELAD -- A Scalable Toolkit for Real-time Interactive Exploration of Large Atmospheric Datasets Schneegans, Simon Neary, Lori Flatken, Markus Gerndt, Andreas Human-Computer Interaction Distributed, Parallel, and Cluster Computing Graphics Technological advances in high performance computing and maturing physical models allow scientists to simulate weather and climate evolutions with an increasing accuracy. While this improved accuracy allows us to explore complex dynamical interactions within such physical systems, inconceivable a few years ago, it also results in grand challenges regarding the data visualization and analytics process. We present STRIELAD, a scalable weather analytics toolkit, which allows for interactive exploration and real-time visualization of such large scale datasets. It combines parallel and distributed feature extraction using high-performance computing resources with smart level-of-detail rendering methods to assure interactivity during the complete analysis process. |
| title | STRIELAD -- A Scalable Toolkit for Real-time Interactive Exploration of Large Atmospheric Datasets |
| topic | Human-Computer Interaction Distributed, Parallel, and Cluster Computing Graphics |
| url | https://arxiv.org/abs/2502.00033 |