Robust detection and visualization of Jet-Stream core lines in atmospheric flow
Jet-streams, their core lines and their role in atmospheric dynamics have been subject to considerable meteorological research since the first half of the twentieth century. Yet, until today no consistent automated feature detection approach has been proposed to identify jet-stream core lines from 3...
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| Main Authors: | , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2018
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| In: |
IEEE transactions on visualization and computer graphics
Year: 2017, Volume: 24, Issue: 1, Pages: 893-902 |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2017.2743989 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1109/TVCG.2017.2743989
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| Author Notes: | Michael Kern, Tim Hewson, Filip Sadlo, Rüdiger Westermann, and Marc Rautenhaus |
| Summary: | Jet-streams, their core lines and their role in atmospheric dynamics have been subject to considerable meteorological research since the first half of the twentieth century. Yet, until today no consistent automated feature detection approach has been proposed to identify jet-stream core lines from 3D wind fields. Such 3D core lines can facilitate meteorological analyses previously not possible. Although jet-stream cores can be manually analyzed by meteorologists in 2D as height ridges in the wind speed field, to the best of our knowledge no automated ridge detection approach has been applied to jet-stream core detection. In this work, we -a team of visualization scientists and meteorologists-propose a method that exploits directional information in the wind field to extract core lines in a robust and numerically less involved manner than traditional 3D ridge detection. For the first time, we apply the extracted 3D core lines to meteorological analysis, considering real-world case studies and demonstrating our method's benefits for weather forecasting and meteorological research. |
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| Item Description: | Date of publication 28 Aug. 2017; date of current version 1 Oct. 2017 Gesehen am 23.10.2019 |
| Physical Description: | Online Resource |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2017.2743989 |