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An Exploratory Framework for Cyclone Identification and Tracking
Indian Inst Sci, India.
Stockholm Univ, Sweden.
Carnegie Mellon Univ, PA 15213 USA.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7285-0483
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2019 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 25, no 3, p. 1460-1473Article in journal (Refereed) Published
Abstract [en]

Analyzing depressions plays an important role in meteorology, especially in the study of cyclones. In particular, the study of the temporal evolution of cyclones requires a robust depression tracking framework. To cope with this demand we propose a pipeline for the exploration of cyclones and their temporal evolution. This entails a generic framework for their identification and tracking. The fact that depressions and cyclones are not well-defined objects and their shape and size characteristics change over time makes this task especially challenging. Our method combines the robustness of topological approaches and the detailed tracking information from optical flow analysis. At first cyclones are identified within each time step based on well-established topological concepts. Then candidate tracks are computed from an optical flow field. These tracks are clustered within a moving time window to distill dominant coherent cyclone movements, which are then forwarded to a final tracking step. In contrast to previous methods our method requires only a few intuitive parameters. An integration into an exploratory framework helps in the study of cyclone movement by identifying smooth, representative tracks. Multiple case studies demonstrate the effectiveness of the method in tracking cyclones, both in the northern and southern hemisphere.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2019. Vol. 25, no 3, p. 1460-1473
Keywords [en]
Cyclone; scalar field; time-varying data; track graph; spatio-temporal clustering; tracking
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Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-154536DOI: 10.1109/TVCG.2018.2810068ISI: 000457824500003PubMedID: 29993808OAI: oai:DiVA.org:liu-154536DiVA, id: diva2:1290538
Note

Funding Agencies|Department of Science and Technology, India [DST/SJF/ETA-02/2015-16]; Joint Advanced Technology Programme, Indian Institute of Science [JATP/RG/PROJ/2015/16]; Swedish e-Science Research Center (SeRC)

Available from: 2019-02-20 Created: 2019-02-20 Last updated: 2019-02-20

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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Language
  • de-DE
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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