Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System
abstract: Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a...
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Online Access: | https://doi.org/10.3390/cli4030043 http://hdl.handle.net/2286/R.I.43206 |
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ftarizonastateun:item:43206 2023-05-15T14:48:11+02:00 Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System Wang, Feng (ASU author) Li, Wenwen (ASU author) Wang, Sizhe (ASU author) College of Liberal Arts and Sciences School of Geographical Sciences and Urban Planning Computational Spatial Science 2016-09-05 15 pages https://doi.org/10.3390/cli4030043 http://hdl.handle.net/2286/R.I.43206 eng eng CLIMATE doi:10.3390/cli4030043 ISSN: 2225-1154 Wang, F., Li, W., & Wang, S. (2016). Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System. Climate, 4(3), 43. doi:10.3390/cli4030043 http://hdl.handle.net/2286/R.I.43206 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by/4.0 CC-BY Text 2016 ftarizonastateun https://doi.org/10.3390/cli4030043 2018-06-30T22:52:59Z abstract: Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a new method which utilizes pressure level data and velocity field is proposed to improve the identification accuracy. In addition, the dynamic, simulative cyclone visualized with a 4D (four-dimensional) wind field further validated the identification result. A knowledge-driven system is eventually constructed for visualizing and analyzing an atmospheric phenomenon (cyclone) in the North Pole. The cyclone is simulated with WebGL on in a web environment using particle tracing. To achieve interactive frame rates, the graphics processing unit (GPU) is used to accelerate the process of particle advection. It is concluded with the experimental results that: (1) the cyclone identification accuracy of the proposed method is 95.6% when compared with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data; (2) the integrated knowledge-driven visualization system allows for streaming and rendering of millions of particles with an interactive frame rate to support knowledge discovery in the complex climate system of the Arctic region. Text Arctic North Pole Arizona State University: ASU Digital Repository Arctic North Pole Climate 4 3 43 |
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Open Polar |
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Arizona State University: ASU Digital Repository |
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ftarizonastateun |
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English |
description |
abstract: Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a new method which utilizes pressure level data and velocity field is proposed to improve the identification accuracy. In addition, the dynamic, simulative cyclone visualized with a 4D (four-dimensional) wind field further validated the identification result. A knowledge-driven system is eventually constructed for visualizing and analyzing an atmospheric phenomenon (cyclone) in the North Pole. The cyclone is simulated with WebGL on in a web environment using particle tracing. To achieve interactive frame rates, the graphics processing unit (GPU) is used to accelerate the process of particle advection. It is concluded with the experimental results that: (1) the cyclone identification accuracy of the proposed method is 95.6% when compared with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data; (2) the integrated knowledge-driven visualization system allows for streaming and rendering of millions of particles with an interactive frame rate to support knowledge discovery in the complex climate system of the Arctic region. |
author2 |
Wang, Feng (ASU author) Li, Wenwen (ASU author) Wang, Sizhe (ASU author) College of Liberal Arts and Sciences School of Geographical Sciences and Urban Planning Computational Spatial Science |
format |
Text |
title |
Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System |
spellingShingle |
Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System |
title_short |
Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System |
title_full |
Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System |
title_fullStr |
Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System |
title_full_unstemmed |
Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System |
title_sort |
polar cyclone identification from 4d climate data in a knowledge-driven visualization system |
publishDate |
2016 |
url |
https://doi.org/10.3390/cli4030043 http://hdl.handle.net/2286/R.I.43206 |
geographic |
Arctic North Pole |
geographic_facet |
Arctic North Pole |
genre |
Arctic North Pole |
genre_facet |
Arctic North Pole |
op_relation |
CLIMATE doi:10.3390/cli4030043 ISSN: 2225-1154 Wang, F., Li, W., & Wang, S. (2016). Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System. Climate, 4(3), 43. doi:10.3390/cli4030043 http://hdl.handle.net/2286/R.I.43206 |
op_rights |
http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by/4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/cli4030043 |
container_title |
Climate |
container_volume |
4 |
container_issue |
3 |
container_start_page |
43 |
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1766319291113668608 |