Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control,
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,
School of Environmental Science and Engineering,
Nanjing University of Information Science and Technology, Nanjing, China
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control,
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology,
School of Environmental Science and Engineering,
Nanjing University of Information Science and Technology, Nanjing, China
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2,916
1,020
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Cumulative views and downloads
(calculated since 09 Mar 2021)
Total article views: 3,243 (including HTML, PDF, and XML)
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EndNote
2,448
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3,243
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PDF: 694
XML: 101
Total: 3,243
Supplement: 148
BibTeX: 99
EndNote: 132
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Cumulative views and downloads
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Total article views: 807 (including HTML, PDF, and XML)
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468
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807
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Cumulative views and downloads
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Viewed (geographical distribution)
Total article views: 4,050 (including HTML, PDF, and XML)
Thereof 3,809 with geography defined
and 241 with unknown origin.
Total article views: 3,243 (including HTML, PDF, and XML)
Thereof 3,104 with geography defined
and 139 with unknown origin.
Total article views: 807 (including HTML, PDF, and XML)
Thereof 705 with geography defined
and 102 with unknown origin.
When discussing the accuracy of a dust forecast, the shape and position of the plume as well as the intensity are key elements. The position forecast determines which locations will be affected, while the intensity only describes the actual dust level. A dust forecast with position misfit directly results in incorrect timing profiles of dust loads. In this paper, an image-morphing-based data assimilation is designed for realigning a simulated dust plume to correct for the position error.
When discussing the accuracy of a dust forecast, the shape and position of the plume as well as...
