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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Total article views: 3,488 (including HTML, PDF, and XML)
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Total
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2,516
872
100
3,488
133
94
129
HTML: 2,516
PDF: 872
XML: 100
Total: 3,488
Supplement: 133
BibTeX: 94
EndNote: 129
Views and downloads (calculated since 09 Mar 2021)
Cumulative views and downloads
(calculated since 09 Mar 2021)
Total article views: 2,715 (including HTML, PDF, and XML)
HTML
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Total
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EndNote
2,075
552
88
2,715
133
85
121
HTML: 2,075
PDF: 552
XML: 88
Total: 2,715
Supplement: 133
BibTeX: 85
EndNote: 121
Views and downloads (calculated since 10 Sep 2021)
Cumulative views and downloads
(calculated since 10 Sep 2021)
Total article views: 773 (including HTML, PDF, and XML)
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BibTeX
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441
320
12
773
9
8
HTML: 441
PDF: 320
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Total: 773
BibTeX: 9
EndNote: 8
Views and downloads (calculated since 09 Mar 2021)
Cumulative views and downloads
(calculated since 09 Mar 2021)
Viewed (geographical distribution)
Total article views: 3,488 (including HTML, PDF, and XML)
Thereof 3,278 with geography defined
and 210 with unknown origin.
Total article views: 2,715 (including HTML, PDF, and XML)
Thereof 2,605 with geography defined
and 110 with unknown origin.
Total article views: 773 (including HTML, PDF, and XML)
Thereof 673 with geography defined
and 100 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...
