School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Zexia Duan
School of Electrical Engineering, Nantong University, Nantong 226019, China
Minghui Yu
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
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4,078
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PDF: 822
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Cumulative views and downloads
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Total article views: 2,647 (including HTML, PDF, and XML)
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2,357
190
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2,647
97
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135
HTML: 2,357
PDF: 190
XML: 100
Total: 2,647
Supplement: 97
BibTeX: 89
EndNote: 135
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Total article views: 2,446 (including HTML, PDF, and XML)
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1,721
632
93
2,446
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150
HTML: 1,721
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Total: 2,446
BibTeX: 123
EndNote: 150
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Viewed (geographical distribution)
Total article views: 5,093 (including HTML, PDF, and XML)
Thereof 5,055 with geography defined
and 38 with unknown origin.
Total article views: 2,647 (including HTML, PDF, and XML)
Thereof 2,628 with geography defined
and 19 with unknown origin.
Total article views: 2,446 (including HTML, PDF, and XML)
Thereof 2,427 with geography defined
and 19 with unknown origin.
This study evaluates various machine learning and statistical methods for interpolating turbulent heat flux data over the Tibetan Plateau. The Transformer model showed the best performance, leading to the development of the Transformer_CNN model, which combines global and local attention mechanisms. Results show that Transformer_CNN outperforms the other models and was successfully applied to interpolate heat flux data from 2007 to 2016.
This study evaluates various machine learning and statistical methods for interpolating...