Institute of Urban Meteorology, China Metrological Administration, Beijing 100089, China
Yuchen Ye
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China
Haikun Zhao
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, 210044, China
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3,159
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146
4,103
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103
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PDF: 798
XML: 146
Total: 4,103
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EndNote: 103
Views and downloads (calculated since 06 Aug 2024)
Cumulative views and downloads
(calculated since 06 Aug 2024)
Total article views: 2,838 (including HTML, PDF, and XML)
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2,459
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2,838
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85
HTML: 2,459
PDF: 356
XML: 23
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Supplement: 128
BibTeX: 44
EndNote: 85
Views and downloads (calculated since 18 Dec 2024)
Cumulative views and downloads
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Total article views: 1,265 (including HTML, PDF, and XML)
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700
442
123
1,265
109
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HTML: 700
PDF: 442
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Total: 1,265
Supplement: 109
BibTeX: 18
EndNote: 18
Views and downloads (calculated since 06 Aug 2024)
Cumulative views and downloads
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Viewed (geographical distribution)
Total article views: 4,103 (including HTML, PDF, and XML)
Thereof 3,993 with geography defined
and 110 with unknown origin.
Total article views: 2,838 (including HTML, PDF, and XML)
Thereof 2,758 with geography defined
and 80 with unknown origin.
Total article views: 1,265 (including HTML, PDF, and XML)
Thereof 1,235 with geography defined
and 30 with unknown origin.
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we...