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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3,855
1,102
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5,144
338
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PDF: 1,102
XML: 187
Total: 5,144
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EndNote: 116
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Cumulative views and downloads
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Total article views: 3,733 (including HTML, PDF, and XML)
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3,118
553
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3,733
191
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HTML: 3,118
PDF: 553
XML: 62
Total: 3,733
Supplement: 191
BibTeX: 62
EndNote: 97
Views and downloads (calculated since 18 Dec 2024)
Cumulative views and downloads
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Total article views: 1,411 (including HTML, PDF, and XML)
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737
549
125
1,411
147
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19
HTML: 737
PDF: 549
XML: 125
Total: 1,411
Supplement: 147
BibTeX: 20
EndNote: 19
Views and downloads (calculated since 06 Aug 2024)
Cumulative views and downloads
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Total article views: 5,144 (including HTML, PDF, and XML)
Thereof 5,043 with geography defined
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Total article views: 3,733 (including HTML, PDF, and XML)
Thereof 3,664 with geography defined
and 69 with unknown origin.
Total article views: 1,411 (including HTML, PDF, and XML)
Thereof 1,379 with geography defined
and 32 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...
