Articles | Volume 16, issue 12
https://doi.org/10.5194/essd-16-5753-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5753-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
Zhiqi Xu
Institute of Urban Meteorology, China Metrological Administration, Beijing 100089, China
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Guwei Zhang
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
Haishan Chen
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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Cited
13 citations as recorded by crossref.
- 50-year wind speed maps for tropical-cyclone-affected regions using best track data K. Chapman-Smith et al. https://doi.org/10.5194/wes-11-1889-2026
- Rising heavy precipitation amid decreasing typhoon contribution in Southeast Asia J. Li et al. https://doi.org/10.1088/1748-9326/ae2e1a
- Prediction of tropical cyclone categories in the North-Western Pacific using a long short-term memory network A. Krishnan et al. https://doi.org/10.3389/feart.2026.1744880
- Coastal compound risk futures and sustainable climate policy payoffs in Caribbean small island developing states X. Jiang et al. https://doi.org/10.1080/17538947.2026.2647501
- Tropical Cyclone Detection and Tracking Using Spatial–Temporal Characteristics of Regional Wind Speed From CYGNSS: Proof of Concept and Assessment X. Ma et al. https://doi.org/10.1109/TGRS.2026.3664983
- A rapid Natech risk assessment framework for industrial enterprises in China: multi-hazard probability assessment and risk level calculation K. Jiang et al. https://doi.org/10.1016/j.jlp.2026.105998
- Extreme precipitation over the western North Pacific influenced by interactions of tropical cyclones with fronts and atmospheric rivers T. Zhao et al. https://doi.org/10.1016/j.atmosres.2026.109003
- Microphysical evolution of the eyewall during intensification and weakening of Typhoon Muifa (2022) D. Wang et al. https://doi.org/10.1016/j.atmosres.2026.109197
- Combining hazard, exposure and vulnerability data to predict historical United States hurricane losses A. Vessey et al. https://doi.org/10.5194/nhess-26-2133-2026
- A Global ERA5-based Tropical Cyclone Wind Field Dataset Enhanced by Integrated Parametric Correction Methods G. Liu et al. https://doi.org/10.1038/s41597-025-05789-w
- Risks of mercury release from disturbance of blue carbon ecosystems F. Zhang et al. https://doi.org/10.1016/j.earscirev.2026.105622
- Extreme wind speeds in tropical cyclones using parametric models P. Renaud et al. https://doi.org/10.5194/wes-11-2521-2026
- Marine Unmanned Surface Vehicle Measurements of Solar Irradiance Under Typhoon Conditions K. Xu et al. https://doi.org/10.3390/drones9060395
13 citations as recorded by crossref.
- 50-year wind speed maps for tropical-cyclone-affected regions using best track data K. Chapman-Smith et al. https://doi.org/10.5194/wes-11-1889-2026
- Rising heavy precipitation amid decreasing typhoon contribution in Southeast Asia J. Li et al. https://doi.org/10.1088/1748-9326/ae2e1a
- Prediction of tropical cyclone categories in the North-Western Pacific using a long short-term memory network A. Krishnan et al. https://doi.org/10.3389/feart.2026.1744880
- Coastal compound risk futures and sustainable climate policy payoffs in Caribbean small island developing states X. Jiang et al. https://doi.org/10.1080/17538947.2026.2647501
- Tropical Cyclone Detection and Tracking Using Spatial–Temporal Characteristics of Regional Wind Speed From CYGNSS: Proof of Concept and Assessment X. Ma et al. https://doi.org/10.1109/TGRS.2026.3664983
- A rapid Natech risk assessment framework for industrial enterprises in China: multi-hazard probability assessment and risk level calculation K. Jiang et al. https://doi.org/10.1016/j.jlp.2026.105998
- Extreme precipitation over the western North Pacific influenced by interactions of tropical cyclones with fronts and atmospheric rivers T. Zhao et al. https://doi.org/10.1016/j.atmosres.2026.109003
- Microphysical evolution of the eyewall during intensification and weakening of Typhoon Muifa (2022) D. Wang et al. https://doi.org/10.1016/j.atmosres.2026.109197
- Combining hazard, exposure and vulnerability data to predict historical United States hurricane losses A. Vessey et al. https://doi.org/10.5194/nhess-26-2133-2026
- A Global ERA5-based Tropical Cyclone Wind Field Dataset Enhanced by Integrated Parametric Correction Methods G. Liu et al. https://doi.org/10.1038/s41597-025-05789-w
- Risks of mercury release from disturbance of blue carbon ecosystems F. Zhang et al. https://doi.org/10.1016/j.earscirev.2026.105622
- Extreme wind speeds in tropical cyclones using parametric models P. Renaud et al. https://doi.org/10.5194/wes-11-2521-2026
- Marine Unmanned Surface Vehicle Measurements of Solar Irradiance Under Typhoon Conditions K. Xu et al. https://doi.org/10.3390/drones9060395
Saved (final revised paper)
Latest update: 10 Aug 2026
Short summary
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...
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