Preprints
https://doi.org/10.5194/essd-2024-329
https://doi.org/10.5194/essd-2024-329
06 Aug 2024
 | 06 Aug 2024
Status: this preprint is currently under review for the journal ESSD.

Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data

Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen

Abstract. Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. The International Best Track Archive for Climate Stewardship (IBTrACS) dataset has been used extensively to estimate TC climatology. However, it has low data coverage, lacking intensity and outer size data for more than half of all recorded storms, and is therefore insufficient as a reference for researchers and decision makers. To fill this data gap, we reconstructed a long-term TC dataset by integrating IBTrACS and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data. This new dataset covers the period 1959–2022, with 3 h temporal resolution. Compared to the IBTrACS dataset, it contains approximately 3–4 times more data points per characteristic. We established machine learning models to estimate the maximum sustained wind speed (Vmax) and radius to maximum wind speed (Rmax) in six basins for which TCs were generated using ERA5-derived 10 m azimuthal median azimuthal wind profiles as input, with Vmax and Rmax data from the IBTrACS dataset used as training data. An empirical wind–pressure relationship and six wind profile models were employed to estimate the minimum central pressure (Pmin) and outer size of the TCs, respectively. Overall, this high-resolution TC reconstruction dataset demonstrated global consistency with observations, exhibiting mean biases of <1 % for Vmax and 3 % for Rmax and Pmin in almost all basins. The new dataset is publicly available from https://doi.org/10.5281/zenodo.12740372 (Xu et al., 2024) and significantly advances our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.

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Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-329', Anonymous Referee #1, 13 Aug 2024
  • RC2: 'Comment on essd-2024-329', Anonymous Referee #2, 14 Sep 2024
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen

Data sets

Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen https://zenodo.org/records/12740372

Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen

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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-hour temporal resolution, using machine learning model. These can be valuable for filling observational data gaps, advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
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