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

A global long-term tropical cyclone (TC) size and intensity reconstruction dataset is generated, covering a time period from 1959 to 2022, with a 3-hour temporal resolution.  The machine learning model was established by taking ERA5-derived 10 m azimuthal mean azimuthal wind profiles in six basins for which TCs were generated as input, while the maximum sustained wind speed and radius of maximum wind from the International Best Track Archive for Climate Stewardship (IBTrACS) was used as the learning target. An empirical wind–pressure relationship and six wind profile models were employed to estimate the minimum central pressure and outer sizes (radial distances from the cyclone center to locations where sustained wind speeds of 34, 50 and 64 knots are observed on surface) of the TCs, respectively. Compared to the IBTrACS dataset, the reconsturction dataset contains approximately 3–4 times more data points per characteristic.

Over all, this dataset is in terms of both coverage and good accuracy.