Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data
<p>Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. The International Best Track Archive for Climate Stewardship (IBTrACS) dataset provides widely used data to estimate TC climatology. However, it has low data coverage, lacking intensity and outer-size dat...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2024-12-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/5753/2024/essd-16-5753-2024.pdf |
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| Summary: | <p>Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. The International Best Track Archive for Climate Stewardship (IBTrACS) dataset provides widely used data 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 reconstruct a long-term TC dataset by integrating IBTrACS and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) data. This reconstructed 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 establish machine learning models to estimate the maximum sustained wind speed (<span class="inline-formula"><i>V</i><sub>max</sub></span>) and radius of maximum wind (<span class="inline-formula"><i>R</i><sub>max</sub></span>) in six basins for which TCs are generated, using ERA5-derived 10 m azimuthal mean azimuthal wind profiles as input, with <span class="inline-formula"><i>V</i><sub>max</sub></span> and <span class="inline-formula"><i>R</i><sub>max</sub></span> data from the IBTrACS dataset used as learning target data. Furthermore, we employ an empirical wind–pressure relationship and six wind profile models to estimate the minimum central pressure (<span class="inline-formula"><i>P</i><sub>min</sub></span>) and outer size of the TCs, respectively. Overall, this high-resolution TC reconstruction dataset demonstrates global consistency with observations, exhibiting mean biases of <span class="inline-formula"><1</span> % for <span class="inline-formula"><i>V</i><sub>max</sub></span> and 3 % for <span class="inline-formula"><i>R</i><sub>max</sub></span> and <span class="inline-formula"><i>P</i><sub>min</sub></span> in almost all basins. The dataset is publicly available from <a href="https://doi.org/10.5281/zenodo.13919874">https://doi.org/10.5281/zenodo.13919874</a> (Xu et al., 2024) and substantially advances our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.</p> |
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| ISSN: | 1866-3508 1866-3516 |