Tropical cyclone frequency - 2022 - National
Data and Resources
This dataset has no data
Additional Info
| Field | Value |
|---|---|
| Source | https://app.mapx.org/static.html?views=MX-0DE6F-F47C0-6DA69&zoomToViews=true#JAAc6 |
| Author | UNEP/GRID-Geneva |
| Maintainer | UNEP/GRID-Geneva |
| Last Updated | December 7, 2022, 08:25 (UTC) |
| Created | December 7, 2022, 08:25 (UTC) |
| GUID | MX-0DE6F-F47C0-6DA69 |
| Issued | 2022-12-01 16:51:23 |
| Language | EN |
| Modified | 2022-12-01 16:51:23 |
| Publisher email | info@mapx.org |
| Publisher name | UNEP/GRID-Geneva |
| Theme | Web Map |
| data_type | geospatial |
| keywords_m49 | WLD |
| projects_description | Opportunity maps based on the combination of natural hazards, ecosystems and population exposure, showing where ecosystems can help prevent or mitigate hazards, reduce exposure to hazard impacts by functioning as natural buffers, and reduce vulnerability by supporting livelihoods |
| projects_id | MX-2LD-FBB-58N-ROK-8RH |
| projects_title | Ecosystem approaches for disaster risk |
| range_end_at_year | 2022 |
| range_start_at_year | 2019 |
| source_abstract | Estimation of global tropical cyclone wind speed probabilities using the STORM dataset. Nadia Bloemendaal, Hans de Moel, Sanne Muis, Ivan D. Haigh & Jeroen C. J. H. Aerts Tropical cyclones (TC) are one of the deadliest and costliest natural disasters. To mitigate the impact of such disasters, it is essential to know extreme exceedance probabilities, also known as return periods, of TC hazards. In this paper, we demonstrate the use of the STORM dataset, containing synthetic TCs equivalent of 10,000 years under present-day climate conditions, for the calculation of TC wind speed return periods. The temporal length of the STORM dataset allows us to empirically calculate return periods up to 10,000 years without fitting an extreme value distribution. We show that fitting a distribution typically results in higher wind speeds compared to their empirically derived counterparts, especially for return periods exceeding 100-yr. By applying a parametric wind model to the TC tracks, we derive return periods at 10 km resolution in TC-prone regions. The return periods are validated against observations and previous studies, and show a good agreement. The accompanying global-scale wind speed return period dataset is publicly available and can be used for high-resolution TC risk assessments. Citation: Bloemendaal, N., de Moel, H., Muis, S. et al. Estimation of global tropical cyclone wind speed probabilities using the STORM dataset. Sci Data 7, 377 (2020). https://doi.org/10.1038/s41597-020-00720-x |
| source_title | Estimation of global tropical cyclone wind speed probabilities using the STORM dataset. |
| spatial | WLD |