Modelling global tropical cyclone wind footprints

Done, James M.; Ge, Ming; Holland, Greg J.; Dima-West, Ioana; Phibbs, Samuel; Saville, Geoffrey R.; Wang, Yuqing

doi:https://doi.org/10.5194/nhess-20-567-2020

Articles | Volume 20, issue 2

Nat. Hazards Earth Syst. Sci., 20, 567–580, 2020

https://doi.org/10.5194/nhess-20-567-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Global- and continental-scale risk assessment for natural...

Nat. Hazards Earth Syst. Sci., 20, 567–580, 2020

https://doi.org/10.5194/nhess-20-567-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 2

Research article 25 Feb 2020

Research article | 25 Feb 2020

Modelling global tropical cyclone wind footprints

James M. Done et al.

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Final revised paper (published on 25 Feb 2020)
Preprint (discussion started on 02 Jul 2019)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review for Modelling Global Tropical Cyclone Wind Footprints', Anonymous Referee #1, 08 Aug 2019
- AC2: 'Author comment in response to RC1', James Done, 04 Nov 2019
SC1: 'Lofty Aims yet Poor Performance?', Bruce Harper, 12 Aug 2019
- AC1: 'Author comment in response to SC1', James Done, 04 Nov 2019
RC2: 'Tropical cyclone surface wind fields', Anonymous Referee #2, 12 Aug 2019
- AC3: 'Author comment in response to RC2', James Done, 05 Nov 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish as is (11 Nov 2019) by James Daniell

ED: Publish subject to minor revisions (review by editor) (25 Nov 2019) by James Daniell

AR by James Done on behalf of the Authors (04 Dec 2019) Author's response Manuscript

ED: Publish subject to technical corrections (25 Jan 2020) by James Daniell

AR by James Done on behalf of the Authors (29 Jan 2020) Author's response Manuscript

Short summary

Assessing tropical cyclone (TC) wind risk is challenging due to a lack of historical TC wind data. This paper presents a novel approach to simulating landfalling TC winds anywhere on Earth. It captures local features such as high winds over coastal hills and lulls over rough terrain. A dataset of over 700 global historical wind footprints has been generated to provide new views of historical events. This dataset can be used to advance our understanding of overland TC wind risk.