Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 4
Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1789-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1789-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 4
Model description paper
 | 
29 Feb 2024
Model description paper |  | 29 Feb 2024

Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding

Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink

Viewed

Total article views: 1,585 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,179 344 62 1,585 50 41
  • HTML: 1,179
  • PDF: 344
  • XML: 62
  • Total: 1,585
  • BibTeX: 50
  • EndNote: 41
Views and downloads (calculated since 01 Nov 2023)
Cumulative views and downloads (calculated since 01 Nov 2023)

Viewed (geographical distribution)

Total article views: 1,585 (including HTML, PDF, and XML) Thereof 1,558 with geography defined and 27 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary

Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.

Read more