Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 11
Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4655-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4655-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 11
Research article
 | 
24 Nov 2025
Research article |  | 24 Nov 2025

Ensemble random forest for tropical cyclone tracking

Pradeebane Vaittinada Ayar, Stella Bourdin, Davide Faranda, and Mathieu Vrac

Viewed

Total article views: 5,444 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
4,117 1,149 178 5,444 445 174 209
  • HTML: 4,117
  • PDF: 1,149
  • XML: 178
  • Total: 5,444
  • Supplement: 445
  • BibTeX: 174
  • EndNote: 209
Views and downloads (calculated since 18 Mar 2025)
Cumulative views and downloads (calculated since 18 Mar 2025)

Viewed (geographical distribution)

Total article views: 5,444 (including HTML, PDF, and XML) Thereof 5,251 with geography defined and 193 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 04 Sep 2026
Download
Short summary
Tracking tropical cyclones (TCs) remains a matter of interest for investigating observed and simulated tropical cyclones. In this study, Random Forest (RF), a machine learning approach, is considered to track TCs. RF associates the TC occurrence or absence with different atmospheric configurations. Compared to trackers found in the literature, it shows similar performance for tracking TCs, better control over false alarms, more flexibility, and reveals key variables for TCs' detection.
Read more
Share
Altmetrics
Final-revised paper
Preprint