Articles | Volume 1, issue 2
https://doi.org/10.5194/wcd-1-313-2020
https://doi.org/10.5194/wcd-1-313-2020
Research article
 | 
13 Jul 2020
Research article |  | 13 Jul 2020

Robust predictors for seasonal Atlantic hurricane activity identified with causal effect networks

Peter Pfleiderer, Carl-Friedrich Schleussner, Tobias Geiger, and Marlene Kretschmer

Viewed

Total article views: 3,244 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,326 811 107 3,244 407 86 86
  • HTML: 2,326
  • PDF: 811
  • XML: 107
  • Total: 3,244
  • Supplement: 407
  • BibTeX: 86
  • EndNote: 86
Views and downloads (calculated since 13 Mar 2020)
Cumulative views and downloads (calculated since 13 Mar 2020)

Viewed (geographical distribution)

Total article views: 3,244 (including HTML, PDF, and XML) Thereof 2,926 with geography defined and 318 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Oct 2024
Download
Short summary
Seasonal outlooks of Atlantic hurricane activity are required to enable risk reduction measures and disaster preparedness. Many seasonal forecasts are based on a selection of climate signals from which a statistical model is constructed. The crucial step in this approach is to select the most relevant predictors without overfitting. Here we show that causal effect networks can be used to identify the most robust predictors. Based on these predictors we construct a competitive forecast model.