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

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Latest update: 01 Mar 2024
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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.