Using Statistical Learning to Predict the Extratropical Transition of Tropical Cyclones

Tropical cyclones (TCs) undergoing extratropical transition (ET) pose a serious threat to coastal regions in the midlatitudes. ET is difficult to forecast even for advanced numerical weather prediction models.

The authors introduce a logistic regression model for TCs undergoing ET in the North Atlantic and the western North Pacific, using elastic net regularization to select predictors and estimate coefficients. Predictors are chosen from the 1979-2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to two days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic, and 92% of those in the western North Pacific. 90% of transition time errors are less than 24 h. Select examples of the model's performance on individual storms illustrate its strengths and weaknesses.

The model may be used to integrate ET into statistical TC risk models used for hazard assessment, or to provide baseline guidance for operational forecasts.