A Long Short-Term Memory Model for Global Rapid Intensification Prediction

Abstract

ABSTRACTRapid intensification (RI) is an outstanding source of error in tropical cyclone (TC) intensity predictions. RI is generally defined as a 24-h increase in TC maximum sustained surface wind speed greater than some threshold, typically 25, 30, or 35 kt (1 kt ≈ 0.51 m s−1). Here, a long short-term memory (LSTM) model for probabilistic RI predictions is developed and evaluated. The variables (features) of the model include storm characteristics (e.g., storm intensity) and environmental variables (e.g., vertical shear) over the previous 48 h. A basin-aware RI prediction model is trained (1981–2009), validated (2010–13), and tested (2014–17) on global data. Models are trained on overlapping 48-h data, which allows multiple training examples for each storm. A challenge is that the data are highly unbalanced in the sense that there are many more non-RI cases than RI cases. To cope with this data imbalance, the synthetic minority-oversampling technique (SMOTE) is used to balance the training data by generating artificial RI cases. Model ensembling is also applied to improve prediction skill further. The model’s Brier skill scores in the Atlantic and eastern North Pacific are higher than those of operational predictions for RI thresholds of 25 and 30 kt and comparable for 35 kt on the independent test data. Composites of the features associated with RI and non-RI situations provide physical insights for how the model discriminates between RI and non-RI cases. Prediction case studies are presented for some recent storms.