Forecasting rapid intensification (hereafter referred to as RI) of tropical cyclones in the Atlantic Basin is still a challenge due to a limited understanding of the meteorological processes that are necessary for predicting RI. To address this challenge, this study considered large-scale processes as RI indicators within tropical cyclone environments. The large-scale processes were identified by formulating composite map types of RI and non-RI storms using NASA MERRA data from 1979 to 2009. The composite fields were formulated by a blended RPCA and cluster analysis approach, yielding multiple map types of RI's and non-RI's. Additionally, statistical differences in the large-scale processes were identified by formulating permutation tests, based on the composite output, revealing variables that were statistically significantly distinct between RI and non-RI storms. These variables were used as input in two prediction schemes: logistic regression and support vector machine classification. Ultimately, the approach identified midlevel vorticity, pressure vertical velocity, 200-850 hPa vertical shear, low-level potential temperature, and specific humidity as the most significant in diagnosing RI, yielding modest skill in identifying RI storms.

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Tropical cyclone (TC) track forecasts have improved in recent decades while intensity forecasts, particularly predictions of rapid intensification (RI), continue to show low skill. Many statistical methods have shown promise in predicting RI using environmental fields, although these methods rely heavily upon supervised learning techniques such as classification. Advances in unsupervised learning techniques, particularly those that integrate nonlinearity into the class separation problem, can improve discrimination ability for difficult tasks such as RI prediction. This study quantifies separability between RI and non-RI environments for 2004–16 Atlantic Ocean TCs using an unsupervised learning method that blends principal component analysis with k-means cluster analysis. Input fields consisted of TC-centered 1° Global Forecast System analysis (GFSA) grids (170 different variables and isobaric levels) for 3605 TC samples and five domain sizes. Results are directly compared with separability offered by operational RI forecast predictors for eight RI definitions. The unsupervised learning procedure produced improved separability over operational predictors for all eight RI definitions, five of which showed statistically significant improvement. Composites from these best-separating GFSA fields highlighted the importance of mid- and upper-level relative humidity in identifying the onset of short-term RI, whereas long-term, higher-magnitude RI was generally associated with weaker absolute vorticity. Other useful predictors included optimal thermodynamic RI ingredients along the mean trajectory of the TC. The results suggest that the orientation of a more favorable thermodynamic environment relative to the TC and midlevel vorticity magnitudes could be useful predictors for RI.