Article
Improvementof typhoon intensity change classification by incorporating an ocean coupling potential intensity index in decision trees
Weather & Forecasting
S Gao，W Zhang，J Liu，II Lin，SC Long
Abstract
Tropical cyclone (TC) intensity prediction, especially in the warning time frame of 24–48 h and for the prediction of rapid intensification (RI), remains a big operational challenge. Sea surface temperature (SST)-based empirical or theoretical maximum potential intensity (MPI) is the most important predictor in statistical intensity prediction schemes and rules derived by data mining techniques. Since underlying during-TC SST usually cannot be observed well by satellites due to rain contamination and cannot be produced timely for operational statistical prediction, an ocean coupling potential intensity index (OC_PI), which is calculated based on pre-TC averaged ocean temperature from the surface down to 100 m, is demonstrated to be important to build the decision tree for the classification of 24-h TC intensity change (dV), i.e., RI (dV ≥ 25 kt) and non-RI (dV < 25 kt). the cross-validation using 2000–2010 data and independent verification using 2011 data are performed. the decision tree with the oc_pi shows a cross-validation accuracy of 83.5% and an independent verification accuracy of 89.6%, which outperforms the decision tree excluding the oc_pi with corresponding accuracies of 83.2% and 83.9%. specifically for ri classification in independent verification, the former decision tree shows a much higher probability of detection and a lower false alarm ratio than the latter one. this study is of great significance for operational tc ri prediction as pre-tc oc_pi can skillfully reduce the overestimation of storm potential intensity by traditional sst-based mpi, especially for the non-ri tcs.
http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-15-0062.1?af=R
