Abstract
The National Typhoon Center of the Korea Meteorological Administration developed a statistical–dynamical typhoon intensity prediction model for the western North Pacific, the CSTIPS-DAT, using a track-pattern clustering technique. The model led to significant improvements in the prediction of the intensity of tropical cyclones (TCs). However, relatively large errors have been found in a cluster located in the tropical western North Pacific (TWNP), mainly because of the large predictand variance. In this study, a decision-tree algorithm was employed to reduce the predictand variance for TCs in the TWNP. The tree predicts the likelihood of a TC reaching a maximum lifetime intensity greater than 70 knots at its genesis. The developed four rules suggest that the pre-existing ocean thermal structures along the track and the latitude of a TC’s position play significant roles in the determination of its intensity. The developed decision-tree classification exhibited 90.0% and 80.5% accuracy in the training and test periods, respectively. These results suggest that intensity prediction with the CSTIPS-DAT can be further improved by developing independent statistical models for TC groups classified by the present algorithm.
Highlights
The accurate prediction of tropical cyclone (TC) intensity is a major task in operational forecasting
The tropical western North Pacific (TWNP) TCs, which belong to Cluster 2 in the CSTIPS-depth-averaged ocean temperature (DAT) model, spend most of their lifetimes over the tropics, where the environmental factors are favorable for their development (Figure 1a)
These results suggest that with prior knowledge of the type at the genthat with prior knowledge of the lifetime maximum intensity (LMI) type at the genesis of a TC, intensity prediction in esis of a TC, intensity prediction in the could be improved through the the TWNP could be improved through the development of independent statisticaldevelopmodels ment of independent statistical models for each classified group
Summary
The accurate prediction of tropical cyclone (TC) intensity is a major task in operational forecasting. A new statistical–dynamical model, the CSTIPS-DAT [2], which uses a clustering technique and depth-averaged ocean temperature (DAT)-based predictors, has facilitated significant improvements in intensity prediction in the western North Pacific (WNP). The tropical western North Pacific (TWNP) TCs, which belong to Cluster 2 in the CSTIPS-DAT model, spend most of their lifetimes over the tropics, where the environmental factors are favorable for their development (Figure 1a). A considerable number of TCs in the said cluster still do not intensify even under favorable conditions, which produces a large breadth of intensity distribution (Figure 2a) and a large predictand variance. The distribution of the lifetime maximum intensity (LMI) in the TWNP is bimodal, characterized by a local minimum (at about 70 knots LMI) that separates the two groups between weakly (1st mode) and strongly developing TCs (2nd mode). Because the CSTIPS-DAT is a multiplelinear-regression-based model, the TWNP cluster was trained to fit well with strong TCs
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