Improvements in typhoon intensity change classification by incorporating an ocean coupling potential intensity index into decision trees

• Main Authors: GAO, Si, ZHANG, Wei, LIU, Jia, LIN, I.-I., CHIU, Long S., CAO, Kai
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5413; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6416&context=sis_research
• Institution: Singapore Management University

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 major 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 the underlying SSTs during TCs usually cannot be observed well by satellites because of rain contamination and cannot be produced on a timely basis for operational statistical prediction, an ocean coupling potential intensity index (OC_PI), which is calculated based on pre-TC averaged ocean temperatures from the surface down to 100 m, is demonstrated to be important in building the decision tree for the classification of 24-h TC intensity change ΔV24, that is, RI (ΔV24 ≥ 25 kt, where 1 kt = 0.51 m s-1) and non-RI (ΔV24