Large-Scale Environment Analysis and Prediction of Tropical Cyclone Rapid Intensification Events

• Main Authors: Yu-Yuan Chang, 張有元
• Other Authors: Hsiao-Chung Tsai
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/7wc4r9

碩士 === 淡江大學 === 水資源及環境工程學系碩士班 === 106 === Rapid intensification (RI) is one of the most challenging issues for operational tropical cyclone (TC) forecasting. According to the National Hurricane Center, a RI event is defined as an increase in the maximum sustained wind speed by at least 30 kt within a 24-h period. The 24-h TC intensity forecast errors are significantly larger during the rapid intensification stage. In this study, various datasets are utilized to investigate the RI events of the western North Pacific TCs. The SHIPS (Statistical Hurricane Intensity Prediction Scheme) Developmental Dataset is used to explore the characteristics of the RI and non-RI events. Recent studies show that the RI events are related to not only the upper ocean thermal structure but also the density and salinity structure. Thus the HYCOM (Hybrid Coordinate Ocean Model) ocean analysis is also used to study the impact of the pre-existing ocean conditions on TC development and intensification. Finally,probabilistic forecast models for the prediction of RI events are developed by using the Quantile Regression(QR) and Logistic Regression(LR) methods. Results show that the QR model that includes the SHIPS predictors and the ETCHP (Effective TC Heat Potential) has the best performance. The area under ROC curve (AUC) of the QR model is 0.88. The probability of detection (POD) is 94%, which is 10% better than for the best LR model. In addition, the false alarm ratio (FAR) is 10% lower. Thus, the QR model can provide more skillful probability forecasts of TC RI events.