風速半徑、環境因子及歷史類比個案對於颱風快速增強機率預報之影響

Impact of Wind Radii, Environmental Factors, and Historical Analogs on the Probabilistic Typhoon Rapid Intensification Forecasting

張楷晧(Kai-Hao Chang)

指導教授 ： 蔡孝忠

淡江大學/工學院/水資源及環境工程學系碩士班/碩士(2021年)

https://doi.org/10.6846/TKU.2021.00773

摘要

本研究之主要目的為颱風強度快速增強(Rapid Intensification；RI)之機率預報模式開發。根據美國國家颶風中心(National Hurricane Center)之定義，熱帶氣旋(Tropical Cyclone)之近中心最大風速若在24小時內增強至少30節(knots)以上，則稱為RI事件。
本研究採用K折交叉驗證方法(K-Fold Cross-Validation)及羅吉斯迴歸(Logistical Regression)，建立不同複雜程度之RI機率預報模式。首先，採用美國聯合颱風警報中心(Joint Typhoon Warning Center)最佳路徑資料以建立RI預報之基本模式，使用颱風中心位置、目前強度、最大風速半徑(Radius of Maximum Wind；RMW)與七級風平均半徑(Average 34-knot Radius；AR34)…等預報因子。然後逐步加入WAIP(Weighted Analog Intensity Prediction)預報強度資料，以及SHIPS開發資料(Statistical Hurricane Intensity Prediction System Developmental Data)之大尺度環境變數，分別建立進階模式與複雜模式，並透過機率式校驗，分析各模式之變數組合對於RI預報之表現。最後以二元式校驗方式，評估模式分別在最佳門檻、保守門檻與積極門檻下之預報差異。
透過機率式校驗之可靠度分析圖(Reliability Diagram)及交叉驗證結果顯示，模式增加輸入變數之後，其所提供的最高機率預報值也隨之上升。藉由ROC曲線(Receiver Operating Characteristic Curve)之分析顯示，當基本模式加入WAIP未來強度變化後，可有效改善整體預報表現。在額外考慮SHIPS大尺度環境變數之後，雖可使得AUC(Area Under Curve)上升，但其提升幅度不如WAIP來得明顯。
二元式校驗結果顯示，若採用積極預報之策略，RI事件的偵測率與誤報率將同時上升，非RI事件的判定能力下降；若提高機率門檻、採用保守預報策略，複雜模式之偵測率高、誤報率低，也具有最高之預兆得分(Threat Score)。校驗結果亦發現，考慮WAIP預報強度對於RI事件的預測能力可有一定程度的提升，但額外加入SHIPS環境場變數僅對保守預報策略有較明顯的幫助。因此本研究建議未來可採用颱風半徑及WAIP，做為颱風RI預報模式之主要輸入變數，進行RI即時預報模式的開發及實際預報應用。

關鍵字

熱帶氣旋 ； 快速增強 ； K折交叉驗證方法 ； 羅吉斯迴歸

並列摘要

The main purpose of this research is to develop a probabilistic typhoon rapid intensification (RI) forecasting model. According to the National Hurricane Center, RI is defined as an increase in the maximum sustained winds of at least 30 knot in a 24-h period.
This research uses the K-fold Cross Validation and the Logistical Regression for developing the probabilistic RI forecast models with different levels of complexity. First, a basic model is developed by using the predictors obtained from the Joint Typhoon Waring Center best track data, such as typhoon center location, current intensity, radius of maximum wind (RMW), average 34-know radius (AR34), etc. Next, the advanced model is created by adding the Weighted Analog Intensity Prediction (WAIP) to the basic model. Finally, the large-scale environmental factors from the Statistical Hurricane Intensity Prediction System (SHIPS) Developmental Data are added to the advanced model to develop the complex model. The probabilistic RI forecast skills for each model are then investigated. Lastly, the binary forecast skills are evaluated by using the best cut-off threshold, the conservative threshold, and the aggressive threshold.
Based on the reliability diagrams and the cross validations, higher RI probabilities can be obtained if more predictors are used. The Receiver Operating Characteristic (ROC) curves show that the performance of the basic model can be improved if the WAIP forecast is considered. Although the AUC (Area Under Curve) can be further improved by adding the SHIPS large-scale environmental factors, the difference is not significant.
According to the categorical binary verifications, the probability of detection and the false alarm ratio of the RI events are both increased if the aggressive forecast strategy is chosen. But the skill on identifying the non-RI events is lower. If the conservative forecast strategy is selected, the complex model has higher probability of detection, lower false alarm ratio, and highest Threat Score. The verification results also show that the RI forecast skill is improved if the WAIP forecast is included in the model. The SHIPS environmental factors, however, are helpful only if the conservative forecast strategy is considered. Therefore, it is suggested that the typhoon radii and the WAIP could be used as the main input variables for developing the operational RI forecast model and the real-time forecast applications.

並列關鍵字

Tropical cyclone ； Rapid Intensification ； K-fold Cross Validation ； Logistical Regression

參考文獻

參考文獻

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