深度學習於熱帶氣旋結構演變與變異度之研究

Abstract

熱帶氣旋（TC）結構之分析對於描繪其特性與評估災害緩減效益具有關鍵重要性。颱風強度（V_max）廣泛應用於描述TC之生命週期與動力過程；而累加動能（integrated kinetic energy, IKE）則被提出作為代表TC整體能量與破壞潛力之指標。數值模擬研究顯示，若能同時分析V_max與IKE，將可對TC結構提供更為整體性的見解。然而，由於IKE資料主要依賴數量有限的飛機觀測，因此關於IKE之研究仍是缺少的。 本研究第一部分使用以不同頻道（可見光、水氣、紅外線）之衛星雲圖訓練，並以SAR與ASCAT風場校驗之人工智慧（AI）模式產出的TC軸對稱風場剖面，用以計算IKE。該資料在與27筆飛機觀測資料比對時之相關係數達R^2 = 0.99，顯示此資料之可信度。本研究進一步於累加動能–強度（K–V）相位圖上探討TC結構之演變與變異性，針對長生命期雙眼牆（concentric eyewall, CE）TC、短生命期CE TC，以及未發生CE之TC進行比較分析。合成分析結果顯示，長生命期CE有助於IKE的增強，而短生命期CE則維持其IKE。K–V相位圖進一步顯示CE影響TC生命期之演變，即由「小而強」發展為「大而強」，最終轉變為「大而弱」的風暴結構直至衰亡。本研究亦探討聖嬰現象（El Niño–Southern Oscillation, ENSO）對TC的生命期演變與變異度之影響，結果顯示，相較於La Niña年，El Niño年之TC具有較高之強度與IKE；惟在不同ENSO相位下，其結構變異度並未顯著不同。 本研究第二部分評估機器學習（machine learning, ML）模型以ERA5環境變數預報IKE及強度之可行性。我們利用Statistical Hurricane Intensity Prediction Scheme（SHIPS）環境變數的資料架構，輸入以不同ML演算法建構的模型，輸出對應的IKE。結果顯示，Transformer模型具有診斷連續156小時IKE之潛力，其演算法能有效萃取時序資訊，進而掌握TC結構隨時間之演變。從誤差隨預報時間之變化來看，掌握初始渦旋IKE與強度對機器學習模型於TC結構預報表現有關鍵的影響。 本研究運用AI模式生成之TC資料，突破以往受限於資料不足之結構分析瓶頸。透過整合IKE與強度，得以於二維架構中系統性分析TC結構於氣候尺度、CE過程及ENSO等不同情境下之演變與變異度。本研究亦驗證AI模型於同時預報強度與IKE之可行性，顯示AI技術對於輔助TC研究之潛力。

Analysis of tropical cyclone (TC) structure is vital for featuring TC and evaluating disaster mitigation. Intensity (V_max) has been widely used to characterize the life cycle and dynamic process of TC, and integrated kinetic energy (IKE) has been proposed as a parameter representing TCs’ overall energy and destructive potential. Numerical TC simulations have shown holistic insights into TC structure by simultaneously analyzing intensity and IKE. However, valid IKE data are limited due to the reliance on infrequent aircraft observations, indicating insufficient investigation of TC IKE. In the first part of this study, an AI-based TC axisymmetric wind profile—trained on multi-channel satellite imagery (visible, water vapor, and infrared) and validated against SAR and ASCAT wind fields—was used to calculate IKE. With an R^2= 0.99 against 27 aircraft observations, the validation showed the feasibility of this AI-based IKE dataset. Further investigations about TC structure were progressed on the IKE-Intensity phase (K-V) diagram. We examined the evolution and variability of long-lived concentric eyewall (CE) TCs, short-lived CE TCs, and no-CE TCs. Composite analysis results showed that long-lived (short-lived) CEs tended to increase (maintain) IKE. The K-V diagram suggested that CE process modulated the TCs life cycle, evolving from small, intense storm to large, intense storm to large, weak storm before demised. We also examined the evolution and variability of TCs influenced by El Niño Southern Oscillation (ENSO). The results indicated that TCs possessed greater Vmax and IKE in El Niño years than in La Niña years, while variability showed no significant differences across ENSO phases. In the second part of this study, machine learning (ML) models were evaluated for their feasibility in forecasting IKE and V_max using ERA5 environmental variables. The models were constructed based on various ML algorithms and adopted the data framework of the Statistical Hurricane Intensity Prediction Scheme (SHIPS), with SHIPS-based environmental variables as inputs and IKE as the predicted output. The results indicate that the Transformer model demonstrates potential for diagnosing IKE over a continuous 156-hour timespan, as its algorithm effectively extracts sequential information to capture the temporal evolution of TC structure. The temporal error progression highlighted the importance of capturing the initial vortex size and intensity for ML models in forecasting TC structure. This study utilizes AI-based TC datasets to perform analyses previously hindered by data limitations. Incorporating IKE alongside V_max allows a two-dimensional assessment of TC structural variability across climate trends, CE process, and ENSO phases. We also assess the feasibility of using AI models to forecast V_max and IKE simultaneously. This study highlights the potential of AI as a key in advancing study on TC.