- 學位論文
颱風雙眼牆形成之邊界層非平衡動力機制
Secondary Eyewall Formation in Tropical Cyclones - Unbalanced Dynamics in the Boundary Layer
摘要
2008年的颱風辛樂克為T-PARC (THORPEX – Pacific Asian Regional Campaign) 實驗的重點觀測颱風，觀測的資料幾乎涵蓋了整個辛樂克的生命史，此珍貴的颱風觀測資料能夠幫助我們了解許多重要的颱風科學問題。 利用一個新發展的渦旋初始化方法，將T-PARC其間所有的觀測資料以EnKF方法同化至WRF模式中，因而得到一組與觀測相符合的辛樂克颱風資料庫。 此組資料庫具有5公里的水平空間解析度，以及每30或2分鐘一筆的模式輸出資料。 在本研究使用此資料庫探討雙眼牆形成的動力問題，並從一個新的觀點提出一個外眼牆形成的概念模型，概念模擬包含先行的流場特徵與對應的動力詮釋。 模擬結果顯示，在外眼牆形成前約一天左右，渦旋外圍的中低層切向風開始增強及向外擴張，並伴隨邊界層內流的增強及維持，此為概念模型中的兩個外眼牆形成之先行流場特徵。 動力分析結果則發現，當渦旋外圍的邊界層內流足夠強時，能夠有效地將較大的絕對角動量往較小地半徑輸送，使得外圍的切向環流增強擴張，最顯著的切向風增強發生在半徑相對來說較小的區間，此快速的切向風增強在邊界層頂附近最為明顯，並形成主眼牆之外的第二個超梯度風極大值區。該處的超地度風隨時間發展，邊界層內流在經過此區間時，伴隨的空氣質點受超梯度力的作用而迅速減速，進而使得邊界層輻合增加，引發在邊界層頂附近的上升運動。 當該區域具有適合對流發展的熱力及動力條件時，此由邊界層非平衡動力所造成的舉升機制，可激發或支持上方深對流的發展。 本研究所提出的外眼牆形成概念模型，強調邊界層的非平衡動力過程，以及其漸進但持續的影響。 邊界層的非平衡動力是持續且漸進地支持超梯度風半徑區間的主、次環流發展，在加上各流場演變特徵之間存在正回饋的機制，經過足夠的影響時間，熱帶氣旋的第二個眼牆對流區即有機會形成於超梯度風發展的半徑區間。 此外，概念模型中所需要的足夠強之外圍邊界層內流，可用以解釋大多數的強颱會經歷至少一次的眼牆置換過程，而強度較弱的熱帶氣旋則少見雙眼牆或多眼牆的結構。 鑒於邊界層非平衡動力在外眼牆形成過程中可能的關鍵角色，本研究認為模式對邊界層動力以及邊界層和自由大氣之間偶合過程的合理詮釋，會增進我們對雙眼牆結構形成與演變機制的了解，並可能進一步提升雙眼牆過程之預報，包含其形成的時間與半徑區間。
並列摘要
Typhoon Sinlaku (2008) is a case in point under T-PARC (THORPEX – Pacific Asian Regional Campaign) with the most abundant flight observations taken and with great potentials to address major scientific issues of tropical cyclones. In a recent study, a new method for vortex initialization based on EnKF data assimilation and the WRF model was adopted to simulate the life cycle of Sinlaku. A high-spatial/temporal-resolution and model/observation-consistent dataset was constructed by continuously assimilating (with an update cycle every 30 minutes) tracks, the mean surface structure of Sinlaku and all available measurement data for Sinlaku during a 4-day period. This dataset provides a unique opportunity to study the dynamical processes of concentric eyewall formation in Sinlaku. Using this previously constructed dataset, this study investigates the evolutionary of the concentric eyewall structure and aim to explore the key dynamical mechanisms and conditions for the secondary eyewall formation (SEF) in a tropical cyclone. Precursors to SEF found in our study suggest a possible application of a newly proposed spin-up paradigm to the SEF problem. We herein present a new model for SEF based on an axisymmetric view of the problem, including precursor characteristics and the associated evolution of the boundary layer flow and a dynamical interpretation. The findings point to a sequence of structure changes that occur in the outer-core region of a mature tropical cyclone, culminating in the formation of a secondary eyewall. The sequence begins with a broadening of the tangential wind field, followed by an increase of the corresponding boundary layer inflow underneath the broadened swirling wind, and an enhancement of a zone of organized convergence in the boundary layer where the secondary eyewall forms. The narrow region of strong boundary layer convergence is associated with the generation of supergradient winds in and just above the boundary layer that acts to rapidly decelerate inflow there. The progressive strengthening of the boundary layer inflow and the generation of an effective adverse radial force therein leads to an eruption of air from the boundary layer to support deep convection outside the primary eyewall in a favorable thermodynamic and kinematic environment. This presented paradigm for SEF is attractive on physical grounds because its simplicity and consistency with a set of 3-D numerical simulations. It is herein suggested that the unbalanced response in the boundary layer to an expanding swirling flow serves as an important mechanism for initiating and sustaining an approximate ring of deep convection in a narrow supergradient-wind zone in the vortex’s outer-core region. This progressive boundary layer control on SEF, involving in a positive feedback loop among the evolving primary and secondary circulation, implies that the boundary layer scheme and its coupling to the interior flow need to be adequately represented in numerical models to improve the understanding of SEF, as well as the accuracy of SEF forecasts, including the timing and preferred radial intervals.