本研究使用高解析度的WRF模式，探討2022年颱風諾盧（Noru）在其最大風速超過定義之快速增強（RI）門檻三倍以上期間，所伴隨的動力與熱力過程。模擬結果顯示，諾盧的劇烈RI特徵包括：眼牆收縮、緊湊的最大風速半徑（RMW），以及在劇烈RI時期的對流爆發。於劇烈RI期間，諾盧展現出常見的位渦（PV）結構特徵，如眼牆PV塔、從眼牆延伸至颱風眼內的高PV橋，以及位於眼內下平流層的高PV核心，這些結構皆於劇烈RI初期渦旋發展過程中逐步形成。諾盧在劇烈RI後期的局部PV極大值可達眼牆區的133 PVU與下平流層附近的691 PVU，顯示出強烈的內核PV異常存在。位渦趨勢收支分析指出，眼牆PV塔的發展主要由強烈的正值的平均PV平流所驅動，而由快速增強的徑向入流所造成邊界層中加強的非絕熱加熱，亦對於劇烈RI期間PV的生成扮演關鍵角色。針對各物理過程對PV傾向的貢獻，透過放寬熱風平衡假設的延伸的Sawyer–Eliassen（SE）方程進行定量分析。SE方程分析結果指出，強烈非絕熱加熱所誘發的橫向環流可產生主要的正值的平均PV平流，是造成眼牆強烈旋轉增強的主因。此外，來自SE方程解所導出的切向風速趨勢的診斷指出，諾盧在劇烈RI初期階段的強化主要由邊界層中的平均徑向平流所驅動，且在邊界層上方則由中度的平均垂直平流所貢獻，而在後期階段期渦流平流作用則扮演較重要角色。諾盧緊湊的內核結構與近RMW處的強烈非絕熱加熱，有助於其劇烈RI，與2020年具有相似RMW但非絕熱加熱較弱、增強速率較慢的颱風谷米（Goni）形成對比。若將諾盧之非絕熱加熱場替換為2018年颱風玉兔（Yutu）所對應之較大RMW與較外側的非絕熱加熱，則其劇烈RI將顯著減弱。相反地，若將原有的非絕熱加熱場向內移動至RMW之內，其劇烈RI反而會變強。
本研究亦探討SE方程解對於不同時間間隔下殘差項的敏感性。結果顯示，殘差項所引發的橫向環流與切向風速趨勢對時間間隔具輕微敏感性，其中5分鐘的時間間隔為誤差可接受範圍。梯度風與靜力平衡的殘差主要影響對流層上層流場，產生短暫且局部的效應；相較之下，切向風速趨勢的殘差則會逐漸增強上層徑向內流、颱風眼內沉降以及內核旋轉增強，進而隨時間導致與非線性模擬結果間的差異擴大。

This study uses a high-resolution WRF model to investigate the dynamic and thermodynamic processes associated with the severe rapid intensification (RI) of Typhoon Noru (2022) during the stage as its increasing maximum wind speed has exceeded three times of the normal RI threshold. The simulation results indicate that Noru exhibits several characteristic features during the severe RI period, such as eyewall contraction, a compact radius of maximum wind (RMW), and intense convective bursts after the onset of severe RI. Furthermore, Noru exhibits common PV features, including an eyewall PV tower, a high-PV bridge from the eyewall into the eye, and a high-PV core in the lower stratosphere within the eye, which form as the vortex develops during the early stage of severe RI. Local PV maxima in Noru reaches up to 133 PVU in the eyewall region and 691 PVU near the lower stratosphere during the severe RI, highlighting the intense inner-core PV anomalies. Analysis of the PV tendency budget reveals that the development of the eyewall PV tower is mainly driven by strong positive mean PV advection, while the enhanced diabatic heating in the boundary layer plays a significant role in generating PV in the boundary layer during the severe RI period. Contributions of different physical processes to the PV tendency have been quantified by solving the extended Sawyer–Eliassen (SE) equation that relaxes the thermal wind balance. The SE analyses indicate that the dominant positive mean PV advection from the transverse circulation induced by intense diabatic heating is mainly responsible for the strong eyewall spinup during severe RI. Besides, analyses of tangential velocity tendency budget induced by the SE solution indicate that the severe RI at the early stage of Noru is mainly driven by the strong spinup from mean radial advection in the boundary layer and moderate spinup from mean vertical advection above, while eddy advection processes play a more important role during the later stage. The compact inner-core structure of Noru and strong diabatic heating near the RMW are beneficial to its severe RI, in contrast to Typhoon Goni (2020), which exhibited a similar RMW but weaker diabatic heating and slower intensification rate. The severe RI of Noru is also reduced when its diabatic heating is replaced with that of Yutu (2018) with a larger RMW and an outer radial diabatic heating. In contrast, the severe RI becomes even stronger when the inherent diabatic heating is shifted inward inside the RMW.
This study also investigates the sensitivity of the SE solution to the residual terms calculated using different time intervals. Transverse circulation and tangential velocity tendency induced from the residual terms are slightly sensitive to time intervals in use, and a 5-minute interval is acceptable for solution errors. Residuals as the deviation from the gradient and hydrostatic balances primarily influence the upper-tropospheric flow, only producing localized effects. However, the residual in the tangential wind tendency equation gradually enhances upper-level inflow, eye downdraft, and inner-core spinup, contributing to the increasing discrepancies over time from the nonlinear simulations.

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