利用向量渦度方程雲解析模型階層式探討影響對流聚集之物理過程

Abstract

本研究構建了一個階層式的模擬框架，旨在解析物理機制，特別是雲微物理和反饋效應，如何影響對流系統的發展及大尺度大氣流場。第一層次的研究探討了微物理參數化對強迫環境中極端降水的作用。結果顯示，使用預測粒子特性（P3）微物理參數化法，由於減少了對流核心中的冰融效應，相較於傳統參數化法，產生了更強的上升氣流並促進了更極端的降水事件。第二層次進一步分析了反饋機制如何在輻射-對流平衡（RCE）情境下影響對流自發聚集（CSA）。這一階段強調了水汽對流反饋（MCF）對於促進對流組織化的重要性，P3方案模擬出更顯著的CSA及其對大尺度環流的深遠影響。最後，該框架比較了兩個雲解析模型—VVM和SCALE，發現不同的物理機制導致了不同的CSA發展過程。VVM呈現出快速的對流驅動聚集過程，而SCALE則展現了較為緩慢的輻射驅動聚集過程，揭示了模型中物理過程如何影響對流聚集過程。綜合來看，這一階層式框架提供了一個分析小尺度對流行為與大尺度反饋效應相互作用的系統化視角，對提升天氣與氣候模型的準確性具有重要的指導意義。

This research presents a hierarchical modeling framework to investigate how physical processes, particularly microphysics and feedback mechanisms, influence convective behavior and large-scale atmospheric circulation. The first level of analysis examines the role of microphysical parameterizations in shaping extreme precipitation under strongly forced conditions. Using the predicted particle properties (P3) microphysics scheme, the study finds that reduced melting effects in convective cores lead to stronger updrafts and more extreme precipitation than traditional schemes. The second level extends the framework to explore how feedback mechanisms drive convective self-aggregation (CSA) within radiative-convective equilibrium (RCE). This stage emphasizes the importance of moisture-convection feedback (MCF) in enhancing convective organization, with P3 simulations demonstrating stronger CSA and its broader influence on large-scale circulation. Finally, the framework compares two cloud-resolving models, VVM and SCALE, revealing that differences in physical processes lead to distinct CSA pathways. VVM follows a rapid, convection-driven aggregation, while SCALE exhibits a slower, radiation-driven pathway, highlighting how model-specific dynamics shape atmospheric behavior. This hierarchical approach provides a structured view of the interaction between small-scale convective processes and large-scale feedback mechanisms, offering valuable insights for improving weather and climate models.