雲解析模式模擬 MJO 抑制相位中集結淺對流雲之特徵

Abstract

在本研究中針對MJO 的抑制相位(suppressed phase) 時，淺至深對流過程(Shallow-to-Deep Transition,SDT) 進行探討。其中，我們可以辨別出對流雲自我集結過程。研究使用三維渦度向量方程式的雲解析模式(VVM)，進行理想化實驗。初始環境場及大尺度沉降場是取自DYNAMO/CINDY 在2011 所進行的觀測，並取自其中的抑制相位的部份。本研究的對流雲自我集結過程，可以經由雲的尺寸分佈來進行辨別。經由深度優先搜尋(DFS) 連結所有相鄰有雲水的網格，使其形成一個雲物件，以利討論的進行。同時，兩個在相鄰的離散時間點上雲物件，可以使用簡單的搜尋法連結，並一個雲物件估計生命週度。本研究顯示，淺至深對流過程中，雲的自我集結效應，可以經由雲的數量及雲的平均大小來辨別。在本研究中平均對流雲大小，在計算的淺至深對流過程中，約有一倍的成長，同時對流雲的數量約增加2.5倍。另外在 SDT 後期與前期的比較中可以發現，後期的質量通量相對大且生命週期長的雲，較前期的比例上有增加的趨勢。本研究同時也指出，對流雲的自我集結效應，對於對流溼化環境貢獻的重要性。

In this study, the self-aggregation process is identified in shallow-to-deep transition of convection during MJO suppressed phase. An idealized experiment is performed using the Vector Vorticity cloud resolving Model (VVM). The initial soundings and subsidence profiles are adopted from the suppressed phase of CINDY/DYNAMO campaign in 2011. The self-aggregation process is identified by the cloud size distribution. The cloud size is calculated using Depth-First Search (DFS) algorithm which treats connected cloudy points as a single cloud object. In addition, the life cycle of the cloud object in discrete model output can also be estimated using a searching algorithm. The result shows that the convective cloud self-aggregation can be identified by the increasing of number and averaged volume during diurnal cycles in the SDT process. The average size of convective clouds increases about 100% during the SDT process and the number of convective clouds becomes 2:5 times larger as well. Our results also show that the ratio of cloud that contains relative larger mass flux and longer life-cycle increase during the SDT process. The results also suggest the importance of convective moistening contributed by self-aggregation of the convective clouds.