Abstract

By introducing a linear diagnostic equation to compute perturbation pressure, the vertical momentum equation of the WRF model in the η coordinate is rederived, which constructs a direct relationship between vertical acceleration and physical factors we concern in the deep convection initiation (DCI) process. Important physical processes to the rapid growth of convective cloud are studied, aiming to fully understand the DCI process in the model. It is found that the DCI process is highly related to a cloud-core vertical acceleration. Due to this acceleration, the updraft is strengthened and the cloud is able to develop higher. Based on the diagnostic results of the vertical momentum equation, some different processes contributing to the high-level cloud-core accelerations in the model are found. A divergent pattern of the three-dimensional wind field is favorable for vertical acceleration. An inner physical process is that the horizontally convergent mass should be pumped up instantly by the vertical updraft to sustain a strong vertical acceleration. Second-order vertical inhomogeneity of perturbation geopotential also has an impact. Because of geopotential changes by vertical velocity (geopotential equation), this factor implies larger vertical motions will induce larger accelerations. The effects of the vertical gradient of perturbation potential temperature and moisture may be cancelled out since phase transitions can heat the convective air, but simultaneously decrease the water vapor content. Moisture makes a direct contribution to vertical acceleration, but is mostly cancelled out by the drag of cloud hydrometeors. Clearly understanding the direct impact of the basic prognostic variables on convection may help to identify the reason why DCI predictions within the model fail.摘要本文将扰动气压利用一个线性诊断关系代替, 重新推导了WRF模式框架地形追随坐标系下的垂直动量方程, 建立了垂直加速与对流触发 (DCI) 影响因子 (如温度, 水汽等) 的直接联系. 研究发现, DCI过程与对流核垂直加速相关, 三维副散, 扰动位势在垂直方向的二阶非均匀性, 扰动位温垂直梯度, 比湿及其垂直梯度, 水凝物拖曳, 均是能够直接影响垂直加速和对流触发的物理因子, 这些量与模式基本预报量相关, 通过解析基本预报量对对流发展的直接影响, 可能有助于理解模式对DCI过程预测失败的原因.

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