一个随机多云对流参数化方案的发展与验证

准确描述云在天气-气候系统中的作用，是数值模式发展面临的最大挑战之一。以往的对流参数化方案对次网格云的描述能力有限，这被认为是数值模式误差和不确定性的重要来源。本项目拟将一个以全新思路构建的随机多云（SMCM）对流参数化方案耦合到WRF模式中。SMCM对流参数化方案中，充分考虑次网格尺度不同类型云的随机过渡过程，使其可以准确描述不同尺度云在多时空尺度上的相互作用。在SMCM对流参数化方案中进一步引入对流动量传输过程，以更准确地模拟次网格尺度对流系统与大尺度环境场之间的能量双向传递过程。在统一的WRF框架下，比较不同对流参数化方案的性能差异和适用性，总结新的SMCM对流参数化方案的优缺点以及区域适用性。通过项目研究，提高模式对云和对流降水的模拟能力，推进数值模式的发展，为提高天气、气候的预测水平提供支撑。

The simulation of cloud-related processes is one of the major challenges in the development of a numerical model. Previous convection parameterization schemes showed insufficient ability to capture the variability of different types of clouds at sub-grid scales, which is an important source of model errors and uncertainties. To overcome this problem, we will couple a new stochastic multi-cloud model (SMCM) convective parameterization to the WRF model. The SMCM scheme can well capture the statistical features of sub-grid clouds and the transition of different types of clouds. We will further introduce the convective momentum transport (CMT) into the SMCM scheme to describe the exchange of momentum and energy between the sub-grid scale and large scales. We will also compare the SMCM scheme with the other convective parameterization schemes of WRF to summarize the advantages and limitations of the SMCM scheme as well as its regional applicability. The implementation of this project is expected to advance the development of numerical models in terms of the simulation of cloud and convection processes, and in turn, improve the numerical weather-climate forecasting.