Entrainment and mixing processes occur during the entire life of a cloud. These processes change the droplet size distribution, which determines rain formation and radiative properties. Since it is a microphysical process, it cannot be resolved in large scale weather forecasting models. Small scale simulations such as Direct Numerical Simulations (DNS) are required to resolve the most minute scale of these processes. The DNS of cloud dynamics are performed by integrating two mathematical models, Eulerian and Lagrangian, in a coupled way. Running DNS is a tedious task as it requires a huge amount of computational resources. In this work, we provide a projection of the required resources for running DNS in different size domains.

Visualizing these large simulations presents an added challenge, as they generate petabytes of data. Visualization plays a vital role in analyzing and understanding these huge data outputs. Here, we experimented with multiple tools to conduct a visual analysis of this data. Two of these tools are well established and tested technologies: ParaView and VAPOR. The others are emergent technologies in the development phase. This data simulation and visualization, in addition to exploring DNS as mentioned above, provided an opportunity to test and improve development of several tools and methods.

夹带和混合过程发生在云的整个生命周期中。这些过程改变了液滴尺寸分布，这决定了雨水的形成和辐射特性。由于它是一个微物理过程，因此无法在大规模天气预报模型中解决。需要使用直接数值模拟 (DNS) 等小规模模拟来解决这些过程的最微小规模。云动力学的 DNS 是通过以耦合方式集成欧拉和拉格朗日两个数学模型来执行的。运行 DNS 是一项繁琐的任务，因为它需要大量的计算资源。在这项工作中，我们提供了在不同大小的域中运行 DNS 所需资源的预测。

可视化这些大型模拟带来了额外的挑战，因为它们会生成 PB 级的数据。可视化在分析和理解这些巨大的数据输出方面起着至关重要的作用。在这里，我们尝试了多种工具来对这些数据进行可视化分析。其中两个工具是成熟且经过测试的技术：ParaView 和 VAPOR。其他是处于开发阶段的新兴技术。这种数据模拟和可视化，除了如上所述探索 DNS 之外，还提供了一个机会来测试和改进多种工具和方法的开发。