Connecting Vertical Velocity and Microphysics at the Subgrid Scale in General Circulation Models (GCMs)

这项研究解决了天气和气候模式中的两个大气边界层参数化问题：湍流与云场的耦合和次网格变率的加入。这项研究的重点将是海洋层积云。该方法依靠概率密度函数(PDF)和“拉丁超立方体抽样”将随机随机性引入到解中，以达到在较粗的参数化中实现子网格变异性的效果，而不需要所谓的“超参数”方法(它明确地表示复杂性，但在区域的一小部分上)的显式复杂性。这一方法应使该方法普遍适用于其他微物理编码，并相对容易改进。这些技术将在一个全球环流模式(GCM)中实施，以测试通过PDF将本地精确的微物理缩放到大型GCM网格盒(一侧数十至数百公里)。目标是避免依赖于栅格框大小的微物理调谐。将进行GCM积分，以测试新参数的影响。智慧的优点：已在单柱模型中成功实施的方法将以普遍的方式扩展到GCM参数。将基于统计的方法扩展到GCM参数化及其测试，是这一领域过去研究的高潮。这项研究的预期更广泛的影响是培养一名研究生，在网上和学术文献中广泛传播研究成果，并改进天气和气候模拟，不仅是在GCM测试中，而且是在广泛的大气模型中。

This research addresses two atmospheric boundary layer parameterization problems in weather and climate models: coupling turbulence with cloud fields and the inclusion of subgrid variability. The focus of this research will be on marine stratocumulus clouds. The approach relies on probability density functions (PDFs) and "Latin hypercube sampling" to introduce stochastic randomness into the solutions to achieve the effects of subgrid variability in coarser parameterizations without the explicit complexity of so-called "super-parameterization" approaches (that explicitly represent the complexity but over a small subset of the region). This approach should make the methodology generally applicable to other microphysics codes and relatively easy to retrofit. These techniques will be implemented in one global circulation model (GCM) to test the scaling of locally accurate microphysics to large GCM grid boxes (tens to hundreds of kilometers on a side) via the PDF. The goal is to avoid microphysical tuning that depends on the grid box size. GCM integrations will be performed to test the impact of the new parameterizations.Intellectual Merit: The methodology, which has been implemented in single-column models with success will be extended to GCM parameterizations in a generalized fashion. The extension of the statistically-based approach to a GCM parameterization and its testing amounts to a culmination of past research in this area. The expected broader impacts of this research are the training of a graduate student, wide dissemination of research results on the web and in the academic literature, and improved simulation of weather and climate in not just the test GCM, but a wide range of atmospheric models.