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

在大气环流模型 (GCM) 中连接亚网格尺度的垂直速度和微观物理

• 批准号: 0618818
• 负责人: Vincent Larson
• 金额: --
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0618818&HistoricalAwards=false
• 关键词: Connecting; Vertical; Velocity; Microphysics; Subgrid

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.

本研究针对天气与气候模式中的两个大气边界层参数化问题：耦合湍流与云场及包含次网格变率。这项研究的重点将是海洋层积云。该方法依赖于概率密度函数（PDF）和“拉丁超立方体采样”的解决方案引入随机随机性，以实现子网格变化的影响，在粗糙的参数化没有明确的复杂性，所谓的“超级参数化”的方法（明确表示的复杂性，但在一个小的子集的区域）。这种方法应使该方法普遍适用于其他微观物理代码和相对容易改造。这些技术将在一个全球环流模式（GCM）中实施，以测试通过PDF将局部精确的微物理学缩放到大型GCM网格框（一侧数十至数百公里）。目标是避免依赖于网格框大小的微物理调整。将进行GCM集成，以测试新的parameterizations.Intellectual优点的影响：该方法，这已被成功地实施在单柱模式将被扩展到GCM参数化在一个广义的时尚。扩展的基于几何的方法的GCM参数化及其测试的过去的研究在这方面达到了高潮。这项研究的预期更广泛的影响是研究生的培训，研究成果在网络和学术文献中的广泛传播，以及不仅在测试GCM中改进天气和气候的模拟，而且在广泛的大气模型中。