Improving convective storm simulations through scale-adaptive and flow-adaptive sub-grid methods

通过尺度自适应和流量自适应子网格方法改进对流风暴模拟

• 批准号: 2438507
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2438507
• 关键词: Improving; convective; storm; simulations; through

The project is concerned with improving the numerical simulations required for short-range numerical weather prediction and small-scale regional climate prediction. Over the past decade, such simulations have become practical at "convection-permitting" resolutions. Convection-permitting simulations have enabled a step change in our forecasting capabilities because individual convective storms can now be simulated directly with the numerical model. Many high-impact weather events are associated with such storms, particularly in cases of flash flooding and damaging wind gusts.There continues to be a steady increase in the use and reliance on such models, and their benefits are well recognized. However, that experience has also revealed some important issues and limitations.a. The simulations are strongly sensitive to the model resolution. Increases in model resolution do not translate into improvements in simulation in any straightforward way. Indeed, we have recently found evidence that key metrics do not converge in current models even at very high resolutions.b. The simulations are strongly sensitive to choices made in the treatment of turbulent mixing.c. The simulations are strongly sensitive to assumptions about cloud formation and microphysics.These issues are strongly related. In reality processes that happen on smaller scales than the model grid interact with and influence the processes that take place on larger scales. In "model world" the smaller scales simply do not exist, but we try to mimic their effects by introducing extra assumptions about turbulent mixing and cloud schemes. It is those assumptions that create problems.In the engineering fluid dynamics community, improved model behaviour as a function of resolution has been achieved using "dynamic" techniques in which key turbulent mixing parameters are no longer prescribed. Rather, they can be computed "on the fly" during the simulation as a function of the evolving flow. The key principle invoked in making the computation is to ensure respect for the proper scale-dependence of the turbulent characteristics of the flow.Compared to the current applications of the technique, simulations of convective storms have additional thermodynamic and microphysical complications (or, as we like to think of it, additional physics that makes the atmosphere produce such rich and interesting forms of flow). This PhD project will extend and enhance the dynamic technique to develop a scale-adaptive and flow-adaptive sub-grid turbulence model that is suitable for simulations of convective storms at convection-permitting scales.

该项目致力于改进短期数值天气预报和小规模区域气候预报所需的数值模拟。在过去的十年里，这样的模拟已经在“允许对流”的分辨率上变得实用。允许对流的模拟使我们的预报能力发生了阶段性的变化，因为现在可以用数值模式直接模拟单个对流风暴。许多高影响的天气事件都与这种风暴有关，特别是在山洪暴发和破坏性阵风的情况下。对这种模式的使用和依赖继续稳步增加，其好处得到了很好的认识。然而，这一经历也暴露了一些重要的问题和局限性。模拟结果对模型分辨率有很强的敏感性。模型分辨率的提高不会以任何直接的方式转化为模拟的改进。事实上，我们最近发现的证据表明，即使在非常高的分辨率下，关键指标在当前的模型中也不会收敛。这些模拟对在处理湍流混合时所做的选择非常敏感。这些模拟对有关云形成和微物理的假设非常敏感。这些问题是密切相关的。在现实中，发生在比模型网格更小的尺度上的过程与更大尺度上发生的过程相互作用并影响它们。在“模式世界”中，较小的尺度根本不存在，但我们试图通过引入关于湍流混合和云图的额外假设来模拟它们的影响。正是这些假设产生了问题。在工程流体动力学领域，已使用不再规定关键湍流混合参数的“动态”技术，实现了作为分辨率函数的改进的模型行为。相反，它们可以在模拟过程中作为不断演变的流动的函数来计算。在进行计算时引用的关键原则是确保尊重气流湍流特征的适当的尺度依赖性。与目前该技术的应用相比，对流风暴的模拟具有额外的热力学和微物理复杂性(或者，正如我们喜欢认为的，额外的物理学使大气产生如此丰富和有趣的流动形式)。本PHD项目将扩展和加强动力学技术，以发展一个尺度自适应和流动自适应的亚网格湍流模式，该模式适用于对流允许尺度上的对流风暴模拟。