Adaptive turbulence modelling to improve high-impact weather forecasts in next generation atmospheric models

自适应湍流建模可改善下一代大气模型中的高影响天气预报

• 批准号: NE/T011351/1
• 负责人: Georgios Efstathiou
• 金额: $ 68.05万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 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=NE%2FT011351%2F1
• 关键词: Adaptive; turbulence; modelling; improve; impact

High-impact weather events are often extremely localised therefore refined spatial resolution is essential for the accurate prediction of such events. However, Numerical Weather Prediction (NWP) might have hit a stalemate as meteorological models move towards the sub-kilometre grid spacing. Even though recent research has shown some improvements in the simulation of heavy rainfall events with increasing horizontal resolution, it has also revealed significant challenges as this improvement is not as pronounced as expected and very sensitive to the treatment of the unresolved turbulence length scales. Those unresolved motions correspond to the dominant scales of boundary layer turbulence and cloud development and mixing with its imminent environment. It seems that the fundamental assumptions behind the parametrization of sub-grid motions at sub-kilometre resolutions need to be revisited.The proposed fellowship aims to provide a step-change in capabilities for forecasting deep convection and subsequent heavy rainfall, through a more physical and dynamic representation of the sub-grid scales in the next generation sub-kilometric NWP models. This will be achieved by dynamically deriving the turbulence length scales in the sub-grid mixing scheme depending on the resolved flow field rather than statically specifying them beforehand. The dynamic method will be first used in an idealised framework to improve the understanding of the coupling between the atmospheric boundary layer with deep convection, by diagnosing the different length scales of mixing in the boundary and cloud layer. This approach can provide further insight on cloud-environment mixing to study the impact of turbulent mixing at the different stages of convection development and identify the feedback between turbulent transport and the synoptic disturbances. A first-order dynamic scheme will then be used prognostically and will be assessed in reproducing convection development against static conventional methods at sub-kilometre resolutions.As a next step, I will develop a novel, scale-aware and flow-adaptive dynamic sub-grid parametrization approach to better represent the unresolved scales by using the resolved scales to determine the intensity of the sub-grid mixing. It will utilise the conservation equations for sub-grid turbulent transport, to provide a more accurate representation of sub-grid motions, through reconstructing the resolved field near the grid scale to dynamically calculate the sub-grid turbulence mixing lengths. Hence, the scheme will be self-contained with minimum tuneable closure parameters. The new approach will be tested in an operational NWP model at very high, sub-kilometre resolutions to validate the ability of model dynamics to explicitly resolve deep convection in realistic case studies, under weak and strong synoptic forcing. This new method, has the potential to improve weather forecasting by enabling weather centres to provide more accurate forecasts of high-impact weather to policy makers and the general public while providing grounds for further research in atmospheric science.

高影响天气事件往往是极端局部性的，因此，精确的空间分辨率对于此类事件的准确预测至关重要。然而，随着气象模型向亚公里网格间距移动，数值天气预报(NWP)可能已经陷入僵局。尽管最近的研究表明，随着水平分辨率的提高，暴雨事件的模拟有所改善，但也揭示了巨大的挑战，因为这种改善并不像预期的那样明显，而且对未分辨的湍流长度尺度的处理非常敏感。这些未解决的运动对应于边界层湍流和云发展的主要尺度，以及与其临近环境的混合。建议的研究金旨在通过对下一代亚公里数值预报模式中的次网格尺度进行更加物理和动态的描述，在预报深对流和随后的暴雨方面提供一个阶梯式的改变。这将通过在子网格混合格式中根据所解析的流场动态地推导湍流长度尺度来实现，而不是预先静态地指定它们。动力学方法将首先在一个理想化的框架中使用，通过诊断边界层和云层中混合的不同长度尺度来提高对大气边界层与深对流之间的耦合的理解。这种方法可以进一步了解云-环境混合，以研究湍流混合在对流发展的不同阶段的影响，并确定湍流输送与天气扰动之间的反馈。作为下一步，我将开发一种新的、具有尺度感知和流动自适应的动态亚网格参数化方法，通过使用分辨的尺度来确定亚网格混合的强度，从而更好地表示未分辨的尺度。它将利用亚网格湍流输运的守恒方程，通过重构网格尺度附近的分辨率场来动态计算亚网格湍流混合长度，从而提供更准确的亚网格运动表示。因此，该方案将是独立的，具有最小的可调闭合参数。新方法将在一个非常高、亚公里分辨率的业务数值预报模式中进行测试，以验证模式动力学在弱天气强迫和强天气强迫下，在实际个例研究中显式解决深对流的能力。这一新方法有可能改善天气预报，使气象中心能够向政策制定者和公众提供更准确的高影响天气预报，同时为大气科学的进一步研究提供依据。

项目成果

Dynamic Subgrid Turbulence Modeling for Shallow Cumulus Convection Simulations Beyond LES Resolutions

超出 LES 分辨率的浅积云对流模拟的动态子网格湍流建模

• 发表时间: 2023
• 期刊: Journal of the Atmospheric Sciences
• 影响因子: 3.1
• 作者: Efstathiou GA