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

高影响力的天气事件往往是非常局部化的，因此精确的空间分辨率对于准确预测此类事件至关重要。然而，数值天气预报（NWP）可能会陷入僵局，因为气象模型朝着亚公里网格间距发展。尽管最近的研究表明，随着水平分辨率的提高，暴雨事件的模拟有了一些改进，但它也揭示了重大的挑战，因为这种改进并不像预期的那样明显，而且对未解决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的发展和混合与其即将到来的环境的主导尺度。似乎需要重新审查以亚公里分辨率对次网格运动进行参数化的基本假设，拟议的研究金旨在通过在下一代亚公里数值预报模式中对次网格尺度进行更加物理和动态的表示，逐步提高预报深对流和随后的暴雨的能力。这将通过动态推导湍流长度尺度的子网格混合方案取决于解决的流场，而不是静态地指定它们预先实现。动力学方法将首先用于一个理想化的框架，以提高对大气边界层与深对流之间的耦合的理解，通过诊断边界层和云层中混合的不同长度尺度。这种方法可以提供云环境混合的进一步见解，研究湍流混合在对流发展的不同阶段的影响，并确定湍流输送和天气扰动之间的反馈。一阶动态方案，然后将被用于explostically和将被评估在再现对流发展对静态传统方法在次公里resolution.As的下一步，我将开发一种新的，规模意识和流动自适应动态子网格参数化方法，以更好地代表未解决的规模，通过使用解决的规模，以确定强度的子网格混合。它将利用亚网格湍流输运的守恒方程，通过重建网格尺度附近的分辨场来动态计算亚网格湍流混合长度，从而提供亚网格运动的更精确的表示。因此，该计划将是独立的最小可调封闭参数。新方法将在一个业务数值预报模式中以非常高的亚公里分辨率进行测试，以验证模式动力学在弱和强天气强迫下明确解决现实案例研究中深对流的能力。这种新方法有可能改进天气预报，使气象中心能够向决策者和公众提供更准确的高影响天气预报，同时为进一步研究大气科学提供基础。

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.