A novel turbulence closure for high-fidelity numerical weather prediction

数值天气预报（NWP）模式中湍流过程的表示是捕捉极端天气事件（如对流风暴、强降雨和伴随的破坏性风）的关键。对流尺度现象在局部表现，但通过大、小尺度运动之间的相互作用产生，因此具有挑战性。传统的湍流模型要求湍流和天气流之间有明确的尺度分离，这意味着所有的湍流都是参数化的。然而，将NWP模式的分辨率提高到亚公里尺度，可以对边界层和云尺度流动进行部分解析。因此，湍流闭包背后的基本假设在这种被称为湍流“灰色地带”的解决机制中并不成立。这对高分辨率NWP产生了重大影响，并限制了亚公里模式的价值。这里提出的新方法有可能克服这些限制，从而实现亚公里模拟的全部价值，并导致更高保真度的业务天气预报。该项目旨在通过开发动态长度尺度闭合来打破灰色区域湍流建模的僵局。该方法将在英国气象局统一模型（UM）中实施，预计将在预测高影响天气事件方面取得重大进展。新的闭包将提供更好的子网格湍流参数化，这将导致在非常高分辨率模型中更忠实地表示已解决的湍流结构。这将导致更准确地模拟大气边界层及其转变的演变，同时也改善了湿对流湍流在尺度上的无缝表现，从而更好地预测对流云的时间和发展。作为起点，新方法将放宽在当前操作的UM湍流混合方案中所做的假设，这是促进适应亚公里分辨率制度的必要步骤。更进一步，将动态方法与高阶闭合相结合，将产生一个真正新颖的模型，能够再现跨尺度的湍流输运转变，从完全解决一直到完全参数化的湍流状态。新方法将从当前可操作的UM格式的动态混合开始，逐步发展到具有自适应闭包参数的高阶动态三维湍流格式。新的动态方法将与传统的静态方案进行评估，并使用WesCon现场活动的数据以及其他现成的观测数据集进行验证。WesCon强调对上升气流和湍流及其与其他过程的相互作用的理解，使其成为验证所提出方法的独特测试平台。与此同时，我们的工作将集中于彻底了解未解决的湍流的长度尺度，特别是在目前知识有限的深风暴云中。我们将仔细评估逐步增加新方法的复杂性所带来的影响，以检验提高天气预报保真度的好处。

The representation of turbulent processes in numerical weather prediction (NWP) models is key for capturing extreme weather events such as convective storms, heavy rainfall and accompanying damaging winds. Convective scale phenomena are manifested locally but are born through the interactions between large and small-scale motions and therefore are challenging to predict. Conventional turbulence modelling requires a clear scale separation between turbulent and synoptic flow implying that all turbulence is parametrised. However, increasing the resolution of NWP models to the sub-kilometric scales makes the boundary layer and the cloud-scale flows partially resolved. Hence, the fundamental assumptions behind turbulence closures do not hold in this resolution regime which is termed the 'grey zone' of turbulence. This results in substantial implications for high-resolution NWP with limitations for the value of sub-kilometric models. The novel method proposed here has the potential to overcome these limitations, thereby realising the full value of sub-kilometre simulations and leading to higher fidelity operational weather forecasts.The project aims to break the deadlock of grey-zone turbulence modelling by developing a dynamic length scale closure. The method will be implemented within the Met Office Unified Model (UM) and is expected to lead to significant improvements in the prediction of high-impact weather events. The novel closure will provide better parametrisation of subgrid turbulence which will result in a more faithful representation of resolved turbulence structures in very high resolution models. This will lead in turn to more accurate simulation of the evolution of the atmospheric boundary layer and its transitions, whilst also improving the representation of moist convective turbulence seamlessly across the scales, thereby better predicting the timing and development of convective clouds. As a starting point, the new method will provide a relaxation of the assumptions made within the current operational UM turbulence blending scheme, a necessary step in order to facilitate adaptation to the sub-km resolution regime. Going a step further and combining the dynamic approach with a higher order closure will result in a truly novel model able to reproduce the transitions of turbulent transport across the scales, from the fully resolved all the way to the fully parametrised turbulence regime.The new method will be developed in steps of varying complexity starting from the dynamic blending of the current operational UM scheme and moving gradually to a higher-order dynamic, 3-dimensional turbulence scheme with self-adapting closure parameters. The new dynamic approaches will be evaluated against the conventional static schemes and validated with data from the WesCon field campaign as well as other readily-available observational datasets. WesCon has an emphasis on the understanding of updrafts and turbulence and their interaction with other processes, making this a unique testbed for the validation of the proposed approach. At the same time our work will focus on thoroughly understanding the length scales of unresolved turbulence, especially in deep storm clouds where current knowledge is limited. We will carefully assess the impact of gradually increasing the complexity of the new method examining the benefits for the increased fidelity of weather forecasts.