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A novel turbulence closure for high-fidelity numerical weather prediction

A novel turbulence closure for high-fidelity numerical weather prediction
用于高保真数值天气预报的新型湍流闭合
批准号:
NE/X018164/1
负责人:
Georgios Efstathiou
金额:
$116.2万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --

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中文摘要
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英文摘要
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.
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Adaptive turbulence modelling to improve high-impact weather forecasts in next generation atmospheric models
  • 批准号:
    NE/T011351/1
  • 项目类别:
    Fellowship
  • 资助金额:
    $68.05万
  • 财政年份:
    2020
  • 负责人:
    Georgios Efstathiou
  • 依托单位:
国内基金
海外基金
流体湍流运动的相关数学分析
  • 批准号:
    10971174
  • 项目类别:
    面上项目
  • 资助金额:
    25.0万元
  • 批准年份:
    2009
  • 负责人:
    肖跃龙
  • 依托单位: