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Diabatic evolution of clouds in a Lagrangian framework: turbulence, vorticity dynamics and precipitation effects

拉格朗日框架中云的非绝热演化：湍流、涡度动力学和降水效应

基本信息

批准号：
EP/T025301/1
负责人：
David Dritschel
金额：
$ 60.43万
依托单位：
University of St Andrews
依托单位国家：
英国
项目类别：
Research Grant
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=EP%2FT025301%2F1
关键词：
Diabatic evolution clouds Lagrangian framework

项目摘要

The importance of clouds in weather and climate has long been recognised, yet accurate cloud modelling is immensely difficult due to the wide range of energetic, spatial and temporal scales involved in cloud formation, growth and decay, and the highly nonlinear nature of these turbulent cloud processes. Indeed, the turbulent behaviour of clouds is responsible for many of the uncertainties in atmospheric models, affecting cloud formation and thereby disrupting the global energy balance in climate simulations, as well as the timing and intensity of precipitation in weather forecasts. Weather and climate models fail to resolve the details of the interactions between clouds and their environment and suffer from a crude representation of important microphysical processes, such as rain and snow formation.These processes can be studied in detail using `large eddy simulation' (LES), a widely-used computational approach employing a fixed discrete grid - a so called `Eulerian' approach. Typically, LES uses grid spacings below 100 metres to resolve such cloud-environment interactions. However, LES still suffers from substantial sensitivity to numerical mixing. This is due to the highly nonlinear nature of the dynamics and thermodynamics of clouds. For the formation of precipitation, regions of high liquid water content are crucial, and numerical mixing can prevent the occurrence of such regions in Eulerian models.We have recently developed a promising new computational model, MPIC (Moist Parcel-In-Cell), which largely overcomes these numerical errors. MPIC deals with the dynamics of clouds in an essentially `Lagrangian' framework, i.e. by explicitly following parcels of fluid rather than approximating this motion on a fixed grid as in LES. The MPIC model represents both dynamics and processes explicitly using Lagrangian parcels that carry a volume, circulation and thermodynamic properties (e.g. potential temperature and moisture content). This approach accurately preserves key parcel properties and avoids problems due to numerical mixing at small scales that are inherent to Eulerian models.Our research aims to adapt the MPIC model for use in realistic atmospheric case studies as well as advance our understanding of the fundamental dynamics of cloud turbulence. In particular, we will investigate how discontinuous diabatic forcing associated with condensation and evaporation modifies turbulence relative to adiabatic conditions. This research presents a novel opportunity to improve our theoretical understanding of generic features of cloud processes. It is especially timely due to the development of a massively parallel version of MPIC, which can resolve detailed processes far beyond the reach of existing numerical models.The potential impacts of this research are vast. Since entrainment in convective clouds strongly influences heavy rainfall, new discoveries about entrainment should enable improved flood forecasting, potentially saving lives, property and business assets. Beyond this project, aspects of the MPIC model may be adopted by numerical weather models to improve the representation of cloud processes and rainfall events. The pathway to this is first to engage scientists at the Met Office, who have agreed to be a project partner. Aspects of the MPIC model will need to be integrated into the latest Met Office model, and the proposed research will make significant progress towards this goal.The proposed research will make substantial advancements in our understanding of fundamental turbulent processes, ultimately leading to improved weather and global climate models. Extensions to the MPIC model will make it well-suited to a range of geophysical applications including cloud simulations and atmospheric chemistry experiments, making it attractive to a wide scientific and industrial audience.

云在天气和气候中的重要性早已被认识到，但由于云的形成、生长和衰减涉及广泛的能量、空间和时间尺度，以及这些湍流云过程的高度非线性性质，精确的云建模非常困难。事实上，云的湍流行为是大气模型中许多不确定性的原因，影响云的形成，从而破坏气候模拟中的全球能量平衡，以及天气预报中的降水时间和强度。天气和气候模式无法解决云与其环境之间相互作用的细节问题，而且对重要的微物理过程，例如雨和雪的形成，只能粗略地表示，这些过程可以用“大涡模拟”详细研究，这是一种广泛使用的计算方法，采用固定的离散网格-一种所谓的“欧拉"方法。通常，LES使用低于100米的网格间距来解决这种云环境相互作用。然而，LES仍然遭受从大量的数值混合的敏感性。这是由于云的动力学和热力学的高度非线性性质。对于降水的形成，高液态水含量的区域是至关重要的，数值混合可以防止在Eulerian模式中出现这样的区域，我们最近开发了一个有前途的新的计算模式，MPIC（Moist Parcel-In-Cell），它在很大程度上克服了这些数值误差。MPIC在本质上是“拉格朗日”框架中处理云的动力学，即通过明确地跟踪流体包裹，而不是像LES那样在固定网格上近似这种运动。MPIC模型明确地使用拉格朗日包裹来表示动力学和过程，拉格朗日包裹携带体积，环流和热力学性质（例如潜在温度和水分含量）。这种方法准确地保留了关键的地块属性，并避免了由于数值混合在小尺度上是固有的欧拉models.Our研究的目的是适应MPIC模型用于现实的大气案例研究，以及推进我们的云湍流的基本动力学的理解问题。特别是，我们将研究如何不连续非绝热强迫与冷凝和蒸发修改湍流相对于绝热条件。这项研究提供了一个新的机会，以提高我们的理论理解的云过程的一般特征。由于MPIC的大规模并行版本的开发，它可以解决远远超出现有数值模型的详细过程，因此这项研究的潜在影响是巨大的。由于对流云中的夹带强烈影响强降雨，有关夹带的新发现应该能够改善洪水预报，从而可能挽救生命，财产和商业资产。除此之外，MPIC模式的某些方面可能会被数值天气模式采用，以改善云过程和降雨事件的表现。实现这一目标的途径首先是让英国气象局的科学家参与进来，他们已经同意成为项目合作伙伴。MPIC模型的各个方面需要被整合到最新的气象局模型中，拟议的研究将朝着这一目标取得重大进展。拟议的研究将在我们对基本湍流过程的理解方面取得实质性进展，最终导致改进天气和全球气候模型。对MPIC模型的扩展将使其非常适合一系列地球物理应用，包括云模拟和大气化学实验，使其对广泛的科学和工业受众具有吸引力。