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

云在天气和气候中的重要性早已被认识到，然而，由于云的形成、增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，精确的云模拟是非常困难的。事实上，云的湍流行为造成了大气模型中的许多不确定性，影响了云的形成，从而扰乱了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。天气和气候模型不能解决云与其环境之间相互作用的细节，并且无法粗略地表示重要的微物理过程，如雨雪形成。这些过程可以使用“大涡模拟”(LES)进行详细研究，这是一种使用固定离散网格的广泛使用的计算方法--即所谓的“欧拉”方法。通常，LES使用100米以下的网格间距来解决云与环境之间的相互作用。然而，大涡模拟仍然对数值混合非常敏感。这是由于云的动力学和热力学的高度非线性性质。对于降水的形成来说，液态水含量高的区域是至关重要的，而数值混合可以防止在欧拉模式中出现这样的区域。我们最近发展了一个很有前途的新的计算模式，MPIC(湿润包裹单元)，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”的框架中处理云的动力学，即通过显式地跟随流体块而不是像在LES中那样在固定网格上近似这种运动。MPIC模型使用带有体积、环流和热力学属性(例如，潜在温度和水分含量)的拉格朗日地块显式地表示动力学和过程。这种方法准确地保留了关键的地块属性，并避免了欧拉模式固有的小尺度数值混合带来的问题。我们的研究旨在使MPIC模式用于实际的大气案例研究，并促进我们对云湍流基本动力学的理解。特别是，我们将研究与凝结和蒸发相关的非连续非绝热强迫如何相对于绝热条件改变湍流。这项研究为提高我们对云过程一般特征的理论理解提供了一个新的机会。这一研究尤其及时，因为大规模并行版本的MPIC可以解决现有数值模式所无法解决的详细过程。由于对流云中的夹带强烈影响暴雨，有关夹带的新发现应该能够改进洪水预报，潜在地拯救生命、财产和商业资产。在这个项目之外，数值天气模式可能会采用MPIC模式的各个方面，以改进对云过程和降雨事件的描述。要做到这一点，首先要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模式的各个方面将需要整合到最新的气象局模式中，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，最终导致天气和全球气候模式的改善。对MPIC模型的扩展将使其非常适合于包括云模拟和大气化学实验在内的一系列地球物理应用，使其对广泛的科学和工业受众具有吸引力。

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.