Circle-A: Parametrizing Convection in the Hard Grey Zone: Modelling the Interaction of Turbulent Cloud processes with Explicit Cloud Dynamics.

Circle-A：硬灰色区域中的对流参数化：对湍流云过程与显式云动力学的相互作用进行建模。

• 批准号: NE/N013735/1
• 负责人: Peter Clark
• 金额: $ 93.14万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FN013735%2F1
• 关键词: Circle; Parametrizing; Convection; Hard; Grey

"Anarchy is the mother of Order", claimed Proudhon, referring to the Anarchist movement. Many might question whether this is true of human behaviour, but it certainly is of the atmosphere. Some of the most extreme weather on earth is associated with deep convective rain storms driven by the heat released and buoyancy generated when water vapour turns into liquid cloud water or ice. These storms start out as small turbulent eddies which grow into violent thunderstorms, the most severe of which preserve their existence by a process of continual regeneration. In the right conditions, storms can organise into much larger structures such as squall lines covering hundreds of kilometres and lasting many hours. The most extreme, violent and beautiful example of organised convection is the tropical cyclone. This ever-larger scale organisation nevertheless remains, in part, controlled by processes occurring at the smallest turbulent scales. If we represent these processes poorly in models used to predict weather and climate, we severely compromise the accuracy of their predictions. Ever-increasing computer power has given us the ability to run higher resolution models that are beginning to represent individual clouds with some realism, but we are far from directly resolving those processes that control the size, intensity, number etc. of clouds. This project is aimed at substantially improving the way we represent the effect of these processes on cloud growth and dissipation in practicable weather forecast models and climate models.We can get so far by theoretical derivation from the fundamental equations of physics, but doing so raises more unanswered questions, such as how can we predict the distribution of water in a cloud, which is highly sensitive to motions within turbulent eddies, given only limited information of average properties over larger scales (e.g. a few km)? We can 'close' the problem by forming hypotheses about dependencies based upon arguments such as scaling and symmetry, but these need testing and calibrating. As part of the project we plan to run a number of ambitious reference simulations of controlled, idealized, convective flows, to provide data to test these hypotheses. We also propose developing a hierarchy of simplified representations of the turbulent flow directed specifically at cost effective modelling of deep convection. In particular, we plan to implement three schemes:1. A 'Rolls-Royce' scheme, with as little approximation as possible. 2. A highly simplified scheme similar to those commonly used but and with a fresh analysis of key parameters.3. A new scheme traceable from 1 but based on a simplification of 1 directed specifically at the representation of deep convective clouds, focussing on vertical motion and resulting condensation separately from horizontal motion.The high resolution data will be analysed much the same way that observational data would be if available, but we also plan to make use of high-resolution models is a way which could never be achieved through observation; we plan to provide information about the 'truth' directly to lower resolution simulations as they run, either turbulent fluxes or mean variables. This can be done in a number of ways to learn about which terms are most important in driving the resolved flow. One exciting and novel way is to use techniques recently developed in the Data Assimilation Research Centre to use 'observations' (in our case, reference high-resolution simulations) to determine objectively which parametrizations best represent the 'missing' terms in a model, the omission of which lead the model to diverge from the observations.

“无政府主义是秩序之母”，蒲鲁东在谈到无政府主义运动时这样说。许多人可能会质疑人类行为是否如此，但它肯定是大气层。地球上一些最极端的天气与深对流暴雨有关，这些暴雨是由水蒸气变成液态云水或冰时释放的热量和浮力驱动的。这些风暴开始时是小的湍流漩涡，然后发展成猛烈的雷暴，其中最严重的雷暴通过不断再生的过程来维持它们的存在。在适当的条件下，风暴可以组织成更大的结构，例如覆盖数百公里并持续数小时的飑线。有组织对流中最极端、最猛烈、最美丽的例子是热带气旋。然而，这种规模越来越大的组织仍然部分地受到最小湍流尺度上发生的过程的控制。如果我们在用于预测天气和气候的模型中表现出这些过程，我们就会严重损害其预测的准确性。不断增长的计算机能力使我们有能力运行更高分辨率的模型，这些模型开始以某种现实主义的方式代表单个云，但我们还远远没有直接解决那些控制云的大小，强度，数量等的过程。这个项目的目的是在实际的天气预报模式和气候模式中，大大改进我们描述这些过程对云的增长和消散的影响的方法。目前，我们可以从物理学的基本方程进行理论推导，但这样做会带来更多未回答的问题，例如，如何预测云中的水分布，这对湍流涡旋中的运动非常敏感，如果在较大尺度（例如几公里）上的平均属性信息有限，我们可以通过基于诸如缩放和对称性之类的参数形成关于依赖性的假设来“关闭”问题，但这些需要测试和校准。作为该项目的一部分，我们计划运行一些雄心勃勃的参考模拟控制，理想化，对流，提供数据来测试这些假设。我们还建议开发一个层次结构的湍流的简化表示专门针对成本效益的深对流建模。我们特别计划推行三项计划：一个“劳斯莱斯”计划，尽可能少的近似。2.一个高度简化的方案，类似于常用的方案，但对关键参数进行了新的分析。一个新的方案可追溯自1，但基于1的简化，专门针对深对流云的表示，侧重于垂直运动和与水平运动分开的凝结，高分辨率数据将以与观测数据（如果有）相同的方式进行分析，但我们也计划利用高分辨率模式，这是一种通过观测永远无法实现的方式;我们计划在较低分辨率的模拟运行时，直接向它们提供有关“真相”的信息，无论是湍流通量还是平均变量。这可以通过多种方式来完成，以了解哪些术语在驱动解析流中最重要。一个令人兴奋的和新颖的方法是使用最近开发的技术在数据同化研究中心使用“观察”（在我们的情况下，参考高分辨率模拟），以客观地确定哪些参数化最好地代表了“失踪”的条款在一个模型中，遗漏导致模型偏离观察。

项目成果

Composited structure of non-precipitating shallow cumulus clouds

非降水浅层积云的复合结构

• DOI: 10.1002/qj.4101
• 发表时间: 2021
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: Gu J

Numerical methods for entrainment and detrainment in the multi-fluid Euler equations for convection

对流多流体欧拉方程中夹带和脱附的数值方法

• DOI: 10.1002/qj.3728
• 发表时间: 2020
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: McIntyre W

Supplementary material to "A climatology of tropical wind shear produced by clustering wind profiles from a climate model"

补充材料

• DOI: 10.5194/gmd-2020-388-supplement
• 发表时间: 2021
• 作者: Muetzelfedt M

Radiation, Clouds, and Self-Aggregation in RCEMIP Simulations

RCMIP 模拟中的辐射、云和自聚集

• DOI: 10.5194/egusphere-egu23-1071
• 发表时间: 2023
• 作者: Holloway C

Evaluation of the Bulk Mass Flux Formulation Using Large-Eddy Simulations

使用大涡模拟评估整体质量通量配方

• DOI: 10.1175/jas-d-19-0224.1
• 发表时间: 2020
• 期刊: Journal of the Atmospheric Sciences
• 影响因子: 3.1
• 作者: Gu J