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.

蒲鲁东在谈到无政府主义运动时声称，“无政府状态是秩序之母”。许多人可能会质疑人类行为是否如此，但大气确实如此。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产生的浮力驱动的。这些风暴一开始是小的湍流漩涡，后来发展成猛烈的雷暴，其中最严重的雷暴通过不断再生的过程来维持其存在。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在一定程度上仍然受到最小动荡规模下发生的流程的控制。如果我们在用于预测天气和气候的模型中表现不佳，我们就会严重损害其预测的准确性。不断增强的计算机能力使我们能够运行更高分辨率的模型，这些模型开始以一定的真实性表示单个云，但我们还远远没有直接解决控制云的大小、强度、数量等的过程。该项目旨在大幅改进我们在实用天气预报模型和气候模型中表示这些过程对云增长和消散影响的方式。到目前为止，我们可以通过从物理基本方程进行理论推导来取得进展，但这样做会带来更多问题悬而未决的问题，例如我们如何预测云中水的分布，云对湍流涡流内的运动高度敏感，仅给出较大尺度（例如几公里）平均属性的有限信息？我们可以通过基于缩放和对称性等参数形成有关依赖关系的假设来“解决”问题，但这些需要测试和校准。作为该项目的一部分，我们计划对受控、理想化、对流进行大量雄心勃勃的参考模拟，以提供数据来检验这些假设。我们还建议开发一个湍流简化表示的层次结构，专门针对深对流的成本有效建模。具体来说，我们计划实施三个方案： 1． “劳斯莱斯”方案，尽可能少的近似。 2. 高度简化的方案，与常用方案相似，但对关键参数进行了全新分析。3.一种新方案，可追溯到 1，但基于 1 的简化，专门针对深对流云的表示，重点关注垂直运动和由此产生的凝结，与水平运动分开。高分辨率数据的分析方式与观测数据的分析方式大致相同如果有的话是可以的，但是我们也计划利用高分辨率模型，这是通过观察永远无法实现的方式；我们计划在运行时直接向低分辨率模拟提供有关“真相”的信息，无论是湍流还是平均变量。这可以通过多种方式来完成，以了解哪些术语对于驱动解析流最重要。一种令人兴奋且新颖的方法是使用数据同化研究中心最近开发的技术来使用“观察”（在我们的例子中，参考高分辨率模拟）来客观地确定哪些参数化最能代表模型中的“缺失”项，即遗漏会导致模型偏离观察结果。

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

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

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

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

Halo Size Around Shallow Cumulus Clouds in the Large Eddy simulations

大涡模拟中浅积云周围的光晕大小

• DOI: 10.5194/egusphere-egu22-10117
• 发表时间: 2022
• 作者: Gu J