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.

骄傲亨说，“无政府状态是秩序的母亲”，指的是无政府主义运动。许多人可能会质疑这是否对人类的行为是正确的，但这当然是气氛。地球上最极端的天气与深对流的暴风雨有关，这是由于水蒸气变成液态云水或冰时释放的热量和浮力所驱动的。这些风暴开始时是小小的动荡的涡流，这些涡流成长为剧烈的雷暴，其中最严重的是通过持续再生的过程来保留其存在。在正确的条件下，风暴可以组织成更大的结构，例如覆盖数百公里和持续数小时的挤压线。有组织的对流的最极端，暴力和美丽的例子是热带气旋。然而，这个越来越大的组织仍然部分地由最小的湍流量表上发生的过程控制。如果我们在用于预测天气和气候的模型中表示这些过程很差，我们会严重损害其预测的准确性。越来越多的计算机功率使我们能够运行更高分辨率模型，这些模型开始用一些现实主义代表单个云，但是我们远非直接解决控制云的大小，强度，数量等的过程。 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 （例如几公里）？我们可以根据诸如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，但是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，对流流的雄心勃勃的参考模拟，以提供数据来检验这些假设。我们还建议开发针对针对深度对流的经济有效建模的湍流简化表示的层次结构。特别是，我们计划实施三个方案：1。 “劳斯莱斯”方案，尽可能少。 2。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。3。一个可追溯的新方案可追溯到1，但基于1个专门针对深度对流云的1个简化，重点关注垂直运动并与水平运动分开导致的凝结。高分辨率数据将与观​​察数据相同的方式分析与观察数据相同的方式，但如果有的话，我们也计划使用高分辨率模型，可以通过观察到高分辨率模型来实现。我们计划在运行时直接提供有关“真相”的信息，以降低分辨率模拟，无论是湍流或平均变量。这可以通过多种方式来了解哪种术语在推动解决流程中最重要。一种令人兴奋的新方法是使用数据同化研究中心最近开发的技术使用“观测”（在我们的情况下，参考高分辨率模拟）来客观地确定哪种参数最能代表模型中的“缺失”项，该模型的遗漏导致模型与观测值不同。

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

A climatology of tropical wind shear produced by clustering wind profiles from the Met Office Unified Model (GA7.0)

• DOI: 10.5194/gmd-14-4035-2021
• 发表时间: 2021-06
• 期刊: Geoscientific Model Development
• 影响因子: 5.1
• 作者: Mark R. Muetzelfeldt;R. Plant;P. Clark;A. Stirling;S. Woolnough