Understanding and Representing Atmospheric Convection across Scales - ParaCon Phase 2

理解和表示跨尺度的大气对流 - ParaCon 第 2 阶段

• 批准号: NE/T003871/1
• 负责人: Robert Plant
• 金额: $ 122.64万
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT003871%2F1
• 关键词: Understanding; Representing; Atmospheric; Convection; across

Cumulus clouds are produced by the vigorous ascent of buoyant air, a process known as convection. The weather and climate of the tropics are dominated by cumulus clouds, and severe weather at all latitudes involves convection. Convection communicates heat and moisture from the Earth's surface throughout the atmosphere. It is the main process controlling the change of temperature and moisture content with height in the tropical atmosphere. On the global scale, cumulus clouds are responsible for the majority of the rainfall, and convection is a crucial component in the overall pattern of the Earth's atmospheric flows.Computer modelling of the atmosphere is essential for both numerical weather prediction (NWP) and climate projections. Society benefits enormously from their outputs to inform decision making on all scales from the individual member of the public to weather-sensitive business activities, the energy sector, the emergency services, and government policy on climate risks. Computer models for NWP and for climate projection divide the atmosphere into boxes with typical horizontal sizes of 10km and 100km respectively. Convective elements such as thunderstorms, on the other hand, are typically only around 1km in size so they cannot be explicitly represented in the models. Instead we must somehow estimate what cumulus clouds will be present in each of the boxes and what their collective effects will be on the larger-scale atmosphere. This is known as a cumulus parameterization.Cumulus parameterization is a stubborn and difficult problem and is the largest single uncertainty that we face. It is a severe and unforgiving test of just how well we understand the fundamental science of convection and its role in the atmosphere. Defects in the existing parameterizations are known to translate into serious deficiencies in weather and climate models. These include errors in the distribution, timing, and intensity of convective rainfall, as well as the behaviour of larger-scale weather systems that are coupled to convection.ParaCon Phase 2 is a wide-ranging plan to redesign the convection parameterization for the Met Office Model, to demonstrate clear improvements in model fidelity and performance, and to lay the groundwork for the next generation of parameterization research.In Phase 1 we have developed a new convection scheme infrastructure called CoMorph, which enables many of the assumptions that are made in such parameterizations to be relaxed, removed or generalized and we have begun the process of developing a formulation based on alternative and more general assumptions. Also in Phase 1 we have performed promising investigations into radically different formulations based on modelling convection as a manifestation of turbulence, and on a multi-fluid approach that relaxes the usual assumptions even further than CoMorph does.In Phase 2 we will continue the development of CoMorph with a view to its adoption for operational forecasting. Building on the work in Phase 1, improved formulations for the components of the scheme will be developed and implemented. The performance of CoMorph will be evaluated in a wide range of test cases. These will include comparison with a suite of high-resolution simulations of idealized convective archetypes conducted in Phase 1, as well as a range of operational-style configurations.In Phase 2 we will also continue to develop the turbulence-based and multi-fluid-based approaches and to evaluate their potential for representing convection in atmospheric models. A key goal will be to clarify the relationship between the three approaches and to understand the extent to which some unification or combination of the approaches might be possible and beneficial.

积云是由有浮力的空气剧烈上升而产生的，这个过程被称为对流。热带地区的天气气候以积云为主，各纬度的恶劣天气都涉及对流。对流将热量和水分从地球表面传递到整个大气层。它是控制热带大气温度和水汽含量随高度变化的主要过程。在全球范围内，积云是大部分降雨的来源，而对流是地球大气流动的整体模式的重要组成部分。大气的计算机模拟对数值天气预报和气候预测都是必不可少的。社会从他们的产出中受益匪浅，为所有规模的决策提供信息，从公众个人到对天气敏感的商业活动，能源部门，紧急服务和政府关于气候风险的政策。数值预报和气候预测的计算机模型将大气划分为水平尺寸分别为10公里和100公里的盒子。另一方面，对流元素，如雷暴，通常只有大约1公里的大小，所以它们不能在模型中明确表示。相反，我们必须以某种方式估计在每个盒子里会出现什么样的积云，以及它们对大尺度大气的集体影响。这就是所谓的积云参数化。积云参数化是一个顽固而困难的问题，也是我们面临的最大的单一不确定性。这是一个严峻而无情的考验，考验我们对对流的基本科学及其在大气中的作用有多了解。已知现有参数化的缺陷会转化为天气和气候模型的严重缺陷。这些误差包括对流降雨的分布、时间和强度，以及与对流耦合的大尺度天气系统的行为。ParaCon第2阶段是一个范围广泛的计划，旨在重新设计气象局模型的对流参数化，以证明模型保真度和性能的明显改善，并为下一代参数化研究奠定基础。在第一阶段，我们开发了一种新的对流方案基础设施，称为CoMorph，它使参数化中的许多假设得以放松，我们已开始根据其他和更一般的假设拟订一项案文。同样在第一阶段，我们已经进行了有前途的调查，从根本上不同的配方的基础上模拟对流作为湍流的表现形式，并在多流体的方法，放宽通常的假设，甚至比CoMorph没有。在第二阶段，我们将继续发展CoMorph，以期采用业务预报。在第一阶段工作的基础上，将制定和实施该计划各组成部分的改进方案。CoMorph的性能将在广泛的测试用例中进行评估。这些将包括与第一阶段中进行的一套理想化对流原型的高分辨率模拟以及一系列操作风格的配置进行比较。在第二阶段中，我们还将继续开发基于连续性和基于多流体的方法，并评估它们在大气模型中代表对流的潜力。一个关键目标是澄清这三种方法之间的关系，并了解这些方法的某种统一或组合在多大程度上是可能的和有益的。

项目成果

A Machine-Learning-Assisted Stochastic Cloud Population Model as a Parameterization of Cumulus Convection

作为积云对流参数化的机器学习辅助随机云种群模型

• DOI: 10.1029/2021ms002808
• 发表时间: 2022
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Hagos S

Improving heavy precipitation forecasting over the western Mediterranean: Benefits of stochastic techniques for model error sampling

改进地中海西部强降水预报：模型误差抽样随机技术的好处

• DOI: 10.5194/egusphere-egu21-6225
• 发表时间: 2021
• 作者: Hermoso A

Evaluating the CoMorph-A parametrization using idealized simulations of the two-way coupling between convection and large-scale dynamics

使用对流和大规模动力学之间双向耦合的理想化模拟来评估 CoMorph-A 参数化

• DOI: 10.1002/qj.4547
• 发表时间: 2023
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: Daleu C

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

Pressure Drag for Shallow Cumulus Clouds: From Thermals to the Cloud Ensemble

浅层积云的压力阻力：从热气流到云团

• DOI: 10.1029/2020gl090460
• 发表时间: 2020
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Gu J