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Understanding and Representing Atmospheric Convection across Scales - ParaCon Phase 2

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:
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
项目状态:
已结题
起止时间:
2019 至 --

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项目成果

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中文摘要
翻译
积云是由浮力空气的剧烈上升产生的,这一过程被称为对流。热带的天气和气候以积云为主,所有纬度的恶劣天气都涉及对流。对流将地球表面的热量和水分传递到整个大气中。它是控制热带大气温度和湿度随高度变化的主要过程。在全球范围内,大部分降雨是由积云造成的,对流是地球大气流动总体格局的关键组成部分。大气的计算机模拟对于数值天气预报和气候预估都是必不可少的。从公众个人到对天气敏感的商业活动、能源部门、应急服务和政府气候风险政策等各个层面的决策提供信息,社会从中获益匪浅。NWP和气候预估的计算机模式将大气分为典型水平大小分别为10公里和100公里的方框。另一方面,像雷暴这样的对流元素通常只有1公里左右的大小,所以它们不能在模型中明确地表示出来。相反,我们必须以某种方式估计每个方框中会出现什么积云,以及它们对更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,是我们面临的最大的单一不确定性。这是一场严峻而无情的考验,考验我们对对流的基础科学及其在大气中的作用的理解程度。已知现有参数化的缺陷会转化为天气和气候模式的严重缺陷。这些误差包括对流降雨的分布、时间和强度方面的误差,以及与对流耦合的大尺度天气系统的行为。ParaCon第二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,以展示模型保真度和性能的明显改进,并为下一代参数化研究奠定基础。在第一阶段,我们开发了一个名为CoMorph的新对流方案基础设施,它使在这种参数化中做出的许多假设可以放松、删除或一般化,我们已经开始基于替代和更一般的假设开发一个公式的过程。同样,在第一阶段,我们也进行了一些有希望的研究,基于对流作为湍流表现形式的建模,以及一种比CoMorph更放松通常假设的多流体方法,对完全不同的公式进行了研究。在第二阶段,我们将继续发展CoMorph,以期将其用于业务预测。在第一阶段工作的基础上,将为该计划的组成部分制定和实施改进的方案。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进行的一套高分辨率理想对流原型模拟的比较,以及一系列操作风格的配置。在第二阶段,我们还将继续发展基于湍流和基于多流体的方法,并评估它们在大气模型中表示对流的潜力。一个关键目标将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可能在多大程度上是有益的。
英文摘要
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.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
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]
通讯作者: Hagos S
DOI: 10.5194/egusphere-egu21-6225
发表时间: 2021
期刊:
影响因子: --
作者: [Hermoso A]
通讯作者: 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]
通讯作者: Daleu 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]
通讯作者: Gu J
共 9 条
    Putting the morph into CoMorph: Adapting convection parametrisation for the hard grey zone
    • 批准号:
      NE/X018512/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $116.52万
    • 财政年份:
      2023
    • 负责人:
      Robert Plant
    • 依托单位:
    Revolutionizing Convective Parameterization
    • 批准号:
      NE/N013743/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $95.37万
    • 财政年份:
      2016
    • 负责人:
      Robert Plant
    • 依托单位:
    GREYBLS: modelling GREY-zone Boundary LayerS
    • 批准号:
      NE/K011502/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $31.52万
    • 财政年份:
      2013
    • 负责人:
      Robert Plant
    • 依托单位:
    Stochastic Parameterization of Deep Convection in Short-Range Ensemble Weather Forecasts
    • 批准号:
      NE/D011493/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $32.43万
    • 财政年份:
      2007
    • 负责人:
      Robert Plant
    • 依托单位:
    海外基金