Revolutionizing Convective Parameterization

彻底改变对流参数化

• 批准号: NE/N013743/1
• 负责人: Robert Plant
• 金额: $ 95.37万
• 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%2FN013743%2F1
• 关键词: Revolutionizing; Convective; Parameterization

The weather and climate of the tropics is dominated by cumulus clouds. These clouds are produced by vigorous convection currents within the atmosphere. The convection communicates heat and evaporation 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, both for global numerical weather prediction (NWP) and for climate projection, divides the atmosphere into boxes with typical horizontal sizes of 20 and 100km respectively. This means that the models are not able to represent convective elements (such as thunderstorms) properly, because these elements are typically only around 1km in size. However, as convection has a crucial role to play in the atmosphere, it must be represented within the models. We have somehow to 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. Computer modelling of the atmosphere is essential for reliable climate projections and weather forecasts. 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 to the insurance sector to the emergency services to government policy on climate risks. Convection parameterization is a stubborn and difficult problem and 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. To give just one example, in many models the predicted convective rainfall is too frequent and too light.RevCon is so named because the project aims at a revolution in convective parameterization by challenging many of the key assumptions that have been made within convective parameterizations for decades. Some of these assumptions are very limiting and known to be poor approximations in many circumstances. We are also convinced that they are unnecessary and that better parameterizations can be achieved without them. This project will establish what the mathematical structure of convection parameterization really should be, with that structure being informed and carefully justified through the detailed analysis of very high-resolution simulations of convective cloud systems. It is a critical contribution to a NERC / Met Office programme in this area because it will provide a necessary starting point for Phase 2 of the programme to build a new-generation parameterization for the Met Office weather forecast and climate model.

热带地区的天气和气候是由积云控制的。这些云是由大气层内强烈的对流产生的。对流将热量和蒸发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，积云是降雨的主要来源，对流是地球大气流动总体模式的重要组成部分。用于全球数值天气预报（NWP）和气候预测的大气计算机模拟将大气划分为典型水平尺寸分别为20和100公里的盒子。这意味着模型不能正确地表示对流元素（如雷暴），因为这些元素通常只有大约1公里的大小。然而，由于对流在大气中起着至关重要的作用，它必须在模型中表示。我们必须以某种方式估计每个盒子中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。大气层的计算机模拟对于可靠的气候预测和天气预报至关重要。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，到保险部门，到紧急服务，再到政府关于气候风险的政策。对流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。举一个例子，在许多模式中，预测的对流降雨太频繁，太轻。RevCon如此命名是因为该项目旨在通过挑战数十年来对流参数化中的许多关键假设来进行对流参数化的革命。这些假设中的一些是非常有限的，并且已知在许多情况下是差的近似。我们还相信，它们是不必要的，没有它们也可以实现更好的参数化。该项目将建立对流参数化的数学结构，通过对对流云系的高分辨率模拟的详细分析，了解并仔细证明该结构的合理性。这是对NERC/气象局计划在这一领域的重要贡献，因为它将为该计划的第二阶段提供必要的起点，为气象局天气预报和气候模式建立新一代参数化。

项目成果

The impact of surface heterogeneity on the diurnal cycle of deep convection

地表非均质性对深层对流日循环的影响

• DOI: 10.1002/qj.4371
• 发表时间: 2022
• 期刊: Quarterly Journal of the Royal Meteorological Society
• 影响因子: 8.9
• 作者: Harvey N

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

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

A Machine Learning Assisted Development of a Model for the Populations of Convective and Stratiform Clouds

机器学习辅助开发对流云和层状云群模型

• DOI: 10.1029/2019ms001798
• 发表时间: 2020
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Hagos S

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