Mesoscale Convective Systems: PRobabilistic forecasting and upscale IMpacts in the grey zonE (MCS:PRIME)

中尺度对流系统：概率预测和灰色地带的高档影响 (MCS:PRIME)

• 批准号: NE/W005530/1
• 负责人: Hannah Christensen
• 金额: $ 78.84万
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FW005530%2F1
• 关键词: Mesoscale; Convective; Systems; PRobabilistic; forecasting

A Mesoscale Convective System (MCS) is an organisation of many convective thunderstorms, each a few km in scale, into a coherent entity on scales of hundreds of km. We use the term to encompass a range of organised convective phenomena, including squall lines, supercells, and mesoscale convective complexes.MCS sit at the intersection between weather and climate. On weather timescales, these long-lived systems produce extreme precipitation and flash flooding. Through their coupling to the large-scale circulation, they play a key role in climate phenomena including the Madden Julian Oscillation (MJO), the Intertropical Convergence Zone (ITCZ), and the Monsoons. The dynamical coupling is two-way: large-scale environmental conditions dictate the likelihood of convective organisation occurring, while in turn the MCS strongly feedback on the dynamics and thermodynamics of the environment. Global numerical weather prediction (NWP) models, with grids of 15-20 km, and climate models, with grids of 50-100 km, cannot represent MCS. Our models operate in the "grey zone" where the phenomenon occurs at scales similar to the grid scale. This means that MCS are not fully resolved, but cannot be parametrised using conventional approaches, which assume that the unresolved process occurs on scales much smaller than the grid scale. Biases in the representation of the MJO, Asian Monsoons and ITCZ, as well as too few strong precipitation events, have been linked to deficiencies in the representation of MCS in models. Furthermore, "forecast busts" over the UK, for which the five- to six-day lead time forecast skill drops to around zero across the world's leading NWP centres, have been linked to a poor representation of MCS upstream over North America. We must improve the representation of MCS in weather and climate models. This project addresses the representation of MCS in the grey zone in a comprehensive and coordinated manner. We will first combine a new global satellite-derived database of MCS with analysis products to assess the predictability of MCS formation and evolution conditioned on the large scales, taking a novel, probabilistic approach. Secondly, several theoretical frameworks have recently been developed which describe the dynamical impact of MCS back onto the large scales. We will critically assess these frameworks, making innovative use of analysis increments from within the data assimilation cycle, to measure the upscale impacts of MCS that are missing from current models.We will use the fundamental understanding gained to develop a new parametrisation of the dynamical coupling between MCS and the larger scales. We will couple our approach to the new CoMorph convection scheme, which is undergoing trials for operational implementation in the UK Met Office's model. While CoMorph shows substantial improvements in initiating organisation, coupling of MCS to the large scales remains a problem. The representation we develop will be stochastic: we will represent the probability of different MCS tendencies conditioned on the resolved scale flow. Stochastic schemes are well suited to the grey zone, where parametrised motions are poorly constrained by the grid-scale variables, and so are very uncertain.Evaluating the new parametrisation will critically test the knowledge gained throughout the project. Having validated our knowledge, we will use the scheme to measure the importance of the dynamical impacts of MCS on climate phenomena including the ITCZ and the MJO. This project will produce a new understanding of the dynamics of MCS formation and upscale impacts. Through close collaboration with the Met Office, we intend to translate this into improved probabilistic forecasts for the UK and wider world. Only with reliable probabilistic forecasts can industry, policy makers, and the humanitarian sector quantify the risks of natural hazards, and act appropriately to protect against those hazards.

中尺度对流系统（MCS）是许多对流雷暴的组织，每个组织的规模几个公里，在数百公里的尺度上进入一个连贯的实体。我们使用该术语涵盖了一系列有组织的对流现象，包括ques虫线，超级电池和中尺度对流综合体。在天气时期，这些长寿命的系统会产生极端的降水和山洪泛滥。通过与大规模循环的耦合，它们在包括Madden Julian振荡（MJO），受热带收敛区（ITCZ）和季风在内的气候现象中起着关键作用。动态耦合是双向的：大规模的环境条件决定了对流组织发生的可能性，而MC又强烈反馈对环境的动力学和热力学。网格为15-20 km的全球数值天气预测（NWP）模型，网格为50-100 km的气候模型不能代表MC。我们的模型在“灰色区域”中运行，该现象以类似于网格尺度的尺度出现。这意味着MC并未完全解决，但不能使用常规方法进行参数化，该方法假设未解决的过程发生在比电网尺度小得多的尺度上。 MJO，亚洲季风和ITCZ的偏见，以及太少的强降水事件与模型中MCS表示的缺陷有关。此外，英国的“预测胸像”为5至六天的领先时间预测技能下降到全球领先的NWP中心的零，与北美上游的MCS代表不佳有关。我们必须改善MC在天气和气候模型中的表示。该项目以全面和协调的方式解决了MC在灰色区域中的代表。我们将首先将新的全球卫星衍生的MC数据库与分析产品相结合，以评估MCS形成和以大规模为条件的MCS形成和进化的可预测性，采用了一种新颖的概率方法。其次，最近已经开发了几个理论框架，这些框架描述了MCS回到大尺度上的动态影响。我们将批判性地评估这些框架，从而使数据同化周期内的分析增量进行创新，以衡量当前模型中缺少的MC的高档影响。我们将使用所获得的基本理解来开发MCS和较大尺度之间动态耦合的新参数。我们将把我们的方法与新的Comorph对流计划相结合，该计划正在接受英国大都会办公室模型的运营实施试验。尽管Comorph在启动组织中显示出很大的改进，但MC与大规模的耦合仍然是一个问题。我们开发的表示将是随机的：我们将代表以解决量表流为条件的不同MC趋势的概率。随机方案非常适合灰色区域，在该区域中，参数化运动受网格尺度变量的约束，因此非常不确定。评估新的参数化将严格测试整个项目中获得的知识。验证了我们的知识后，我们将使用该方案来衡量MC对包括ITCZ和MJO在内的气候现象的动态影响的重要性。该项目将对MCS组和高档影响的动态产生新的了解。通过与大都会办公室的密切合作，我们打算将其转化为对英国和更广阔世界的改进的概率预测。只有使用可靠的概率预测，行业，政策制定者和人道主义部门才能量化自然危害的风险，并采取适当的行动以防止这些危害。

项目成果

Improving and Assessing Organized Convection Parameterization in the Unified Model

改进和评估统一模型中的有组织对流参数化

• DOI: 10.5194/egusphere-egu24-8367
• 发表时间: 2024
• 作者: Zhang Z

Environmental Precursors to Mesoscale Convective Systems

中尺度对流系统的环境前体

• DOI: 10.5194/egusphere-egu23-16303
• 发表时间: 2023
• 作者: Muetzelfeldt M