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，每个对流雷暴的尺度为几公里，在数百公里的尺度上成为一个连贯的实体。我们使用这个术语来涵盖一系列有组织的对流现象，包括飑线，超级单体和中尺度对流复合体。MCS位于天气和气候之间的交叉点。在天气时间尺度上，这些长期存在的系统会产生极端降水和山洪暴发。通过它们与大尺度环流的耦合，它们在气候现象中起着关键作用，包括马登朱利安振荡（MJO），热带辐合带（ITCZ）和季风。动力学耦合是双向的：大规模的环境条件决定了对流组织发生的可能性，而反过来MCS强烈反馈环境的动力学和热力学。网格为15-20公里的全球数值天气预报模式和网格为50-100公里的气候模式不能代表MCS。我们的模型在“灰色地带”运行，在那里现象发生在类似于网格尺度的尺度上。这意味着MCS没有完全解决，但不能使用传统的方法参数化，这假设未解决的过程发生在尺度上比网格尺度小得多。MJO，亚洲季风和ITCZ的代表性的偏见，以及太少的强降水事件，已被链接到MCS的代表性在模型中的缺陷。此外，英国上空的“预报萧条”，在世界领先的数值预报中心，5至6天的提前期预报技能下降到零左右，这与北美上空MCS上游的代表性差有关。我们必须改进MCS在天气和气候模式中的代表性。该项目以全面和协调的方式处理监控监在灰色地带的代表权问题。我们将首先将联合收割机结合一个新的全球卫星来源的MCS数据库与分析产品，以评估MCS的形成和演变的可预测性条件下的大规模，采取一种新的，概率的方法。其次，最近已经发展了几个理论框架，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，创新性地利用数据同化周期内的分析增量，以测量当前模型中缺失的MCS的高档影响。我们将利用所获得的基本理解来开发MCS与更大尺度之间动力学耦合的新参数化。我们将把我们的方法与新的CoMorph对流方案结合起来，该方案正在英国气象局的模型中进行试验。虽然CoMorph在启动组织方面有了很大的改进，但MCS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：我们将代表不同的MCS趋势的概率条件下解决规模流。随机方案非常适合灰色区域，其中参数化运动受网格尺度变量的约束很差，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。在验证了我们的知识之后，我们将使用该方案来衡量MCS对气候现象（包括ITCZ和MJO）的动力影响的重要性。该项目将使人们对MCS的形成和高级影响的动力学有新的认识。通过与英国气象局的密切合作，我们打算将其转化为对英国和更广泛世界的更好的概率预测。只有通过可靠的概率预测，工业界、政策制定者和人道主义部门才能量化自然灾害的风险，并采取适当行动防范这些灾害。

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