Criticality: A Theory for Understanding and Forecasting Deep Convective Initiation

临界性：理解和预测深对流起始的理论

• 批准号: 0757189
• 负责人: Adam Houston
• 金额: $ 18.91万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757189&HistoricalAwards=false
• 关键词: Criticality; Theory; Understanding; Forecasting; Deep

Intellectual Merit: Deep convection in the atmosphere involves the vertical transport of heat and moisture through a considerable fraction of the troposphere. It plays a significant role in regulating the water cycle and thus regional and local variations in the occurrence of deep convection can significantly impact how water resources are managed. Deep convection can also produce large hail, damaging winds, tornadoes, and flooding rains and can therefore pose a significant threat to health and safety. Improving forecasts of deep convection will enable better assessment and management of these threats and will also enable more effective management of local and regional water resources. Improving forecasts of the initiation of deep convection is essential to the overall improvement to forecasts of deep convection. However, consistently accurate predictions of deep convective initiation (DCI) can only be possible if there is an understanding of the fundamental regulating mechanisms. The first objective of the research is to advance the state of knowledge by solidifying the theory of criticality, a new conceptual paradigm for DCI that captures its fundamental regulating mechanisms. Criticality is a concept that was introduced in a recent article by the PI and uses the non-linear relationship between buoyancy and dilution to define two convective regimes: a supercritical regime in which DCI is likely and a subcritical regime in which DCI is unlikely. With criticality the probability of DCI is seen to depend not on the likelihood that parcels will become unstable but on the likelihood that parcels will become supercritical. Solidifying the concept of criticality will rely on a combination of numerical experiments conducted with a both a three dimensional (3D) cloud-resolving model and an idealized one dimensional (1D) model of criticality. Because criticality is defined by the relationship between buoyancy and dilution, the 3D experiments will focus on examining the sensitivity of DCI to both buoyancy and dilution. Experiments with the 1D criticality model are designed to isolate the role of criticality from the more complex dynamics and microphysics operating within a convective cloud. The 1D criticality model will be applied to the 3D simulations in an effort to determine the reliability of using criticality to discriminate between environments that do and do not yield DCI. The second objective of the research is to develop metrics for quantifying criticality. These metrics will enable the analysis planned to fulfill the first objective. They will also be instrumental in meeting the third objective of this work. The third objective is to determine how well criticality metrics in particular and criticality in general discriminate between observed environments that do and do not support DCI. Statistical analysis will serve to quantify how well various criticality metrics predict DCI. These results will then be compared to other metrics used for forecasting DCI. Broader Impacts: An improved understanding of deep convection is clearly important to society and improved forecasts of DCI will provide direct and immediate benefit. This work not only aims to improve understanding of DCI but, through the development and testing of criticality metrics, involves concrete steps that will enable the application of this work to the operational forecasting of DCI. This work will also enable the PI to solidify partnerships between the University of Nebraska and NOAA through the future collaborative development of an interactive tool for forecasting DCI. This work also aims to foster the integration of research and education by leading to the thesis/dissertation work of a graduate student. Results from the research will be disseminated to the scientific and operational forecasting communities through publication in peer-reviewed professional journals, presentations at professional meetings, and seminars at the host institution and elsewhere. Direct interaction with the operational forecasting community will also be sought so that these results can be expediently and effectively disseminated to operational forecasters.

智力优势：大气中的深层对流涉及热量和水分通过对流层相当大一部分的垂直输送。 它在调节水循环方面发挥着重要作用，因此深对流发生的区域和地方变化可以显著影响水资源的管理方式。 深层对流也会产生大冰雹、破坏性的风、龙卷风和洪水，因此会对健康和安全构成重大威胁。 改进对深层对流的预测将有助于更好地评估和管理这些威胁，也有助于更有效地管理地方和区域水资源。 改进深对流形成的预报是提高深对流预报水平的关键。然而，一致准确的预测深对流的启动（DCI）只有可能的，如果有一个基本的调节机制的理解。 研究的第一个目标是通过巩固临界理论来推进知识状态，临界理论是DCI的一个新的概念范式，它抓住了DCI的基本调节机制。 临界性是PI在最近的一篇文章中引入的一个概念，它使用浮力和稀释之间的非线性关系来定义两种对流状态：可能发生DCI的超临界状态和不可能发生DCI的亚临界状态。 在临界状态下，DCI的概率不取决于包裹变得不稳定的可能性，而是取决于包裹变得超临界的可能性。巩固临界性的概念将依赖于与三维（3D）云解析模型和理想化的一维（1D）临界性模型进行的数值实验的组合。由于临界性是由浮力和稀释度之间的关系定义的，因此3D实验将侧重于检查DCI对浮力和稀释度的敏感性。一维临界模型实验的目的是隔离的作用，从更复杂的动力学和微观物理学的对流云内运行的临界。1D关键性模型将应用于3D模拟，以确定使用关键性区分产生和不产生DCI的环境的可靠性。研究的第二个目标是制定量化关键性的指标。这些指标将使计划的分析能够实现第一个目标。它们还将有助于实现这项工作的第三个目标。第三个目标是确定关键性指标，特别是关键性一般区分观察到的环境，做和不支持DCI。 统计分析将用于量化各种关键性指标预测DCI的程度。 然后将这些结果与用于预测DCI的其他指标进行比较。更广泛的影响：加深对深对流的了解显然对社会很重要，改进DCI的预报将提供直接和直接的好处。 这项工作不仅旨在提高对DCI的理解，而且通过关键性指标的开发和测试，涉及将这项工作应用于DCI业务预测的具体步骤。 这项工作还将使PI能够通过未来合作开发预测DCI的交互式工具来巩固内布拉斯加大学和NOAA之间的伙伴关系。这项工作的目的还在于促进研究和教育的一体化，导致论文/论文工作的研究生。 研究结果将通过在同行评审的专业期刊上发表、在专业会议上介绍以及在主办机构和其他地方举办研讨会等方式传播给科学和业务预报界。 还将寻求与业务预报界的直接互动，以便将这些结果方便有效地传播给业务预报员。