Criticality: A Theory for Understanding and Forecasting Deep Convective Initiation

智能优点：大气中的深层对流涉及热量和水汽通过对流层相当一部分的垂直输送。它在调节水循环方面发挥着重要作用，因此，发生深对流的区域和地方差异会对水资源的管理产生重大影响。深度对流还可能产生大冰雹、破坏性大风、龙卷风和洪涝暴雨，因此可能对健康和安全构成重大威胁。改进对深对流的预报将能够更好地评估和管理这些威胁，也将能够更有效地管理当地和区域水资源。改进对启动深对流的预报对于全面改进对深对流的预报是至关重要的。然而，只有了解了深对流启动的基本调控机制，才有可能对深对流启动(DCI)做出一致准确的预测。研究的第一个目标是通过巩固临界性理论来推进知识的状态，临界性理论是一种新的概念范式，可以捕捉到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个对流区域：可能发生DCI的超临界区域和不可能存在DCI的亚临界区域。对于临界性，DCI的概率被认为不取决于地块变得不稳定的可能性，而取决于地块变得超临界的可能性。要巩固临界性的概念，就必须结合使用三维(3D)云分辨模式和理想化的一维(1D)临界性模式进行的数值试验。由于临界程度是由浮力和稀释度之间的关系定义的，3D实验将重点检查DCI对浮力和稀释度的敏感度。一维临界模型的实验旨在将临界的作用与对流云中更复杂的动力学和微物理操作隔离开来。一维临界性模型将被应用于3D模拟，以努力确定使用临界性来区分产生和不产生DCI的环境的可靠性。这项研究的第二个目标是制定衡量关键程度的指标。这些指标将使计划中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程度度量特别是关键程度在多大程度上区分支持和不支持DCI的观察到的环境。统计分析将用来量化各种关键指标对DCI的预测效果。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：改进对深对流的了解显然对社会很重要，改进对DCI的预测将提供直接和即时的好处。这项工作不仅旨在提高对预防犯罪的了解，而且通过制定和测试关键程度指标，涉及能够将这项工作应用于预防犯罪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用于预测DCI的互动工具，巩固内布拉斯加州大学和NOAA之间的伙伴关系。这项工作还旨在通过引导研究生的论文/论文工作来促进研究和教育的整合。研究结果将通过在同行评议的专业期刊上发表、在专业会议上作陈述以及在主办机构和其他地方的研讨会上发表，向科学和业务预测界传播。还将寻求与业务预测界的直接互动，以便能够方便和有效地将这些结果传播给业务预测人员。

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.