Criticality: A Theory for Understanding and Forecasting Deep Convective Initiation

University Of Nebraska-Lincoln, Lincoln NE

Investigators

Abstract

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.