Detecting Synoptic-Scale Precursors of Tornado Outbreaks

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

Intellectual Merit - Historically, synoptic-scale signals have played an elusive role in discriminating between tornado outbreak days and severe thunderstorm days without substantial tornadic activity. A question that needs to be answered is: To what extent are tornado outbreaks attributable to processes on the synoptic-scale rather than on the mesoscale? To explore this question, a series of numerical simulations will be performed that commence from smoothed, synoptic-scale, initial conditions. These simulations will be run for lead-times of one to three days. The exclusion of mesoscale observational data is necessary to establish a baseline for determining the relationship between synoptic-scale signals and tornado outbreaks. Two mesoscale numerical models will be utilized. These models will be initialized using composite gridded fields from the NCEP/NCAR reanalysis data, which has a horizontal grid spacing of about 200 km. A family of such composites will be developed using Empirical Orthogonal Functions that filter the data such that only the dominant synoptic-scale modes are retained. A range of meteorological covariates, including Convective Available Potential Energy (CAPE), low-level wind shear, storm-relative helicity, relative vorticity, and relative humidity will be used as proxy variables for the occurrence of tornadoes. The covariates are necessary, as even the most sophisticated mesoscale models currently can predict supercell formation and motion but are incapable of explicitly and routinely predicting tornadoes. The Principal Investigator will investigate the spatial and temporal correlations between the simulated fields associated with tornado outbreak cases and cases involving primarily non-tornadic severe weather. Statistical exploration of the outbreak and non-outbreak cases will enhance physical understanding of the relationships between the synoptic environment and tornado outbreaks. A high statistical correlation between the outbreak and non-outbreak cases will imply that even the most important tornado events are controlled primarily at sub-synoptic scales. Such a finding would have clear implications for operational observing strategies and for research programs. Alternatively, if low correlations are found, then further study aimed at diagnosis of those processes that connect the synoptic scales to tornado outbreaks is likely to prove very fruitful. Broader Impacts -This effort will advance scientific research while promoting graduate training and the results of the research will be incorporated into teaching courses in a wide range of subject areas including numerical weather prediction, statistics, and advanced forecasting skills classes. The modeling advances and databases created by the Investigators will be made directly available to the broader scientific and operational communities. The scientific discoveries will generate societal benefits, as they will assist in refining the ability to predict severe weather, particularly tornadic supercells.