Synoptic-Scale Influences on Outbreaks of Severe Convection

University Of Oklahoma Norman Campus, Norman OK

Investigators

Abstract

Severe thunderstorms and the damaging weather they produce (including large hail, strong straight-line winds and tornadoes) are by nature small scale phenomena occupying narrow segments of space and time. As such, local extreme events such as tornadoes are not resolved even by advanced mesoscale numerical models. Recent evidence nonetheless suggests that clusters of such severe weather events (termed "outbreaks") may be anticipated on a regional basis with lead times of 2-3 days or more, although the ability of models to discriminate tornadic outbreaks from primarily non-tornadic ones is apparently diminished during the spring season when damaging storms are most frequent. This study will investigate the limits of predictability (viz. maximum lead times) over which contrasting outbreak types may be reliably forecast by the MM5 and WRF (Weather Research & Forecasting) community models when initialized using only spatially coarse synoptic-scale information on atmospheric structure. Derived meteorological covariates (summary parameters used to evaluate the potential for contrasting outbreak types) will be analyzed through a combination of subjective and objective statistical techniques. This analysis will cover the period 1970-2003. Initial results suggest that dynamical measures including shear-related quantities such as storm-relative helicity are favored over thermodynamically-based measures such as Convective Available Potential Energy (CAPE) in these long-range forecasts. This invites further exploration of why certain pathways may be preferred for communicating large-scale atmospheric influences down to more the scale of individual thunderstorms. Evidence of seasonal modulation of ability to discern tornadic vs. non-tornadic outbreaks will be further explored, as will forecast skill based both upon single model runs and more computationally demanding ensemble runs. The utility of model-resolved storm properties such as updraft strength in discriminating outbreak type will also be evaluated. The intellectual merit of this work rests on improved ability to run and interpret mesoscale forecast models to reliably anticipate major outbreaks of severe weather, to discern the nature of these outbreaks with lead times of several days, and to gain improved understanding of atmospheric processes connecting large and small scales. Broader impacts of this work will include benefits from increased lead time for the public to prepare for hazardous weather events, through immediate communication of these advances through cooperation and collaboration with operational forecasters at NOAA's Storm Prediction Center and Australian Bureau of Meteorology, as well as more traditional links and graduate student education.