Objective Classification of Tornadic and Nontornadic Severe Weather Outbreaks

Abstract

Tornadoes often strike as isolated events, but many occur as part of a major outbreak of tornadoes. Nontornadic outbreaks of severe convective storms are more common across the United States but pose different threats than do those associated with a tornado outbreak. The main goal of this work is to distinguish between significant instances of these outbreak types objectively by using statistical modeling techniques on numerical weather prediction output initialized with synoptic-scale data. The synoptic-scale structure contains information that can be utilized to discriminate between the two types of severe weather outbreaks through statistical methods. The Weather Research and Forecast model (WRF) is initialized with synopticscale input data (the NCEP-NCAR reanalysis dataset) on a set of 50 significant tornado outbreaks and 50 nontornadic severe weather outbreaks. Output from the WRF at 18-km grid spacing is used in the objective classification. Individual severe weather parameters forecast by the model near the time of the outbreak are analyzed from simulations initialized at 24, 48, and 72 h prior to the outbreak. An initial candidate set of 15 variables expected to be related to severe storms is reduced to a set of 6 or 7, depending on lead time, that possess the greatest classification capability through permutation testing. These variables serve as inputs into two statistical methods, support vector machines and logistic regression, to classify outbreak type. Each technique is assessed based on bootstrap confidence limits of contingency statistics. An additional backward selection of the reduced variable set is conducted to determine which variable combination provides the optimal contingency statistics. Results for the contingency statistics regarding the verification of discrimination capability are best at 24 h; at 48 h, modest degradation is present. By 72 h, the contingency statistics decline by up to 15%. Overall, results are encouraging, with probability of detection values often exceeding 0.8 and Heidke skill scores in excess of 0.7 at 24-h lead time.

An Assessment of Areal Coverage of Severe Weather Parameters for Severe Weather Outbreak Diagnosis

“…The first method uses principal component analysis of gridded fields as a means of data mining, and the principal component scores are used to train and test statistical models. This method was introduced by Mercer et al (2009) andShafer et al (2010b) to discriminate tornadic and nontornadic outbreaks using mesoscale model output. The second method uses information regarding the areal coverage and magnitudes of a severe weather parameter (or a combination of parameters) as a means of distinguishing outbreaks.…”

“…Previous studies (e.g., Mercer et al 2009;Shafer et al 2009Shafer et al , 2010b have limited the scope to major tornado and primarily nontornadic outbreaks, in which prototypical cases of each type were identified by D06. However, most severe weather outbreaks fall in between these two categories (SD10; SD11), which prompted additional investigation into the ability of reanalysis data to distinguish the most significant severe weather outbreaks FIG.…”

Automated Processing of Doppler Radar Data for Severe Weather Warnings

“…So the issuance of warnings requires a very fine balance of decision-making that takes into account lead time, climatology, societal risk behaviour, social-economic infrastructure, warning service capacity and many other regional, political and societal factors (Baumgart et al, 2008;Dunn, 1990;Hammer and Schmidlin, 2002;Mercer et al, 2009;Schmeits et al, 2008;Westefeld et al, 2006;Wilson et al, 2004). Nowcasts in general are user dependent (Baumgart et al, 2008).…”

Spatial–temporal clustering of tornadoes

“…However, it must be recognized that the definition of a tornado outbreak is somewhat arbitrary (Mercer et al, 2009). Ideally, the definition of a tornado outbreak would be the occurrence of multiple tornadoes within a particular synoptic-scale weather system, but the spatial and temporal limits on the weather system are subject to arbitrary distinction (Glickman, 2000).…”