Forecaster Perceptions and Climatological Analysis of the Influence of Convective Mode on Tornado Climatology and Warning Success

Abstract

Tornadogenesis occurs in a variety of storm types, or convective modes, each having a unique climatology and challenges in their detection and warning. Some warnings result in false alarms, meaning no tornado occurred within the warning polygon. We used a mixed-methods approach to assess how convective mode--discrete supercell, cell in cluster, cell in line, or quasi-linear convective system (QLCS)--affects the tornado climatology and National Weather Service (NWS) procedures within three County Warning A reas (CWAs): Memphis (MEG), Nashville (OHX), and Morristown (MRX). We used three data sets: tornadoes (2003-2014) categorized by convective mode, false alarms (2012-2016) categorized by convective mode, and 11 interviews of NWS forecasters. The CWAs had no significant difference in mode frequency when removing replication from multiple-tornado events. However, when outbreaks were included, discrete supercell and QLCS signals were identified in MEG and OHX, respectively. Convective mode, season, and time of day were strongly associated. Tornadic discrete supercells followed a traditional severe weather pattern of spring a nd daytime occurrences, and caused fewer false alarms. More QLCS tornadoes happened at night and in winter. Cells in lines and clusters accounted for larger proportions of events in the false alarm data set than the tornado data set.Forecasters noted challenges in detecting tornadoes in convective modes other than discrete supercells, including short-lived QLCS tornadoes. Key forecaster concerns other than convective mode included storm speed, outbreaks, and lack of ground-truthing at night. Forecasters differed in their motivation to either warn on every tornado or avoid false alarms.

Tornado activity, house prices, and stock returns

“…This makes challenging the development of a unique process through which scientists can detect different tornadoes and thus set reliable warnings. Ellis, Burow, Gassert, Mason, and Porter (2019) do not find any evidence of a significant relationship between tornado-genesis and the probability of a warning success. It is thus evident that predicting hurricanes and tropical storms is not as challenging as predicting tornadoes (or anticipating their direction).…”

Mean storms: Composites of radar reflectivity images during two decades of severe thunderstorm events

“…To create this dataset, images centred on the starting location of tornado reports from 1996 to 2017 were manually assigned to one of the aforementioned CSMs . Although the classifications are subjective, we followed the guidance of previous work (e.g., Gallus Jr et al ., 2008; Smith et al ., 2012; Ashley et al ., 2019; Ellis et al ., 2019)—specifically, (a) QLCSs are identified by noting a linear organization of pixels ≥40 dBZ (i.e., at least a 3 to 1 length to width ratio) with a length of at least 100 km, (b) Cellular cases are identified by noting a circular organization to the ≥40 dBZ pixels in the vicinity of the report, and that contiguous circular region is entirely within a 100 × 100 km box around the report, and (c) Tropical cases are those that occurred near a HURDAT track (Landsea et al ., 2015). The reports were gathered from the Southern United States (i.e., Oklahoma, Texas, Arkansas, Louisiana, Mississippi, Tennessee, Alabama, Florida, Georgia, South Carolina, and North Carolina).…”

“…Examples of how these data are used include storm warning verification (Brooks and Correia Jr., 2018), generating hazard climatologies (Brooks et al ., 2003; Allen and Tippett, 2015; Edwards et al ., 2018), informing teleconnection relationships (Allen et al ., 2015), exploring changes in the spatiotemporal occurrence of events (Brooks et al ., 2014; Gensini and Brooks, 2018), vulnerability and exposure analyses (Strader et al ., 2017), and environmental analyses (Thompson et al ., 2012). Of particular interest in the field of severe thunderstorm research has been the automated identification of convective storm mode (CSM) in weather radar data through manual (Smith et al ., 2012; Ellis et al ., 2019) or automated approaches (Haberlie and Ashley, 2018; Gagne II et al ., 2019; McGovern et al ., 2019; Jergensen et al ., 2020).…”

Relationship of Convection Initiation and Subsequent Storm Strength to Ensemble Simulated Environmental Conditions during IOP3b of VORTEX Southeast 2017

“…Severe weather outbreaks in the southeastern United States frequently occur during the cool and transition seasons (e.g., Brooks et al 2003;Smith et al 2012;Thompson et al 2012;Childs et al 2018) within active synoptic patterns containing translating baroclinic systems (e.g., Galway and Pearson 1981;Gaffin and Parker 2006;Sherburn et al 2016). Although the passage of these midlatitude weather systems are generally well predicted, forecasting the likelihood of severe weather and tornadoes in the Southeast remains a significant challenge for operational meteorologists at all lead times (e.g., Anderson-Frey et al 2019;Ellis et al 2019).…”