Meteorological Overview of the Devastating 27 April 2011 Tornado Outbreak

Carey

Weather and Forecasting

et al. 2017

Self Cite

Thirty-nine thunderstorms are examined using multiple-Doppler, polarimetric and total lightning observations to understand the role of mixed phase kinematics and microphysics in the development of lightning jumps. This sample size is larger than those of previous studies on this topic. The principal result of this study is that lightning jumps are a result of mixed phase updraft intensification. Larger increases in intense updraft volume (≥ 10 m s −1 ) and larger changes in peak updraft speed are observed prior to lightning jump occurrence when compared to other non-jump increases in total flash rate. Wilcoxon-Mann-Whitney Rank Sum testing yields p-values ≤0.05, indicating statistical independence between lightning jump and non-jump distributions for these two parameters. Similar changes in mixed phase graupel mass magnitude are observed prior to lightning jumps and non-jump increases in total flash rate. The p-value for graupel mass change is p=0.096, so jump and non-jump distributions for graupel mass change are not found statistically independent using the p=0.05 significance level. Timing of updraft volume, speed and graupel mass increases are found to be 4 to 13 minutes in advance of lightning jump occurrence. Also, severe storms without lightning jumps lack robust mixed phase updrafts, demonstrating that mixed phase updrafts are not always a requirement for severe weather occurrence. Therefore, the results of this study show that lightning jump occurrences are coincident with larger increases in intense mixed phase updraft volume and peak updraft speed than smaller non-jump increases in total flash rate.

Section: A Radar Datamentioning

confidence: 99%

Kinematic and Microphysical Significance of Lightning Jumps versus Nonjump Increases in Total Flash Rate

Carey

Weather and Forecasting

et al. 2017

Self Cite

“…Because of the infrequent occurrence of violent tornadoes, research that combined all EF2+ tornadoes together caused any EF4 and EF5 tornado environmental signals to be overwhelmed. For example, for the 27 April 2011 tornado outbreak, there were 15 violent tornadoes but 184 non-violent tornadoes (Knupp et al 2014). Therefore, this study classifies cases in the following way: violent tornadoes (EF4 and EF5), significant tornadoes (EF2 and EF3), and weak tornadoes (EF0 and EF1).…”

Section: Introductionmentioning

confidence: 99%

Relationship of Low-Level Instability and Tornado Damage Rating Based on Observed Soundings

Hampshire¹,

Mosier²,

Ryan³

et al. 2018

J. Operational Meteor.

A database of upper-air soundings was collected for weak (EF0/EF1), significant (EF2/EF3), and violent (EF4/ EF5) tornadoes that occurred within 100 km and 6 h of the rawindsonde observation. After case filtering and quality control, a total of 50 proximity soundings for violent tornadoes and randomized samples of 100 proximity soundings for significant tornadoes and 102 for weak tornadoes were obtained. Key convective parameters were analyzed between the tornado datasets. Low-level instability parameters (0-3-km lapse rates and 0-3-km mixed-layer convective available potential energy) were noteworthy predictors of the highest tornado damage rating, whereas mixed-layer lifted condensation level, wind shear, and effective storm relative helicity displayed little predictive skill distinguishing significant and violent tornado environments. The ability of the significant tornado parameter (STP) to discriminate between significant and violent tornadoes also was analyzed. This analysis found that STP does statistically discriminate between violent and significant tornadoes, with mixedlayer convective available potential energy the best discriminator of its variables. Because of the skill in the lowlevel instability parameters, this study also offers a new violent tornado parameter that includes the low-level instability fields in order to better differentiate between significant and violent tornado environments. ABSTRACT (Manuscript

Nat. Hazards Earth Syst. Sci.

“…4a are dominated by a complex sequence of linear tracks that we have previously discussed. Knupp et al (2013) suggest that this 26 April outbreak of tornadoes was associated with a quasi-linear convective squall line. The pattern seen in Fig.…”

Section: Discussionmentioning

confidence: 80%

“…The resulting random generation of tornadoes both in space (the several supercells) and time (for each supercell) would lead to a near-random distribution of data points. The thunderstorms on 26 April 2011 are much less likely to be supercellular than those on 27 April 2011 (Knupp et al, 2013).…”

Section: Clustering Analysis Of Tornadoesmentioning

confidence: 92%

Spatial–temporal clustering of tornadoes

Malamud

Turcotte

Brooks

2016

Abstract. The standard measure of the intensity of a tornado is the Enhanced Fujita scale, which is based qualitatively on the damage caused by a tornado. An alternative measure of tornado intensity is the tornado path length, L. Here we examine the spatial-temporal clustering of severe tornadoes, which we define as having path lengths L ≥ 10 km. Of particular concern are tornado outbreaks, when a large number of severe tornadoes occur in a day in a restricted region. We apply a spatial-temporal clustering analysis developed for earthquakes. We take all pairs of severe tornadoes in observed and modelled outbreaks, and for each pair plot the spatial lag (distance between touchdown points) against the temporal lag (time between touchdown points). We apply our spatial-temporal lag methodology to the intense tornado outbreaks in the central United States on 26 and 27 April 2011, which resulted in over 300 fatalities and produced 109 severe (L ≥ 10 km) tornadoes. The patterns of spatial-temporal lag correlations that we obtain for the 2 days are strikingly different. On 26 April 2011, there were 45 severe tornadoes and our clustering analysis is dominated by a complex sequence of linear features. We associate the linear patterns with the tornadoes generated in either a single cell thunderstorm or a closely spaced cluster of single cell thunderstorms moving at a near-constant velocity.