A Reassessment of the Percentage of Tornadic Mesocyclones

Trapp, Robert J.; Stumpf, Gregory J.; Manross, Kevin L.

doi:10.1175/waf864.1

Cited by 94 publications

(54 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MDA output for each volume scan was then analyzed to determine which detections met the criteria to be considered a mesocyclone. Similar to Trapp et al (2005), detections were classified as mesocyclones if a circulation of rank 5 2 or greater was 1) detected at or below 5 km above radar level, 2) observed throughout a vertical depth $3 km, and 3) persisted for a period longer than one radar volume scan (5-6 min). For further details, see Stumpf et al (1998) and Trapp et al (2005).…”

Section: Sampling and Testing Methodology A Thunderstorm Selectionmentioning

confidence: 99%

“…The method used to verify severe weather follows that used by a number of severe storms research projects (Carey et al 2003;Jones et al 2004;Stumpf et al 1998;Trapp et al 2005). Severe storm reports taken from the National Climatic Data Center (NCDC) Storm Events Database were compared in time and space to storms observed in visible satellite imagery.…”

Section: Data Sourcesmentioning

confidence: 99%

“…Severe storm reports taken from the National Climatic Data Center (NCDC) Storm Events Database were compared in time and space to storms observed in visible satellite imagery. This database contains point severe weather reports, so, as suggested by Witt et al (1998), a time window was selected [similar to that of Jones et al (2004), Stumpf et al (1998), and Trapp et al (2005)] to extend the utility of a single point report over a longer period of time. Employing a time window also helped to minimize errors due to nonmeteorological factors, such as inaccurately reported times.…”

Section: Data Sourcesmentioning

confidence: 99%

See 2 more Smart Citations

A Preliminary Statistical Study of Correlations between Inflow Feeder Clouds, Supercell or Multicell Thunderstorms, and Severe Weather

Mazur

Weaver

Haar

2009

Weather and Forecasting

View full text Add to dashboard Cite

This study examines the relationship between severe weather and organized lines of cumulus towers, called feeder clouds, which form in the inflow region of supercell and multicell thunderstorms. Using Geostationary Operational Environmental Satellite (GOES) imagery, correlations between the occurrence of feeder clouds and severe weather reports are explored. Output from the Weather Surveillance Radar-1988 Doppler (WSR-88D) mesocyclone detection algorithm (MDA) is also assessed for a subset of the satellite case days. Statistics from the satellite and radar datasets are assembled to estimate not only the effectiveness of feeder cloud signatures as sole predictors of severe weather, but also the potential utility of combining feeder cloud analysis with the radar's MDA output.Results from this study suggest that the formation of feeder clouds as seen in visible satellite imagery is often followed by the occurrence of severe weather in a storm. The study finds that feeder cloud signatures by themselves have low skill in predicting severe weather. However, if feeder clouds are observed in a storm, there is a 77% chance that severe weather will occur within 30 min of the observation. For the cases considered, the MDA turns out to be the more effective predictor of severe weather. However, results show that combined predictions (feeder clouds plus mesocyclones) outperform both feeder cloud signatures and the MDA as separate predictors by ;10%-20%. Thus, the presence of feeder clouds as observed in visible imagery is a useful adjunct to the MDA in diagnosing a storm's potential for producing severe weather.

show abstract

Section: Sampling and Testing Methodology A Thunderstorm Selectionmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

Section: Data Sourcesmentioning

confidence: 99%

See 1 more Smart Citation

A Preliminary Statistical Study of Correlations between Inflow Feeder Clouds, Supercell or Multicell Thunderstorms, and Severe Weather

Mazur

Weaver

Haar

2009

Weather and Forecasting

View full text Add to dashboard Cite

show abstract

“…Doppler weather radars, and especially those mounted on flatbed trucks that can get close to storms, can identify regions of circulation developing within the storms (e.g., Wurman et al 1996;Bluestein et al 2007). However, only a minority of storms that exhibit circulations go on to produce tornadoes (e.g., Trapp et al 2005). Meteorologists have developed various theories for tornado formation (e.g., Markowski et al 2003, Davies-Jones 2008), but they are not able to identify the specific storm that will produce a tornado.…”

Section: Introductionmentioning

confidence: 99%

Enhanced spatiotemporal relational probability trees and forests

McGovern

Troutman

Brown

et al. 2012

Data Min Knowl Disc

View full text Add to dashboard Cite

Many real world domains are inherently spatiotemporal in nature. In this work, we introduce significant enhancements to two spatiotemporal relational learning methods, the spatiotemporal relational probability tree and the spatiotemporal relational random forest, that increase their ability to learn using spatiotemporal data. We enabled the models to formulate questions on both objects and the scalar and vector fields within and around objects, allowing the models to differentiate based on the gradient, divergence, and curl and to recognize the shape of point clouds defined by fields. This enables the model to ask questions about the change of a shape over time or about its orientation. These additions are validated on several real-world hazardous weather datasets. We demonstrate that these additions enable the models to learn robust classifiers that outperform the versions without these new additions. In

show abstract

“…19 However, many supercells do not produce tornadoes, 20,21 and our understanding of conditions that lead to tornadic supercells is inadequate. The dynamics of supercell storms are generally understood, but the conditions that lead to tornadogenesis are unclear.…”

mentioning

confidence: 99%

Numerical Simulation of a Tornado Generating Supercell

Proctor

Ahmad

2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

View full text Add to dashboard Cite

The development of tornadoes from a tornado generating supercell is investigated with a large eddy simulation weather model. Numerical simulations are initialized with a sounding representing the environment of a tornado producing supercell that affected North Carolina and Virginia during the Spring of 2011. The structure of the simulated storm was very similar to that of a classic supercell, and compared favorably to the storm that affected the vicinity of Raleigh, North Carolina. The presence of mid-level moisture was found to be important in determining whether a supercell would generate tornadoes. The simulations generated multiple tornadoes, including cyclonic-anticyclonic pairs. The structure and the evolution of these tornadoes are examined during their lifecycle. Nomenclature

show abstract

A Reassessment of the Percentage of Tornadic Mesocyclones

Cited by 94 publications

References 9 publications

A Preliminary Statistical Study of Correlations between Inflow Feeder Clouds, Supercell or Multicell Thunderstorms, and Severe Weather

A Preliminary Statistical Study of Correlations between Inflow Feeder Clouds, Supercell or Multicell Thunderstorms, and Severe Weather

Enhanced spatiotemporal relational probability trees and forests

Numerical Simulation of a Tornado Generating Supercell

Contact Info

Product

Resources

About