The National Severe Storms Laboratory Tornado Detection Algorithm

Mitchell, Elyne; Vasiloff, Steven V.; Stumpf, Gregory J.; Witt, Arthur; Eilts, Michael D.; Johnson, J. T.; Thomas, Kevin W.

doi:10.1175/1520-0434(1998)013<0352:tnsslt>2.0.co;2

Cited by 110 publications

(66 citation statements)

References 33 publications

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“…Although existing algorithms designed to detect thunderstorms, mesocyclones and tornado vortex signatures, such as thunderstorm identification, tracking, analysis and nowcasting (TITAN; Dixon and Wiener, 1993); storm cell identification and tracking (SCIT; Johnson et al, 1998); the National Severe Storms Laboratory (NSSL) Tornado vortex signature detection algorithm (TDA; Mitchell et al, 1998); the NSSL mesocyclone detection algorithm (MDA; Stumpf et al, 1998); w2segmotion (Lakshmanan et al, 2007(Lakshmanan et al, , 2009; Thunderstorm observation by radar (ThOR; Barjenbruch and Houston, 2006;Barjenbruch, 2008;Lahowetz et al, 2010) and enhanced-TITAN (E-TITAN; Han et al, 2009) include a tracking component, many recommendations to improve tracking in legacy algorithms have been proposed. For example, Johnson et al (1998) propose that SCIT should incorporate different estimates of initial storm motion throughout the domain and impose limits upon the ability of tracks to change direction.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Johnson et al (1998) propose that SCIT should incorporate different estimates of initial storm motion throughout the domain and impose limits upon the ability of tracks to change direction. Moreover, many existing algorithms (e.g., Johnson et al, 1998;Mitchell et al, 1998;Stumpf et al, 1998) limit their tracking to within a single-radar domain rather than a larger domain. This study describes a new algorithm, the advanced algorithm for tracking objects (AALTO) that combines many of the best features of existing tracking algorithms while addressing many of the known limitations of legacy tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

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The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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ABSTRACT:The advanced algorithm for the tracking of objects (AALTO) constructs tracks from objects, such as thunderstorms or mesocyclones, detected by multiple weather radars at irregular time intervals. It is important to have high accuracy in tracking thunderstorms to generate skilful forecasts and high-quality climatologies and, fundamentally, to ensure that any derived product from tracks captures only that particular storm, and in its entirety. AALTO incorporates many of the best practices of existing tracking algorithms and techniques employed by meteorologists in constructing tracks. AALTO differs from existing algorithms designed to track meteorological phenomena that manifest in radar data in the following ways: (1) AALTO is designed to track objects from multiple radars, enabling analysis over a larger domain than if a single radar was used; (2) improved tracking is realized through improved initial motion estimates and directional thresholding and (3) AALTO looks both at the track history and at the subsequent possible positions along a track when constructing the best possible tracks, mimicking the approach that would be taken by a human meteorologist. Verification was done using metrics that were objectively determined to distinguish between good and degraded tracks; a description of the approach to determine the appropriate metrics is presented. An overview of the AALTO tracking procedure and an example case are presented in this study.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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“…In a tornado detection algorithm (TDA) developed by the National Severe Storms Laboratory (NSSL), a minimum ∆V r of 11 m s -1 between an azimuthally adjacent velocity gate pair is required to be considered of potential interest (Mitchell et al 1998). Additionally, ~3% of mesocyclones detected by NSSL's mesocyclone detection algorithm (MDA) are associated with tornadoes, and most missed tornadoes are EF-0 or occasionally EF-1 (Jones et al 2004).…”

Section: Other Tornado Indicatorsmentioning

confidence: 99%

Potential Tornado Warning Improvement Resulting from Utilization of the TDS in the Warning Decision Process

Broeke¹

2017

J. Operational Meteor.

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Polarimetric tornadic debris signatures (TDSs) provide a means of confirming that a strong wind field has lofted debris to the altitude of the radar beam. They can increase confidence in an ongoing tornado, which may be noted in tornado warning text to increase the sense of urgency. They may also serve as indicators of weak tornadoes that may otherwise go unwarned. Polarimetric data have been available since 2012 on a large portion of the U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network and since May 2013 for the entire network. It is anticipated that use of the polarimetric TDS in the tornado warning process should evolve through time. Thus, this paper presents an overview of how the TDS was used in the warning process during the 16 months when widespread polarimetric data were first becoming available (February 2012-May 2013). During this period, 22.5% of tornado warning texts for TDS-producing events mentioned the TDS. It is estimated that utilization of the TDS in the warning decision process could result in ~45 previously unwarned tornadoes being warned annually, and in ~65 tornado warnings being issued with a less negative lead time.Examples are shown that demonstrate potential operational usefulness of the TDS. ABSTRACT (Manuscript

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“…There are algorithmic radar techniques for the identification of all of these severe weather features (Dance and Potts, 2002;Donaldson and Desrochers, 1990;Johnson et al, 1998;Joe et al, 2004;Kessler and Wilson, 1971;Lakshmanan et al, 2003;Lakshmanan and Smith, 2009;Lenning et al, 1998;Mitchell et al, 1998;Stumpf et al, 1998;Winston, 1998;Witt et al, 1998a, Witt et al, 1998b. The efficacy of the detection depends on the radar scan strategy and quality of the radar (range, azimuth resolution, cycle time, sensitivity, elevation angles, number of elevation tilts, etc Heinselman et al, 2008;Lakshmanan et al, 2006;Marshall and Ballantyne, 1975;McLaughlin et al, 2009;Vasiloff, 2001).…”

Section: Thunderstormmentioning

confidence: 99%