TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology

Dixon, Michael; Wiener, Gerry

doi:10.1175/1520-0426(1993)010<0785:ttitaa>2.0.co;2

Cited by 798 publications

(651 citation statements)

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“…AALTO was developed to incorporate practices from radar-based tracking algorithms such as TITAN (Dixon and Wiener, 1993), SCIT , the NSSL MDA , w2segmotion (Lakshmanan et al, 2007(Lakshmanan et al, , 2009, ThOR (Barjenbruch and Houston, 2006;Barjenbruch, 2008;Lahowetz et al, 2010), E-TITAN (Han Scharenbrouch et al, 2010;Root et al, 2011;Lakshmanan et al, 2013;Miller et al, 2013) and satellite-based tracking algorithms (Zahraei et al, 2012;Sieglaff et al, 2013). Each of these algorithms adopts different approaches to forecasting object motion, searching for new objects and identifying the best object to use in continuing a track.…”

Section: Algorithm Designmentioning

confidence: 99%

“…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%

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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: Algorithm Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The advanced algorithm for tracking objects (AALTO)

Limpert

Houston

Lock

2015

Meteorological Applications

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“…The goal of Henry's study was very similar to the goal of this study, but the data were somewhat different. In Henry's study, storms were defined by a single reflectivity threshold, 35 dBZ e , and a minimum volume of 50 km 3 , using the Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm (Dixon and Wiener 1993). Single and multicelled storms were analyzed separately and storm characteristics were sampled every 30 min.…”

Section: Introductionmentioning

confidence: 99%

Radar Reflectivity–Derived Thunderstorm Parameters Applied to Storm Longevity Forecasting

MacKeen¹,

Brooks²,

Elmore³

1999

Wea. Forecasting

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In order for the Federal Aviation Administration (FAA) to use airspace more efficiently during thunderstorm events, accurate storm longevity forecasts are needed. Relationships between 16 radar reflectivity-derived storm characteristics and storm longevity are examined to determine which, if any, of the storm characteristics are strongly related to storm lifetime. Such relationships are potentially useful for the development of storm longevity forecasts. The study includes 879 storms that formed over the Memphis, Tennessee, area during 15 late spring and summer convective days. Statistical analyses comparing all 16 storm characteristics to the observed remaining lifetime show that these storm characteristics are not good predictors for storm remaining lifetime.

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“…Previous studies defined the convective cell as a group of 3D contiguous grids that exceed the reflectivity and volume thresholds (Dixon and Wiener, 1993;Johnson et al, 1998;Han et al, 2009a). The same definition is used in this study.…”

Section: Identification Of Convective Cellsmentioning

confidence: 99%

“…Rosenfeld, 1987;Dixon and Wiener, 1993;Johnson et al, 1998;Handwerker, 2002;Han et al, 2009a;Dance et al, 2010). Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN, Dixon and Wiener, 1993) and Storm Cell Identification and Tracking (SCIT, Johnson et al, 1998) are well-known algorithms.…”

Section: Introductionmentioning

confidence: 99%

Radar‐based cell tracking with fuzzy logic approach

Jung

Lee

2015

Meteorological Applications

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This study presents a new algorithm Fuzzy logic Algorithm for Storm Tracking (FAST) for tracking convective cells in 3D Cartesian co-ordinates. A fuzzy logic approach was applied to track cells with objectively determined membership functions (MFs) and weights that were derived from a large set of convective cell tracks (2326 pairs of convective cells). Three feature parameters (cell motion speed, area change ratio and axis transformation ratio) were selected by Bayesian information criterion and combined through fuzzy logic. FAST was evaluated with three skill scores using 2561 (2456) pairs of convective cells with (without) mergers and splits. The performance was tested in comparison with Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) as a baseline method. For reference tracks without merger and split, the critical success index (CSI) of FAST reaches about 0.89. FAST shows better skill scores (CSI = 0.92 and 0.89 at speed limitations of 16.1 m s −1 and 25.0 m s −1 , respectively) than those of TITAN (CSI = 0.89 and 0.82 at the same speed limitations) and is independent of the number of convective cells and the speed limitation. When mergers and splits are considered in the reference, the tracking performance of FAST (FAST MS ) with a merger and split process is always better than that of TITAN (TITAN MS ) with a merger and split process and is independent of speed limitations. When FAST (=FAST noMS ) does not include a merger and split process, its tracking performance is better than that of TITAN MS at speed limitation of > 20 m s −1 .

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TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology

Cited by 798 publications

References 0 publications

The advanced algorithm for tracking objects (AALTO)

The advanced algorithm for tracking objects (AALTO)

Radar Reflectivity–Derived Thunderstorm Parameters Applied to Storm Longevity Forecasting

Radar‐based cell tracking with fuzzy logic approach

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