The National Severe Storms Laboratory Mesocyclone Detection Algorithm for the WSR-88D*

Stumpf, Gregory J.; Witt, Arthur; Mitchell, E. DeWayne; Spencer, Phillip L.; Johnson, J. T.; Eilts, Michael D.; Thomas, Kevin W.; Burgess, Donald W.

doi:10.1175/1520-0434(1998)013<0304:tnsslm>2.0.co;2

Cited by 167 publications

(116 citation statements)

References 24 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

View full text Add to dashboard Cite

show abstract

“…Consequently, several algorithms have been developed (Stumpf et al 1998;Zrnic et al 1985;Desrochers and Donaldson 1992) for automated mesocyclone signature detection. The National Severe Storm Laboratory (NSSL) MDA (Stumpf et al 1998) is one such algorithm and is an enhancement to the Build 9 WSR-88D Mesocyclone Algorithm (B9MA) (Zrnic et al 1985). Compared to B9MA, NSSL MDA identifies a broader spectrum of mesocyclones and has an improved probability of mesocyclone feature detection.…”

Section: Mesocyclonesmentioning

confidence: 99%

“…The detection algorithm has three steps: (1) identify 1-dimensional (1D) shear segments based on Rankine Vortex, (2) identify two-dimensional (2D) shear regions in a scan sweep, and (3) Aggregate collocated shear regions (along the elevation scans) into 3-dimensional (3D) mesocyclone signatures. A known criteria is used to determine the strength of the shear segments (Stumpf et al 1998). The algorithm builds 2D shear regions using a region-growing technique (Gonzalez and Woods 1992), i.e., grouping those 1D shear segments adjacent to each other into a 2D region.…”

Section: Mesocyclonesmentioning

confidence: 99%

“…The algorithm builds 2D shear regions using a region-growing technique (Gonzalez and Woods 1992), i.e., grouping those 1D shear segments adjacent to each other into a 2D region. A feature vector is calculated for each 2D shear region and its features include central azimuth angle, central range, central height, diameter, shear, maximum Gate-to-Gate Velocity Difference (GTGVD), rotational velocity difference (Vrot), and mesocyclone strength index, as defined in Stumpf et al (1998). For a 3D mesocyclone signature, a number of features such as base and top heights, core base and top heights are calculated in addition to the mean values of the corresponding 2D features.…”

Section: Mesocyclonesmentioning

confidence: 99%

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Real-time storm detection and weather forecast activation through data mining and events processing

Plale

Vijayakumar

et al. 2008

Earth Sci Inform

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Each year across the USA, destructive weather events disrupt transportation and commerce, resulting in both loss of lives and property. Mitigating the impacts of such severe events requires innovative new software tools and cyberinfrastructure through which scientists can monitor data for specific severe weather events such as thunderstorms and launch focused modeling computations for prediction and forecasts of these evolving weather events. Bringing about a paradigm shift in meteorology research and education through advances in cyberinfrastructure is one of the key research objectives of the Linked Environments for Atmospheric Discovery (LEAD) project, a large-scale, interdisciplinary NSF funded project spanning ten institutions. In this paper we address the challenges of making cyberinfrastructure frameworks responsive to realtime conditions in the physical environment driven by the use cases in mesoscale meteorology. The contribution of the research is two-fold: on the cyberinfrastructure side, we propose a model for bridging between the physical environment and e-Science 1 workflow systems, specifically through events processing systems, and provide a proof of concept implementation of that model in the context of the LEAD cyberinfrastructure. On the algorithmic side, we propose efficient stream mining algorithms that can be carried out on a continuous basis in real time over large volumes of observational data.

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Reconstruction of a meteotsunami in Lake Erie on 27 May 2012: Roles of atmospheric conditions on hydrodynamic response in enclosed basins

Anderson

Bechle

et al. 2015

JGR Oceans

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On 27 May 2012, atmospheric conditions gave rise to two convective systems that generated a series of waves in the meteotsunami band on Lake Erie. The resulting waves swept three swimmers a 0.5 mi offshore, inundated a marina, and may have led to a capsized boat along the southern shoreline. Analysis of radial velocities from a nearby radar tower in combination with coastal meteorological observation indicates that the convective systems produced a series of outflow bands that were the likely atmospheric cause of the meteotsunami. In order to explain the processes that led to meteotsunami generation, we model the hydrodynamic response to three meteorological forcing scenarios: (i) the reconstructed atmospheric disturbance from radar analysis, (ii) simulated conditions from a high‐resolution weather model, and (iii) interpolated meteorological conditions from the NOAA Great Lakes Coastal Forecasting System. The results reveal that the convective systems generated a series of waves incident to the southern shore of the lake that reflected toward the northern shoreline and reflected again to the southern shore, resulting in spatial wave focusing and edge wave formation that combined to impact recreational users near Cleveland, OH. This study illustrates the effects of meteotsunami development in an enclosed basin, including wave reflection, focusing, and edge wave formation as well as temporal lags between the causative atmospheric conditions and arrival of dangerous wave conditions. As a result, the ability to detect these extreme storms and predict the hydrodynamic response is crucial to reducing risk and building resilient coastal communities.

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The National Severe Storms Laboratory Mesocyclone Detection Algorithm for the WSR-88D*

Cited by 167 publications

References 24 publications

The advanced algorithm for tracking objects (AALTO)

The advanced algorithm for tracking objects (AALTO)

Real-time storm detection and weather forecast activation through data mining and events processing

Reconstruction of a meteotsunami in Lake Erie on 27 May 2012: Roles of atmospheric conditions on hydrodynamic response in enclosed basins

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