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contributor authorMitchell, E. De Wayne
contributor authorVasiloff, Steven V.
contributor authorStumpf, Gregory J.
contributor authorWitt, Arthur
contributor authorEilts, Michael D.
contributor authorJohnson, J. T.
contributor authorThomas, Kevin W.
date accessioned2017-06-09T14:55:00Z
date available2017-06-09T14:55:00Z
date copyright1998/06/01
date issued1998
identifier issn0882-8156
identifier otherams-2960.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4166845
description abstractThe National Severe Storms Laboratory (NSSL) has developed and tested a tornado detection algorithm (NSSL TDA) that has been designed to identify the locally intense vortices associated with tornadoes using the WSR-88D base velocity data. The NSSL TDA is an improvement over the current Weather Surveillance Radar-1988 Doppler (WSR-88D) Tornadic Vortex Signature Algorithm (88D TVS). The NSSL TDA has been designed to address the relatively low probability of detection (POD) of the 88D TVS algorithm without a high false alarm rate (FAR). Using an independent dataset consisting of 31 tornadoes, the NSSL TDA has a POD of 43%, FAR of 48%, critical success index (CSI) = 31%, and a Heidke skill score (HSS) of 46% compared to the 88D TVS, which has a POD of 3%, FAR of 0%, CSI of 3%, and HSS of 0%. In contrast to the 88D TVS, the NSSL TDA identifies tornadic vortices by 1) searching for strong shear between velocity gates that are azimuthally adjacent and constant in range, and 2) not requiring the presence of an algorithm-identified mesocyclone. This manuscript discusses the differences between the NSSL TDA and the 88D TVS and presents a performance comparison between the two algorithms. Strengths and weaknesses of the NSSL TDA and NSSL?s future work related to tornado identification using Doppler radar are also discussed.
publisherAmerican Meteorological Society
titleThe National Severe Storms Laboratory Tornado Detection Algorithm
typeJournal Paper
journal volume13
journal issue2
journal titleWeather and Forecasting
identifier doi10.1175/1520-0434(1998)013<0352:TNSSLT>2.0.CO;2
journal fristpage352
journal lastpage366
treeWeather and Forecasting:;1998:;volume( 013 ):;issue: 002
contenttypeFulltext


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