The National Severe Storms Laboratory Tornado Detection AlgorithmSource: Weather and Forecasting:;1998:;volume( 013 ):;issue: 002::page 352Author:Mitchell, E. De Wayne
,
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;2Publisher: American Meteorological Society
Abstract: The 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.
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contributor author | Mitchell, E. De Wayne | |
contributor author | Vasiloff, Steven V. | |
contributor author | Stumpf, Gregory J. | |
contributor author | Witt, Arthur | |
contributor author | Eilts, Michael D. | |
contributor author | Johnson, J. T. | |
contributor author | Thomas, Kevin W. | |
date accessioned | 2017-06-09T14:55:00Z | |
date available | 2017-06-09T14:55:00Z | |
date copyright | 1998/06/01 | |
date issued | 1998 | |
identifier issn | 0882-8156 | |
identifier other | ams-2960.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4166845 | |
description abstract | The 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. | |
publisher | American Meteorological Society | |
title | The National Severe Storms Laboratory Tornado Detection Algorithm | |
type | Journal Paper | |
journal volume | 13 | |
journal issue | 2 | |
journal title | Weather and Forecasting | |
identifier doi | 10.1175/1520-0434(1998)013<0352:TNSSLT>2.0.CO;2 | |
journal fristpage | 352 | |
journal lastpage | 366 | |
tree | Weather and Forecasting:;1998:;volume( 013 ):;issue: 002 | |
contenttype | Fulltext |