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Caren Marzban
,
E. De Wayne Mitchell
, and
Gregory J. Stumpf

Abstract

It is argued that the strength of a predictor is an ill-defined concept. At best, it is contingent on many assumptions, and, at worst, it is an ambiguous quantity. It is shown that many of the contingencies are met (or avoided) only in a bivariate sense, that is, one independent variable (and one dependent variable) at a time. Several such methods are offered after which data produced by the National Severe Storms Laboratory’s Tornado Detection Algorithm are analyzed for the purpose of addressing the question of which storm-scale vortex attributes based on Doppler radar constitute the “best predictors” of tornadoes.

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J. T. Johnson
,
Pamela L. MacKeen
,
Arthur Witt
,
E. De Wayne Mitchell
,
Gregory J. Stumpf
,
Michael D. Eilts
, and
Kevin W. Thomas

Abstract

Accurate storm identification and tracking are basic and essential parts of radar and severe weather warning operations in today’s operational meteorological community. Improvements over the original WSR-88D storm series algorithm have been made with the Storm Cell Identification and Tracking algorithm (SCIT). This paper discusses the SCIT algorithm, a centroid tracking algorithm with improved methods of identifying storms (both isolated and clustered or line storms). In an analysis of 6561 storm cells, the SCIT algorithm correctly identified 68% of all cells with maximum reflectivities over 40 dBZ and 96% of all cells with maximum reflectivities of 50 dBZ or greater. The WSR-88D storm series algorithm performed at 24% and 41%, respectively, for the same dataset. With better identification performance, the potential exists for better and more accurate tracking information. The SCIT algorithm tracked greater than 90% of all storm cells correctly.

The algorithm techniques and results of a detailed performance evaluation are presented. This algorithm was included in the WSR-88D Build 9.0 of the Radar Products Generator software during late 1996 and early 1997. It is hoped that this paper will give new users of the algorithm sufficient background information to use the algorithm with confidence.

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Arthur Witt
,
Michael D. Eilts
,
Gregory J. Stumpf
,
J. T. Johnson
,
E. De Wayne Mitchell
, and
Kevin W. Thomas

Abstract

An enhanced hail detection algorithm (HDA) has been developed for the WSR-88D to replace the original hail algorithm. While the original hail algorithm simply indicated whether or not a detected storm cell was producing hail, the new HDA estimates the probability of hail (any size), probability of severe-size hail (diameter ≥19 mm), and maximum expected hail size for each detected storm cell. A new parameter, called the severe hail index (SHI), was developed as the primary predictor variable for severe-size hail. The SHI is a thermally weighted vertical integration of a storm cell’s reflectivity profile. Initial testing on 10 storm days showed that the new HDA performed considerably better at predicting severe hail than the original hail algorithm. Additional testing of the new HDA on 31 storm days showed substantial regional variations in performance, with best results across the southern plains and weaker performance for regions farther east.

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E. De Wayne Mitchell
,
Steven V. Vasiloff
,
Gregory J. Stumpf
,
Arthur Witt
,
Michael D. Eilts
,
J. T. Johnson
, and
Kevin W. Thomas

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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Arthur Witt
,
Michael D. Eilts
,
Gregory J. Stumpf
,
E. De Wayne Mitchell
,
J. T. Johnson
, and
Kevin W. Thomas

Abstract

This paper discusses some important issues and problems associated with evaluating the performance of radar-based severe storm detection algorithms. The deficiencies of using Storm Data as a source of verification are examined. Options for equalizing the time- and space scales of the algorithm predictions and the corresponding verification data are presented. Finally, recommendations are given concerning the different evaluation procedures that are available.

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