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Caren Marzban
and
Arthur Witt

Abstract

The National Severe Storms Laboratory has developed algorithms that compute a number of Doppler radar and environmental attributes known to be relevant for the detection/prediction of severe hail. Based on these attributes, two neural networks have been developed for the estimation of severe-hail size: one for predicting the severe-hail size in a physical dimension, and another for assigning a probability of belonging to one of three hail size classes. Performance is assessed in terms of multidimensional (i.e., nonscalar) measures. It is shown that the network designed to predict severe-hail size outperforms the existing method for predicting severe-hail size. Although the network designed for classifying severe-hail size produces highly reliable and discriminatory probabilities for two of the three hail-size classes (the smallest and the largest), forecasts of midsize hail, though highly reliable, are mostly nondiscriminatory.

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Arthur Witt
and
Stephan P. Nelson

Abstract

The relationship between a storm's divergent outflow magnitude at upper levels and maximum hailstone size was investigated by analyzing single-Doppler radar data for 49 severe hailstorms. Two different techniques were developed for use with single-Doppler radar data to estimate the magnitude of divergent outflows. The developed techniques show considerable skill at estimating the maximum hailstone size produced by a thunderstorm, with the correlation coefficients between the parameters used and maximum hailstone size being as high as 0.89. The data also show that estimates of maximum hailstone size have an 80% chance of being accurate to within ±1.4 cm. The developed techniques are computationally simple and should be useful for real-time estimation of the maximum hailstone size likely to be produced by a thunderstorm.

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Harold E. Brooks
,
Arthur Witt
, and
Michael D. Eilts

The question of who is the “best” forecaster in a particular media market is one that the public frequently asks. The authors have collected approximately one year's forecasts from the National Weather Service and major media presentations for Oklahoma City. Diagnostic verification procedures indicate that the question of best does not have a clear answer. All of the forecast sources have strengths and weaknesses, and it is possible that a user could take information from a variety of sources to come up with a forecast that has more value than any one individual source provides. The analysis provides numerous examples of the utility of a distributions-oriented approach to verification while also providing insight into the problems the public faces in evaluating the array of forecasts presented to them.

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Valliappa Lakshmanan
,
Travis Smith
,
Kurt Hondl
,
Gregory J. Stumpf
, and
Arthur Witt

Abstract

With the advent of real-time streaming data from various radar networks, including most Weather Surveillance Radars-1988 Doppler and several Terminal Doppler Weather Radars, it is now possible to combine data in real time to form 3D multiple-radar grids. Herein, a technique for taking the base radar data (reflectivity and radial velocity) and derived products from multiple radars and combining them in real time into a rapidly updating 3D merged grid is described. An estimate of that radar product combined from all the different radars can be extracted from the 3D grid at any time. This is accomplished through a formulation that accounts for the varying radar beam geometry with range, vertical gaps between radar scans, the lack of time synchronization between radars, storm movement, varying beam resolutions between different types of radars, beam blockage due to terrain, differing radar calibration, and inaccurate time stamps on radar data. Techniques for merging scalar products like reflectivity, and innovative, real-time techniques for combining velocity and velocity-derived products are demonstrated. Precomputation techniques that can be utilized to perform the merger in real time and derived products that can be computed from these three-dimensional merger grids are described.

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

Abstract

The National Severe Storms Laboratory (NSSL) has developed a mesocyclone detection algorithm (NSSL MDA) for the Weather Surveillance Radar-1988 Doppler (WSR-88D) system designed to automatically detect and diagnose the Doppler radar radial velocity patterns associated with storm-scale (1–10-km diameter) vortices in thunderstorms. The NSSL MDA is an enhancement to the current WSR-88D Build 9.0 Mesocyclone Algorithm (88D B9MA).

The recent abundance of WSR-88D observations indicates that a variety of storm-scale vortices are associated with severe weather and tornadoes, and not just those vortices meeting previously established criteria for mesocyclones observed during early Doppler radar studies in the 1970s and 1980s in the Great Plains region of the United States. The NSSL MDA’s automated vortex detection techniques differ from the 88D B9MA, such that instead of immediately thresholding one-dimensional shear segments for strengths comparable to predefined mesocyclone parameters, the initial strength thresholds are set much lower, and classification and diagnosis are performed on the properties of the four-dimensional detections. The NSSL MDA also includes multiple range-dependent strength thresholds, a more robust two-dimensional feature identifier, an improved three-dimensional vertical association technique, and the addition of time association and trends of vortex attributes. The goal is to detect a much broader spectrum of storm-scale vortices (so that few vortices are missed), and then diagnose them to determine their significance. The NSSL MDA is shown to perform better than the 88D B9MA at detecting storm-scale vortices and diagnosing significant vortices.

Operational implications of the NSSL MDA are also presented. In light of the new WSR-88D observations of storm-scale vortices and their association with severe weather and tornadoes, it is clear that the operational paradigms of automated vortex detection require changes.

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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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Laurie G. Hermes
,
Arthur Witt
,
Steven D. Smith
,
Diana Klingle-Wilson
,
Dale Morris
,
Gregory J. Stumpf
, and
Michael D. Eilts

Abstract

The Federal Aviation Administration's Terminal Doppler Weather Radar (TDWR) system was primarily designed to address the operational needs of pilots in the avoidance of low-altitude wind shears upon takeoff and landing at airports. One of the primary methods of wind-shear detection for the TDWR system is the gust-front detection algorithm. The algorithm is designed to detect gust fronts that produce a wind-shear hazard and/or sustained wind shifts. It serves the hazard warning function by providing an estimate of the wind-speed gain for aircraft penetrating the gust front. The gust-front detection and wind-shift algorithms together serve a planning function by providing forecasted gust-front locations and estimates of the horizontal wind vector behind the front, respectively. This information is used by air traffic managers to determine arrival and departure runway configurations and aircraft movements to minimize the impact of wind shifts on airport capacity.

This paper describes the gust-front detection and wind-shift algorithms to be fielded in the initial TDWR systems. Results of a quantitative performance evaluation using Doppler radar data collected during TDWR operational demonstrations at the Denver, Kansas City, and Orlando airports are presented. The algorithms were found to be operationally useful by the FAA airport controllers and supervisors.

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