Gregory J. Stumpf

The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm

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 dB Z 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.

The Warning Decision Support System–Integrated Information

Abstract The Warning Decision Support System–Integrated Information (WDSS-II) is the second generation of a system of tools for the analysis, diagnosis, and visualization of remotely sensed weather data. WDSS-II provides a number of automated algorithms that operate on data from multiple radars to provide information with a greater temporal resolution and better spatial coverage than their currently operational counterparts. The individual automated algorithms that have been developed using the WDSS-II infrastructure together yield a forecasting and analysis system providing real-time products useful in severe weather nowcasting. The purposes of the individual algorithms and their relationships to each other are described, as is the method of dissemination of the created products.

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

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 stren...

An Enhanced Hail Detection Algorithm for the WSR-88D

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.

A Neural Network for Tornado Prediction Based on Doppler Radar-Derived Attributes

Abstract The National Severe Storms Laboratorys (NSSL) mesocyclone detection algorithm (MDA) is designed to scotch for patterns in Doppler velocity radar data that are associated with rotating updrafts in severe thunderstorms. These storm-scale circulations are typically precursors to tornados and severe weather in thunderstorms, yet not all circulations produce such phenomena. A neural network has been designed to diagnose which circulations detected by the NSSL MDA yield tornados. The data used both for the training and the testing of the network are obtained from the NSSL MDA. In particular, 23 variables characterizing the circulations are selected to be used as the input nodes of a feed-forward neural network. The output of the network is chosen to be the existence/nonexistence of tornados, based on ground observations. It is shown that the network outperforms the rule-based algorithm existing in the MDA, as well as statistical techniques such as discriminant analysis and logistic regression. Additio...

An Automated Technique to Quality Control Radar Reflectivity Data

Abstract Echoes in radar reflectivity data do not always correspond to precipitating particles. Echoes on radar may result from biological targets such as insects, birds, or wind-borne particles; from anomalous propagation or ground clutter; or from test and interference patterns that inadvertently seep into the final products. Although weather forecasters can usually identify and account for the presence of such contamination, automated weather-radar algorithms are drastically affected. Several horizontal and vertical features have been proposed to discriminate between precipitation echoes and echoes that do not correspond to precipitation. None of these features by themselves can discriminate between precipitating and nonprecipitating areas. In this paper, a neural network is used to combine the individual features, some of which have already been proposed in the literature and some of which are introduced in this paper, into a single discriminator that can distinguish between “good” and “bad” echoes (i...

A Real-Time, Three-Dimensional, Rapidly Updating, Heterogeneous Radar Merger Technique for Reflectivity, Velocity, and Derived Products

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.

The National Severe Storms Laboratory Tornado Detection Algorithm

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...

A Reassessment of the Percentage of Tornadic Mesocyclones

Abstract A large set of data collected by numerous Weather Surveillance Radar-1988 Doppler (WSR-88D) units around the United States was analyzed to reassess the percentage of tornadic mesocyclones. Out of the 5322 individual mesocyclone detections that satisfied the relatively stringent WSR-88D Mesocyclone Detection Algorithm objective criteria, only 26% were associated with tornadoes. In terms of height or altitude of mesocyclone base, 15% of midaltitude mesocyclone detections were tornadic, and more than 40% of low-altitude mesocyclone detections (e.g., those with bases ≤ 1000 m above radar level) were tornadic. These results confirm that a low-altitude mesocyclone is much more likely to be associated with a tornado than is a midaltitude mesocyclone, and more generally, that the percentage of tornadic mesocyclones is indeed lower than previously thought.

The Tornadoes of 3 May 1999: Event Verification in Central Oklahoma and Related Issues

Abstract The tornado events of 3 May 1999 within the county warning area of the Norman, Oklahoma, office of the National Weather Service are reviewed, emphasizing the challenges associated with obtaining accurate information about the existence, timing, location, and intensity of individual tornadoes. Accurate documentation of tornado and other hazardous weather events is critical to research, is needed for operational assessments, and is important for developing hazard mitigation strategies. The situation following this major event was unusual because of the high concentration of meteorologists in the area, relative to most parts of the United States. As a result of this relative abundance of resources, it is likely that these tornadoes were reasonably well documented. Despite this unique situation in central Oklahoma, it is argued that this event also provides evidence of a national need for a rapid-response scientific and engineering survey team to provide documentation of major hazardous weather event...