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.

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 Ն19 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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Tornadic vortex signatures (TVSs) of 52 tornadoes were identified and analyzed, then characterized as either descending or nondescending. This characterization refers to a known tendency of radar-observed tornadic vortices, namely, that of their initial detection aloft and then of their subsequent descent leading to tornadogenesis. Only 52% of the sampled TVSs descended according to this archetypal model. The remaining 48% were detected first near the ground and grew upward or appeared nearly simultaneously over a several kilometer depth; these represent primary modes of tornado development that have been explained theoretically. The descendingnondescending TVSs were stratified according to attributes of the tornado and TVS. Significantly, tornadoes within quasi-linear convective systems tended to be associated with nondescending TVSs, identification of which provided a mean tornado lead time of 5 min. Two case studies are presented for illustrative purposes. On 1 July 1997 in southern Minnesota, nondescending TVSs and associated tornadogenesis were revealed in the leading edge of a squall line, with a squall linesupercell merger, and later during that day, with the cyclonic bookend vortex of a bow echo. On 22 June 1995 in southern Colorado, a low-topped supercell storm produced a tornado that was associated with a descending TVS.

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