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The Storm Cell Identification and Tracking Algorithm: an Enhanced JUNE 1998 JOHNSON ET AL. 263 The Storm Cell Identi®cation and Tracking Algorithm: An Enhanced WSR-88D Algorithm J. T. JOHNSON,* PAMELA L. MACKEEN,*1 ARTHUR WITT,* E. DEWAYNE MITCHELL,*1 GREGORY J. STUMPF,*1 MICHAEL D. EILTS,* AND KEVIN W. T HOMAS*1 * NOAA, Environmental Research Laboratories, National Severe Storms Laboratory, Norman, Oklahoma 1 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma (Manuscript received 28 February 1997, in ®nal form 9 December 1997) ABSTRACT Accurate storm identi®cation 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 Identi®cation 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 identi®ed 68% of all cells with maximum re¯ectivities over 40 dBZ and 96% of all cells with maximum re¯ectivities of 50 dBZ or greater. The WSR-88D storm series algorithm performed at 24% and 41%, respectively, for the same dataset. With better identi®cation performance, the potential exists for better and more accurate tracking infor- mation. 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 suf®cient background information to use the algorithm with con®dence. 1. Introduction storm system identi®cation, tracking, and cell motion products. Many other useful severe weather-related The network of Doppler radars called Weather Sur- products are discussed in Klazura and Imy (1993). veillance Radar-1988 Doppler (WSR-88D) have been The subject of this paper is the Storm Cell Identi®- purchased, installed, and maintained by the United cation and Tracking (SCIT) algorithm; many similar al- States National Weather Service (NWS), the Federal gorithms have been developed over the past 40 years. Aviation Administration (FAA), and the Department of These algorithms have either used cross correlation to Defense. This modernized network has replaced the ag- determine the movement of storms or storm-cell cen- ing WSR-57 and WSR-74 radars and become the pri- troid techniques to individually identify and track mary severe weather warning tool used by the govern- storms. Both techniques have strengths and weaknesses: ment and the private sector. Some advantages of this correlation tracking algorithms tend to provide more network are nationwide coverage, Doppler capabilities, accurate speed and direction information on larger areas and a suite of severe weather detection algorithms. A of re¯ectivity, while centroid identi®cation and tracking more complete description of the WSR-88D system is algorithms can track individual isolated storms more given in Crum and Alberty (1993). effectively (Jackson 1993) and can provide cell char- Severe weather detection algorithms are a key ele- acteristic information. In addition to these two schemes, ment of the WSR-88D system for numerous reasons. a tracking method called combinational optimization, Such algorithms can provide severe weather potential which combines both techniques, has been more re- and detection products to a forecaster during severe cently developed (Dixon and Wiener 1993). weather warning operations. The WSR-88D algorithms Cross correlation analysis of the re¯ectivity ®eld was used in NWS of®ces across the country can detect me- one of the ®rst tracking techniques developed. This socyclone and tornadic vortex signatures in the Doppler method may be used to track the entire re¯ectivity ®eld velocity data and furnish continuous isolated storm and (to determine the general motion of the entire ®eld), isolated echo regions (to determine the motion of iso- lated storms or storm systems), or cells within a large Corresponding author address: J. T. Johnson, NOAA/ERL/NSSL, echo region (to determine the motion of cells within a 1313 Halley Circle, Norman, OK 73069. storm system) (Crane 1979). Examples of operational E-mail: [email protected] cross correlation algorithms include the Integrated Ter- Unauthenticated | Downloaded 10/02/21 04:33 AM UTC 264 WEATHER AND FORECASTING VOLUME 13 minal Weather System storm cell information algorithm (Klingle-Wilson et al. 1993) and the Advanced Traf®c Management System cross-correlation tracker algorithm (Jackson and Jesuroga 1995). Both of these algorithms were initially developed to provide automated storm tracking for the FAA. The cross-correlation techniques were followed by storm cell centroid identi®cation and tracking tech- niques. Wilk and Gray (1970) applied a centroid iden- ti®cation technique to the WSR-57 radar signal in order to estimate storm motion and precipitation. Their 2D storm centroid technique identi®ed areas of contiguous FIG. 1. Identi®cation of 50, 45, 40, 35, and 30 dBZ thresholded echo above a set re¯ectivity level threshold. An inten- storm segments along a radial. sity-weighted centroid, used to de®ne the storm location, was determined for each identi®ed area. This technique and variations thereof were also applied by Zittel (1976), 2. The SCIT algorithm Brady et al. (1978), and others. Crane (1979) further a. Data processing requirements developed this type of scheme to de®ne the individual 3D cell cores within clustered or large re¯ectivity ech- The SCIT algorithm processes volumetric re¯ectivity oes. A few years later, Rosenfeld (1987) developed an information from WSR-88D base data (Crum and Al- algorithm that tracked the cell re¯ectivity maxima and berty 1993) on a radial-by-radial basis. No velocity data are processed by the algorithm. No special data ®ltering its encompassing echo. Both algorithms determined the or thresholding, apart from the radar data acquisition cell volume characteristics associated with identi®ed ground clutter suppression, error detection, etc., is per- storm cells. Brady, Crane, and Rosenfeld automated formed before data processing begins. their cell identi®cation and tracking schemes by imple- menting cell centroid tracking. Boak et al. (1977) de- veloped a centroid identi®cation algorithm and Forsyth b. Detection methodology (1979) developed a centroid tracking technique. These Three-dimensional storm identi®cation is performed latter two algorithms became the basis for the WSR- in stages, beginning with one-dimensional (1D) data 88D storm series algorithm (prior to Build 9.0). processing. The purpose of the 1D portion of the al- This paper discusses SCIT, an algorithm that was de- gorithm is to identify storm ``segments'' in the radial veloped to identify, characterize, track, and forecast the re¯ectivity data. Storm segments are de®ned as runs, short-term movement of storm cells identi®ed in three along a radial, of contiguous range gates (sample vol- dimensions. It is based on centroid identi®cation and umes) whose re¯ectivities are above a speci®ed thresh- tracking techniques because of the technique's capabil- old (REFLECTIVITY 1±7; a list of thresholds used by ity to track individual storms and to provide cell char- the algorithm is given in appendix A). As illustrated in acteristic information. The SCIT algorithm's develop- Fig. 1, when a re¯ectivity value above REFLECTIVITY ment was motivated by the relatively poor performance is ®rst encountered along a radial, subsequent gates hav- (NSSL 1995) of the WSR-88D storm series algorithms ing re¯ectivity above REFLECTIVITY are grouped to- in situations of closely spaced storms (lines, clusters). gether, until a re¯ectivity value below REFLECTIVITY The use of seven re¯ectivity thresholds (30, 35, 40, 45, is encountered. If this re¯ectivity value is not more than 50, 55, 60 dBZ) in the SCIT algorithm, as opposed to DROPOUT REF DIFF below REFLECTIVITY, DROP- the one re¯ectivity threshold (30 dBZ) used in the WSR- OUT COUNT is incremented, and the process contin- 88D storm series algorithms, greatly improves identi- ues. However, when a re¯ectivity value more than ®cation performance. For instance, within a line or clus- DROPOUT REF DIFF is encountered or when the drop- ter of storms, individual storm cells are detected by the out counter (DROPOUT COUNT) reaches a speci®ed SCIT algorithm, whereas only one storm may be de- value (default 5 2), the process is terminated and a tected by the WSR-88D storm series algorithms. Since storm segment is created. In Fig. 2, three gray-scaled SCIT identi®es individual storms, rather than storm sys- storm segments are shown. Their braced dropout and tems, individual storm cells may be tracked, and their termination values further illustrate that only dropouts characteristics, such as maximum re¯ectivity, may be within the segment are saved. The dropout method has determined and trended. Thus, the evolution of cell- been implemented in an attempt to keep related features based characteristics is available to the forecaster. This together. paper describes the details of this algorithm, which has A storm segment is saved if its radial length (ending been implemented as part of the WSR-88D system range±beginning range) is greater than a preset threshold (Build 9.0), including its strengths, weaknesses, and op- (SEGMENT LENGTH 1±7; default 5 1.9 km). The erational performance. primary difference between SCIT and the WSR-88D Unauthenticated | Downloaded 10/02/21 04:33 AM UTC JUNE 1998 JOHNSON ET AL. 265 FIG. 3. Outlined blocks represent 30-dBZ thresholded storm seg- ments. All the 30-dBZ storm segments are combined into a 2D com- ponent, assuming that they meet the azimuthal and range criteria.
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