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A new method for identifying and tracking convective cells is proposed for the statistical analysis of convective cells embedded within mesoscale convective systems using a two dimensional radar reflectivity dataset. The algorithm for the identification and tracking of convective cells determines convective regions with radar reflectivity exceeding a given constant threshold. The threshold value is gradually increased to detect a cell region with a single peak of reflectivity. The algorithm includes a new cell-merging and -splitting scheme that assumes the conservation of total area of convective cells and the maintenance of their relative locations when merging or splitting occurs, and is termed AITCC (Algorithm for the Identification and Tracking Convective Cells). The AITCC performance was evaluated in an analysis of 2004 non-severe convective cells (30–40 dBZ) and in 1268 linkages of convective cells (i.e., two successive observations of the same convective cell) observed within meso-β convective systems in the Meiyu frontal region. We demonstrated that the AITCC decreased the number of incorrect cell assignments, especially in situations where convective cells are located close together. AITCC showed promising performance (false-alarm-rate < 10%) in the tracking of weak convective cells (30–40 dBZ) that seemed to be difficult for the previous tracking algorithms. AITCC is expected to enable us to calculate the statistical features of convective cells from their development to dissipation. tions have reported that multiple CCs occur as convective region within MCS (Houze et al. 1990; Parker and Johnson 2000). To understand the dynamics or microphysics of the MCS, it is necessary to accurately trace individual cells from their development to dissipation. However, it is difficult to trace the evolution of a cell because 1) CCs may undergo complex processes over the course of their life cycles, including merging and splitting; and 2) the number of CCs during severe weather tends to be too large to enable tracking by manual methods. Corresponding author: Shingo Shimizu, National Research Institute for Earth Science and Disaster Prevention, Tennoudai 3-1, Tsukuba, Ibaraki 305-0006, Japan. E-mail: shimizus@bosai.go.jp ©2012, Meteorological Society of Japan Journal of the Meteorological Society of Japan Vol. 90, No. 6 870 Based on radar reflectivity, automated algorithms have been developed in some nowcasting systems for the purpose of cell identification and tracking (TITAN: Dixon and Wiener 1993; SCIT: Johnson et al. 1998; TRACE3D: Handwerker 2002; TRT: Hering et al. 2004; CELLTRACK: Kyznarova and Novak 2009). Most such identification algorithms define a CC as a contiguous region around the local maximum in the reflectivity field for a given height. Two types of schemes are employed for CC identification: constant threshold schemes (TITAN, SCIT and CELLTRACK), and adaptive threshold schemes (TRACE3D and TRT). Constant threshold schemes enclose a reflectivity region that exceeds a certain threshold value. This threshold value depends on the type of storm: 40–50 dBZ for individ ual CCs, 30–40 dBZ for a group of CCs, and 25–30 dBZ for an MCS (see Dixon and Wiener 1993). However, when using the constant threshold, it cannot be guaranteed that a single local maximum in reflectivity will exist in the detected target. In contrary, the adaptive threshold method enables the selection of the lowest reflectivity threshold that includes the single local maximum that distinguishes a given CC from nearby CCs (Handwerker 2002). The adaptive threshold method enables the detection of different CCs at individual threshold values depending on the stage of their life cycle (Hering et al. 2004). However, a weakness of this method is that it is unsuitable for statistical analysis because the cell definition varies among different cells. The greatest difficulty encountered in previous tracking algorithms is the handling of cell merging and splitting, which commonly occurs when many CCs are located close together (e.g., in a squall line). It is generally difficult to distinguish the splitting of a cell into smaller cells from the generation of a new cell near a pre-existing cell. The accurate treatment of merging and splitting is important in tracking the evolution of cells and determining the duration of cell lifetime. However, none of the existing tracking algorithms is able to handle merging and splitting in the case of many cells within a small area. The aim of the present study is to develop an automated cell identification and tracking method that is able to trace a cell from the developing to dissipating stages using a sophisticated merging and splitting scheme. The scheme assumes the conservation of total area and the maintenance of their relative locations between two successive time points. To this end, we developed the Algorithm for Identification and Tracking of Convective Cell using constant and adaptive threshold (AITCC) which enables us to conduct the statistic analysis of CCs that merge or split and that are embedded in an MCS. The performance of AITCC is evaluated using observational data obtained during the Meiyu frontal season when non-severe convective cells (30–40 dBZ) are frequently embedded in meso-β convective systems (Zhang et al. 2006). In this study, in order to detect a meso β convective system in a Meiyu frontal region, the value of 30 dBZ is set as a constant threshold and convective cells are detected at a slightly higher threshold value ( i.e., an adaptiv threshold). A lower threshold value of reflectivity would have negativ effect on the quality of the tracking routines because more object would be detected. Therefore, the tracking of weak convective cells within a meso-β convective system is a challenging but necessary task in order to reveal the total life cycle of convective cells in the Meiyu frontal region. Table 1. List of abbreviations. Abbreviation full name CC CG CCR OVR MV Alone-Child CC (CG) AOR PF PV RC APN Convective Cell convective Cell Group Cross-CoRrelatioin optimal OVeRlapping-area Movement Vector of CG or CC CC (CG) that was not assignmented by any Parent-CC (CG) Accumulated Occupation Ratio Penalty Function Principal Vector Removal of Crossing assignment Additional assignment between two Parallel Neighboring assignments Table 1. List of abbreviations. region var before after T-test NC R NET –ω500 301 57 –0.00053 267 –8 –0.0071 98% 99% 99% YH R NET –ω500 408 172 0.025 469 242 0.028 98% 98% 85% SC R NET –ω500 556 236 0.035 700 396 0.044 99% 99% 99% S. SHIMIZU and H. UYEDA December 2012 871 2. Methodology and Data 2.1 Input data for AITCC Most algorithms (e.g., TITAN, SCIT, TRACE3D, TRT, and CELLTRACK) use three-dimensional reflectivity information for the detection of both shallow and deep convective cells. For the tracking procedure, a three-dimensional cell centroid is calculated (e.g., TI TAN, SCIT, or TRACE3D), and the centroids of the former scan are assigned to those of the more recent scan. Otherwise, three-dimensional reflectivity is projected onto a horizontal plane (e.g., the vertical maximum projection of reflectivity: TRT or CELLTRACK) and the tracking is performed using two dimensional objects. However, reflectivity data near the ground surface are often contaminated by ground clutter, and reflectivity data around the melting layer are affected by a bright band that is caused by an intense reflection from melting ice-phase precipitation in the stratiform region; these reflectivity data may be incorrectly interpreted as a convective cell. Therefore, these previous algorithms seek to reduce the occurrence of data contamination and false interpretation. TITAN uses a clutter map that is computed from a number of Cartesian volumes sampled during a period with no significant weather, and TRACE3D ignores reflectivity data from below 1 km in height as well as cells with a very small vertical extent. CELLTRACK uses a relatively high reflectivity threshold (44 dBZ) to distinguish convective cells from ground clutter. AITCC also requires three-dimensional radar reflectivity data as input (i.e., a constant altitude plane positioning indicator (CAPPI) dataset). However, the cell identification and tracking procedures are implemented based on a two-dimensional reflectivity field at a height of 3 km above sea level (ASL). Three-dimensional reflectivity data are only used to calculate the “bright band fraction” proposed by Chen et al. (2003) as explained below. Thus, AITCC is limited when detecting shallow convective cell below 3 km ASL. However, most of the convective cells observed in the Meiyu frontal region are expected to be found at a height of 3 km ASL because climatologically, the statistical analysis by Zhang et al. (2006) indicates that this height is their reflectivity peak. The lower reflectivity threshold (30 dBZ) used in AITCC permits the detection of the early stages of a convective cell more quickly than that possible using previous algorithms (e.g., the maximum reflectivities in the convective cell must exceed 45 dBZ in TRACE3D, and 44 dBZ in CELLTRACK). However, reflectivity within the bright band or around ground clutter often exceeds 30 dBZ (Handwerker 2002; Kyznarova and Novak 2009), which would impede the performance of cell identification and tracking. To avoid contamination by the bright band, AITCC performs a classification of precipitation type based on three-dimensional reflectivity according to the “bright band fraction method” (Chen et al. 2003), and the stratiform region is excluded from analysis. Near the radar site, the CAPPI dataset includes data-void regions due to the radar scan geometry. In such situations, precipitation type is classified based on two-dimensional reflectivity (at a height of 3 km ASL) as proposed by Steiner et al. (1995) to eliminate the stratiform region. During the Meiyu season, the height of the bright band is climatologically loc

kisho shushi. dai1shu/kisho shushi. dai2shu/journal of the meteorological society of japan

Algorithm for the Identification and Tracking of Convective Cells Based on Constant and Adaptive Threshold Methods Using a New Cell-Merging and -Splitting Scheme