Automatic Identification of Storm Cells Using Doppler Radars

Transcription

1 NO.3 HU Sheng, GU Songshan, ZHUANG Xudong and LUO Hui 353 Automatic Identification of Storm Cells Using Doppler Radars HU Sheng 1,2 ( ), GU Songshan 1 ( ), ZHUANG Xudong 2 ( ), and LUO Hui 3 ( ) 1 Nanjing University of Information Science & Technology, Nanjing Guangzhou Central Meteorological Observatory, Guangzhou Shaanxi Provincial Meteorological Bureau, Xi an (Received May 28, 2007) ABSTRACT Three storm automatic identification algorithms for Doppler radar are discussed. The WSR-88D Build 7.0 (B7SI) tests the intensity and continuity of the objective echoes by multiple-prescribed thresholds to build 3D storms, and when storms are merging, splitting, or clustered closely, the detection errors become larger. The B9SI algorithm is part of the Build 9.0 Radar Products Generator of the WSR-88D system. It uses multiple thresholds of reflectivity, newly designs the techniques of cell nucleus extraction and closestorms processing, and therefore is capable of identifying embedded cells in multi-cellular storms. The strong area components at a long distance are saved as 2D storms. However, the B9SI cannot give information on the convection strength of storm, because texture and gradient of reflectivity are not calculated and radial velocity data are not used. To overcome this limitation, the CSI (Convective Storm Identification) algorithm is designed in this paper. By using the fuzzy logic technique, and under the condition that the levels of the seven reflectivity thresholds of B9SI are lowered, the CSI processes the radar base data and the output of B9SI to obtain the convection index of storm. Finally, the CSI is verified with the case of a supercell occurring in Guangzhou on 11 August The computational and analysis results show that the two rises of convection index matched well with a merging growth and strong convergent growth of the supercell, and the index was when the supercell was the strongest, and then decreased. Correspondingly, the height of the maximum reflectivity, detected by the radar also reduced, and heavy rain also occurred in a large-scale area. Key words: storm identification, nucleus extraction, fuzzy logic technique, convection index 1. Introduction Severe weather detection algorithms are a key element of the Doppler radar system. Such algorithms can provide severe weather potential and detection products to a forecaster during severe weather warning operations. The subject of this paper is the automatic identification of storm cells. Many similar algorithms have been developed over the past 30 years. A pattern-recognition technique is given by Rinehart (1981) to determine internal storm motions. Liu et al. (1991) applied a 2D centroid identification technique in order to track and warn severe storms. The technique identifies 2D storms by checking the intensity of contiguous echoes in CAPPI (constant-altitude plan position indicator). At the same time, other 2D identification algorithms, such as moment invariant identification and fuzzy clustering identification, have been designed. These algorithms described above cannot give information on vertical structure of storm cells, because they only process 2D reflectivity field. Furthermore, a 3D centroid identification technique is discussed by Austin and Bellon ( 1982), which regards a storm cell as a 3D contiguous region of significant reflectivity values above a specified threshold and calculates some features including storm volume, maximum reflectivity, and overhang. This 3D technique and variations thereof have been applied in the WSR-88D Build 7.0 storm series algorithms (B7SI) by Rosefeld (1987) and Dixon and Wiener (1993). The B7SI tests the intensity and continuity of the objective echoes by multiple-prescribed thresholds to build 3D storms, and when storms are merging, splitting, or clustered closely, the detection errors become larger. Vance (1993) and Witt and Johnson (1993) found that a line or cluster of storms with several individual Supported jointly by the Guangdong Natural Science Foundation under Grant No and the Guangzhou Municipal Science and Technology Program (06A ). Corresponding author: guyuesheng@tqyb.com.cn.

2 354 ACTA METEOROLOGICA SINICA VOL.21 strong cells might be detected as one storm by the B7SI, which caused some features of storm to be wrong. Therefore they improved the verticalcorrelation technique to resolve the problem. An enhanced storm identification algorithm is documented by Johnson et al. (1997), which has been implemented as part of the WSR-88D Build 9.0 system (B9SI). The use of seven reflectivity thresholds in the B9SI, as opposed to the one reflectivity threshold used in the B7SI, greatly improves identification performance. 2. Automatic identification algorithms of storms Three storm cell identification algorithms are addressed in this section. Firstly, a 3D identification algorithm is designed and has been applied in the WSR- 88D Build 7.0 Storm Series Algorithms (B7SI). The B7SI tests the intensity and continuity of the objective echoes by multiple-prescribed thresholds to build 3D storms and has been adopted in Chinese NEXRAD system. Secondly, based on the B7SI, an enhanced storm identification algorithm has been developed and implemented as part of the WSR-88D Build 9.0 system (B9SI). The B9SI uses seven reflectivity thresholds and newly designs the techniques of cell nucleus extraction and close-storms processing to reduce errors in situations of closely spaced storms (lines and clusters). Finally, the CSI (Convective Storm Identification) algorithm is designed in this section. By using the fuzzy logic technique, and under the condition that the levels of the seven reflectivity thresholds of the B9SI are lowered, the CSI processes the radar base data and the output of B9SI to obtain convection index of a storm. 2.1 The B7SI The B7SI consists of storm segment and storm centroid. Table 1 shows part of the operational parameters in storm segment. The storm segment algorithm performs the radial processing required to identify storms. It identifies radial sequences or segments from Doppler radar data. The segments are runs of contiguous sample volumes greater than or equal to the minimum reflectivity threshold and have a combined length greater than or equal to the segment length threshold. A specified number of contiguous reflectivity values below the minimum reflectivity threshold may be included within each segment, if the values do not fall below the dropout reflectivity threshold (Part A in Fig.1). The storm centroid algorithm determines in two steps. In step 1, regions of significant reflectivity at known elevation angles are determined by finding contiguous segments as Fig.1. A general schematic for storm identification (Part A: identifying storm segments; Part B: defining a storm component; and Part C: constituting a storm cell).

3 NO.3 HU Sheng, GU Songshan, ZHUANG Xudong and LUO Hui 355 Table 1. Operational parameters of storm segment in the B7SI algorithm Operational parameters Definition Default Dropout number threshold The maximum number of sample volumes below the minimum 2 reflectivity threshold and above the dropout reflectivity threshold that may be in a storm segment Dropout reflectivity The minimum effective reflectivity of sample volumes that are 25 threshold below the reflectivity threshold that may be in a storm segment (dbz) Minimum reflectivity The minimum effective reflectivity in a sample volume to be 30 in a storm segment (dbz) Segment length threshold The minimum length of a segment (km) 4.2 Table 2. Operational parameters of storm centroid in the B7SI algorithm Operational parameters Definition Default Adjacent minimum The minimum number of adjacent azimuths required to define 1 a storm component ( ) Overlap threshold The minimum number of sample volumes that must overlap 2 in range for adjacent azimuths to be identified as part of the same storm component Maximum storms The maximum number of storms that can be defined in a volume 20 a function of azimuth (Part B in Fig.1). These regions are defined as components. In step 2, storm components at different elevation angles are correlated vertically along the Z-axis. Vertical correlation means finding those storm components at different elevation angles whose centers of mass are closest in proximity with respect to the X Y -plane (Part C in Fig. 1). The operational parameters of storm centroid are given in Table 2. The number of identified storms in a volume may be below or equal to the maximum storms threshold. 2.2 The B9SI Detection errors of the B7SI become larger when storms are merging, splitting, or clustered closely. To overcome the problem, the B9SI uses seven reflectivity thresholds as opposed to the one reflectivity threshold used in the B7SI, and newly designs the techniques of cell nucleus extraction, vertical-overlay cells combining, 2D storms classifying in a long range and closely spaced storms processing. Table 3 shows the operational parameters added in the B9SI. (1) Seven reflectivity thresholds the primary difference between the B9SI and B7SI is that the B9SI storm segment algorithm repeats the process using seven different reflectivity thresholds (reflectivity 1-7: 60, 55, 50, 45, 40, 35, and 30 dbz), whereas the B7SI storm segment algorithm uses only one threshold (30 dbz). Figure 2 shows an example for storm segments of different lengths identified by using multiple thresholds. (2) Cell nucleus extraction it is employed to isolate the cells from surrounding areas of lower reflectivity. If the centroid of a higher-reflectivity threshold component falls within the area of a lower-reflectivity threshold component, the latter component will be discarded. (3) Vertical-overlay cells combining occasionally, when several cells are clustered closely, the algorithm may divide a single storm cell into two or more 3D cells, each at roughly the same horizontal location but separated by a vertical gap. Under certain circumstances, the collocated 3D cells may be combined to form a single cell. Fig.2. Storm segments of different lengths identified using thresholds of 30, 35, 40, 45, and 50 dbz, respectively, along a radial.

4 356 ACTA METEOROLOGICA SINICA VOL.21 (4) Two-dimensional storm classifying after all possible 3D associations are completed, the remaining non-associated lowest-elevation 2D centroids located beyond a site-adaptable range threshold can be classified as 2D storm cells. This new classification would identify storm cells as under-sampled by the radar due to their range from the radar. (5) Closely spaced storms processing since the B9SI detects more storm cells than the B7SI, there can be situations where numerous cells are identified in close proximity to one another. This may result in undesirable crowding of displayed output and poor tracking performance. In such situations, it is preferable to retain only the stronger and taller storm cells. Thus, if the distance between the centroids of two cells is less than horizontal deleting, and if a difference in depth greater than depth deleting exists between the two cells, the weaker or shorter would be discarded. Table 3. Operational parameters added in the B9SI Operational parameters Definition Default Reflectivity 1-7 Seven reflectivity thresholds used for the one- and two-dimensional 30, 35, 40, 45, 50, 55, and 60 parts of the storm cell building process (dbz) Site-adaptable range The range threshold used to classify the remaining non-associated 175 lowest-elevation 2D components as 2D storm cells (km) Horizontal deleting The distance threshold for deleting storms in close proximity (km) 5.0 Depth deleting The depth threshold used to determine that the weaker cell should 4.0 be deleted in order to avoid cell crowding when two cells are in close proximity (km) 2.3 The CSI The B9SI cannot give information on the convection strength of storms, because texture and gradient of reflectivity are not calculated and radial velocity data are not used. To overcome this problem, the CSI algorithm is designed in this paper. By using the fuzzy logic technique, and under the condition that the levels of the seven reflectivity thresholds of the B9SI are lowered, the CSI processes the radar base data and the output of the B9SI to obtain the convection index of a storm (I c ). Figure 3 shows the flow chart for the CSI algorithm. The CSI algorithm proceeds stepwise. Firstly, seven reflectivity thresholds of the B9SI are lowered. This may result in many more storm cells being identified. Secondly, a set of features of storm morphology are combined to describe convective characteristics of storm cells, and every feature is given a weight. Finally, the likelihood values that the features match with the convective characteristics of storm cells are calculated by a fuzzy logic engine. The convection index is the weighted average of all the likelihood values, and signs the convective strength of a storm instantly. Fig.3. Flow chart for the CSI algorithm. Steps of the processing include: inputting the radar base data and the output of the B9SI, generating the features derived from the base data fields, using a fuzzy logic engine to determine the initial interesting output, and finally outputting the convection index.

5 NO.3 HU Sheng, GU Songshan, ZHUANG Xudong and LUO Hui Feature field generation In the recent 10 years, storm researches have been focusing on the convective characteristics. Steiner et al. (1995) processed 2D reflectivity field to separate radar echoes into convective and stratiform regions using the BET technique. The convective and stratiform regions are separated on the basis of the intensity and sharpness of the echoes peak. The peaks indicate the centers of the convective regions. An improved scheme for convective/stratiform echo classification using the 3D reflectivity structure has been developed by Biggerstaff and Listenmaa (2000). The scheme performs different tests to determine if the radar reflectivity is correctly classified. These tests require that several parameters would be calculated at each grid point. Firstly, the vertical lapse rate of reflectivity is calculated. Toracinta et al. (1996) studied several MCSs in Southeast Texas and found a median lapse rate in the convective cores of near 2.0 dbz km 1, similar to those found by Zipser and Lutz (1994) for mid-latitude continental convection. Secondly, a bright-band fraction (BBF) is calculated at each grid point. A grid column is considered to contain a bright band when and only when the reflectivity maximum is within ±1.5 km of the melting level and the reflectivity above the maximum decreases substantially with height. Finally, the magnitude of the 2D horizontal gradient of radar reflectivity, computed by using log-scale reflectivity (dbz), is determined at each grid point. After testing several values of horizontal gradient, a 3.0-dBz km 1 threshold was chosen, with stratiform (convective) echo associated with gradients less (greater) than this value. The CSSA (convective/stratiform separation algorithm) as described by Seo et al. (2002) relies on three attributes: the maximum reflectivity in the vertical, the vertically averaged local spatial correlation of reflectivity, and the local spatial correlation of the top-height of apparent convective core. Seo et al. (2002) also recognized that vertically integrated liquid water (VIL) is a skillful attribute for convective/stratiform separation. To develope the algorithms for the REC (radar echo classifier; Kessinger et al., 2003), the characteristics of each scatter type are examined through the feature fields. The feature fields are some quantities (e.g., mean, median, gradient, and texture) that are derived from the radar base data fields of reflectivity, radial velocity, and spectrum width. Some meteorologists in China have also analyzed the convective characteristics of storms using Doppler radar data and variational assimulation data in order to improve short-term prediction of mesoscale severe weather. In this paper, the texture of reflectivity (Z Texture ), the vertical gradient of reflectivity ( Z H ), the information of the horizontal gradient (Z SIGN ), the vertically integrated liquid water (Q VIL ), and the standard deviation of radial velocity (σ V ) are combined and given a weight respectively to calculate convection index of a storm (Table 4). Table 4. Features and their weight coefficients used in the fuzzy logic engine Z Features Z Texture Z SIGN H Q VIL σ V Weight coefficients The texture feature of reflectivity is the mean squared difference of the reflectivity field and is calculated over the local area as shown in Eq.(1): Z Texture = { Nbeams j=1 [ Ngates i=2 [(N gates 1) N beams ] (Z i,j Z i 1,j ) 2 ]}/. (1) The vertical gradient of reflectivity, defined as the decrease in reflectivity with increasing height above ground in the 3-km layer above the height of maximum reflectivity, is calculated. Because the height of the maximum reflectivity can change in the convective system s lifetime, the vertical gradient for a 3-km layer above the height of the maximum reflectivity is used instead of a fixed height calculation. The information of the horizontal gradient is the mean sign of the reflectivity change. Z SIGN is computed as the average of the signs of the change in reflectivity from gate to gate within the local area by using Eq.(2). The sign of reflectivity change for each gate is considered to be +1, 0, or 1 if the difference in reflectivity between that gate and the gate immediately nearer the radar is positive, zero, or

6 358 ACTA METEOROLOGICA SINICA VOL.21 negative, respectively. A large absolute value of Z SIGN (near unity) can indicate strong reflectivity gradients such as those within convective precipitation. If (Z i+1 Z i )>0, then Z SIGNi =+1; If (Z i+1 Z i )=0, then Z SIGNi =0; If (Z i+1 Z i )<0, then Z SIGNi = 1; N Z SIGN = Z SIGNi /N. (2) i=1 The VIL algorithm describes the conversion of weather radar reflectivity data into liquid-water content based on theoretical studies of drop-size distributions and empirical studies on the relationship between reflectivity factor and liquid-water content. In this paper, the maximum VIL value of storm is selected as Q VIL. The standard deviation of the radial velocity is calculated over the local area by using Eq.(3): σ V = { Nbeams j=1 [ Ngates i=1 (V i,j V ) 2 ]}/ (N gates N beams ). (3) Fuzzy logic technique After all features are calculated, they are input into the fuzzy logic engine. Membership functions are applied to the values of the feature fields to scale them to match the characteristics of the convective storm under consideration. Membership functions are stepwise linear functions that scale the feature fields to have values between zero and unity. By using the feature field histograms as shown in Fig.4, membership functions are devised by the following steps. (1) A source of radar base data has been used to develope the CSI algorithm: 127 scans are from a CIN-98SA system located in Guangzhou, 394 storm cells are identified by the B9SI with lowered reflectivity thresholds totally, and 125 cells among these storms are classified as vigorously convective storms by experts. To define vigorously convective storms, the experts classify severe storms that produce large hail with a diameter of at least 2 cm, wind gusts in excess of 25 m s 1, tornado, or precipitation in excess of 20 mm h 1, then these severe storms at their mature stages are defined as vigorously convective storms. (2) The histogram plots (Fig.4) are made for the feature fields using the dataset of all the storms and vigorously convective storms. (3) After construction of the histograms, they are examined to find the best discriminators between common storms and vigorously convective storms. Using a feature field histogram, the membership function is devised so that the characteristic that matches with a vigorously convective storm has a maximum likelihood value. For instance, a severe storm at its mature stage has the vertical gradient of reflectivity near zero in the cell cores. Therefore, the appropriate membership function will scale the vertical gradient values of 0 db km 1 to a value of unity, i.e., the maximum degree of likelihood. Values of the vertical gradient far from zero, such as 4.5 db km 1, will be scaled to have a likelihood value of 0. Defining the appropriate value of the vertical gradient where zero likelihood should begin on the membership function depends on the data distribution for the feature. An analysis on the feature of Z SIGN has been done similarly. For the vigorously convective storms, the percentage of storms with the value of Z SIGN in excess of 0.4 is 92.8%, and the percentage of storms with the value less than 0.15 is zero. But for all the storms, Z SIGN has a broad distribution. Among 394 storms, there are 18 storms with the values of Z SIGN greater than 0.8, and 17 storms are classified as vigorously convective storms by experts. The results show that the feature of Z SIGN is a good discriminator for vigorously convective storms. For features with a narrow distribution, membership functions will also have a narrow distribution. Features with a broad distribution will have membership functions with a broad distribution. The membership functions for the features are shown in Fig Performance of three cell identification algorithms for a typical case Many storms occurred, developed, and dissipated in Guangdong Province on 11 August In the situation that storms were merging, splitting, or clustered closely, three cell identification algorithms were examined completely.

7 NO.3 HU Sheng, GU Songshan, ZHUANG Xudong and LUO Hui 359 Fig.4. Patterns of features (a) Z Texture, (b) Z SIGN, (c) Z, (d) QVIL, and (e) σv for all storms (dotted line) H and strong convective storms (solid line). 3.1 Comparison between the B7SI and B9SI When storms were isolated at 1315 Beijing Time (BT), both the B7SI and B9SI performed well (Fig.6). A storm band, consisting of four separated cells, was identified accurately. The appropriate position and area of each cell in the band were given by the B7SI and B9SI. The storm volumes given by the B9SI were less than those given by the B7SI because of using the technique of seven reflectivity thresholds in the B9SI.

8 360 ACTA METEOROLOGICA SINICA VOL.21 Fig.5. Membership functions of all features (a) Z Texture, (b) Z SIGN, (c) Z, (d) QVIL, and (e) σv. H About 2 h later, isolated storms developed and moved. Some of them were merging, splitting, and clustered closely. Figure 7 shows the output of the B7SI and B9SI at 1451 BT. Some closely spaced storms, pointed out by the yellow arrow in Fig.7, were identified as one big storm by the B7SI. But the B9SI newly designs the techniques of seven reflectivity thresholds and cell nucleus extraction, and therefore is capable of identifying embedded cells in multi-cellular storms. When a storm was splitting (pointed out by the white arrow in Fig.7), the positions and areas of the newly split cells were obtained by the B9SI. The B7SI could not give the information on storm splitting because it classified them as one storm. Some closely spaced storms were selected in the yellow rectangle in Fig.7, which were magnified in Fig.8. The B9SI identified more separated cells than those identified by the B7SI. Furthermore, the 2D storm, pointed out by the red arrow in Fig.8, was identified by the B9SI and discarded by the B7SI. Analyzing the radar base data, the 2D component in the lowest elevation was given by both the B7SI and B9SI. The B7SI discarded the 2D component because no vertical association was made with it. However, the non-associated lowest-elevation 2D component located beyond a site-adaptable range threshold could be classified as 2D storm by the B9SI. 3.2 Comparison among the output of the B7SI, B9SI, and CSI At 1609 BT, a band echo with several cells was separated into the east and west part, as shown in the blue rectangle and the yellow rectangle, respectively. Figure 9 shows the outputs of the B7SI, B9SI, and CSI. These 3D storm identification algorithms were performed in stages, beginning at the first gate along a radial. When the echoes in the yellow rectangle were passing by the radar site, none of these algorithms gave the band feature. The B7SI and B9SI classified the band echo as two isolated storms (storms 1 and 2), and the CSI identified storms 3 and 4 besides storms 1 and 2 because of the lowered reflectivity thresholds. Table 5 shows the convective indices and features of four storms in the yellow rectangle given by the CSI. The convective indices of storms 2 and 4 were a little more than those of storms 1 and 3, which means the convection strengths of storms 2 and 4 are much stronger. Storm 4 was smaller than storm 1, and the maximum reflectivity of storm 4 was lower than that of storm 1. Nevertheless, Q VIL of storm 4 was higher than that of storm 1, and the vertical core of reflectivity in storm 4 was deeper than that in storm 1. Detecting by the radar, storms 2 and 4 merged to form a supercell about 1 h later. The newly formed supercell afftected Shenzhen and Dongguan seriously. Storm 1, damaging Guangzhou City severely, became weaker soon. In the blue rectangle, the B7SI classified the multi-cellular storm as one storm as usual and the central position of the storm was not appropriate. The output of the B9SI and CSI was similar. Table 6 shows the convective index and features of each storm in the blue rectangle. Since storm 5 was at the top point of bow echo and the convection motion was the

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11 NO.3 HU Sheng, GU Songshan, ZHUANG Xudong and LUO Hui 363 Table 5. The features and the convection indices for four storms in the yellow rectangle Z Storm number Z Texture Z SIGN H Q VIL σ V I C Table 6. The features and the convection indices for six storms in the blue rectangle Z Storm number Z Texture Z SIGN H Q VIL σ V I C Aliasing (1) Aliasing (1) D (2) 22.5 Aliasing (1) ND (3) Notes: (1) σ V cannot be calculated because of Doppler velocity aliasing; (2) Z cannot be calculated because it is a 2D storm H cell; (3) I C cannot be acquired if two or more features are not calculated. strongest, the convection index (I C ) of storm 5 was the largest. The convection indices of storms 5, 6, and 7 were more than 0.5, so forecasters should pay more attention to them. When the convection index of a storm is more than or equal to 0.5, the storm is signed with a red circle. When the index is less than 0.5, the storm is signed with a blue circle. Those storms without convection indices are circled in white. Five sensitivity experiments are designed for storms 1, 2, 3, 4, 6, and 8, as described in detail below: Exp. A: sensitivity experiment without Z Texture ; Exp. B: sensitivity experiment without Z SIGN ; Exp. C: sensitivity experiment without Z H ; Exp. D: sensitivity experiment without Q VIL ; Exp. E: sensitivity experiment without σ V. The purpose of the first three experiments is to discuss relative importance of the effect of each reflectivity feature on convection index. Experiments D and E are designed to focus on the effect of the maximum VIL and the standard deviation of radial velocity on convective strength. Storms 5, 7, and 10 are not tested because at least one of their features can not be calculated. The convection index of storm 9 is only 0.147, and thus storm 9 is discarded. Table 7 shows the results of five sensitivity experiments. The convection indices for storms except storm 6, calculated in Exp.A, are less than those in the CSI, which means the feature of Z Texture plays an role on enhancing the convection index in the fuzzy logic engine. The two features of Z SIGN and Z H are less sensitive because the convection indices in Exps. B and C are mostly close to the counterparts in the CSI. When a storm is at its mature stage, both outflow and inflow are stronger, resulting in fluctuations of the standard deviation of radial velocity. The fluctuation of convection index is verified with the result of Exp.E. 3.3 Temporal variations of the convection index of a supercell A supercell with a merging growth and strong convergent growth occurred in Guangzhou on 11 August In this section, the convection index of the supercell was obtained during its evolution. Figure 10 shows the temporal variations of the convection index. The computational and analysis results show that the Table 7. The convection indices of the several sensitivity experiments Storm number I C Exp.A Exp.B Exp.C Exp.D Exp.E

12 364 ACTA METEOROLOGICA SINICA VOL.21 Fig.10. Temporal variations of the convection index of a supercell occurring in Guangzhou on 11 August 2004 during its evolution. two rises of convection index during the periods ( and BT) matched well with the merging growth and strong convergent growth. The maximum index was when the supercell was strongest at 1521 BT. Then, the supercell decreased and the height of the maximum reflectivity, detected by the radar, also reduced. Correspondingly, heavy rain occurred in a large-scale area. 4. Concluding remarks The B7SI tests the intensity and continuity of the objective echoes by multiple-prescribed thresholds to build a 3D storm. Furthermore, the B9SI uses seven reflectivity thresholds and newly designs the techniques of cell nucleus extraction and closely spaced storms processing in order to reduce errors in situations of clustered storms. By using the fuzzy logic technique and under the condition that the levels of the seven reflectivity thresholds of the B9SI are lowered, the CSI processes the radar base data and the output of the B9SI to obtain the convection index of a storm. Analysis results show that both the B7SI and B9SI perform very well in the situation of isolated storms. When storms are merging, splitting, or clustered closely, the B9SI is capable of identifying embedded cells in multi-cellular storms and the B7SI often classifies them as one storm. For band echoes, the B9SI gives the information of band by combining a set of cell cores, but the B7SI classifies band as a storm as usual. The B9SI also remains non-associated lowest-elevation 2D components. After checked by a site-adaptable range threshold, some components could be classified as 2D storms. The CSI may identify more storms because of lowered reflectivity thresholds. A set of features are combined to obtain convection index of a storm by using a fuzzy logic engine. Analyzing temporal variations of the convection index of a storm, forecasters can get information of convective strength. The CSI is verified with the case of a supercell occurring in Guangzhou on 11 August The computational and analysis results show that the two rises of convection index match very well with a merging growth and strong convergent growth of the supercell, and the index was when the supercell was strongest, and then decreased. Correspondingly, the height of the maximum reflectivity, detected by the radar also reduced, and heavy rain also occurred in a large-scale area. The CSI needs to be verified with more cases and the effect on the processing of fuzzy logic technique may become worse when one or more features are not calculated. REFERENCES Austin, G. L., and A. Bellon, 1982: Very short-range forecast of precipitation by the objective extrapolation of radar and satellite data. Nowcasting. Broning, A. K., Ed., Academic Press, Biggerstaff, M. I., and A. Listenmaa, 2000: An improved scheme for convectice/stratiform echo classification using radar reflectivity. J. Appl. Meteor., 39, Dixon, M., and G. Wiener, 1993: Thunderstorm identification, tracking, analysis and nowcasting. J. Atmos. Oceanic Technic., 10, Johnson, J. T., P. L. Mackeen, and A. Witt, 1997: The

13 NO.3 HU Sheng, GU Songshan, ZHUANG Xudong and LUO Hui 365 storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Weather & Forecast, 13, Kessinger, C., S. Ellis, and J. V. Andel, 2003: The radar echo classifier: A fuzzy logic algorithm for the WSR- 88D. Preprints, 3rd Conf. on Artificial Intelligence Applications to the Environmental Science, Amer. Meteor. Soc. Liu Liping, Xu Baoxiang, and Wang Zhijun, 1991: preliminary study of tracking and warning severe storms by the method of tracking echo centroids. Plateau Meteorology, 10(3), (in Chinese) Li Yongping, Zhu Guofu, and Xue Jishan, 2004: Microphysical retrieval from Doppler radar reflectivity using variational data assimilation. A Acta Meteorologica Sinica, 62(6), (in Chinese) Qiu Chongjian and Yu Jinxiang, 2000: Use of Doppler radar in improving short-term prediction of mesoscale weather. Acta Meteorologica Sinica, 58(2), (in Chinese) Rinehart, R. E., 1981: A pattern-recognition technique for use with convention weather radar to determine internal storm motion, recent progress in radar meteorology. Atmos. Tech., 13, Rosefeld, D., 1987: Object method for analysis and tracking of convective cells as seen by radar. J. Atmos. Oceanic Technic., 4, Steiner, M., R. A. Houze, and S. E. Yuter, 1995: Climatological characterization of three dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor., 36, Seo, D. J., Ding F., and R. Fulton, 2002: Final report interagency MOU among the NEXRAD program. The WSR-88D Radar Operation Center and the NWS Office of Hydrologic Development, Hydrology Laboratory, Office of Hydrologic Development, National Weather Service, Silver Spring, MD. Toracinta, E. R., K. I. Mohr, E. J. Zipser, and R. E. Orville, 1996: A comparison of WSR-88D reflectivities, SSM1I brightness temperatures, and lightning for mesoscale convective systems in Texas. Part I: Radar reflectivity and lightning. J. Appl. meteor., 39, Vance Mansur M., 1993: Examples of the strength and weakness of the WSR-88D storm tracking products. Preprints, 26th National Convention Conference on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., Witt, A., and J. T. Johnson, 1993: An enhanced storm cell identification and tracking algorithm. Preprints, 26th National Convention Conference on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., Zheng Yuanyuan, Yu Xiaoding, and Fang Chong, 2004: Analysis of a strong classic supercell storm with Doppler weather radar data. Acta Meteorologica Sinica, 62(3), (in Chinese) Zipser, E. J., and K. Lutz, 1994: The vertical profile of radar reflectivity of convective cells: A strong indicator of storm intensity and lightning probability? Mon. Wea. Rev., 122,