The Evaluation of WSR-88D Hail Detection Algorithm over Guizhou Region

Wang Jin; Liu Liping

Vol.22, NO.1, 2011

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Article Navigation > Journal of Applied Meteorological Science > 2011 > 22(1): 96-106

Wang Jin, Liu Liping. The evaluation of WSR-88D hail detection algorithm over Guizhou Region. J Appl Meteor Sci, 2011, 22(1): 96-106.

Citation:

Wang Jin, Liu Liping. The evaluation of WSR-88D hail detection algorithm over Guizhou Region. J Appl Meteor Sci, 2011, 22(1): 96-106.

Wang Jin, Liu Liping. The evaluation of WSR-88D hail detection algorithm over Guizhou Region. J Appl Meteor Sci, 2011, 22(1): 96-106.

Citation:

Wang Jin, Liu Liping. The evaluation of WSR-88D hail detection algorithm over Guizhou Region. J Appl Meteor Sci, 2011, 22(1): 96-106.

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The Evaluation of WSR-88D Hail Detection Algorithm over Guizhou Region

Wang Jin^{1
,
,},
Liu Liping²

1.
Guizhou Provincial Meteorological Bureau, Guiyang 550002
2.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2010-03-16
Rev Recd Date: 2010-10-18
Publish Date: 2011-02-28

Abstract

Abstract

The evaluation databases for the WSR-88D hail detection algorithm have been built by using hail observation data of 504 hail prevention spots in Guizhou and Doppler radar data of Guiyang during 8 of severe hail cases from 2005 to 2006, filtered by specific conditions including definition of severe hail, observation range along the cell track, and time-window methodology, etc. These conditions make the databases provide a more accurate picture of algorithm performance. The algorithms are evaluated using the probability of detection (D_PO), false alarm ratio (R_FA), and critical success index (I_CS) statistics.It shows that POH (probability of hail) threshold of 50% get the highest R_FA, and different POH thresholds get similar I_CS, suggesting that it is unreliable to use POH as the only parameter for hail detection in Guizhou region. The difference of climatology between Guizhou area and central Switzerland where the initial POH curve is derived is the crucial cause why POH algorithm becomes unreliable and gets higher R_FA.Assigning POSH (probability of severe hail) 30% leads to the highest I_CS score in Guizhou region, but this threshold does not always get the best performance in these 8 severe hail cases. The difference of WTSM (Warning Threshold Selection Model) in different climatic region is the main cause why the default POSH algorithm gets a bad performance. An improved WTSM will predict the optimum I_SH threshold for each day more accurately. It will help ensure that the POSH threshold of 50% always corresponds to the largest possible I_CS every day. The re-evaluation of the improved POSH algorithm shows that it has decreased the hail detection R_FA, and gets a higher performance of severe hail detection in Guizhou.
- hail detection algorithm;
- evaluation of WSR-88D algorithm;
- POSH;
- WTSM

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References(22)

Figures and Tables

Fig. 1 Distribution of hail reports for 8 hail cases observed by Guiyang radar

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Fig. 2 Diagram of 5 storms tracking of a hailstorm case occurred over the west part of Guizhou on 24 April 2006

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Fig. 3 Sketch of two convective cells with identical POH and noticeable difference in intensity

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图 4 2006年4月9日15:15贵阳雷达1.5°仰角风暴识别图 (相邻距离圈之间距离为30 km)(a) 及风暴1在l线上的垂直剖面图 (b)

Fig. 4 1.5° elevation PPI reflectivity and storm identification displays of Guiyang radar at 15:15 9 April 2006

(the distance between odjacent circles is 30 km) (a) and cross section of storm 1 as line l in Fig. 4a(b)

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Fig. 5 Plots of I_CS for the range of I_SH of the study

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Fig. 6 Plot of best I_SH vs freezing level

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Table 1 Performance results of POH algorithm for 8 hail cases

冰雹概率/%	记录总数	n_成功	n_虚警	n_漏报	D_PO	R_FA	I_CS
10	562	341	196	25	0.93	0.37	0.61
20	534	323	186	25	0.93	0.37	0.60
30	510	310	174	26	0.92	0.36	0.61
40	484	295	161	28	0.91	0.35	0.61
50	463	285	150	28	0.91	0.34	0.62
60	434	265	138	31	0.90	0.34	0.61
70	393	232	121	40	0.85	0.34	0.59
80	337	194	98	45	0.81	0.34	0.58
90	250	139	64	56	0.71	0.32	0.54
100	176	90	29	66	0.58	0.24	0.49
注：记录总数表示以对应冰雹概率作为冰雹探测预警阈值时进入降雹校验数据库的记录数。

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Table 2 Performance results of POSH algorithm for 8 hail cases

强冰雹概率/%	记录总数	n_成功	n_虚警	n_漏报	D_PO	R_FA	I_CS
0	362	192	163	7	0.96	0.46	0.53
10	194	115	69	10	0.92	0.38	0.59
20	157	99	47	11	0.90	0.32	0.63
30	119	82	25	13	0.86	0.23	0.68
40	86	50	16	21	0.70	0.24	0.57
50	63	27	9	28	0.49	0.25	0.42
60	39	9	5	35	0.20	0.36	0.18
70	9	2	3	41	0.05	0.60	0.04

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Table 3 POSH performance for individual days

日期	统计量	强冰雹概率/%
日期	统计量	0	10	20	30	40	50	60
2005-05-01	记录数	60	31	21	17	9	2	1
	n_成功	46	24	21	17	9	2	1
	n_虚警	13	6	5	1	0	0	0
	n_漏报	1	1	1	1	2	6	6
	D_PO	0.78	0.80	0.81	0.94	1	1	1
	R_FA	0.22	0.20	0.19	0.06	0	0	0
	I_CS	0.77	0.77	0.78	0.89	0.82	0.25	0.14
2005-05-02	记录数	52	33	32	29	25	14	6
	n_成功	24	22	21	21	19	12	1
	n_虚警	28	9	9	6	4	0	0
	n_漏报	0	2	2	2	2	2	5
	D_PO	0.46	0.71	0.70	0.78	0.83	1	1
	R_FA	0.54	0.29	0.30	0.22	0.17	0	0
	I_CS	0.46	0.67	0.66	0.72	0.76	0.85	0.17
2006-04-09	记录数	62	19	14	9	7	4	0
	n_成功	24	7	6	6	4	0	0
	n_虚警	37	11	7	2	2	1	0
	n_漏报	1	1	1	1	1	3	3
	D_PO	0.39	0.39	0.46	0.75	0.67	0	0
	R_FA	0.61	0.61	0.54	0.25	0.33	1	0
	I_CS	0.39	0.37	0.43	0.67	0.57	0	0
2006-04-24	记录数	63	43	37	23	14	12	9
	n_成功	40	29	28	16	7	6	2
	n_虚警	22	13	8	4	1	0	0
	n_漏报	1	1	1	3	6	6	7
	D_PO	0.65	0.69	0.78	0.80	0.88	1	1
	R_FA	0.35	0.31	0.22	0.20	0.12	0	0
	I_CS	0.63	0.67	0.76	0.70	0.50	0.50	0.22

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References

[1]	Forsyth D E, Bjerkacs C, Petrocchi P. Modular Radar Analysis Software System (MRASS)//Preprints, 20th National Conventional Conference on Radar Meteorology. 1981: 696-699.
[2]	Lemon L R. On the Use of Storm Structure for Hail Identification//Preprints, 18th Conf on Radar Meteorology. 1978: 203-206.
[3]	Witt A, Eilts M D, Stumpf G J, et al. An enhanced hail detection algorithm for the WSR-88D. Wea Forecasting, 1998, 13: 286-303. doi: 10.1175/1520-0434(1998)013<0286:AEHDAF>2.0.CO;2
[4]	Witt A. Comparison of the Performance of Two Hail Detection Algorithms Using WSR-88D Data//Preprints, 26th Int Conf on Radar Meteorology. Amer Meteor Soc, 1993: 154-156.
[5]	Witt A, Johnson J T. An Enhanced Storm Cell Identification and Tracking Algorithm//Preprints, 26th Int Conf on Radar Meteorology. Amer Meteor Soc, 1993:141-143.
[6]	Witt A, Mitchell E D, Johnson J T, et al. Evaluating the performance of WSR-88D severe storm detection algorithms. Wea Forecasting, 1998, 13: 513-518. doi: 10.1175/1520-0434(1998)013<0513:ETPOWS>2.0.CO;2
[7]	Winston H A, Ruthi L J. Evaluation of RADAP Ⅱ severe storm detection algorithms. Bull Amer Meteor Soc, 1986, 67: 145-150. doi: 10.1175/1520-0477(1986)067<0145:EORISS>2.0.CO;2
[8]	Eric L, Henry E F. An evaluation of WSR-88D severe hail algorithms along the northeastern Gulf Coast. Wea Forecasting, 1998, 13: 1029-1044. doi: 10.1175/1520-0434(1998)013<1029:AEOWSH>2.0.CO;2
[9]	Mather G K, Treddenick D, Parsons R. An observed relationship between the height of the 45-dBZ contours in storm profiles and surface hail reports. J Appl Meteor, 1976, 15 : 1336-1340. doi: 10.1175/1520-0450(1976)015<1336:AORBTH>2.0.CO;2
[10]	Waldvogel A, Schmid W, Federer B. The kinetic energy of hailfalls. Part Ⅰ: Hailstone spectra. J Appl Meteor, 1978, 17: 515-520. doi: 10.1175/1520-0450(1978)017<0515:TKEOHP>2.0.CO;2
[11]	Waldvogel A, Federer B, Schmid W, et al. The kinetic energy of hailfalls. Part Ⅱ: Radar and hailpads. J Appl Meteor, 1978, 17: 1680-1693. doi: 10.1175/1520-0450(1978)017<1680:TKEOHP>2.0.CO;2
[12]	Waldvogel A, Grimm P. Criteria for the detection of hail cells. J Appl Meteor, 1979, 18: 1521-1525. doi: 10.1175/1520-0450(1979)018<1521:CFTDOH>2.0.CO;2
[13]	Foster D S, Bates F C. A hail size forecasting technique. Bull Amer Meteor Soc, 1956, 37: 135-141.
[14]	Miller R C. Notes on Analysis and Severe-storm Forecasting Procedures of the Air Force Global Weather Central. Tech Rep 200 (Rev), Air Weather Service (MAC), US Air Force, 1972: 1-190.
[15]	王瑾, 刘黎平.基于GIS的贵州省冰雹分布与地形因子关系分析.应用气象学报, 2008, 19(5):627-634. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20080515&flag=1
[16]	Browning K A, Foote G B. Airflow and hail growth in supercell storms and some implications for hail suppression. Quart J Roy Meteor Soc, 1976, 102: 499-534. doi: 10.1002/(ISSN)1477-870X
[17]	葛润生, 姜海燕, 彭红.北京地区雹暴气流结构的研究.应用气象学报, 1998, 9(1):1-7. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=19980101&flag=1
[18]	朱君鉴, 刁秀广, 黄秀韶.一次冰雹风暴的CINRAD/SA产品分析.应用气象学报, 2004, 15(5):579-589. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040571&flag=1
[19]	廖玉芳, 俞小鼎, 郭庆, 等.一次强对流系列风暴个例的多普勒天气雷达资料分析.应用气象学报, 2003, 14(6):656-662. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030683&flag=1
[20]	Marshall J S, Palmer W M. The distribution of raindrops with size. J Meteor, 1948, 5: 165-166. doi: 10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2
[21]	Changnon S A. Hailstreaks. J Atmos Sci, 1970, 27: 109-125. doi: 10.1175/1520-0469(1970)027<0109:H>2.0.CO;2
[22]	Kessinger C J, Brandes E A, Smith J W. A Comparison of the NEXRAD and NSSL Hail Detection Algorithms//Preprints, 27th Conf on Radar Meteorology. Amer Meteor Soc, 1995: 603-605.