Revised radar precipitation prediction based on ground clutter filter by using the SRTM data

doi:10.11755/j.issn.1006-7639(2022)-02-0296

Abstract

Abstract:

Based on the SRTM (shuttle radar topography mission)data, the ground clutter and other clutters around Tianshui radar station were filtered, then the Z-I function with localized parameters was established on the basis of six precipitation processes with three types in Southeast Gansu after filtering the ground clutter and other clutters of radar data, and at last the reflectivity factor of Xifeng new generation weather radar in Qingyang was compared with the one in Tianshui within the coincidence range. The results show that SRTM data can well simulate the distribution of ground clutter; radar reflectivity is ahead of precipitation; the Z-I function with localized parameters in Tianshui, which had a smaller A and bigger b, is significantly different to common ones; Tianshui new generation weather radar may have a systematic problem of low echo intensity.

Key words: precipitation retrieval based on radar, SRTM, ground clutter, leading correlation, Z-I function

摘要：

利用SRTM资料建立天水雷达站周边地物分布,滤除雷达回波中的地物回波和其他杂波。在此基础上基于陇东南6次3类降水过程,进行Z-I关系参数本地化试验,最后比较庆阳西峰新一代天气雷达和天水雷达探测重合范围内的反射率因子。结果表明:SRTM资料可以很好地模拟地物回波的分布;雷达反射率因子相对于降水有一定超前;本地化的Z-I关系参数与其他地区有明显区别,A值偏小而b值偏大;天水新一代天气雷达可能存在系统性的回波强度偏低。

关键词: 雷达反演降水, SRTM, 地物回波, 超前相关, Z-I关系

CLC Number:

P412.25

ZHAO Wen, WANG Chenghai, ZHANG Qiang, YUE Ping, ZHAO Ning, DU Lili. Revised radar precipitation prediction based on ground clutter filter by using the SRTM data[J]. Journal of Arid Meteorology, 2022, 40(2): 296-307.

赵文, 王澄海, 张强, 岳平, 赵宁, 杜莉丽. 基于SRTM数据地物滤波改进雷达降水估测[J]. 干旱气象, 2022, 40(2): 296-307.

Figures/Tables 14

Tab.1 Information of the precipitation data

过程时间	类型	平均每小时体扫次数/次	R≥6 mm 站点数	降水累计总时次/次
2011年7月20日18:00至21日01:00	对流性降水,局地有冰雹	11	42	336
2011年7月28日06:00—12:00	混合性降水	11	85	595
2013年5月23日21:00至24日03:00	对流性降水,局地有冰雹	11	63	441
2013年7月8日02:00—17:00	非对流性降水	12	120	1920
2013年7月21日16:00至22日04:00	混合性降水	12	112	1456
2013年7月24日18:00至25日12:00	非对流性降水	12	102	1938

Fig.1 The effective earth’s radius model

Fig.2 The diagram of calculating the height

Tab.2 The common DEM data

数据源	空间分辨率	覆盖范围	下垫面
etopo5	5'	全球	陆地、海洋
etopo2	2'	全球	陆地、海洋
etopo1	1'	全球	陆地、海洋
GEBCO	1'或30″	全球	陆地、海洋
GTOPO30	30″	全球	陆地
SRTM30_PLUS	30″(约1 km)	(180°W—180°E, 81°S—81°N)	陆地、海洋
SRTM15_PLUS	15″(约500 m)	(180°W—180°E, 81°S—81°N)	陆地、海洋
SRTM	3″(约90 m)	(180°W—180°E, 60°S—60°N)	陆地
ASTER GDEM	1″(约30 m)	(180°W—180°E, 83°S—83°N)	陆地

Fig.3 The elevation of the region around Tianshui radar station (Unit: m)

Fig.4 The blocking angle of the region around Tianshui radar station (a) standard refraction, (b) critical refraction

Fig.5 The diagram of blockage of radar beam (a) radial direction, (b) normal direction

Fig.6 The blockage rate of radar beam for standard atmospheric refraction (Unit: %) (a) 0.5°elevation angle, (b) 1.5°elevation angle

Fig.7 Correlation coefficient between moving average value of lg Z and 1 hour lg I during 6 precipitation processes (The number 12 represents that the reflectivity factors participating in the moving average are with in the precipitation hour; and number 11 represents that the last reflectivity factor is removed and the one before the first values are added to the front for moving average, and so on)

Tab.3 The results of optimal fitting

类型	A	b
对流性降水	5	2.3
混合性降水	20	1.6
非对流性降水	8	1.9

Fig.8 The comparation of Z-I function with different parameters (a) RATIO, (b) ARE, (c) RMSE, (d) COR

Fig.9 The inversion-effect coefficient of two empirical formulas with respect to the optimal fitting formula

Fig.10 The detection ranges of two radars

Fig.11 Comparison of reflectivity factors in overlapping area of Tianshui (a, c, e) and Xifeng (b, d, f) radars under different types of precipitation (a, b) convective precipitation, (c, d) mixed precipitation, (e, f) non-convective precipitation

References 46

[1]	俞小鼎. 短时强降水临近预报的思路与方法[J]. 暴雨灾害, 2013, 32(3):202-209.
[2]	CHRISMAN J, RINDERKNECHT D, HAMILTON R. WSR-88D clutter suppression and its impact on meteorological data interpretation[C]// The First WSR-88D User’s Conference. Norman, OK: WSR-88D Operational Support Facility, 1995:9-20.
[3]	GOSS S M, CHRISMAN J N. An introduction to WSR-88D clutter suppression, and some tips for effective suppression utilization[C]// The First WSR-88D User’s Conference. Norman, OK: 1995:1-16.
[4]	孙召平, 张持岸, 张建云. 一种基于高斯模型的自适应地物杂波滤波算法[J]. 太赫兹科学与电子信息学报, 2013, 11(2):250-253.
[5]	HAYKIN S, DENG C. Classification of radar clutter using neural networks[J]. IEEE Transactions on Neural Networks, 1991, 2(6):589-600. DOI URL
[6]	GRECU M, KRAJEWSKI W F. An efficient methodology for detection of anomalous propagation echoes in radar reflectivity data using neural networks[J]. Journal of Atmospheric and Oceanic Technology, 2000, 17(2):121-129. DOI URL
[7]	BERENGUER M, SEMPERE-TORRES D, CORRAL C, et al. A fuzzy logic technique for identifying nonprecipitating echoes in radar scans[J]. Journal of Atmospheric and Oceanic Technology, 2006, 23(9):1157-1180. DOI URL
[8]	KESSINGER C, ELLIS S, VANANDEL J, et al. The AP clutter mitigation scheme for the WSR-88D[C]// 31st Conference on Radar Meteorology. Seattle, WA: Amer Meteor Soc, 2003:526-529.
[9]	KESSINGER C, ELLIS S, VANANDEL J. P1.6 The radar echo classifier: a fuzzy logic algorithm for the WSR-88D[C]// 3rd Conference on Artificial Intelligence Applications to the Environmental Science. Long Beach, CA: American Meteorological society, 2003:1-11.
[10]	MOSZKOWICZ S, CIACH G J, KRAJEWSKI W F. Statistical detection of anomalous propagation in radar reflectivity patterns[J]. Journal of Atmospheric and Oceanic Technology, 1994, 11(4):1026-1034. DOI URL
[11]	SIGGIA A, PASSARELLI R, Jr. Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation[C]// The third European Conference on Radar Meteorology (ERAD). Visby, Sweden, 2004:421-424.
[12]	NGUYEN C M, CHANDRASEKAR V. Gaussian model adaptive processing in time domain (GMAP-TD) for weather radars[J]. Journal of Atmospheric and Oceanic Technology, 2013, 30(11):2571-2584. DOI URL
[13]	刘黎平, 吴林林, 杨引明. 基于模糊逻辑的分步式超折射地物回波识别方法的建立和效果分析[J]. 气象学报, 2007, 65(2):252-260.
[14]	江源, 刘黎平, 庄薇. 多普勒天气雷达地物回波特征及其识别方法改进[J]. 应用气象学报, 2009, 20(2):203-213.
[15]	李丰, 刘黎平, 王红艳, 等. C波段多普勒天气雷达地物识别方法[J]. 应用气象学报, 2014, 25(2):158-167.
[16]	杜牧云, 刘黎平, 胡志群, 等. 双线偏振多普勒雷达资料质量分析[J]. 气象学报, 2013, 71(1):146-158.
[17]	吴欢, 黄兴友. X波段双线偏振雷达回波强度衰减和地物回波识别订正[J]. 气象科学, 2014, 34(1):32-38.
[18]	陈艳, 李柏, 何建新, 等. 在天气雷达信号处理器中用IQ信号消减地物杂波[J]. 气象科技, 2015, 43(4):569-575.
[19]	谢丽萍, 王德旺, 黄宁立, 等. 基于云雷达的大气0 ℃层亮带识别[J]. 干旱气象, 2016, 34(3):472-480.
[20]	孙伟, 徐芬. 基于偏度方法的地物杂波识别及去除[J]. 干旱气象, 2018, 36(3):522-528.
[21]	杜言霞, 吴勇凯, 程思, 等. 综合识别法去除风廓线雷达地物杂波的可行性研究[J]. 干旱气象, 2019, 37(1):166-172.
[22]	袁正腾, 陈正洪, 陈英英, 等. SWAN 雷达拼图VIL产品中鄂西南地物回波特征及其剔除方法[J]. 干旱气象, 2014, 32(1):147-150.
[23]	ANDRIEU H, CREUTIN J, DELRIEU G, et al. Use of a weather radar for the hydrology of a mountainous area. Part I: radar measurement interpretation[J]. Journal of Hydrology, 1997, 193(1):1-25. DOI URL
[24]	ARCHIBALD E. Enhanced clutter processing for the UK weather radar network[J]. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 2000, 25(10):823-828. DOI URL
[25]	DELRIEU G, CREUTIN J D, ANDRIEU H. Simulation of radar mountain returns using a digitized terrain model[J]. Journal of Atmospheric and Oceanic Technology, 1995, 12(5):1038-1049. DOI URL
[26]	DJAFRI A, HADDAD B. Mapping of weather radar ground clutter using the digital elevation model (srtm)[J]. Signal & Image Processing, 2012, 3(4):135-151.
[27]	GONZALEZ-RAMIREZ E. Weather radar data analysis oriented to improve the quality of rainfall estimation[D]. Bristol: University of Bristol, 2005.
[28]	RICO-RAMIREZ M A, GONZALEZ-RAMIREZ E, CLUCKIE I, et al. Real-time monitoring of weather radar antenna pointing using digital terrain elevation and a Bayes clutter classifier[J]. Meteorological Applications, 2009, 16(2):227-236. DOI URL
[29]	韩雁飞, 吴仁彪, 李海. 基于双门限控制的机载气象雷达地杂波抑制方法[J]. 雷达学报, 2013, 2(1):97-104.
[30]	李壮志, 张尤赛. 基于DEM的雷达地杂波仿真[J]. 计算机仿真, 2007, 24(5):176-178.
[31]	秦娟, 吴仁彪, 苏志刚, 等. 基于地形可视性分析的机载气象雷达地杂波剔除方法[J]. 电子与信息学报, 2012, 34(2):351-355.
[32]	王曙东, 裴翀, 郭志梅, 等. 基于SRTM数据的中国新一代天气雷达覆盖和地形遮挡评估[J]. 气候与环境研究, 2011, 16(4):459-468.
[33]	万玉发, 杨洪平. 多普勒天气雷达站址视程的客观分析技术[J]. 应用气象学报, 2000, 11(4):440-447.
[34]	肖艳姣. 新一代天气雷达三维组网技术及其应用研究[D]. 北京:中国气象科学研究院, 南京: 南京信息工程大学, 2007.
[35]	刘艳. 多普勒天气雷达地物杂波时域和频域抑制研究[D]. 成都: 成都信息工程学院, 2007.
[36]	KADI A P, LOUPAS T. On the performance of regression and step-initialized IIR clutter filters for color Doppler systems in diagnostic medical ultrasound[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 1995, 42(5):927-937. DOI URL
[37]	裴旭, 马东立. 等效地球半径法应用中的问题[J]. 北京航空航天大学学报, 2005, 31(4):455-458.
[38]	BLAKE L V. Radar range-performance analysis[M]. Norwood, MA: Artech House, 1980:1-472.
[39]	SeiSman全球地形起伏数据总结[EB/OL] [2022-02-11]. https://blog.seisman.info/global-relief-models/.
[40]	张泉. 中国地区SRTM数据高程误差的分布特征[D]. 西安: 西北大学, 2015.
[41]	刘勇. 中国西北部高山高原地区SRTM3数据质量评价[J]. 兰州大学学报(自然科学版), 2008, 44(6):1-7.
[42]	徐良, 彭光雄, 沈蔚. 基于ArcGIS的SRTM缺失数据修复处理方法[J]. 城市勘測, 2011(1):5-10.
[43]	庄薇, 刘黎平, 胡志群. 青藏高原零度层亮带的识别订正方法及在雷达估测降水中的应用[J]. 气象, 2013, 39(8):1004-1013.
[44]	ZHANG J, LANGSTON C, HOWARD K. Brightband identification based on vertical profiles of reflectivity from the WSR88D[J]. Journal of Atmospheric & Oceanic Technology, 2008, 25(10):1859-1872.
[45]	张乐坚, 程明虎, 陶岚. CINRAD-SA/SB 零度层亮带识别方法[J]. 应用气象学报, 2010, 21(2):171-179.
[46]	杨杰, 刘黎平, 赵城城, 等. 雷达估测对流性降水的误差空间分布及Z-R关系的优化[J]. 高原气象, 2015,(6):1785-1796. DOI