[1]孙京,肖艳姣,冷亮.基于多普勒天气雷达体扫资料的下击暴流预警方法研究[J].自然灾害学报,2019,28(02):118-126.[doi:10.13577/j.jnd.2019.0213]
 SUN Jing,XIAO Yanjiao,LENG Liang.Damaging downbursts warning algorithm using the Doppler weather radar scanning data[J].,2019,28(02):118-126.[doi:10.13577/j.jnd.2019.0213]
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基于多普勒天气雷达体扫资料的下击暴流预警方法研究
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《自然灾害学报》[ISSN:/CN:23-1324/X]

卷:
28
期数:
2019年02期
页码:
118-126
栏目:
出版日期:
2019-04-28

文章信息/Info

Title:
Damaging downbursts warning algorithm using the Doppler weather radar scanning data
作者:
孙京 肖艳姣 冷亮
中国气象局武汉暴雨研究所, 暴雨监测预警湖北省重点实验室, 湖北 武汉 430205
Author(s):
SUN Jing XIAO Yanjiao LENG Liang
1. Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, China
关键词:
多普勒天气雷达下击暴流预警指标云模型BP神经网络
Keywords:
Doppler weather radardamaging downburstswarning indicescloud modelBP neural network
分类号:
P456.9;X43
DOI:
10.13577/j.jnd.2019.0213
摘要:
本文利用新一代多普勒天气雷达体扫数据、自动气象探空站和地面大风测站资料,对2009-2013年湖北省大风天气过程的风暴特征量进行相关统计分析,通过云模型和支持向量机(SVM)等方法确定了包含环境、反射率和径向速度特征的9个下击暴流雷达预警指标。基于已确定的预警指标,分别利用Bayes和BP神经网络两种方法建立了下击暴流预报模型,通过识别结果检验表明,两种算法均能有效区分大风与非大风。Bayes方法大风击中率(POD)可以达到81.8%,大风和非大风预报准确率为86.7%,虚警率(FAR)和失误率(FOM)分别为5.2%和18.1%,TS评分0.77; BP神经网络非线性预报方法对大风的识别准确率为84%。进一步证明了提出的下击暴流雷达指标的可预报性和实用性。
Abstract:
Based on a Doppler radar volume data, meteorological sounding and wind data, the characteristic quantity of downbursts from 2009-2013 in Hubei province are statistically analyzed, and the nine main radar warning indices of downbursts are given by the cloud model and support vector machine, which includes the characteristics of environment, radar reflectivity and radar-velocity. On the basis, the downburst prediction models are established by the method of Bayes and BP neural network. The identification results show that the methods can differentiate the downburst and non-downburst, and the POD of Bayes is 81.8%, the forecast accuracy of downburst and non-downburst is 86.7%, and the FAR and FOM are 5.2% and 18.1%, respectively. The TS score of model is 0.77; the forecast accuracy of downburst based on the nonlinear prediction of BP neural network is 84%, which further proves the predictability and practicability of the downburst warning indices.

参考文献/References:

[1] Fujita T T, Byers H R. Spearhead echo and downbursts in the crash of an airliner[J]. Mon Wea Rev, 1977, 105:129-146.
[2] 李宏海, 欧进萍. 我国下击暴流的时空分布特性[J]. 自然灾害学报, 2015, 24(6):9-18. LI Honghai, OU Jinping. Spatiotemporal distribution characterisitcs of downburst in China[J]. Journal of Natural Disasters, 2015, 24(6):9-18. (in Chinese)
[3] 郑永光, 田付友, 孟智勇, 等."东方之星"客轮翻沉事件周边区域风灾现场调查与多尺度特征分析[J].气象, 2016, 42(1):1-13. ZHENG Yongguang, TIAN Fuyou, MENG Zhiyong, et al. Survey and multi0scale characteristics of wind damage caused by convective storms in the surrounding area of the capsizing accident of cruise ship"Dongfangzhixing"[J]. Meteorological Monthly, 2016,42(1):1-13. (in Chinese)
[4] 孟智勇, 姚聃, 白兰强, 等. 基于实地灾害调研和雷达观测对"东方之星"倾覆地点附近强风的估计[J]. 科学通报, 2016, 61(7):797-798. MENG Zhiyong, YAO Dan, BAI Lanqiang,et al. Wind estimation around the shipwreck of oriental star based on field damage surveys and radar observations[J]. Sci Bull, 2016, 61:330-337. (in Chinese)
[5] Darrah R P. On the relationship of severe weather to radar lops[J]. Mon Wea Rev, 1978, 106(9):1332-1339.
[6] Fujita T T. Objective, operation, and results of Project NIMROD, Preprints[C]//11th Conf. on Severe Local Storms, Kansas City, MO, Amer Meteor Soc, 1979, 259-266.
[7] Johns R H and Hirt W D. Derechos:Widespread convectively induced windstorms[J].Wea Forecasting, 1987, 2:32-49.
[8] Przybylinski R W, Gery W J. The reliability of the bow echo as an important severe weather signature[C]//Preprints, 13th Conf on Severe Local Storms. Tulsa, OK, Amer Meteor Soc, 1983, 270-273.
[9] Kessinger C J,Parsons D B, Wilson J W. Observations of a storm containing miso cyclones, downbursts, and horizontal vortex circulations[J]. Mon Wea Rev, 1998, 116(10):1959-1982.
[10] Roberts R D, Wilson J W. A proposed microburst nowcasting procedure using single-Doppler radar[J]. J Appl Meteor, 1989, 28(4):285-303.
[11] 李国翠, 刘黎平, 连志鸾, 等. 利用雷达回波三维拼图资料识别雷暴大风统计研究[J].气象学报, 2014, 72(1):168-181. LI Guocui, LIU Liping, LIAN Zhiluan, et al. Statistical study of the identification of thunderstorm gale based on the radar 3D mosaic data[J]. Acta Meteorological Sinica, 72(1):168-181. (in Chinese)
[12] Lemon, L.R. and D.W. Burgess. Supercell associated deep convergence zone revealed by a WSR-88D[C]//Preprints, 26th Conf. on Radar Meteor., Norman, Amer. Meteor. Soc.,1993, 206-208.
[13] Przybylinski R W. The bow echo:Observations, numerical simulation, and severe weather detection methods[J]. Wea Forecasting, 1995, 10, 203-218.
[14] Smith T M, Elmore K L, Dulin S A. A damaging downburst prediction and detection algorithm for the WSR-88D[J]. Wea Forecasting, 2004b,19:240-250.
[15] 杨淑华, 王丽丽, 梁进秋, 等. 山西北部一次飑线大风的多普勒雷达特征[J]. 自然灾害学报, 2011, 20(3):113-119.YANG Shuhua, WANG Lili, LIANG Jinqiu, et al. Feature ofDoppler radar data about a squall line strong wind in north of Shanxi Province[J]. Journal of Natural Disaster, 2011,20(3):113-119. (in Chinese)
[16] 罗辉, 张杰, 朱克云, 等.下击暴流的雷达预警量化指标研究[J].气象学报, 2015, 73(5):853-867. LUO Hui, ZHANG Jie, ZHU Keyun, et al. Study of the radar quantitative index of forewarning downburst[J]. Acta Meteorological Sinica, 2015, 73(5):853-867. (in Chinese)
[17] 周康辉, 郑永光, 王婷波, 等, 基于模糊逻辑的雷暴大风和非雷暴大风区分方法[J]. 气象, 2017, 43(7):781-791. (in Chinese)ZHOU Kanghui, ZHENG Yongguang, WANG Tingbo. Fuzzy logic algorithm of thunderstorm gale identification using multisource data[J]. Meteorological Monthly, 2017, 43(7):781-791. (in Chinese)
[18] 李德毅, 刘常昱. 论正态云模型的普适性[J].中国工程科学, 2004, 6(8):30-32.LIi Deyi, LIU Changyu. Study on the universality of the normal cloud model[J]. Engineering Science,2004, 6(8):30-32. (in Chinese)
[19] 王健峰, 张磊, 陈国兴, 等. 基于改进的网格搜索法的SVM参数优化[J].应用科技, 2012, 39(3):28-31. WANG Jianfeng, ZHANG Lei, CHEN Guoxing, et al. A parameter optimization method for an SVM based on improved grid seach algorithm[J]. Applied Science and Technology, 2012, 39(3):28-31. (in Chinese)
[20] 蒋宗礼. 人工神经网络导论[M]. 北京:高等教育出版社, 2001, 7-13. JIANG Zongli. Introduction to Artificial Neural Network[M]. Beijing:Higher Education Press,2011, 7-13.(in Chinese)

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备注/Memo

备注/Memo:
收稿日期:2018-06-04;改回日期:2018-12-13。
基金项目:政府间国际科技创新合作重点专项(2016YFE0109400);国家自然科学基金项目(41275008);湖北省气象局年轻科技人员专项(2016Q03)
作者简介:孙京(1986-),女,助理研究员,硕士,主要从事强对流天气研究.E-mail:jing20100210@sina.com
更新日期/Last Update: 1900-01-01