RESEARCH ON SHEAR DETECTION ALGORITHM BASED ON DOPPLER WEATHER RADAR FOR LOW-ELEVATION DOPPLER VELOCITY
-
摘要: 低空风切变是影响航空器起飞和着陆安全的重要因素，利用多普勒天气雷达径向速度数据，从风的空间和时间变化上对低空风切变的识别进行了研究，并利用一次飑线过程和一次低空急流过程的资料对识别算法进行了验证。识别算法的核心是分别计算二维合成风切变、垂直风切变和时间风切变。在计算二维合成风切变时，先利用风切变强度因子自适应地选择拟合“窗口”的大小，再利用最小二乘线性拟合方法，得到水平风切变。结果表明：自适应多尺度最小二乘法得到的合成风切变，在低空风切变识别效果、切变连续性和边缘数据处理等方面都优于我国多普勒天气雷达的PUP合成风切变；垂直风切变反映了雷达径向速度的高低空配置情况；时间风切变可提供径向速度随时间的变化情况。算法还可应用于民航机场低空风切变的识别和预警。Abstract: Low-level wind shear is a serious threat to flight safety during the take-off and landing phases. This paper studies the recognition of spatiotemporal low-level wind shear by using radial velocity data from a Doppler radar, and verification is made according to a squall line event and a low-level jet event. The main idea of the recognition algorithm is to calculate two-dimensional wind shear combination, vertical wind shear and temporal wind shear. To get the two-dimensional wind shear combination, an adaptive fitting "window" according to the wind shear intensity factor should be firstly determined, and the least squares linear fitting technique is then adopted within the "window". The results show that the adaptive multi-scale least squares wind shear combination is better than that of PUP provided by Doppler weather radar system. The advantage is reflected on wind shear detect effect, shear continuity and the outer edge "false" shear. Vertical wind shear refers to the speed variation between two adjacent elevations, and temporal wind shear refers to the speed change with time. The recognition algorithm and results can provide reliable information for airport service and early warning on low-level wind shear.
-
图 3 2009年6月3日23:30徐州飑线过程多普勒天气雷达回波及低空风切变识别结果
a. 0.53 °仰角反射率因子；b. 1.45 °仰角反射率因子；c. 0.53 °仰角径向速度；d. 1.45 °仰角径向速度；e. PUP合成风切变图(显示阈值2.0×10-3 s-1)；f.自适应多尺度最小二乘法合成风切变(显示阈值2.0×10-3 s-1)；g. 0.53 °与1.45 °仰角间垂直风切变；h. 6月3日23: 18与6月3日23: 30间时间风切变(显示阈值0.5 m/(s·min))。
-
[1] 王秀春, 顾莹, 李程.航空气象[M].北京:清华大学出版社, 2014: 139-149. [2] Organization ICAO. Manual on low-level wind shear and turbulence[M]. International Civil Aviation Organization, 2005: 21-22. [3] 赵丽艳.基于多普勒天气雷达的风切变预警算法研究[D].天津: 中国民航大学, 2016. http://cdmd.cnki.com.cn/Article/CDMD-10059-1016775960.htm [4] WILSON J W, ROBERTS R D, KESSINGER C J, et al. Microburst wind structure and evaluation of Doppler radar for airport wind shear detection[J]. J Appl Meteor, 1984, 23(6): 898-915. doi: 10.1175/1520-0450(1984)023<0898:MWSAEO>2.0.CO;2 [5] UYEDA H, ZRNIC D S. Automatic detection of Gust fronts[J]. J Atmos Ocean Techno, 1986, 3(1): 36-50. doi: 10.1175/1520-0426(1986)003<0036:ADOGF>2.0.CO;2 [6] CAMPBELL S D, OLSON S H. Recognizing low-altitude wind shear hazards from Doppler weather radar: An artificial intelligence approach[J]. J Atmos Ocean Techno, 1987, 4(1): 5-18. doi: 10.1175/1520-0426(1987)004<0005:RLAWSH>2.0.CO;2 [7] EVANS J E, DUCOT E R. The Integrated Terminal Weather System (ITWS)[J]. Lincoln Laboratory Journal, 1994, 7(192): 449-474. http://cn.bing.com/academic/profile?id=e7d1bce6b55fdaf9bb7e50e91efc47d3&encoded=0&v=paper_preview&mkt=zh-cn [8] BOILLEY A, MAHFOUF J F, LAC C. High resolution numerical modeling of low level wind-shear over the Nice-Cote d'Azur airport[J]. Proceedings of the 13th AMS Conference on Mountain Meteorology, 2008: 11-15. http://cn.bing.com/academic/profile?id=ef190c8bed5298725d6fa5844cbbcc40&encoded=0&v=paper_preview&mkt=zh-cn [9] AUGROS C, TABARY P, ANQUEZ A, et al. Development of a nationwide, low-level wind shear mosaic in France[J]. Wea Forecasting, 2013, 28(5): 1 241-1 260. doi: 10.1175/WAF-D-12-00115.1 [10] 段鹤, 严华生, 王晓君.滇南飑线的发生环境及其多普勒雷达回波特征[J].热带气象学报, 2012, 28(1): 68-76. doi: 10.3969/j.issn.1004-4965.2012.01.008 [11] 王珊珊.多普勒天气雷达合成切变模块设计[J].科技信息, 2007(18): 82-83. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-HBKJ200811006037.htm [12] 王楠, 刘黎平, 徐宝祥, 等.利用多普勒雷达资料识别低空风切变和辐合线方法研究[J].应用气象学报, 2007, 18(3): 314-320. doi: 10.3969/j.issn.1001-7313.2007.03.007 [13] 魏耀, 张兴敢.多普勒天气雷达合成切变算法及改进方法的研究[J].电子与信息学报, 2010, 32(1): 43-47. http://d.old.wanfangdata.com.cn/Periodical/dzkxxk201001009 [14] 胡琦.基于多普勒气象雷达的风切变预测研究[D].上海: 上海交通大学, 2012. http://cdmd.cnki.com.cn/Article/CDMD-10248-1012019809.htm [15] WANG L, WEI M, YANG T. An advanced algorithm for recognizing wind shear using airborne Doppler weather radar[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2015, 229(14): 2547-2558. doi: 10.1177/0954410015580653 [16] 周生辉, 魏鸣, 张培昌, 等.多普勒雷达反演风场的风切变识别研究[J].热带气象学报, 2015, 31(1):119-127. http://www.itmm.org.cn/rdqxxb/ch/reader/view_abstract.aspx?file_no=20150114&flag=1 [17] 蒋立辉, 赵丽艳, 熊兴隆.基于自适应多尺度的梯度搜索风切变预警算法研究[J].科学技术与工程, 2015, 15(31):1-6. doi: 10.3969/j.issn.1671-1815.2015.31.001 [18] 杜爱军.多普勒雷达退速度模糊及风切变的仿真与识别[D].南京: 南京信息工程大学, 2016. http://cdmd.cnki.com.cn/Article/CDMD-10300-1016194501.htm [19] 纪雷, 王振会, 黄兴友, 等.机载W波段雷达衰减订正方法的不同云型模拟对比研究[J].热带气象学报, 2018, 34(2): 260-267. http://www.itmm.org.cn/rdqxxb/ch/reader/view_abstract.aspx?file_no=20180212&flag=1 [20] 寇蕾蕾, 李应超, 楚志刚, 等. C波段双偏振多普勒天气雷达资料分析及在定量估计降水中的应用研究[J].热带气象学报, 2018, 34(4): 460-471. http://www.itmm.org.cn/rdqxxb/ch/reader/view_abstract.aspx?file_no=20180403&flag=1 [21] 丁治英, 夏蘩, 高松, 等.一次飑线过程中雷达回波的组织结构变化及成因分析[J].热带气象学报, 2017, 33(3): 323-333. http://www.itmm.org.cn/rdqxxb/ch/reader/view_abstract.aspx?file_no=20170304&flag=1 [22] 张晓辉, 王云峰, 李刚, 等.多普勒雷达资料同化的稀疏化方式对暴雨过程的影响研究[J].大气科学学报, 2016, 39(3): 409-416. http://d.old.wanfangdata.com.cn/Periodical/njqxxyxb201603013 [23] 沈菲菲, 闵锦忠, 许冬梅, 等. Hybrid ETKF-3DVAR方法同化多普勒雷达速度观测资料Ⅰ:模拟资料试验[J].大气科学学报, 2016, 39(1): 81-89. http://d.old.wanfangdata.com.cn/Periodical/hwyhmb201503002 [24] WOODFIELD A A, WOODS J F. Worldwide experience of wind shear during 1981-1982[C]. AGARD flight mechanics panel Conf. on 'Flight Mechanics and system design lessons from Operational Experience', 1983, AGARD CP No. 347. [25] 梁海河, 张沛源, 葛润生.多普勒天气雷达风场退模糊方法的研究[J].应用气象学报, 2002, 13(5): 591-599. doi: 10.3969/j.issn.1001-7313.2002.05.008 [26] 郑佳锋.新一代天气雷达阵风锋识别算法研究[D].成都: 成都信息工程学院, 2013. http://cdmd.cnki.com.cn/Article/CDMD-10621-1013361608.htm -
下载:
点击查看大图
图(5)
计量
- 文章访问数: 361
- HTML全文浏览量: 5
- PDF下载量: 134
- 被引次数: 0
