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高原气象  2018, Vol. 37 Issue (1): 264-274    DOI: 10.7522/j.issn.1000-0534.2016.00143
论文     
基于多普勒天气雷达体扫资料的MARC特征自动识别算法
肖艳姣1,2
1. 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;
2. 中国气象科学研究院灾害天气国家重点实验室, 北京 100081
An Algorithm of Recognizing Automatically MARC Signature Using the Doppler Weather Radar Volume Scanning Data
XIAO Yanjiao1,2
Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, Hubei, China
 全文: PDF(23605 KB)  
摘要: 下击暴流是对流风暴最常产生的天气现象,预报其初始爆发的时间是强对流风暴预报中最具挑战性的内容之一。而较为显著的中层径向辐合(Mid-Altitude Radial Convergence,MARC)特征是下击暴流的预警指标之一,预报时效为10~30 min。基于多普勒天气雷达体扫资料的三维MARC特征自动识别算法首先采用二维局地LLSD方法计算径向速度的径向散度切变,其次是基于径向散度切变数据,使用类似SCIT算法的SCRCZI算法进行风暴单体三维径向辐合区的自动识别,然后使用基于反射率因子数据识别的风暴位置对与强风暴无关的三维径向辐合区进行消空处理,最后被保留下来的三维径向辐合区就是被识别出来的三维MARC特征。该算法能较好地识别出与强风暴相关的三维MARC特征,包括表现为非典型"正-负速度区域对"的径向辐合区。使用武汉雷达观测的强风暴个例体扫资料,分析了一个产生下击暴流的强风暴反射率因子和径向速度回波演变特征,并对MARC识别算法进行了检验。结果表明:在最低仰角径向速度图上首次出现辐散特征之前的3个体扫和之后出现辐散特征的3个体扫里,该算法都识别出了强风暴较为显著的三维MARC特征,其平均高度为3.9 km,平均厚度为2.5 km,最强辐合高度位于3.0~4.6 km之间,平均最强辐合量为-58×10-4 s-1,预报时效为18 min。
关键词: 多普勒天气雷达MARC特征自动识别    
Abstract: Damaging downbursts on the ground are the most common weather phenomenon produced by severe convective storm. One of the challenges in the severe storm warning process is forecasting the initial onset of damaging winds. A prominent MARC signature is a Doppler radar-velocity based precursor towards forecasting the initial onset of damaging downburst in a strong storm system. The lead time from the initial identification of MARC signature to the first reports of severe wind damage is about 10~30 minutes. In this paper, an automatic recognition algorithm of three-dimensional MARC signature was proposed. First of all, the algorithm uses a two-dimensional, local, linear least squares (LLSD) method to calculate the radial divergences of radial velocities. Secondly, the algorithm uses storm cell radial convergence zone identification (SCRCZI) algorithm which is similar to SCIT algorithm to recognize three-dimensional radial convergence zone based on the radial divergent shear field. And lastly, the locations of storm cells identified using modified SCIT algorithm are used to remove those three-dimensional radial convergence zones unrelated to storm. The algorithm can well identify three-dimensional MARC signatures, including those radial convergence zones not characterized by a symmetric positive/negative velocity pair. The reflectivity and radial velocity evolution characteristics of a strong storm producing a downburst have been analyzed and MARC recognition algorithm has been tested using Wuhan Doppler weather radar observations. The results show that the algorithm is very efficient, the prominent MARC signatures were recognized in six volume scans including three volume scans before the divergence signature appeared in radial velocity image at the lowest tilt for the first time and subsequent three volume scans. In this process, the average height of MARC was 3.9 km, the average depth was 2.5 km, and the strongest magnitudes of MARC were located between 3.0 and 4.6 km, mean magnitudes of the strongest convergence is -58×10-4 s-1, the lead time from the initial identification of MARC to the first reports of the strong divergence signature at the lowest tilt was 18 minutes.
Key words: Doppler weather radar    MARC signature    automatic recognition
收稿日期: 2015-11-03 出版日期: 2018-02-20
ZTFLH:  P406  
基金资助: 公益性行业(气象)科研专项(GYHY201306008);灾害天气国家重点实验室开放课题(2013LASW-B15)
作者简介: 肖艳姣(1971-),女,湖北天门人,正研高工,主要从事天气雷达应用研究和开发.E-mail:Yanjiao.xiao@163.com
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引用本文:

肖艳姣. 基于多普勒天气雷达体扫资料的MARC特征自动识别算法[J]. 高原气象, 2018, 37(1): 264-274.

XIAO Yanjiao. An Algorithm of Recognizing Automatically MARC Signature Using the Doppler Weather Radar Volume Scanning Data. PLATEAU METEOROLOGY, 2018, 37(1): 264-274.

链接本文:

http://www.gyqx.ac.cn/CN/10.7522/j.issn.1000-0534.2016.00143        http://www.gyqx.ac.cn/CN/Y2018/V37/I1/264

Eilts M D, Johnson J T, Mitchell E D, et al, 1996. Damaging downburst prediction and detection algorithm for the WSR-88D[C]. Preprints, 18 th Conf on Severe Local Storms, San Francisco, Amer Meteor Soc, 541-545.
Elmore K M, Albo E D, Goodrich R K, et al, 1994. NASA/NCAR airborne and ground-based wind shear studies[M]. Final Report, contract no. NCC1-155, 343.
Fujita T T, Byers H R, 1977. Spearhead echo and downburst in the crash of an airliner[J]. Mon Wea Rev, 105:129-146.
Fujita T T, 1978. Manual of downburst identification for project NIMROD. Satellite and Mesometeorology Research Paper No. 156[C]//Department of Geophysical Sciences, University of Chicago, 104.
Fujita T T, 1979. Objectives, operations and results of project NIMROD[C]//Preprints, 11th Conf on Severe Local Storms. Boston, CA, Amer Meteor Soc, 259-266.
Hjelmfelt M R, 1988. Structure and life cycle of microburst outflows observed in Colorado[J]. J Appl Meteor, 27:900-927.
Johnson J T, MacKeen P L, Witt A, et al, 1998. The storm cell identification and tracking algorithm:an enhanced WSR-88d algorithm[J]. Wea Forecasting, 13:263-276.
Lemon L R, Burgess D W, 1993. Supercell associated deep convergence zone revealed by a WSR-88D[C]//Preprints, 26th Int Conf on Radar Meteorology, Norman, OK, Amer Meteor Soc, 206-208.
Lemon L R, Parker S, 1996. The Lahoma storm deep convergence zone:Its characteristics and role in storm dynamics and severity[C]//Preprints, 18th Conf on Severe Storms, San Francisco, CA, Amer Meteor Soc, 70-74.
Przybylinski R W, Gery W J, 1983. 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, 270-273.
Przybylinski R W, Lin Y J, Schmocker G K, et al, 1995. The use of real-time WSR-88D, profiler, and conventional data sets in forecasting a northeastward moving derecho over eastern Missouri and central Illinois[C]//Preprints, 14th Conf on Weather Analysis and Forecasting. Dallas, Amer Meteor Soc, 335-342.
Roberts R D, Wilson J W, 1989. A proposed microburst nowcasting procedure using single-Doppler radar[J]. J Climate Appl Meteor, 28:285-303.
Schmocker G K, Przybylinski R W, Lin Y J, 1996. Forecasting the initial onset of damaging downburst winds associated with a mesoscale convective system (MCS) using the mid-altitude radial convergence (MARC) signature[C]//Preprints, 15th Conf on Weather Analysis and Forecasting, Norfolk, VA, Amer Meteor Soc, 306-311.
Smith T M, Elmore K L, 2004a. The use of radial velocity derivatives to diagnose rotation and divergence[C]//Preprints, 11th Conf on Aviation, Range, and Aerospace, Hyannis, MA, Amer Meteor Soc, P5. 6.
Smith T M, Elmore K L, Dulin S A, 2004b. A damaging downburst prediction and detection algorithm for the WSR-88D[J]. Wea Forecasting, 19:240-250.
Wilson J W, Roberts R D, Kessinger C, et al, 1984. Microburst wind structure and evaluation of Doppler radar for airport wind shear detection[J]. J Climate Appl Meteor, 23:898-915.
陈贵川, 谌芸, 乔林, 等, 2011. 重庆"5·6"强风暴天气过程成因分析[J]. 气象, 37(7):871-879. Chen G C, Chen Y, Qiao L, et al, 2011. The causation analysid of the 6 May 2010 severe windstorm weather process in Chongqing[J]. Meteor Mon, 37(7):871-879.
李国翠, 刘黎平, 连志鸾, 等, 2014. 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, 72(1):168-181. Li G C, Liu L P, Lian Z L, et al, 2014. Statistical study of the identification of thunderstorm gale based on the radar 3D mosaic data[J]. Acta Meteor Sinica, 72(1):168-181.
慕熙昱, 党人庆, 陈秋萍, 等, 2007. 一次飑线过程的雷达回波分析与数值模拟[J]. 应用气象学报, 18(1):42-49. Mu X Y, Dang R Q, Chen Q P, et al, 2007. Radar data analysis and numerical simulation of a squall line[J]. J Appl Meteor Sci, 18(1):42-49.
肖艳姣, 李中华, 张端禹, 等, 2008. "07·7"鄂东南强对流天气的多普勒雷达资料分析[J]. 暴雨灾害, 27(2):213-218. Xiao Y J, Li Z H, Zhang D Y, et al, 2008. Analysis of "07·7" severe convective events with China new generation weather radar data[J]. Torrential Rain Disaster, 27(2):213-218.
肖艳姣, 万玉发, 王珏, 等, 2012. 一种自动多普勒雷达速度退模糊算法研究[J]. 高原气象, 31(4):1119-1128. Xiao Y J, Wan Y F, Wang J, et al, 2012. Study of an automated Doppler radar velocity dealiasing algorithm[J]. Plateau Meteor, 31(4):1119-1128.
肖艳姣, 万玉发, 王志斌, 2016. 业务多普勒天气雷达双PRF径向速度资料分析和质量控制[J]. 高原气象, 35(4):1112-1122. Xiao Y J, Wan Y F, Wang Z B, et al, 2016. Analysis and quality contral of dual PRF velocity data for operational Doppler weather radars[J]. Plateau Meteor, 35(4):1112-1122. DOI:10. 7522/j. issn. 1000-0534. 2015. 00039.
徐琪, 幕熙昱, 刘韻蕊, 等, 2015. 南京空域一次高空致灾冰粒过程的可预报性分析[J]. 高原气象, 34(1):258-268. Xu Q, Mu X Y, Liu Y R, et al, 2015. Analysis of rredictability on a high-altitude hail/graupel disaster weather in Nanjing airspace[J]. Plateau Meteor, 34(1):258-268. DOI:10. 7522/j. issn. 1000-0534. 2013. 00105.
姚叶青, 俞小鼎, 张义军, 等, 2008. 一次典型飑线过程多普勒天气雷达资料分析[J]. 高原气象, 27(2):373-381. Yao Y Q, Yu X D, Zhang Y J, et al, 2008. Analysis on a typical squall line case with Doppler weather radar data[J]. Plateau Meteor, 27(2):373-381.
俞小鼎, 周小刚, 王秀明, 2012. 雷暴与强对流临近天气预报技术进展[J]. 气象学报, 70(3):311-337. Yu X D, Zhou X G, Wang X M, 2012. The advances in the nowcasting techniques on thunderstorms and severe convection[J]. Acta Meteor Sinica, 70(3):311-337.
王萍, 牛智勇, 2014. 基于多普勒天气雷达数据的中层径向辐合自动识别及其与强对流天气的相关性研究[J]. 物理学报, 63(1):424-436. Wang P, Niu Z Y, 2014. Automatic recognition of mid-altitude radial convergence and study on the relationship between the convergence and strong convective weather based on Doppler weather radar data[J]. Acta Physica Sinica, 63(1):424-436.
王秀明, 俞小鼎, 周小刚, 等, 2012. "6·3"区域致灾雷暴大风形成及维持原因分析[J]. 高原气象, 31(2):504-514. Wang X M, Yu X D, Zhou X G, et al, 2012. Study on the formation and evolution of ‘6·3’ damage wind[J]. Plateau Meteor, 31(2):504-514.
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