高原气象

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2018 , Vol. 37 >Issue 1: 264 - 274

DOI: https://doi.org/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
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  • 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;中国气象科学研究院灾害天气国家重点实验室, 北京 100081

收稿日期: 2015-11-03

  网络出版日期: 2018-02-28

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）

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An Algorithm of Recognizing Automatically MARC Signature Using the Doppler Weather Radar Volume Scanning Data

  • XIAO Yanjiao
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  • Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research, Institute of Heavy Rain, China Meteorological Administration, Wuhan 430205, Hubei, China

Received date: 2015-11-03

  Online published: 2018-02-28

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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

本文引用格式

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 . DOI: 10.7522/j.issn.1000-0534.2016.00143

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 rada; MARC signature; automatic recognitio

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