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Flash Cell Identification, Tracking and Nowcasting with Lightning Data
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摘要: 该文提出了一种新的雷暴识别、追踪与外推方法。该方法基于地闪数据，利用密度极大值快速搜索聚类算法实现雷暴的识别，采用Kalman滤波算法实现雷暴的追踪与外推。应用该方法处理了2013年的全国地闪定位数据，同时利用多普勒天气雷达等数据对选取的个例进行评估。结果表明：该方法能有效识别雷暴并对其进行实时追踪，且能有效处理雷暴分裂与合并的情况；算法具有较好的0~60 min的临近外推预报能力，各项性能指标整体与TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting) 算法接近，在30 min时效有更好的表现。该方法能够实时监测、预报全国雷暴发生发展状况，对于0~60 min临近预报具有一定参考价值。Abstract: Lightning, accompanying with convective storms in the whole lifecycle, can reflect the development of storms effectively. The national lightning detection network makes it possible to get lightning location data instantly all over China, which would be highly valuable in convective system monitoring. A new method for flash cell identification, tracking and nowcasting is proposed. Using cloud-to-ground lightning location data over China, a new cluster algorithm of fast searching and density peaks identifying, is utilized to recognize the flash cells by clustered flashes. Time and area distribution characteristics of flashes are used in identification. Second, Kalman filtering is used to track the moving path of cells, considering cell spitting and merging conditions. Finally, based on the previous path, the linear moving path in next 60 min is predicted with Kalman filtering.lightning location data in 2013 are analyzed by this method. Doppler radar data are applied to evaluate its performance, which proves its effectiveness on identification and track for thunderstorm split and merge. The overall performance is as better as TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting) for thunderstorm nowcasting in 60 minutes, and even better from some aspects. The probability of detection of nowcasting for 10 min is about 0.7, about 0.6 for 30 min and 0.2 for 60 min, respectively. The probability of detection and critical success index decrease dramatically with time, and the false alarm rate increases rapidly in 60 min. All of those mean that the linear nowcasting would be more reliable in near time, and it is meaningful in 0-60 min forecast.One case is analyzed in detail, which shows that flashes only appear in deep convective systems that usually companies with severe weather. Furthermore, flashes disappears obviously in the dissipative stage of storms, which would be an indicator for predicting the end of convective systems.
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Key words:
- lightning;
- thunderstorm;
- identification;
- track;
- nowcasting
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图 3 雷暴识别、追踪与外推流程
Fig. 3 The process of flash cells identification, track and nowcasting
图 4 雷暴识别效果示意图
(椭圆为根据闪电簇分布自动生成的雷暴区) (a) 闪电聚类效果 (红点为闪电)，(b) 识别的雷暴活动区与雷达回波对比
Fig. 4 The lightning clusters identification
(the ellipse denotes the boundary of cluster) (a) the clustered result (the red dot denotes lightning), (b) the clustered result compared with radar echo
图 5 2013年3月20日华南地区雷暴追踪路径与闪电分布
(黑色虚线为雷暴追踪路径，S为雷暴开始标志，数字为雷暴对应的时刻，下同；红点为负地闪, 蓝点为正地闪)
Fig. 5 The lightning distribution and track paths of thunderstorms in South China on 20 Mar 2013
(the black dash line denotes the tracked path, S is the start symbol, number denotes is the corresponding moment, red point denotes negative flash, blue point denotes positive flash)
图 6 2013年3月20日华南地区雷暴移动情况
Fig. 6 The track paths of thunderstorms in South China on 20 Mar 2013
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