气候变化领域集成服务门户(CCPortal): Solving the storm split-merge problem—A combined storm identification; tracking algorithm

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DOI	10.1016/j.atmosres.2018.12.007
	Solving the storm split-merge problem—A combined storm identification; tracking algorithm
	Zan B.; Yu Y.; Li J.; Zhao G.; Zhang T.; Ge J.
发表日期	2019
ISSN	0169-8095
起始页码	335
结束页码	346
卷号	218
英文摘要	Many storm identification and tracking algorithms based on radar data have been designed and widely used in weather forecasting. However, most of these algorithms have concentrated on storm cells. As a convective storm splits (merges), many storm cells will be generated (disappeared) within close proximity to each other resulting in errors in storm tracking and analysis of storm evolution. To resolve this, a new combined storm identification tracking algorithm (referred as CSIT) is devised after detailed testing of various existing convective storm identification and tracking methods. The connected neighborhoods labeling and a lower radar reflectivity threshold(30dBz) is used in CSIT, which ensures that newly formed storms can be detected. Moreover, with a lower reflectivity threshold, merger of convective cells is regarded as one convective storm, thus reducing the occurrence of storm split-merge. In terms of storm tracking, five tracking algorithms with different storm motion estimation, search radius calculation, and matching principles are designed and quantitatively evaluated using the contingency table approach and objective method. The best-performing algorithm that considers different situations of storm splitting and merging is selected to devise CSIT and the optimal thresholds for two parameters in the algorithm (i.e. maximum matching distance and search radius adjust factor) is determined through a series of sensitivity tests and objective evaluation using six years of warm season (JJA) radar data from 2012 to 2017. The devised storm identification and tracking algorithm has the potential to be applied to other data, such as cloud top brightness temperature from satellites, lightning frequency and other model output variables. The objective evaluation method does not rely on manual tracking results and thus can be used to improve and adapt automatic tracking algorithms for different situations (different storm types, regions etc). © 2018 Elsevier B.V.
英文关键词	Algorithm evaluation; Split-merge; Storm identification; Storm tracking
语种	英语
scopus关键词	Merging; Motion estimation; Reflection; Storms; Tracking (position); Tracking radar; Algorithm evaluation; Automatic tracking algorithms; Brightness temperatures; Lightning frequency; Objective evaluation; Radar reflectivities; Splitting and merging; Storm identification; Weather forecasting; algorithm; brightness temperature; convective system; parameterization; radar; storm track; weather forecasting
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162323
作者单位	Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Pingliang Land Surface Process and Severe Weather Research Station, Chinese Academy of Sciences, Pingliang, Gansu 744015, China; Institute for Climate and Global Change Research, School of Atmospheric Sciences, Nanjing University, Nanjing, China; ARC Centre of Excellence for Climate Extremes and Climate Change Research Centre, University of New South Wales, Sydney, Australia
推荐引用方式 GB/T 7714	Zan B.,Yu Y.,Li J.,等. Solving the storm split-merge problem—A combined storm identification; tracking algorithm[J],2019,218.
APA	Zan B.,Yu Y.,Li J.,Zhao G.,Zhang T.,&Ge J..(2019).Solving the storm split-merge problem—A combined storm identification; tracking algorithm.Atmospheric Research,218.
MLA	Zan B.,et al."Solving the storm split-merge problem—A combined storm identification; tracking algorithm".Atmospheric Research 218(2019).