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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 | 01698095 |
| 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 |
| URL | https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85058706893&doi=10.1016%2fj.atmosres.2018.12.007&partnerID=40&md5=484d0cee635a9413e166d4256ccdef39 |
| 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
 |
| 来源机构 | 中国科学院西北生态环境资源研究院 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/77340 |
| 推荐引用方式 GB/T 7714 | Zan B.； Yu Y.； Li J.； Zhao G.； Zhang T.； Ge J.. Solving the storm split-merge problem—A combined storm identification, tracking algorithm[J]. 中国科学院西北生态环境资源研究院,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.； Yu Y.； Li J.； Zhao G.； Zhang T.； Ge J.."Solving the storm split-merge problem—A combined storm identification, tracking algorithm".Atmospheric Research 218(2019). |
| 条目包含的文件 | 条目无相关文件。 | |||||
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