globalchange  > 气候变化事实与影响
DOI: 10.1016/j.atmosres.2018.12.007
Scopus ID: 2-s2.0-85058706893
Solving the storm split-merge problem—A combined storm identification, tracking algorithm
Author: Zan B.; Yu Y.; Li J.; Zhao G.; Zhang T.; Ge J.
Source Publication: Atmospheric Research
ISSN: 1698095
Publishing Year: 2019
Volume: 218
pages begin: 335
pages end: 346
Language: 英语
Keyword: Algorithm evaluation ; Split-merge ; Storm identification ; Storm tracking
Scopus Keyword: 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
English Abstract: 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.
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Affiliation: 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

Recommended Citation:
Zan B.,Yu Y.,Li J.,et al. Solving the storm split-merge problem—A combined storm identification, tracking algorithm[J]. Atmospheric Research,2019-01-01,218
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