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Solving the storm split-merge problem-A combined storm identification, tracking algorithm

机译:解决风暴分裂合并问题-风暴识别,跟踪组合算法

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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).
机译:已经设计了许多基于雷达数据的风暴识别和跟踪算法,并将其广泛用于天气预报。但是,大多数这些算法都集中在风暴单元上。随着对流风暴的分裂(合并),许多风暴单元将在彼此紧邻的位置生成(消失),从而导致风暴跟踪和风暴演化分析中的错误。为了解决这个问题,在对各种现有的对流风暴识别和跟踪方法进行了详细测试之后,设计了一种新的组合风暴识别跟踪算法(称为CSIT)。 CSIT中使用了连接的邻域标签和较低的雷达反射率阈值(30dBz),以确保可以检测到新形成的风暴。此外,在较低的反射率阈值下,对流单元的合并被视为对流风暴,因此减少了风暴分裂合并的发生。在风暴跟踪方面,设计了五种具有不同风暴运动估计,搜索半径计算和匹配原理的跟踪算法,并使用列联表法和客观方法进行了定量评估。选择考虑风暴分裂和合并的不同情况的最佳算法来设计CSIT,并通过一系列敏感性测试和目标确定算法中两个参数(即最大匹配距离和搜索半径调整因子)的最佳阈值使用2012年至2017年的六年暖季(JJA)雷达数据进行评估。设计的风暴识别和跟踪算法有可能应用于其他数据,例如卫星的云顶亮度温度,闪电频率和其他模型输出变量。客观评估方法不依赖于人工跟踪结果,因此可以用于针对不同情况(不同的风暴类型,区域等)改进和调整自动跟踪算法。

著录项

  • 来源
    《Atmospheric research》 |2019年第4期|335-346|共12页
  • 作者单位

    Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Land Surface Proc & Climate Change Cold &, 320 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China|Univ Chinese Acad Sci, Beijing 100049, Peoples R China;

    Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Land Surface Proc & Climate Change Cold &, 320 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China|Chinese Acad Sci, Pingliang Land Surface Proc & Severe Weather Res, Pingliang 744015, Gansu, Peoples R China;

    Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Land Surface Proc & Climate Change Cold &, 320 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China|Chinese Acad Sci, Pingliang Land Surface Proc & Severe Weather Res, Pingliang 744015, Gansu, Peoples R China;

    Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Land Surface Proc & Climate Change Cold &, 320 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China|Chinese Acad Sci, Pingliang Land Surface Proc & Severe Weather Res, Pingliang 744015, Gansu, Peoples R China;

    Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Land Surface Proc & Climate Change Cold &, 320 Donggang West Rd, Lanzhou 730000, Gansu, Peoples R China|Chinese Acad Sci, Pingliang Land Surface Proc & Severe Weather Res, Pingliang 744015, Gansu, Peoples R China;

    Nanjing Univ, Inst Climate & Global Change Res, Sch Atmospher Sci, Nanjing, Jiangsu, Peoples R China|Univ New South Wales, ARC Ctr Excellence Climate Extremes, Sydney, NSW, Australia|Univ New South Wales, Climate Change Res Ctr, Sydney, NSW, Australia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Storm identification; Storm tracking; Split-merge; Algorithm evaluation;

    机译:风暴识别;风暴跟踪;拆分合并;算法评估;

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