双月刊

ISSN 1006-9895

CN 11-1768/O4

中尺度涡旋客观识别与三维追踪的新方法及其效果评估
作者:
作者单位:

中国科学院大学

作者简介:

通讯作者:

基金项目:


A New Objective Identification Method for Mesoscale Vortices: Three-dimensional Tracking and its Quantitative Evaluation
Author:
Affiliation:

University of Chinese Academy of Sciences

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    中尺度涡旋是引发强降水等一系列气象灾害的重要天气系统之一。中尺度涡旋识别是对其进行研究的重要基础。目前,如何客观准确地识别中尺度涡旋并对其进行较全面的评估仍是一项充满挑战的任务。本文从我国中尺度涡旋的基本特征出发,提出了一种基于风场和相对涡度的涡旋识别标准、并发展了适用于高分辨率格点数据的中尺度涡旋客观识别算法。该算法能准确识别出中尺度气旋性环流并定位涡旋中心,较现有常规中尺度涡旋识别方法而言,具有误判率低,定位精度高等特点。本文将该客观识别算法应用于长江流域频发的三类中尺度涡旋(高原涡,西南涡,大别山涡)的识别中,结果表明对于不同时间段、不同分辨率的再分析资料(逐6小时 0.5°×0.5°的 NCEP CFSR 再分析资料、逐小时 0.25°×0.25°的ERA5再分析资料),本识别算法对三类中尺度涡旋均有较好的识别效果。本文基于1979-2020年共42年暖季(5-9月)大别山涡的数据集(共计36357时次)对新发展的中尺度涡旋客观识别算法进行了定量评估,结果表明本算法能够长期稳定的识别涡旋,42年的平均命中率为95.5%。此外,本文提出了涡旋连续性判定和三维追踪方案,较现有常规中尺度涡旋追踪方法具有显著优势。

    Abstract:

    Mesoscale vortex (MV) is one of the most important weather systems that causes precipitation and meteorological disasters in China. However, there is no universal standard of MV identification , and the MV objective identification is still an urgent problem to be solved. Based on the main features of MVs in China, a new objective identification algorithm (new algorithm for short) suitable for high-precision grid data is developed in this study, by combining the wind and vorticity. The new algorithm can accurately identify the mesoscale cyclonic circulation and locate the vortex center, with lower false rate and higher positioning accuracy than existing identification methods. The new algorithm is applied to three kinds of mesoscale vortices (Plateau vortex (TPV), Southwest vortex (SWV) and Dabie Mountain vortex(DBV)) frequently occurring along the Yangtze river basin, the results show that the new algorithm performed well in all the three kinds of MVs with almost insensitive to applied period or data resolution (6-hourly 0.5°×0.5°NCEP CFSR reanalysis data, hourly 0.25°×0.25°ERA5 reanalysis data). Based on the DBV activity dataset of DBV (the total DBV occurrence is 36357 ) in the warm season (May - September) during 1979 - 2020, the new algorithm is quantitatively evaluated. The evaluations prove that the new algorithm can identify MV in a long-term and stable way, with an average hit rate of 95.5%. In addition, this paper proposes a 3D MV tracking scheme, which has significant advantages over traditional tracking methods.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-09-17
  • 最后修改日期:2021-11-09
  • 录用日期:2021-11-23
  • 在线发布日期: 2021-11-23
  • 出版日期: