双月刊

ISSN 1006-9895

CN 11-1768/O4

基于高分辨率再分析风场的高原涡三维识别技术及初步应用
作者:
作者单位:

1.中国科学院大气物理研究所;2.国网浙江省电力科学研究院

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基金项目:

本研究受到国网浙江省电力公司科技项目(5211DS19001W),国家自然科学基金委项目(41775046和42075002),中国科学院青年创新促进会项目,国家重大科技基础设施项目“地球系统数值模拟装置”以及“高原与盆地暴雨旱涝灾害四川省重点实验室开放研究基金项目”共同资助。


A three-dimensional objective identification of the Tibetan Plateau vortex based on wind field
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Affiliation:

The Institute of Atmospheric Physics, Chinese Academy of Sciences

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    摘要:

    高原涡是生成于青藏高原主体的一类浅薄中尺度涡旋系统,其发生频繁、影响范围广、造成灾害强,是我国最重要的致灾中尺度系统之一。全面揭示高原涡的统计特征是本领域研究的重要基础。其中,高原涡的精准识别是认识其统计特征的关键。随着高时空分辨率再分析资料的出现,高原涡的研究有了更好的数据基础,然而,无论是人工识别方法还是基于较粗分辨率的客观识别算法都难以高效地适用于当前的新再分析资料。因此,亟需发展一种高精度的、适用于高时空分辨率再分析资料的高原涡客观识别方法。本文提出了一种适用于高分辨率再分析资料、基于风场的限制涡度高原涡客观识别算法(Restricted-vorticity based Tibetan-Plateau-vortex identifying algorithm; 简称RTIA)。该方法首先判断高原涡候选点,然后以候选点为中心,划分多个象限,通过象限平均风场限定条件和象限组逆时针旋转(北半球)条件确定高原涡中心,无需复杂计算及对各气压层分别设定阈值,即可快速实现高原涡的水平和垂直追踪。基于1979-2020年共42个暖季(5-9月)、15466个高原涡(共计99090时次)大样本的评估表明,RTIA方法识别高原涡的平均命中率超过95%,平均空报率低于9%,平均漏报率少于5%,可以十分准确地对高原涡进行识别。此外,评估还表明RTIA方法应用于不同空间分辨率的再分析资料(如0.5o或0.25o)时,仍能保持高原涡识别的高准确率,其识别结果主要受涡旋自身强度的影响,对弱涡旋的识别精度比强涡旋偏低。该方法对其他中尺度涡旋识别也具有一定的借鉴意义。

    Abstract:

    Tibetan Plateau vortex (TPV for short) is a kind of shallow mesoscale vortex system generated in the main body of the Tibetan Plateau. It occurs frequently, affects a wide range and causes strong disasters. It is one of the most important disaster-causing mesoscale systems in China. To fully reveal the statistical characteristics of TPVs is to lay the important basis for the study of TPVs. Among them, the accurate identification of TPVs is the key to the statistical characteristics of TPVs. With the emergence of reanalysis data with high spatial and temporal resolution, the study of TPVs has a better data basis. However, neither artificial identification method nor objective identification algorithm based on coarser resolution can be effectively applied to the current new reanalysis data. In this paper, a restricted vorticity based TPV identifying algorithm is proposed, which is suitable for high resolution reanalysis data. The method first determines TPV candidate points, divides multiple octants with the candidate points as the center, and determines the center of TPV by limiting conditions of average wind field in octants and counterclockwise rotation (the Northern Hemisphere ) conditions of octant group. The advantage of this approach is that the horizontal and vertical tracing of vortices can be detected quickly without complicated calculation and different thresholds for each pressure layer. A large sample evaluation of 15,466 TPVs (99,090 hours in total) in 42 warm seasons (May-September) from 1979 to 2020 shows that the average hit ratio of RTIA is more than 95%, the average false alarm ratio is less than 9%, and the average missing report rate is less than 5%. Therefore, the RTIA can accurately identify the centers of TPVs. In addition, the test results also show that when RTIA is applied to the reanalysis data with different spatial resolutions (e.g., 0.5°or 0.25°), the high accuracy of TPV identification can still be maintained. The identification results are mainly affected by the strength of vortexes themselves, and the identification accuracy of weak vortexes is lower than that of strong vortexes. This method can be used as a reference for the identification of other mesoscale vortexes.

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  • 收稿日期:2021-07-19
  • 最后修改日期:2021-12-01
  • 录用日期:2021-12-07
  • 在线发布日期: 2022-01-05
  • 出版日期: