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汤欢, 傅慎明, 孙建华, 等. 2023. 基于高分辨率再分析风场的高原涡三维识别技术及应用[J]. 大气科学, 47(3): 698−712. doi: 10.3878/j.issn.1006-9895.2112.21127
引用本文: 汤欢, 傅慎明, 孙建华, 等. 2023. 基于高分辨率再分析风场的高原涡三维识别技术及应用[J]. 大气科学, 47(3): 698−712. doi: 10.3878/j.issn.1006-9895.2112.21127
TANG Huan, FU Shenming, SUN Jianhua, et al. 2023. Three-Dimensional Objective Identification of the Tibetan Plateau Vortex Based on a Reanalysis Wind Field with a High Spatial and Temporal Resolution [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(3): 698−712. doi: 10.3878/j.issn.1006-9895.2112.21127
Citation: TANG Huan, FU Shenming, SUN Jianhua, et al. 2023. Three-Dimensional Objective Identification of the Tibetan Plateau Vortex Based on a Reanalysis Wind Field with a High Spatial and Temporal Resolution [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 47(3): 698−712. doi: 10.3878/j.issn.1006-9895.2112.21127

基于高分辨率再分析风场的高原涡三维识别技术及应用

Three-Dimensional Objective Identification of the Tibetan Plateau Vortex Based on a Reanalysis Wind Field with a High Spatial and Temporal Resolution

  • 摘要: 高原涡(TPV)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0.5°或0.25°)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: The Tibetan Plateau vortex (TPV) is a shallow mesoscale vortex system in the Tibetan Plateau’s main body. It occurs regularly, affects a wide area, and causes strong disasters. It is a major disaster-causing mesoscale system in China. To fully show the statistical characteristics of TPVs, a crucial basis for TPV research must be established. The accurate identification of TPVs is the key to the statistical characteristics of TPVs. TPV research has a better data basis with the emergence of reanalysis data with a high spatial and temporal resolution. However, neither an artificial identification approach nor an objective identification algorithm based on a coarser resolution can be effectively used for the current new reanalysis data. In this study, a restricted vorticity-based TPV identifying algorithm is proposed, which is suitable for high-resolution reanalysis data. This approach first determines the TPV candidate points, divides several octants with the candidate points as the center, and determines the center of the TPV by restricting the conditions of the average wind field in the octant and counterclockwise rotation (Northern Hemisphere) conditions of the octant group. This method can quickly identify the horizontal and vertical tracing of vortexes without complicated calculations 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 exceeds 95%, the average false alarm ratio is below 9%, and the average missing report rate is below 5%. Thus, the RTIA can correctly identify the centers of TPVs. Furthermore, the test results show that the high accuracy of TPV identification can still be maintained when RTIA is applied to the reanalysis data with different spatial resolutions (e.g., 0.5°or 0.25°). The identification results are primarily affected by the strength of the vortexes themselves, and the identification accuracy of weak vortexes is lower than that of strong vortexes. This approach can be used as a reference for identifying other mesoscale vortexes.

     

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