Three-Dimensional Objective Identification of the Tibetan Plateau Vortex Based on a Reanalysis Wind Field with a High Spatial and Temporal Resolution
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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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图 1 高原涡客观识别（RTIA）方法流程图
Figure 1. Flowchart of the RTIA (Restricted-vorticity-based Tibetan-Plateau-vortex Identifying Algorithm) method
图 2 （a–d）2009年7月3日14:00（协调世界时，下同）和（e–h）2010年6月1日23:00的500 hPa流场（灰色箭头）分布：（a、e）涡度（阴影，单位：10−5 s−1）；（b、f）限制涡度（阴影，单位：10−5 s−1）；（c、g）有效涡度（阴影，单位：10−5 s−1）；（d、h）位势高度场（阴影区，单位：gpm）。蓝色线表示高原边界；符号“×”表示高原涡可能的中心，（a、e）由涡度的极大值确定，（b、f）由限制涡度的极大值确定，（c、g）由有效涡度的极大值确定，（d、h）由位势高度的极小值确定
Figure 2. Distributions of 500 hPa streamline field (gray line with arrows) at 1400 UTC July 3, 2009 (left column) and 2300 UTC June 1, 2010 (right column): Panels (a, e) Relative vorticity (shading, units: 10−5 s−1); (b, f) restricted vorticity (shading, units: 10−5 s−1); (c, g) effective vorticity (shading, units: 10−5 s−1); (d, h) indicate the geopotential height (shading, units: gpm). Blue lines show the Tibetan Plateau boundaries; the black crosses show the candidate TPV centers, in (a, e) the centers are determined using the maxima of relative vorticity, in (b, f) the centers are determined using the maxima of restricted vorticity, in (c, g) the centers are determined using the maxima of effective vorticity, in (d, h) the centers are determined using the minima of geopotential height
图 3 （a）四象限和（b）八象限限制涡度涡旋逆时针环流识别方案示意图[红色的点
${O}'$ 表示候选点中心，识别半径Rf确定以${O}'$ 为起始点用于计算的风场数据，红色箭头表示各象限平均风场（u表示纬向风，v表示经向风）满足气旋性旋转，黑色虚线箭头表示各象限组（如第一象限和第二象限，第二象限和第三象限等）平均风向呈现逆时针旋转]Figure 3. Schematic of the four-quadrant (a) and eight-octant (b) vortex counterclockwise identification approach, where the large red dot at the center is the center of the candidate vortex (
${O}'$ ), and the identification radius (Rf) determined the wind data from the center of the candidate vortex (${O}'$ ) used for the calculation. The red arrow indicates that the octant-averaged wind should satisfy the cyclonic pattern, as shown by u (zonal wind) and v (meridional wind). The thick black dashed circle with arrows indicates the average wind direction of the octant group (e.g., the first and second octants; the second and third octants) is counterclockwise
图 4 高原涡源地的识别范围（阴影表示地形，单位：m；黑色线表示高原边界）
Figure 4. Identification area (red rectangle) of the TPV source (the shaded area indicates terrain, units: m; the black solid line indicates the Tibetan Plateau’ s boundary)
图 5 高原涡垂直追踪示意图。2018年5月14日06:00 450～550 hPa流场，阴影为相对涡度，单位：10−5 s−1，紫色线为高原边界线，R表示垂直搜索半径，
$ {C}_{k} $ 表示该层（第k层）追踪的高原涡中心，$ {C}_{k+1} $ 表示向下一层追踪的高原涡中心，$ {C}_{k-1} $ 表示向上一层追踪的高原涡中心Figure 5. Schematic of TPV Vertical Tracking. Streamlines field at 0600 UTC May 14, 2018, from 450 to 550 hPa; the shaded area indicates relative vorticity, units: 10−5 s−1; the purple solid line shows the boundary of the Tibetan Plateau; R is the vertical search radius;
$ {C}_{k} $ represents the center of TPV tracked by the pressure layer (pressure layer k);$ {C}_{k+1} $ represents the center of TPV tracked by the next lower pressure layer;$ {C}_{k-1} $ represents the center of TPV tracked by the next upper-pressure layer
图 6 2002年7月（a）26日15:00、（b）27日15:00、（c）29日10:00 500 hPa风场（黑色风羽，一根长羽为4 m s−1）、高度场（蓝色等值线，单位：gpm）、涡度场（阴影区，单位：10−5 s−1）及RTIA方法识别的高原涡中心（绿点）分布，褐色线为高原边界
Figure 6. Distributions of 500 hPa wind field (black wind barb, a full bar indicates 4 m
$ {\mathrm{s}}^{-1} $ ), geopotential height field (blue contours, units: gpm), relative vorticity field (shading, units: 10−5 s−1) and the center of the TPVs (green dots) by RTIA at (a) 1500 UTC July 26, (b) 1500 UTC July 27, and (c) 1000 UTC July 29, 2002. The brown lines indicate the Tibetan Plateau’ s boundaries
图 7 2002年7月27日15:00 200～700 hPa风场（黑色风羽，一根长羽为4 m s−1）、高度场（蓝色等值线，单位：gpm）、涡度场（阴影区，单位：10−5 s−1）及RTIA方法识别的高原涡中心（绿点）分布
Figure 7. Distributions of wind field (black wind barbs, a full bar indicates 4 m s−1), geopotential height field (blue contours, units: gpm), relative vorticity field (shading, units: 10−5 s−1), and the TPVs’ center (green dots) by RTIA with 50 hPa intervals from 200 to 700 hPa at 1500 UTC July 27, 2002
图 8 2000年5月25日（a）21:00、（b）22:00、（c）23:00 500 hPa流场分布。阴影为相对涡度，单位：10−5 s−1；红点代表高原涡中心
Figure 8. Distributions of 500 hPa stream fields (shading: relative vorticity, units: 10−5 s−1; red dots: the centers of the TPVs) at (a) 2100 UTC, 2200 UTC, and (c) 2300 UTC May 25, 2000
图 9 1979～2020年5～9月高原涡生命史分布（柱子上方数字表示生命史在该区间内的高原涡数量占全部高原涡的百分比，橙色表示RTIA方法，绿色表示实况）
Figure 9. The lifespan distribution of TPVs from May to September during 1979–2020, the number above the column represents the percentage of the number of TPVs within this lifespan period in the total TPVs from the RTIA (orange) and the actual situation (green)
图 10 1979～2020年5～9月高原涡各时次2°×2°风场相关系数（绿线表示该年风场相关系数从小到大排第5百分位对应的值，灰色柱子是第10百分位对应的值，红线是该年所有时次风场相关系数的平均值）
Figure 10. Temporal correlation coefficient of 2°×2° wind field of TPVs from May to September during 1979–2020. The green line represents the value corresponding to the 5th percentile of wind field correlation coefficient from small to large in this year, the gray column represents the value corresponding to the 10th percentile, and the red line is the average value of all temporal wind correlation coefficients in this year
图 11 1979～2020年5～9月高原涡RTIA效果评估（a）箱形图（线段最高点为最大值，线段最低点为最小值，箱型上部框线为第75%分位值，箱形下部框线为第25%分位值，箱内线为中位数，箱内点为平均值）以及（b）年变化（红线：命中率，蓝线：空报率，黑线：漏报率）
Figure 11. (a) Box-and-whisker plot (the lower and upper whiskers cover the minima and maxima value, the boxes cover the 25th–75th percentiles, the horizontal lines in the boxes mark the median value, and the dots represent the mean value) and (b) annual variations (the red line is the hit ratio, the blue line is the false alarm ratio, and the black line is the missing ratio) of TPVs from the RTIA from May to September during 1979–2020
表 1 基于ERA5风场的不同涡旋中心识别的效果验证
Table 1. Validation of the effect of identifying different vortex centers based on the ERA5 wind field
样本时段 涡旋类型 检出数 命中率 空报率 漏报率 1999年5～9月 高原涡（500 hPa） 3099 95.98% 5.29% 4.02% 2008年5～6月 西南涡（700 hPa） 413 95.72% 2.42% 4.28% 2015年7～8月 大别山涡（850 hPa） 479 96.06% 3.34% 3.94% 
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