﻿ 位涡倾向在Muifa台风路径转折中的应用 位涡倾向在Muifa台风路径转折中的应用
 大气科学  2018, Vol. 42 Issue (2): 281-291 PDF

1 中国科学院大气物理研究所云降水物理与强风暴实验室, 北京 100029
2 中国科学院大学, 北京 100049
3 中国民用航空飞行学院空中交通管理学院, 四川广汉 618307
4 成都信息工程大学大气科学学院, 成都 610225

Application of Potential Vorticity Tendency in Track Recurvature Study of Typhoon Muifa
YUAN Min1,2,3, PING Fan1, LI Guoping4
1 Laboratory of Cloud−Precipitation Physics and Severe Storms, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2 University of Chinese Academy of Sciences, Beijing 100049
3 College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan, Sichan Province 618307
4 College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225
Abstract: Horizontal advection and diabatic heating terms in the potential vorticity tendency equation are used to diagnose the twice track changes of typhoon Muifa based on ECMWF data. The results show that horizontal advection is about one order of magnitude larger than diabatic heating; large-scale steering flows represented by horizontal advection mainly contributed to the first track change; horizontal advection and diabatic heating both contributed to the second track change, while horizontal advection controlled the direction and diabatic heating restrained its track recurvature.
Key words: Potential vorticity tendency      Horizontal advection      Diabatic heating      Asymmetric structure
1 引言

2 位涡倾向

p坐标下的位涡方程的表达式为

 $P = - g\left[ {(\zeta + f)\frac{{\partial \theta }}{{\partial p}} + \frac{{\partial u}}{{\partial p}}\frac{{\partial \theta }}{{\partial y}} - \frac{{\partial v}}{{\partial p}}\frac{{\partial \theta }}{{\partial x}}} \right],$ (1)

 $\begin{array}{l} \frac{{{\rm{d}}P}}{{{\rm{d}}t}} = - g\left[ {\frac{{\partial \theta }}{{\partial p}}\frac{{{\rm{d}}(\zeta + f)}}{{{\rm{d}}t}} + (\zeta + f)\frac{{\rm{d}}}{{{\rm{d}}t}}\left({\frac{{\partial \theta }}{{\partial p}}} \right) + } \right.\\ \left. {\;\;\;\;\;\;\;\;\;\;\;\frac{{\rm{d}}}{{{\rm{d}}t}}\left({\frac{{\partial u}}{{\partial p}}\frac{{\partial \theta }}{{\partial y}} - \frac{{\partial v}}{{\partial p}}\frac{{\partial \theta }}{{\partial x}}} \right)} \right]. \end{array}$ (2)

 $\begin{array}{l} \frac{{{\rm{d}}(\zeta + f)}}{{{\rm{d}}t}} = - (\zeta + f)\left({\frac{{\partial u}}{{\partial x}} + \frac{{\partial v}}{{\partial y}}} \right) + \\ \;\;\;\;\;\;\;\;\left({\frac{{\partial \omega }}{{\partial y}}\frac{{\partial u}}{{\partial p}} - \frac{{\partial \omega }}{{\partial x}}\frac{{\partial v}}{{\partial p}}} \right) + \frac{{\partial {F_y}}}{{\partial x}} - \frac{{\partial {F_x}}}{{\partial y}}, \end{array}$ (3)

 $\begin{array}{l} \frac{{{\rm{d}}P}}{{{\rm{d}}t}} = - g\left[ {(\zeta + f)\frac{\partial }{{\partial p}}(\frac{{{\rm{d}}\theta }}{{{\rm{d}}t}}) + \frac{{\partial u}}{{\partial p}}\frac{\partial }{{\partial y}}(\frac{{{\rm{d}}\theta }}{{{\rm{d}}t}}) - \frac{{\partial v}}{{\partial p}}\frac{\partial }{{\partial x}}(\frac{{{\rm{d}}\theta }}{{{\rm{d}}t}})} \right] - \\ \;\;\;\;\;g\left[ {(\frac{{\partial {F_y}}}{{\partial x}} - \frac{{\partial {F_x}}}{{\partial y}})\frac{{\partial \theta }}{{\partial p}} + \frac{{\partial {F_x}}}{{\partial p}}\frac{{\partial \theta }}{{\partial y}} - \frac{{\partial {F_y}}}{{\partial p}}\frac{{\partial \theta }}{{\partial x}}} \right], \end{array}$ (4)

 $\frac{{\partial P}}{{\partial t}} = {F_1} + {F_2} + {F_3} + {F_4}.$ (5)

 ${F_1} = - u\frac{{\partial P}}{{\partial x}} - v\frac{{\partial P}}{{\partial y}}.$ (6)

 ${F_2} = - \omega \frac{{\partial P}}{{\partial p}},$ (7)

 ${F_3} = - g\left[ {(\zeta + f)\frac{\partial }{{\partial p}}\left({\frac{{{\rm{d}}\theta }}{{{\rm{d}}t}}} \right) + \frac{{\partial u}}{{\partial p}}\frac{\partial }{{\partial y}}\left({\frac{{{\rm{d}}\theta }}{{{\rm{d}}t}}} \right) - \frac{{\partial v}}{{\partial p}}\frac{\partial }{{\partial x}}\left({\frac{{{\rm{d}}\theta }}{{{\rm{d}}t}}} \right)} \right].$ (8)

 ${F_4} = - g\left[ {\left({\frac{{\partial {F_y}}}{{\partial x}} - \frac{{\partial {F_x}}}{{\partial y}}} \right)\frac{{\partial \theta }}{{\partial p}} + \frac{{\partial {F_x}}}{{\partial p}}\frac{{\partial \theta }}{{\partial y}} - \frac{{\partial {F_y}}}{{\partial p}}\frac{{\partial \theta }}{{\partial x}}} \right].$ (9)

3 Muifa台风外部环流和内部结构分析

 图 1 Muifa台风最佳路径 Figure 1 The best track of typhoon Muifa
3.1 环流特征

Muifa台风路径转折期间，500 hPa中高纬度对流层中层的大气环流呈两脊一槽的纬向形分布，且槽和脊的位置相对固定，Muifa的两次路径转向就是在上述相对稳定的环流背景下展开。第一次路径转折之前（2日00:00，图 2a），Muifa向偏北方向移动，西太平洋副热带高压的脊线呈西南—东北向，并西伸到Muifa南侧，其北侧有两个断裂的小高压体不断发展增强。2日12:00（图 2b），Muifa北侧两个断裂的小高压体发展强大并与大洋中部的副热带高压连通，使其向北运动受到阻挡，Muifa在三面都被高压体包围的形式下向西偏转。在3日00:00（图 2c），副热带高压脊线由西南—东北向演变成东—西向，此时Muifa完成了第一次路径转折并在副热带高压南侧偏东气流的引导下向西移动。在此期间，Muifa东侧160°E附近的“苗柏”台风正在生成发展并向西移动。

 图 2 2011年8月ECMWF资料的500 hPa位势高度场（等值线，单位：gpm）和风场（箭头，单位：m s−1）：（a）2日00:00；（b）2日12:00；（c）3日00:00；（d）4日12:00；（e）5日06:00；（f）6日00:00 Figure 2 Geopotential height (contours, units: gpm) and wind (vectors, units: m s−1) from ECMWF data at 500 hPa: (a) 0000 UTC 2 August 2011; (b) 1200 UTC 2 August 2011; (c) 0000 UTC 3 August 2011; (d) 1200 UTC 4 August 2011; (e) 0600 UTC 5 August 2011; (f) 0000 UTC 6 August 2011

 图 3 ECMWF资料演算的850 hPa水汽通量（阴影，单位：g kg−1 m s−1）和风场（箭头，单位：m s−1）：（a）2日00:00；（b）2日12:00；（c）3日00时；（d）4日12:00；（e）5日12:00；（f）6日12:00 Figure 3 Moisture fluxes (shaded, units: g kg−1 m s−1) and wind fields (vectors, units: m s−1) from ECMWF data at 850 hPa derived from ECMWF analysis product: (a) 0200 UTC 2 Aug; (b) 1800 UTC 2 Aug; (c) 1200 UTC 3 Aug; (d) 0000 UTC 4 Aug; (e) 0000 UTC 6 Aug; (f) 0000 UTC 7 Aug
3.2 引导气流

 图 4 （a）全风速引导气流垂直分布的时间演变（单位：m s−1）；DLM（实线）和Muifa（虚线）移动速度（b）纬向分量和（c）经向分量的演变，单位：m s−1 Figure 4 (a) Temporal variation of steering flow profiles (vectors, units: m s−1); temporal variations of (b) zonal and (c) meridional components of deep-layer mean steering flow (DLM, solid line, units: m s−1) and Muifa moving speed (dashed line, units: m s−1)

3.3 内部结构特征

 图 5 MTSAT卫星红外云图和TMI/SSMI 85GHz频道的红外亮温的叠加图（单位：K）：（a）2日08:24；（b）2日20:55；（c）4日12:10；（d）4日18:42；（e）5日12:52；（f）5日 Figure 5 TRMM microwave imager (TMI) or Special Sensor Microwave Imager (SSMI) 85 GHz IR (Infrared Radiation) brightness temperature (color, units: K) superimposed on MTSAT satellite IR cloud imagery: (a) 0824 UTC 2 August; (b) 2055 UTC 2 August; (c) 1210 UTC 4 August; (d) 1842 UTC 4 August; (e) 1252 UTC 5 August; (f) 2345 UTC 6 August

 图 6 TRMM卫星云水含量的质量加权垂直积分（第一行，单位：kg m−2）及沿红线的垂直剖面（第二行, 单位：kg m−3）：（a、d）4日12:10；（b、e）4日18:43；（c、f）5日12:53 Figure 6 Mass-weighted vertical integrals (first line, units: kg m−2) and vertical cross sections (second line, units: kg m−3) of TRMM estimated cloud water content: (a, d) 1210 UTC 4 August; (b, e) 1843 UTC 4 August, (c, f) 1253 UTC 5 August

4 位涡倾向在Muifa路径转折过程的诊断

 图 7 HA（第一行）和PVT（第二行）的垂直积分（单位：10−6 m3 K s−2 kg−1）：（a、d）02日00:00；（b、e）02日12:00；（c、f）03日00:00。黑色符号表示当前台风中心位置；红色符号代表 6小时后台风中心位置 Figure 7 Vertical integrals of (a, b, c) HA (Horizontal Advection) and (d, e, f) PVT (Potential Vorticity Tendency): (a, d) 0000 UTC 2 August; (b, e) 1200 UTC 2 August; (c, f) 0000 UTC 3 August. Black symbol: current position of Muifa's center; red symbol: position of Muifa' center after 6 h

 图 8 2011年8月（a–c）04日06:00、（d–f）05日00:00、（g–i）05日12:00以及（j–l）06日06:00 HA（左列）、DH（中间列）和PVT（右列）垂直积分的演变（单位：10−6 m3 K s−2 kg−1）。黑色符号：当前台风中心位置；红色符号：6小时后台风中心位置 Figure 8 Vertical integrals of HA (left column), DH (middle column), and PVT (right column) in 2011: (a–c) 0600 UTC 4 August; (d–f) 0000 UTC 5 August; (g–i) 1200 UTC 5 August; (j–l) 0600UTC 6 August. Black symbol: current position of Muifa's center, red symbol: position of Muifa' center after 6 h
5 结论和探讨

（1）分析Muifa台风两次路径转折过程的外部大尺度环流，发现第一次路径转折主要受副热带高压脊线调整的影响；而中高纬西风槽的接近，副热带高压南落是造成第二次路径转折的因素。引导气流的分析结果表明：第一次路径转折过程中，深层平均引导气流速度和台风移动速度的变化趋势一致；第二次路径转折过程中两者的波动特征明显，两者的对应关系不如第一次转折期间显著。

（2）卫星资料揭示了Muifa台风两次路径转折过程的内部对流结构，发现第一次路径转折过程中眼墙对流结构一直维持较均匀的对称结构；第二次路径转折过程中，台风眼墙出现了明显的非对称结构，眼墙对流活动由南侧眼墙转移到东侧眼墙。

（3）位涡倾向方程揭示出两次路径转折的机制：第一次路径转折期间，DH对PVT的影响很小，PVT主要受到HA控制，PVT能较好地指示台风的转向和移动，因此，外部环流因素是第一次路径转折的主要原因。第二次路径转折过程中，PVT受到HA和DH的共同控制，其中HA对PVT的影响较大，主要控制台风的移向，台风向着HA和PVT的正值区运动，DH对台风转向有抑制作用，因此，第二次路径转折受到外部环流和内部结构的共同影响。

（4）本文的研究发现在第二次路径转折过程中伴随着眼墙对流结构的非对称变化，但究竟是台风路径转折造成眼墙对流的非对称结构，还是眼墙对流的非对称结构引起台风的路径转折？因此，下一步研究将展开高分辨的数值模拟，在模拟出Muifa台风两次路径转折的基础上，对引导气流与台风环流的相互作用以及眼区的动力与微物理过程的相互作用等台风内外部过程展开深入的研究。

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