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Covariation Relationship Between Tropical Cyclone Intensity and Size Change over the Northwest Pacific
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摘要: 热带气旋强度和尺度是衡量其破坏性的重要指标。利用美国联合热带气旋警报中心（JTWC）最佳路径数据集和全球飓风强度统计预测计划（SHIPS）再分析数据集，对2004—2020年7—11月西北太平洋热带气旋的强度和尺度（26 m·s-1大风平均半径）时空变化及协同变化特征统计分析发现，热带气旋强度与尺度在10月均达到峰值，主要表现为大而强且海上生命史长的热带气旋占比高于其他月份。热带气旋尺度伴随着强度增强（减弱）而增大（收缩），达到生命史最大尺度的时间平均滞后于最大强度时间40 h，且尺度快速膨胀及达到最大尺度的平均位置比发生快速增强和最大强度的位置更接近陆地。热带气旋初始尺度影响其最大尺度。71%大尺度涡旋在后期发展为大涡旋，其中发展为强热带气旋（不小于59 m·s-1）的占比为59%。热带气旋26 m·s-1大风平均半径对后期尺度的影响可达66 h，说明尺度预报中不能忽略初始尺度的影响。热带气旋最大尺度增长率发生在中等强度条件下（25~50 m·s-1），而最大强度增长率发生在中小尺度26 m·s-1大风平均半径（50~100 km）范围。在高空辐散强、相对湿度高、海洋热含量大，且中等及较弱环境垂直风切变条件下，尺度更易向外扩展，甚至发生快速膨胀。Abstract: Tropical cyclone (TC) has brought huge losses to coastal areas, whose intensity and size are both important indicators of destruction. The Northwest Pacific is the area with the most TCs generated. Due to the lack of effective observation methods and monitoring information, the TC operational centers of coastal countries or regions have not yet established complete TC outer-core size prediction and testing service. Thus, to select factors that significantly impact TC size changes and improve TC size forecast, statistical analysis is carried out on the climatological characteristics and the lifetime covariation characteristics of intensity and outer-core size (selected as the radius of damaging-force winds, R26) over the Northwest Pacific from July to November during 2004-2020, using the tropical cyclone best track data from JTWC (Joint Typhoon Warning Center) and SHIPS (Statistical Hurricane Intensity Prediction Scheme) reanalysis data. The results show that TC intensity and size peak in October, mainly showing a higher proportion of strong and large-sized TCs with longer lifetime at sea than in other months. Generally, the TC size expands with the increase in intensity and shrinks as the TC weakens. TCs reach the lifetime maximum size (LMS) later than the lifetime maximum intensity (LMI), with a mean lag time of 40 hours. Compared to the TC rapid intensification and LMI, the mean meridional positions of TC rapid expansion and LMS are closer to the coastal continent. Initial vortex size of TC affects the size development, especially LMS. Specifically, 58% of small initial vortices maintain the size in the small to medium category, while 71% of vortices with large initial size develop to large vortices in later periods, with 59% intensify to strong TCs (no less than 59 m·s-1) at LMI stage. Compared to small initial vortices, vortices with larger initial sizes tend to attain the greater integrated kinetic energy. The size of the latter stage has a high correlation (no less than 0.45) with the initial R26 for 66 h, indicating that the initial size of TCs can be a key predictor. The peak of size change rate (ΔR26) is located at moderate intensity (25~50 m·s-1) and the peak intensity change rate (ΔVmax) is located at medium and small size (50-100 km). The outer-core size is more likely to expand outward and even leads to rapid expansion under the conditions of stronger upper air divergence, higher relative humidity, larger ocean heat content and moderate vertical shear.
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图 1 热带气旋尺度(a)及强度(b)月际平均分布
(误差线表示达到0.05显著性水平区间)
Fig. 1 Distribution of monthly tropical cyclone size(a) and intensity(b)
(error bars denote 0.05 significant level range)
图 2 热带气旋尺度(a)和强度(b)样本频数及占比分布
Fig. 2 Monthly frequencies and percentages of tropical cyclone size(a) and intensity(b)
图 3 热带气旋尺度和强度经向(a)及纬向(b)分布
(误差线表示达到0.05显著性水平区间)
Fig. 3 Meridional(a) and zonal(b) distribution of tropical cyclone size and intensity
(error bars denote 0.05 significant level range)
图 4 热带气旋尺度与强度变化及生命史最大值与快速增长区域分布
(a)ΔR26 (填色，红色表示正值，蓝色表示负值) 与最大尺度位置(黑色三角) 及最大尺度平均位置(黄色空心圆)，(b)ΔVmax (填色，红色表示正值，蓝色表示负值) 与最大强度位置(黑色三角) 及最大强度平均位置(黄色空心圆)，(c)尺度增大频数(填色) 与快速膨胀位置(黑色圆点) 及快速膨胀平均位置(黄色空心圆)，(d)强度增强频数(填色) 与快速增强位置(黑色圆点) 及快速增强平均位置(黄色空心圆)
Fig. 4 Distributions of tropical cyclone size and intensity change rate, lifetime maximum and rapid growth
(a)ΔR26 (the shaded, the red denotes positive, the blue denotes negative) and locations of lifetime maximum size (triangles), the average position of lifetime maximum size (the yellow circle), (b)ΔVmax (the shaded, the red denotes positive, the blue denotes negative) and locations of lifetime maximum intensity (triangles), the average position of lifetime maximum intensity (the yellow circle), (c)frequency of expansion cases (the shaded) and the locations of rapid expansion cases (dots) with the average position of rapid expansion cases (the yellow circle), (d)frequency of intensification cases (the shaded), the locations of rapid intensification cases (dots) with the average position of intensification cases (the yellow circle)
图 5 不同尺度初始涡旋生命史维持比率箱线图
Fig. 5 Box plots of the maintenance rate of different size vortexes
图 6 不同尺度初始涡旋最大尺度累积频率分布
Fig. 6 Cumulative relative frequencies of different size vortexes
图 7 ΔVmax与R26(a)、风裙参数(b)空间内的样本分布，ΔR26与Vmax(c)、ΔVmax(d)空间内的样本分布
Fig. 7 Scatter plots of ΔVmax against R26(a), the wind skirt parameter(b), and ΔR26 against Vmax(c), ΔVmax(d)
图 8 累计动能随强度变化
(曲线表示累计动能拟合中值)
Fig. 8 Scatter plot of integrated kinetic energy against Vmax
(color curves denote fitting median)
图 9 初始大、小涡旋沿转向和直行两类路径的R26分布
Fig. 9 Comparison of R26 along recurving and straight-moving tracks for large and small initial vortexes
表 1 环境因子与其后72 h内R26的24 h变化的相关系数
Table 1 Correlation coefficients of environmental factors to ΔR26 within 72 hours
环境因子 与热带气旋中心距离/km 滞后时间/h 12 24 36 48 60 72 海表温度 200~800 0.23* 0.17* 0.10* 0.05 0.01 -0.02 海洋热含量 200~800 0.32* 0.29* 0.23* 0.17* 0.09* 0.01 850 hPa相对涡度 0~1000 0.00 -0.04 -0.06* -0.05 -0.05 -0.06 200 hPa散度 0~1000 0.15* 0.13* 0.11* 0.08* 0.03 0.01 850 hPa至200 hPa环境垂直风切变 200~800 -0.10* -0.03 0.01 0.03 0.06 0.05 700 hPa至500 hPa相对湿度 200~800 0.22* 0.18* 0.13* 0.09* 0.06* 0.02 200 hPa相对角动量的涡度能量辐合 100~600 -0.12* -0.05* 0.00 -0.01 0.00 0.01 850 hPa至700 hPa平均温度平流 0~500 -0.19* -0.20* -0.18* -0.13* -0.06* -0.01 注：*表示达到0.05显著性水平。 
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[1] Musgrave K D, Taft R K, Vigh J L, et al. Time evolution of the intensity and size of tropical cyclones. J Adv Model Earth Syst, 2012, 4(3): M08001. [2] Merrill R T. A comparison of large and small tropical cyclones. Mon Wea Rev, 1984, 112: 1408-1418. doi: 10.1175/1520-0493(1984)112<1408:ACOLAS>2.0.CO;2 [3] Weatherford C L, Gray W M. Typhoon structure as revealed by aircraft reconnaissance, Part Ⅱ: Structural variability. Mon Wea Rev, 1988, 116: 1044-1056. doi: 10.1175/1520-0493(1988)116<1044:TSARBA>2.0.CO;2 [4] 胡邦辉, 谭言科, 王举. 热带气旋海面最大风速半径的计算. 应用气象学报, 2004, 15(4): 427-435. doi: 10.3969/j.issn.1001-7313.2004.04.005Hu B H, Tan Y K, Wang J. Calculation of maximum wind velocity radius of tropical cyclone on sea surface. J Appl Meteor Sci, 2004, 15(4): 427-435. doi: 10.3969/j.issn.1001-7313.2004.04.005 [5] 孔莉莎, 张秀芝. 西北太平洋历史台风风场重建模型参数试验. 应用气象学报, 2022, 33(1): 56-68. doi: 10.11898/1001-7313.20220105Kong L S, Zhang X Z. Sensitive experiments on reconstruction model of historical typhoon wind field in the Northwest Pacific Ocean. J Appl Meteor Sci, 2022, 33(1): 56-68. doi: 10.11898/1001-7313.20220105 [6] Goerss J S, Sampson C R, Gross J M. A history of western North Pacific tropical cyclone track forecast skill. Wea Forecasting, 2004, 19(3): 633-638. doi: 10.1175/1520-0434(2004)019<0633:AHOWNP>2.0.CO;2 [7] DeMaria M, Knaff J A, Sampson C R. Evaluation of long-term trend in tropical cyclone intensity forecasts. Meteor Atmos Phys, 2007, 97: 19-28. doi: 10.1007/s00703-006-0241-4 [8] 冯佳宁, 端义宏, 徐晶, 等. 雷达资料同化对2015年台风彩虹数值模拟改进. 应用气象学报, 2017, 28(4): 399-413. doi: 10.11898/1001-7313.20170402Feng J N, Duan Y H, Xu J, et al. Improving the simulation of Typhoon Mujigae(2015) based on radar data assimilation. J Appl Meteor Sci, 2017, 28(4): 399-413. doi: 10.11898/1001-7313.20170402 [9] 霍振华, 李晓莉, 陈静, 等. 基于背景场奇异向量的CMA全球集合预报试验. 应用气象学报, 2022, 33(6): 655-667. doi: 10.11898/1001-7313.20220602Huo Z H, Li X L, Chen J, et al. CMA global ensemble prediction using singular vectors from background field. J Appl Meteor Sci, 2022, 33(6): 655-667. doi: 10.11898/1001-7313.20220602 [10] 施丽娟, 许小峰, 李柏, 等. 雷达资料在登陆台风"桑美"数值模拟中的应用. 应用气象学报, 2009, 20(3): 257-266. doi: 10.3969/j.issn.1001-7313.2009.03.001Shi L J, Xu X F, Li B, et al. Application of Doppler radar data to the landfalling Typhoon Saomai simulation. J Appl Meteor Sci, 2009, 20(3): 257-266. doi: 10.3969/j.issn.1001-7313.2009.03.001 [11] Fiorina M, Elsberry R L. Some aspects of vortex structure related to tropical cyclone motion. J Atmos Sci, 1989, 46(7): 975-990. doi: 10.1175/1520-0469(1989)046<0975:SAOVSR>2.0.CO;2 [12] 麻素红, 张进, 沈学顺, 等. 2016年GRAPES_TYM改进及对台风预报影响. 应用气象学报, 2018, 29(3): 257-269. doi: 10.11898/1001-7313.20180301Ma S H, Zhang J, Shen X S, et al. The upgrade of GRAPE_TYM in 2016 and its impacts on tropical cyclone prediction. J Appl Meteor Sci, 2018, 29(3): 257-269. doi: 10.11898/1001-7313.20180301 [13] Schubert W H, Hack J J. Inertial stability and tropical cyclone development. J Atmos Sci, 1982, 39: 1687-1697. doi: 10.1175/1520-0469(1982)039<1687:ISATCD>2.0.CO;2 [14] Pendergrass A G, Willoughby H E. Diabatically induced secondary flows in tropical cyclones. Part Ⅰ: Quasi-steady forcing. Mon Wea Rev, 2009, 137: 805-821. doi: 10.1175/2008MWR2657.1 [15] Vigh J L, Schubert W H. Rapid development of the tropical cyclone warm core. J Atmos Sci, 2009, 66: 3335-3350. doi: 10.1175/2009JAS3092.1 [16] Xu J, Wang Y. Sensitivity of the simulated tropical cyclone inner-core size to the initial vortex size. Mon Wea Rev, 2010, 138: 4135-4157. doi: 10.1175/2010MWR3335.1 [17] Cocks S B, Gray W M. Variability of the outer wind profiles of western North Pacific typhoons: Classifications and techniques for analysis and forecasting. Mon Wea Rev, 2002, 130(8): 1989-2005. doi: 10.1175/1520-0493(2002)130<1989:VOTOWP>2.0.CO;2 [18] Hill K A, Lackmann G M. Influence of environmental humidity on tropical cyclone size. Mon Wea Rev, 2009, 137: 3294-3315. doi: 10.1175/2009MWR2679.1 [19] Carr L E, Elsberry R L. Models of tropical cyclone wind distribution and beta-effect propagation for application to tropical cyclone track forecasting. Mon Wea Rev, 1997, 125(12): 3190-3209. doi: 10.1175/1520-0493(1997)125<3190:MOTCWD>2.0.CO;2 [20] Lu X, Yu H, Lei X. Statistics for size and radial wind profile of tropical cyclones in the western North Pacific. Acta Meteor Sinica, 2011, 25: 104-112. doi: 10.1007/s13351-011-0008-9 [21] Evans C, Hart R E. Analysis of the wind field evolution associated with the extratropical transition of Bonnie(1998). Mon Wea Rev, 2008, 136(6): 2047-2065. doi: 10.1175/2007MWR2051.1 [22] Chan K T F, Chan J C L. Angular momentum transports and synoptic flow patterns associated with tropical cyclone size change. Mon Wea Rev, 2013, 141: 3985-4007. doi: 10.1175/MWR-D-12-00204.1 [23] Knaff J A, Seseske S A, DeMaria M, et al. On the influences of vertical wind shear on symmetric tropical cyclone structure derived from AMSU. Mon Wea Rev, 2004, 132: 2503-2510. doi: 10.1175/1520-0493(2004)132<2503:OTIOVW>2.0.CO;2 [24] 何立富, 陈双, 郭云谦. 台风利奇马(1909)极端强降雨观测特征及成因. 应用气象学报, 2020, 31(5): 513-526. doi: 10.11898/1001-7313.20200501He L F, Chen S, Guo Y Q. Observation characteristics and synoptic mechanisms of Typhoon Lekima extreme rainfall in 2019. J Appl Meteor Sci, 2020, 31(5): 513-526. doi: 10.11898/1001-7313.20200501 [25] 郑倩, 毛程燕, 丁丽华, 等. 台风利奇马(1909)与台风摩羯(1814)云特征对比. 应用气象学报, 2022, 33(1): 43-55. doi: 10.11898/1001-7313.20220104Zheng Q, Mao C Y, Ding L H, et al. Comparison of cloud characteristics between Typhoon Lekima(1909) and Typhoon Yagi(1814). J Appl Meteor Sci, 2022, 33(1): 43-55. doi: 10.11898/1001-7313.20220104 [26] 刘涛, 端义宏, 冯佳宁, 等. 台风利奇马(1909)双眼墙特征及长时间维持机制. 应用气象学报, 2021, 32(3): 289-301. doi: 10.11898/1001-7313.20210303Liu T, Duan Y H, Feng J N, et al. Characteristics and mechanisms of long-lived concentric eyewalls in Typhoon Lekima in 2019. J Appl Meteor Sci, 2021, 32(3): 289-301. doi: 10.11898/1001-7313.20210303 [27] Ooyama K. Numerical simulation of the life cycle of tropical cyclones. J Atmos Sci, 1969, 26: 3-40. [28] Riehl H. Tropical Meteorology. New York: McGraw Hill Book, 1954: 1-392. [29] Wang S, Toumi R. A historical analysis of the mature stage of tropical cyclones. Int J Climatol, 2018, 38: 2490-2505. [30] Kaplan J, DeMaria M. Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Wea Forecasting, 2003, 18: 1093-1108. [31] Powell M D, Reinhold T A. Tropical cyclone destructive potential by integrated kinetic energy. Bull Amer Meteor Soc, 2007, 88(4): 513-526. [32] Chan J C L, Williams R T. Analytical and numerical studies of the beta-effect in tropical cyclone motion. Part Ⅰ: Zero mean flow. J Atmos Sci, 1987, 44: 1257-1265. [33] 陈可鑫, 陈光华, 向纯怡, 等. 基于MTCSWA风场资料对西北太平洋热带气旋风场结构的气候统计特征研究. 气候与环境研究, 2020, 25(6): 588-600. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH202006002.htmChen K X, Chen G H, Xiang C Y, et al. Statistical characteristics of wind field structures of tropical cyclones over the western North Pacific based on MTCSWA data. Climatic Environ Res, 2020, 25(6): 588-600. https://www.cnki.com.cn/Article/CJFDTOTAL-QHYH202006002.htm [34] Moyer A C, Evans J L, Powell M. Comparison of observed gale radius statistics. Meteor Atmos Phys, 2007, 97: 41-55. [35] Xu J, Wang Y. Dependence of tropical cyclone intensification rate on sea surface temperature, storm intensity, and size in the western North Pacific. Wea Forecasting, 2018, 33(2): 523-537. [36] Chen K, Chen G, Rao C, et al. Relationship of tropical cyclone size change rate with size and intensity over the western North Pacific. Atmos Ocean Sci Lett, 2021, 14(3): 100040. [37] Knaff J A, Sampson C R, Chirokova G. A global statistical-dynamical tropical cyclone wind radii forecast scheme. Wea Forecasting, 2017, 32(2): 629-644. -
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