“碧利斯”与“圣帕”引发湘东南特大暴雨雷达回波对比分析

利用多普勒天气雷达、常规气象观测等资料,从雷达气象学和中小尺度天气学出发,对引发湘东南特大暴雨的两次强热带风暴"碧利斯"与超强台风"圣帕"进行了对比分析.结果表明:两次过程均为东风带系统影响,地形增幅作用显著.螺旋带状、弥合加波阶段是造成强降水的主要时段.均有低质心暖性降水虽波特征,降水效率高."列车效应"是造成特大暴雨的关键,但形成方式不同:"碧利斯"主要由带状回波形成,而"圣帕"主要由回波在同一区域加强形成."碧利斯"回波强度、回波顶高均大于"圣帕"过程,对流更旺盛,范围小而雨强大,与西风带冷式切变线暴雨回波相似."圣帕"回波均匀,持续时间长,与西风带暖式切变线暴雨回波特征相似,回波移动缓慢,降水总量大.两次过程都有暖平流上叠加辐合风场的特征,形成了有利于强降水的环境背景.中γ尺度"大风核"造成有组织的次级环流,可能是"列车效应"形成和维持的主要原因.谱宽表明,受地面的摩擦作用,低层有强烈的湍流和乱流,有利于低压中心从低层减弱.中层为强而稳定的气流,有利于气旋强度的维持,形成长时间强降水.     

两次严重影响湖南的登陆台风水汽场特征数值模拟

针对造成湖南省特大暴雨过程的"碧利斯"和"圣帕"两次台风,利用气象、水文加密观测资料及NCEP再分析资料,结合中尺度数值预报模式AREM的模拟结果,对它们独特的水汽场特征进行了对比分析.结果表明:这两次台风的共同特点是有两条主要水汽输送通道,即与西南季风相联系的偏南风水汽通道和与台风低压环流相联系的偏北风水汽通道;凝结降水的水汽主要来源于低层风场辐合和水汽平流,并通过局地垂直运动再将其输送到中高层;在湘东南强降水区上空始终存在强的水汽水平辐合和水汽垂直输送,比较而言,"圣帕"台风暴雨区上空水汽通量更强,但水汽通量辐合强度却小于"碧利斯"台风,水汽辐合层也不及后者深厚,但前者由于自身旋转性强,低压环流中心南部的切变较长时间维持,并自东向西转动,使得强降水持续时间更长,过程雨量更大,影响范围也更大."碧利斯"水汽主要源地较"圣帕"更加偏南,水汽辐合更强,与南海季风的相互作用更显著,降水时段集中,局部地区短时间内的降雨强度甚至超过了"圣帕".     

引起“碧利斯”强降水的MCS数值模拟研究

利用多种观测资料和数值模拟,对0604号强热带风暴碧利斯登陆后在湖南、广东等地引发强降水的中尺度对流系统活动特征进行了分析.结果表明,在"碧利斯"登陆后西行减弱过程中,由于西南季风的持续维持,"碧利斯"减弱后的低压环流中仍保持有强降水所需的充足水汽供应,造成局地强降水的MCS十分活跃.ARPS模式较好地模拟了7月15日发生在湖南南部的中尺度降雨过程,并揭示出"碧利斯"变性过程中,环境风场垂直切变结构强迫的次级环流决定了MCS活动特点,同时利用湿Q矢量诊断了低压次级环流的垂直运动特征.造成这次强降水过程的MCS在台风低压切变线以北的偏北潮湿气流中生成发展,低层偏北急流造成的动力辐合效应、对流不稳定性层结的建立是MCS在湖南南部迅速发展的重要原因.     

相似路径热带气旋“海棠”(0505)和“碧利斯”(0604)暴雨对比分析

一般认为相似路径台风的影响大致相似,但实际上相似路径台风的风雨分布尤其是暴雨分布往往有很大差异,因此,对相似路径热带气旋“海棠”(0505)和“碧利斯”(0604)暴雨成因的对比分析有助于加强台风暴雨发生机制的认识和预报。“海棠”(0505)和“碧利斯”(0604)逐日降水分布对比分析表明,两者登陆前降水分布类似,而登陆后降水分布差异比较大。利用NCEP/GFS 1 °×1 °分析资料对热带气旋登陆前后天气形势、水汽通量和水汽通量散度进行诊断分析,结果表明：“海棠”(0505)和“碧利斯”(0604)登陆前引起浙闽沿海地区大降水主要是热带气旋外围偏东气流和地形共同影响下形成。“海棠”登陆后,维持在浙江东部沿海东南风急流不断输送水汽到“海棠”倒槽内引起浙东南沿海强降水,深入内陆后,降水主要由“海棠”自身环流携带的水汽辐合引起的,降水比沿海地区明显减弱;而“碧利斯”登陆后,有明显的南海季风环流输送水汽并入热带气旋南侧环流,在其南侧形成偏南风急流,使南侧水汽输送得到明显加强,造成“碧利斯”南侧水汽通量辐合,北侧水汽通量辐散,南侧降水比北侧降水强很多;深入内陆后,“碧利斯”环流仍维持并引导北方槽后弱冷空气渗透到其西南侧,使南侧降水进一步增幅。本文还探讨了包括热带气旋外核在内区域平均垂直风切变和热带气旋强降水落区的关系,结果表明：“海棠”和“碧丽斯”大暴雨落区均对应于暴雨区区域平均垂直风切矢量左侧水汽通量散度负值区。“海棠”垂直风切变矢量平行于移动路径并指向移动路径后方是造成“海棠”强降水分布在其移动路径右侧的重要原因,“碧利斯”垂直风切变矢量平行于移动路径并指向移动路径前方是造成“碧利斯”强降水分布在其移动路径左侧的重要原因。因此,利用垂直风切结合水汽输送条件可以作热带气旋大暴雨落区预报可能是一种比较有效的方法。     

"北冕"与"黑格比"影响广西的对比分析

通过资料分析和物理量诊断对两个影响广西的热带气旋"北冕"、"黑格比"进行对比,发现:"北冕"强降水主要集中在低压环流的西南侧,随着中心移动强降水中心也自东向西移动,降水持续时间较长;"黑格比"强降水强度更强,范围更大,移速较快.     

两个偏北路径影响广西台风的降水差异分析

通过对影响广西的1209号台风"苏拉"与0604号台风"碧利斯"这两个台风生成的季节、移动路径、强度、对广西的影响分析可以看出:副高西侧的东南气流对登陆后台风能够造成的降水强度有非常重要的意义;西南季风能否卷入到台风环流中也是其发展、维持、造成强降水的重要因素。台风登陆影响广西时会逐渐减弱,如果这时在西北太平洋靠近我国沿海有较强台风活动时,会对水汽和能量的输送有削弱作用,从而减小广西地区的降水。正是因为缺少这些因素,"苏拉"对广西造成的降水范围、强度都明显比"碧利斯"的小。     

0604号强热带风暴碧利斯异常强降水过程的诊断分析

基于0604号强热带风暴“碧利斯”异常强降水对我国造成的巨大影响,使用地面加密观测资料、卫星云图资料、NCEP/NCAR再分析资料及中尺度模式输出的产品,对此次强热带风暴登陆减弱后缓慢移动且长时间维持而引发的湖南、广东等地强降水过程进行了诊断分析。结果表明：受西太平洋副热带高压、北方大陆高压、青藏高原东部高压及低纬赤道高压的包围,低压移动缓慢,而西南季风气流及副高西南侧的东南气流源源不断地输送水汽到低压环流中,有利于其强度经久不衰,低压持续存在。对中尺度数值预报产品和物理量场的分析发现,水汽供应充沛,低层气流辐合性较强,垂直对流旺盛,是这次强降水发生的有利条件。     

秋季孟加拉湾风暴影响云南降水差异的对比分析

利用关岛联合台风警报中心提供的北印度洋热带风暴资料、NCEP/NCAR再分析资料和云南125个气象站的逐日降水资料,选取两次秋季强孟加拉湾风暴个例,对其移动路径和影响云南降水进行了对比分析。结果表明,两次孟加拉湾风暴的强度相近,移动路径和登陆位置不同,受其影响云南降水在强度和范围上存在明显差异。200 hPa南压高压位置、引导气流的差异导致了孟加拉湾风暴移动路径和登陆位置不同;孟加拉湾风暴环流与冷空气相互作用是产生云南强降水的重要机制;低层孟加拉湾风暴东侧西南大风区的移动对降水的形成也具有重要作用,一方面将风暴中大量的水汽和能量输送到云南,另一方面大风区的风速辐合有助于维持必要的动力学条件。受500 hPa西太平洋副热带高压西伸脊点和低层西南风强度的影响,水汽输送、辐合强度和维持时间存在差异,高低空不同的环流形势配置导致了风暴影响云南降水的动力结构、垂直运动等方面存在明显差异,而这些差异是两次孟加拉湾风暴造成云南不同降水分布特征的重要原因。     

强热带风暴“碧利斯”的多普勒雷达回波分析

通过对强热带风暴“碧利斯”转向西行减弱成的热带低压环流在滇东南地区造成的大到暴雨过程中主要降水时段多普勒雷达回波强度和径向速度场特点的分析。分析表明，此次强降水是多种天气系统相互作用的结果。第三、四时段滇东南南部的暴雨是长时间维持在其上空的强降雨云团和持续的大风区造成的。     

三个典型登闽空心结构台风强降水分布差异分析

利用常规气象观测资料和NCEP(National Centers for Environmental Prediction)再分析资料对1710"海棠"、1307"苏力"和0709"圣帕"这三个典型登闽空心台风分别于2017年7月31日、2013年7月13日和2007年8月19日产生的强降水分布差异采用诊断方法分析。结果表明:高层辐散分流通道显著不同,"海棠"与"圣帕"为反气旋性流出气流,"苏力"属于单一流出通道。环境风垂直切变方向对"海棠"与"苏力"大降水落区有一定预报指示意义。湿度锋区及干舌侵入位置的差异对三个台风空心结构形成及强降水分布的差异有重要影响。湿位涡场均呈现下负上正分布,表明大气在低层为对流不稳定状态。湿正压项是"海棠"与"圣帕"台风MCS发展的主要不稳定条件,而湿正压项和湿斜压项均对"苏力"台风MCS发展起加强作用,其中湿正压项对MCS非对称分布起主要作用。     

CHARACTERISTICS AND TRENDS OF CLIMATIC EXTREMES IN CHINA DURING 1959–2014

The spatial and temporal variations of daily maximum temperature(Tmax), daily minimum temperature(Tmin), daily maximum precipitation(Pmax) and daily maximum wind speed(WSmax) were examined in China using Mann-Kendall test and linear regression method. The results indicated that for China as a whole, Tmax, Tmin and Pmax had significant increasing trends at rates of 0.15℃ per decade, 0.45℃ per decade and 0.58 mm per decade,respectively, while WSmax had decreased significantly at 1.18 m·s~(-1) per decade during 1959—2014. In all regions of China, Tmin increased and WSmax decreased significantly. Spatially, Tmax increased significantly at most of the stations in South China(SC), northwestern North China(NC), northeastern Northeast China(NEC), eastern Northwest China(NWC) and eastern Southwest China(SWC), and the increasing trends were significant in NC, SC, NWC and SWC on the regional average. Tmin increased significantly at most of the stations in China, with notable increase in NEC, northern and southeastern NC and northwestern and eastern NWC. Pmax showed no significant trend at most of the stations in China, and on the regional average it decreased significantly in NC but increased in SC, NWC and the mid-lower Yangtze River valley(YR). WSmax decreased significantly at the vast majority of stations in China, with remarkable decrease in northern NC, northern and central YR, central and southern SC and in parts of central NEC and western NWC. With global climate change and rapidly economic development, China has become more vulnerable to climatic extremes and meteorological disasters, so more strategies of mitigation and/or adaptation of climatic extremes,such as environmentally-friendly and low-cost energy production systems and the enhancement of engineering defense measures are necessary for government and social publics.     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.     

AN ATTRIBUTION ANALYSIS OF CHANGES IN POTENTIAL EVAPOTRANSPIRATION IN THE BEIJING–TIANJIN–HEBEI REGION UNDER CLIMATE CHANGE

Based on the measurements obtained at 64 national meteorological stations in the Beijing–Tianjin–Hebei (BTH) region between 1970 and 2013, the potential evapotranspiration (ET0) in this region was estimated using the Penman–Monteith equation and its sensitivity to maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Vw), net radiation (Rn) and water vapor pressure (Pwv) was analyzed, respectively. The results are shown as follows. (1) The climatic elements in the BTH region underwent significant changes in the study period. Vw and Rn decreased significantly, whereas Tmin, Tmax and Pwv increased considerably. (2) In the BTH region, ET0 also exhibited a significant decreasing trend, and the sensitivity of ET0 to the climatic elements exhibited seasonal characteristics. Of all the climatic elements, ET0 was most sensitive to Pwv in the fall and winter and Rn in the spring and summer. On the annual scale, ET0 was most sensitive to Pwv, followed by Rn, Vw, Tmax and Tmin. In addition, the sensitivity coefficient of ET0 with respect to Pwv had a negative value for all the areas, indicating that increases in Pwv can prevent ET0 from increasing. (3) The sensitivity of ET0 to Tmin and Tmax was significantly lower than its sensitivity to other climatic elements. However, increases in temperature can lead to changes in Pwv and Rn. The temperature should be considered the key intrinsic climatic element that has caused the "evaporation paradox" phenomenon in the BTH region.     

Revisiting the Concentration Observations and Source Apportionment of Atmospheric Ammonia

正While China’s Air Pollution Prevention and Control Action Plan on particulate matter since 2013 has reduced sulfate significantly, aerosol ammonium nitrate remains high in East China. As the high nitrate abundances are strongly linked with ammonia, reducing ammonia emissions is becoming increasingly important to improve the air quality of China. Although satellite data provide evidence of substantial increases in atmospheric ammonia concentrations over major agricultural regions, long-term surface observation of ammonia concentrations are sparse. In addition, there is still no consensus on     

COMPARATIVE ANALYSIS OF VARIOUS DATA OF AIR–SEA TEMPERATURE DIFFERENCE AND ITS VARIATION ACROSS SOUTH CHINA SEA IN THE PAST 35 YEARS

Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;