恩施“6·24”暴雨中尺度系统特征分析

利用常规观测资料、自动区域站雨量、卫星TBB资料、雷达资料,对恩施州2016年6月24—25日发生的一次大范围暴雨过程进行分析。结果表明:本次强降水,具有典型的两槽一脊"单阻型"梅雨环流特征,在有利的大尺度环流背景下,在高空槽、低层低涡切变、西南急流、地面中尺度辐合线等中尺度天气系统的共同影响、相互作用下,形成了此次大范围强降水。此次暴雨空间上分布不均,局地性强,表现为明显的中尺度对流性特征,雷达回波图上降水性质表现为混合型降水,暴雨的直接影响系统是中β尺度对流系统,且中β尺度对流系统在多个中尺度对流云团合并后加强,时间尺度约为5 h。此次暴雨过程是在上干冷下暖湿强的大气层结不稳定条件下,梅雨锋、边界层辐合线和地形槽的触发作用将前期积累的能量释放产生的强对流天气,同时,副高外围西南气流将南海和西太平洋的水汽向恩施输送,为暴雨的发生提供了有利的条件。     

四川盆地西部两次暖区暴雨过程分析

利用地面、高空观测资料、卫星和多普勒雷达资料、ERA-Interim再分析资料,对2012年8月17日(12·08)和2017年7月16日(17·07)四川盆地西部两次暖区暴雨过程的环境条件、中尺度对流系统、雷达回波特征和动力抬升条件等预报着眼点进行了对比分析。结果表明:两次过程均出现在低层偏南暖湿气流和地面热低压之中,12·08暴雨发生在副高边缘,水汽输送条件更好,对流持续时间更长,17·07暴雨发生在高空低涡切变后部,高层冷平流使得位势不稳定更强,对流强度更剧烈;12·08暴雨中尺度对流系统沿副高外围自南向北缓慢移动,具有明显列车效应,其强回波质心高度较低,属于积状云为主的混合性降水,17·07暴雨中尺度对流系统在高空低涡后部偏北气流引导下自北向南快速移动,其强回波质心高度较高,属于积状云降水;地面辐合线为对流的发生发展提供了较好的动力触发条件,两次过程强降水均随着地面辐合线的生成而发生,且强降水中心出现在中尺度辐合线附近,并随着辐合线而移动。     

低空暖式切变线引发局地特大暴雨成因分析

利用常规观测、加密自动站降水及NCEP 1°×1°再分析资料,结合铜仁多普勒雷达观测资料对2015年7月14日夜间发生在贵州省松桃县的一次局地特大暴雨进行分析,结果发现:此次局地特大暴雨是发生在中层500 h Pa中低纬"ω"环流型稳定形势下;低层高湿高能环境为暴雨提供了充足的水汽和能量;地面弱冷空气入侵激发了对流的产生,是产生强降水的触发条件;梵净山地形对气流既有抬升又有增强气流辐合的作用,对流单体不断在其迎风坡产生;雷达回波显示对流回波单体沿着地面中尺度辐合线生成、发展、合并、移动和消亡,出现了明显的列车效应,地面辐合线对对流单体起着组织、加强和引导作用;暴雨区位于850 h Pa暖式切变线南侧、地面冷锋前部、地面中尺度辐合线北侧,而地面中尺度辐合线北侧1~1.5个纬距内是强降水的主要落区。     

贵州一次辐合线锋生极端暴雨过程的中尺度特征分析

利用地面常规观测资料及加密自动站观测资料、FY-2G卫星云图、多普勒天气雷达产品和ERA5再分析资料,对2021年5月10日贵州东部和北部的一次辐合线锋生极端暴雨过程中尺度特征进行分析,初步探讨其形成机制。结果表明：此次极端暴雨过程发生在低涡切变背景下,低层强盛的南风为中尺度对流系统发生发展输送了充足的水汽和不稳定能量,地面辐合线及锋生提供了触发条件,暴雨区主要位于云团云顶亮温低值中心西侧或南侧梯度大值区,并沿地面辐合线呈东西向带状分布,最强降水发生在对流云团合并阶段。辐合线锋生作用在其西侧频繁触发对流单体,新生对流单体沿地面辐合线东移发展,持续影响贵州东部和北部地区。在降水最强的两个阶段,雷达回波呈现暖云和悬垂结构特征。地面辐合线及其锋生、上游降水带来的降温增压以及持续增强的南风有利于暴雨区水汽辐合增强,而垂直方向上纬向、经向中尺度次级环流上升支正好位于特大暴雨中心附近,有利于中尺度对流系统维持和增强。     

闽北一次初春强降水的成因分析

利用常规观测资料、NCEP 1°×1°再分析资料、新一代天气雷达资料,对2012年3月6日-7日福建省北部地区暴雨成因进行诊断分析。结果表明:此次降水过程在高空西风槽南下带来的冷空气与西南暖湿气流交汇的背景下产生,暴雨区位于低空西南急流北侧、低层切变南侧、地面冷锋附近;强降水落区位于层结不稳定的湿区中,低层辐合、高层辐散有利于对流发展;干冷空气的侵入时高层高值位涡库向北向下伸展,促使中低层气旋涡度发展,从而导致强降水的发生;雷达回波分析表明,低层暖平流、高层冷平流、区域上空辐合形势都有利于对流性降水的产生。     

鲁西南至鲁中一次暴雨过程成因分析

利用实况观测资料、中尺度自动站资料、中国气象局物理量分析资料和泰山多普勒雷达资料对2013 年7 月18 日发生在鲁西南至鲁中的暴雨过程进行了诊断分析。结果表明：强降水由500hPa西风槽、700hPa 切变线、850hPa低涡、地面辐合线、以及副热带高压西北边缘的暖湿气流共同影响造成。低层前期明显的持续升温为暴雨的产生创造了良好的热力条件,副高外围的水汽输送为此次暴雨提供了充足的水汽,同时暴雨区不稳定能量的维持和层结对流不稳定的结构,有利于暴雨的产生。地面中尺度辐合线的生成和发展, 是这次暴雨产生的启动机制, 暴雨的分布与地面辐合线的走向基本一致。强降水期间,沿低层切变线北侧东北气流南下的冷空气与暖空气交汇,使对流加强、降水强度加大。另外,泰安地处鲁中山区向西南开口的山谷的南部,偏南气流的迎风坡,有地形产生的偏南风的辐合和抬升,地形造成的辐合上升运动对泰安地区第一个强降水时段降水具有明显的增幅作用。两个强降水时段雷达回波为混合型降水回波,反射率因子强度一般在30～35dBz,最强达40dBz,其中第一个强降水时段回波对流发展的高度更高。特殊的地势地貌也是此次暴雨产生的重要原因。     

鄂伦春自治旗一次暴雨天气过程诊断分析

文章综合利用常规观测资料、雷达回波资料、卫星资料,对2015年8月2—3日发生在鄂伦春旗南部的暴雨天气过程从环流背景和中尺度特征方面进行诊断分析,结果表明:500hPa高空槽和850切变线形成前倾结构,前倾槽结构为暴雨的发生发展提供了热力和动力条件;700、850hPa西南急流对鄂伦春旗低层增温增湿作用明显,加之地面、850hPa东南急流辐合上升,增加了大气层结不稳定度,这两支急流在鄂伦春旗上空形成了强烈的水汽通量的辐合。与地形因素相关联的近地面层切变辐合促成下边界层辐合是此次暴雨过程的触发点,其附近陆续生成的中等强度块状的对流单体,形成暴雨。此次暴雨过程的中尺度特征:影响云带主要由有序的中尺度对流云团和中尺度对流复合体组成,降水时空变率大,雷达回波强度达到60dBz,走向与低空切边线移动方向一致。     

2003年淮河流域特大暴雨期间低纬环流分析

利用NCEP NCAR提供的OLR和气象要素场再分析资料 ,对 2 0 0 3年 6～ 7月淮河流域特大暴雨期间西南季风和东亚季风以及热带低纬地区环流的异常特征及其对梅雨暴雨的贡献进行了分析。结果表明 ,暴雨期间 ,长江流域附近地区维持准稳定的比常年明显偏强的南风带 ,北方冷空气亦比常年偏强 ;三次强降雨过程 ,低层均表现为稳定的强南风与阶段性增强的偏北气流的经向辐合。三次强降水过程中有两次 ,来自西北太平洋的东南气流的水汽输送占主导地位。异常环流分析表明 ,淮河流域强降雨时段 ,东南亚季风和南海季风异常偏弱 ;赤道西北太平洋地区东风气流比常年同期异常偏强。诊断分析认为 ,高原东部和南部地区对流活动比常年同期偏弱 ,80～1 1 5°E地区越赤道气流比常年异常偏弱 ,可能是西南季风异常偏弱原因。而偏强的WAKER环流可能是西北太平洋地区低纬地区东风异常偏强的原因。另外 ,印度尼西亚地区低层气流辐合异常偏强有利于副高的稳定和加强 ,进而使我国东南部地区长时间维持强异常偏强的东南气流 ,从而为淮河流域的持续暴雨提供了源源不断的来自南海和西北太平洋的暖湿气流     

一次地形加强贵州暖区暴雨成因分析

利用常规气象资料、FNL再分析及雷达卫星资料等对2015年5月26—27日发生在贵州一次区域性暖区暴雨进行分析,结果表明:此次特大暴雨发生前期副高稳定少动,暴雨临近时高空影响槽东移,中低层切变线南压及暖湿气流辐合加强触发了暴雨的发生,暴雨发生在辐合线附近,无明显的冷空气影响,为典型的暖区暴雨;雷公山地形增强了气流辐合和地形抬升使其西南气流垂直上升运动较强,低空为强辐合区;低层高温高湿高能环境,有利于降水效率的提高,切变线两侧风速风向的辐合为暴雨的发生发展提供了充足的动力条件;降水过程强回波呈低质心、降水效率高的热带降水型特征,多个对流单体不断生消发展,在黔东南西部表现为明显的"列车效应";卫星云图显示特大暴雨落区及强度与TBB的变化有很好的对应关系。     

2017年9月一次夏季暴雨天气的特征分析

利用卫星云图、雷达、探空和地面自动气象站等气象资料,对2017年9月12日福建省西部的一场暴雨过程进行特征分析,探讨在副热带高压控制下出现较大范围暴雨过程的原因,结果表明:东亚大槽深入副热带高压北部,使得冷暖气流交汇于长江中下游以南地区,为处于副热带高压控制下的福建西部午后出现强降水创造有利环流条件;低层东北冷湿气流、中高层西南暖湿气流和地面冷锋是主要影响系统;副高控制下的福建西部白天升温明显,增强该地区上空的层结不稳定,沿海有云系增温不明显和台风"泰利"外围环流增强了闽浙沿海低层东北气流,两者共同作用增强地面冷锋强度和走向,有利于冷锋向西南移动,触发福建西部强对流的发展;从雷达回波特征看,该次过程既具有块状对流性强降水回波特征,又具有絮状稳定性降水回波特征,是锋面降水与局地热对流降水叠加结果。     

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;