黄土高原一次β中尺度大暴雨特征及成因分析

利用卫星云图、多普勒雷达资料和NCEP资料等,对2010年9月18日20：00-19日08：OO黄土高原发生的一次β中尺度大暴雨过程的大尺度环境场、中尺度影响系统以及触发机制等进行综合分析。结果表明;4个中尺度径向速度辐合是β中尺度大暴雨的直接影响系统,列车效应是β中尺度大暴雨形成的原因之一;气压持续降低,配合2rain平均风速急剧增大、而后风向突变,或配合先风向突变、而后2min平均风速急剧增大,是β中尺度大暴雨形成条件之一;地面能量比场“Ω”系统东侧小能量比低值舌的活动,也是口中尺度大暴雨的另一触发机制;云高和液态累积含水量（VIL）的配合,对大暴雨的产生具有指示作用。     

天津一次局地大暴雨中尺度对流系统组织化特征与成因

应用常规观测资料与地面加密自动站、卫星云图、多普勒雷达等多种非常规观测资料以及雷达变分同化分析系统(VDRAS)分析场资料,对2013年7月1日天津南部大暴雨中尺度对流系统的结构、演变特征及其成因进行了分析。结果表明:(1)大暴雨发生在副热带高压边缘暖湿气流、低空700—850 h Pa暖性切变线、高低空急流有利配置的背景下,属暖区暴雨。(2)大暴雨由若干β中尺度对流云团合并加强后的α中尺度对流云团造成,对应雷达,强降雨是由西南方向不断移入天津南部的γ中尺度对流单体发展加强,并先后组织成若干东—西向带状β中尺度对流系统先东北后偏东方向移动造成的,在大港南部有列车效应,具有典型的热带型降水回波特征。(3)逆风区的出现、中空急流向低层伸展,低空急流、超低空急流先后形成并加强,是降水强度加强的重要原因。(4)地面中尺度切变线的维持、加强和中尺度低压倒槽东移、发展、入海加强为中尺度气旋,是先后造成对流单体发展加强并组织成带状中尺度对流系统的两个中尺度系统。(5)近地层中尺度切变线是地面中尺度切变线形成的原因,对流单体前侧的偏南冷性水平出流的叠加,一方面增强了沿切变线的辐合,一方面也加大了低层的水汽输送;带状对流系统后侧的偏北冷性水平出流与东南气流形成的中尺度切变线是地面中尺度气旋形成的原因。     

弱天气背景下鲁南一次暖区大暴雨触发 与维持机制分析

利用区域自动站资料、ERA5再分析资料、FY-2F云顶亮温资料和多普勒雷达资料等,对2019年8月5—6日鲁南大暴雨过程的环流背景、环境场条件、中尺度对流系统（MCS）演变特征及其触发机制进行了分析。结果表明：（1）这次暴雨过程发生在副热带高压边缘的弱天气强迫背景下,大尺度环流形势配置不是很有利;（2）深厚的湿层、较低的抬升凝结高度（LCL）和自由对流高度（LFC）、上干下湿的不稳定层结为大暴雨的产生提供了有利的环境条件;（3）暖区强降水发生在鲁中山脉向苏北平原的过渡带上,呈狭长带状,5日午后和夜间先后生成的准静止β中尺度对流系统（MβCS）共同导致大暴雨过程的发生,小时强降水中心主要出现在MβCS云团TBB梯度大值区附近;（4）5日午后鲁南和6日凌晨枣庄中部强降水的触发机制为地面中尺度辐合线,MCS沿着辐合线不断新生和发展,形成“列车效应”,造成大暴雨。6日凌晨临沂西北部强降水由850 hPa露点锋触发,鲁中山脉峡谷风效应和迎风坡的动力抬升作用促使MCS增强发展;（5）强降水的持续与850 hPa露点锋、冷池和边界层暖湿气流增强引起的地面辐合线的长时间维持有关。     

一次秋季台风倒槽大暴雨过程诊断及中尺度分析

利用FNL再分析资料,结合加密自动站、多普勒雷达、卫星资料和数值模式预报产品,对2018年9月16—17日长三角地区一次典型的秋季台风倒槽大暴雨进行了分析。结果表明:大暴雨是在远距离台风倒槽、低空急流和高空槽共同影响下,由冷暖空气持续交汇激发的4个中尺度对流云团活动造成。第一阶段长江口区强暴雨发生在3号云团快速增强期间,暴雨出现在云团北侧TBB梯度大值区中,雨强随云顶温度降低快速增强;4号云团缓慢东移造成第二阶段暴雨,降水累积效应使长江口区降水量进一步加大。东北风(偏北风)与东南风(偏东风)形成的地面中尺度辐合线是暴雨的关键触发机制,气旋性辐合中心的形成对雨团增幅具有重要作用。多普勒雷达径向速度场上中气旋的形成提前于强暴雨增幅约30 min,具有良好的先兆性和预报预警意义。(超)低空急流持续的水汽和能量输送、高低空急流耦合及冷空气侵入形成的倾斜上升支和垂直环流圈、上干冷下暖湿的对流不稳定层结有利于中尺度暴雨云团的形成和维持。表征冷暖空气结合的地面辐合线位置是暴雨落区预报的关键,对于秋季台风倒槽暴雨,要特别重视冷空气对暴雨的触发和增幅作用,基于实况资料监测及时订正模式预报结论。     

一次孟加拉湾风暴和冷空气影响下滇西大暴雨中尺度分析

应用MICAPS资料, 通过天气诊断分析, 结合FY-2卫星云图及德宏CINRAD-CC雷达体扫资料, 分析了发生在2004年5月18日滇西地区的大暴雨过程。发现本次大暴雨过程天气尺度影响系统为初夏孟加拉湾风暴及南下冷锋切变; 大暴雨发生在高能高湿的水汽辐合中心、700 hPa螺旋度正值区及湿 Q 矢量散度大值辐合区内; 卫星云图上, 多个β-中尺度对流系统在大暴雨区发展; 多普勒雷达回波为絮状混合型降水回波, 强度在30~44 dBz之间, 频繁出现的逆风区、低空急流、中尺度辐合线等中小尺度系统是造成本次大暴雨的直接影响系统。     

2011年“7·25”山东乳山特大暴雨成因分析

利用常规资料、地面加密资料、多普勒雷达资料和ＮＣＥＰ再分析资料等,对2011年7月25日发生在山东省乳山市一次超历史极值的特大暴雨过程进行分析。结果表明：本次特大暴雨是由高空西风槽、低层切变线与副热带高压边缘的低空急流共同影响所致;由低层前期强盛的低空偏南暖湿气流输送使半岛上空低层高温高湿,形成上干冷下暖湿的对流性不稳定层结;近地面向岸风的侧向辐合产生气旋式切变线,是本次暴雨的启动机制,大暴雨的分布与地面辐合线的走向基本一致。此外,半岛上空超低空偏南急流的加强,使中尺度切变线北抬,进而受乳山倒喇叭口地形影响,发展成了中气旋,产生了强降水超级单体风暴。而强降水超级单体风暴造成的短时强降水,是本次暴雨致灾的重要原因。     

“7·23”大暴雨动力机理的双多普勒雷达反演

使用地基双多普勒雷达MUSCAT三维风场反演技术,利用宜昌和荆州双多普勒雷达同步体积扫描资料,对2002年7月22-23日湖北省境内的一次混合型大暴雨进行了三维风场的反演。分析表明,中低层的中尺度气旋和气旋性切变线是触发和维持此次大暴雨的重要动力因素;低层辐合,高层辐散的动力配置也有利于强降水系统的发生和发展。     

“12.7.21”西南涡极端强降雨的成因分析

利用常规观测资料、ECMWF分析场、区域自动站、多普勒雷达及SWAN系统产品等资料对2012年7月21日西南涡暴雨过程及盘龙极端强降雨进行分析。分析发现：此次过程是“北槽南涡”形势下,地面冷空气触发西南涡其东侧辐合上升运动强烈发展,高层强辐散,因而产生了对流性暴雨天气过程;冷空气从西侧侵入西南涡是925 hPa “S”形冷锋形成的直接原因,也是地面辐合线形成的重要因素;极端短时强降雨就发生在西南涡东侧中尺度雨带的中部偏北区域,有地面辐合线相配合,降雨最强时MCC冷云中心TBB达最低值。雷达回波表明：西南涡两侧冷暖空气的交绥促进了β中尺度气旋式环流的形成;偏南风低空急流为强降雨提供了充足的水汽,增强了中低层的垂直风切变,有利于强降水超级单体风暴的发展和维持;盘龙的极端短时强降雨是β中尺度气旋式环流中,伴随有深厚中气旋的强降水超级单体风暴在环流中心附近持续发展的结果。     

一次局地大暴雨的落区分析与预报

应用常规天气资料、地面加密自动站资料、FY-2C红外TBB资料和多普勒雷达资料,并引用中尺度对流复合体(MCC)β中尺度单元(MBE)移动概念模型,对2007年7月18日天津地区出现的强雷雨、局地大暴雨天气进行了分析。结果表明:局地大暴雨是在大范围的有利天气条件下产生的,降水具有明显的β中尺度强对流系统特征;强降水出现在"人"字型回波带的头部,落区位置与中气旋的位置相对应;从地面加密自动站资料也能很好地分析出强降水雨区的位置和移动方向。通过分析FY-2C红外TBB资料表明:强降水出现在MCC中冷云顶区的右后侧,且降水强度在MCC中出现强冷云顶区时达到最强。应用MCCβ中尺度单元(MBE)移动的概念模型,通过判断MBE的移动,可以很好地预报出强降水下一时刻的具体落区位置,从而为该地区强雷雨、局地大暴雨落区的短时临近预报提供一种新的方法。     

滇缅脊前一次弱对流形成的局地大暴雨过程分析

利用常规观测资料、加密自动站资料、NCEP 1°×1°每6 h再分析资料和多普勒雷达资料,用尺度分离和物理量诊断分析方法,对2005年8月7日发生在云南新平平掌的局地大暴雨进行综合分析,结果表明:低层700 hPa滇缅脊前哀牢山沿线切变线内生成的中尺度气旋,是产生局地强降水的主要天气系统;局地大暴雨发生在低层辐合、中高层辐散的弱对流环境中,低层局地强水汽辐合为此次大暴雨提供了水汽条件;局地大暴雨发生前垂直螺旋度低层为辐合上升,强降水发生在辐合上升运动减弱期;大暴雨发生在对流云团温度梯度迅速增大的位置,多普勒雷达回波强度和回波顶高均无强对流特征;局地大暴雨发生地有逆风区形成,不断南下补充的新对流单体,使得β中尺度回波长时间维持,是导致局地大暴雨发生的重要原因。     

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;