2017年川东北首场暴雨过程水汽条件分析

本文利用NCEP1°×1°6小时再分析资料及常规观测资料,针对2017年5月2～3日发生在四川盆地东北部的一次暴雨天气过程的水汽条件进行分析。其结果表明:(1)暴雨开始前,水汽含量猛增,湿层厚度增加,并在整个过程中维持高湿状态;暴雨区水汽随时间由南向北逐渐增加。(2)暴雨区水汽总收支在整个时段都为正值,水汽收入大值与两个降水时段的开始时间重合,经向的水汽输入在此次过程中起主要作用。(3)水汽的来源主要是孟加拉湾和南海,水汽输送以850～700hPa为主,由南至北逐渐减弱,南部湿层更厚,与大暴雨落区相对应。(4)第一阶段降水开始时,水汽低层辐合、高层辐散,两阶段降水之间辐合减弱,然后又增强并持续到过程结束。      

西南低涡东移引发重庆暴雨的综合诊断

利用NCEP/NCAR Reanalysis 1°×1°格点资料和MICAPS实时观测资料,使用水汽散度垂直通量、湿螺旋度等新型诊断物理量,对2009年8月2~4日发生在重庆地区由西南低涡东移引发的暴雨做了综合分析。结果表明:水汽主要在大气低层850hPa附近积聚,上升运动强,水汽的辐合上升区域与降水大值区较吻合。500hPa湿z-螺旋度负值区水平分布与相应时段降水落区和强降水中心的分布对应较好,垂直分布上:暴雨区低层正涡度、水汽辐合旋转上升与高层负涡度、水汽辐散相配合,是触发暴雨的有利动力机制。      

渭河流域三次暴雨过程水汽和上升运动的垂直结构比较

利用常规高空观测资料和NCEP/NCAR 6 h再分析资料等,着重从水汽和上升运动的垂直结构上对发生在渭河流域的三次致灾暴雨过程进行了比较分析。结果表明:三次暴雨过程具有相似的水汽通量散度场垂直结构,即低层辐合、中层或高层辐散,但低层辐合远大于其上层辐散,低层强水汽通量辐合不仅为暴雨区提供了充沛水汽,也导致并促使水汽在垂直方向上从低层向高层输送,从而增强大气垂直上升运动发展;600 hPa(或400 hPa)水汽辐合或辐散突然增强,预示降水强度将增大,其突然减弱,则标志着强降水趋于结束;三次暴雨过程中,强降水主要出现在整层上升运动形成前后和450 hPa附近垂直上升运动增强最快时段内。     

低涡影响下的西北地区东部暴雨个例分析

利用常规气象观测资料、陕西区域自动站观测资料、多源融合逐小时0.05°×0.05°格点降水资料、 NCEP1°×1°再分析资料和卫星探测资料对2019年8月2—4日西北地区东部一次由低涡向东北方向移动引发的暴雨过程进行了诊断分析。结果表明：500 hPa中纬度低槽、副热带高压、低槽携带的弱冷空气及来自台风外围和副高外围的暖湿空气为此次暴雨过程提供了非常有利的条件;大气整层水汽通量及水汽通量散度在本次低涡暴雨预报中具有一定的意义,水汽通量的突增对应降水的增强,水汽通量大值中心叠加强水汽通量辐合区对应强降水落区;500 hPa正涡度平流使低涡移动发展,对暴雨的发展和移动有很好的指导意义,对流层低层温度平流对低涡移动路径方面有良好预报指示作用;对流层上层高位涡向下伸展,分裂的高值扰动可促使中低层气旋涡度发展,影响低涡加强,对流层中低层700 hPa附近上正下负的分布形态促进了不稳定能量的释放,有利于低涡暴雨的发生;暴雨期间卫星云图上表现为逗点型云带,与低涡系统对应良好。     

初夏四川盆地东北部一次暴雨过程分析

本文利用NCEP1°×1°6小时再分析资料及常规观测资料,针对2014年6月2日发生在四川盆地东北部的一次暴雨天气过程进行分析,并就风与温度与2011年5月1～2日的过程进行对比,其结果表明:(1)此次过程主要是由华北冷涡后的冷平流、东南气流引导的暖湿空气及低层偏东气流引导的冷空气共同作用而形成。(2)两次过程都存在低层偏东气流引导华北低涡中冷空气入川,且与强降水落区有较好的对应关系。(3)强降水关键区在整个过程中一直处于高湿状态。(4)低层水汽输送主要以由东向西和由南向北输送为主。(5)强降水爆发期从400hPa以下皆为水汽的辐合上升运动,且在中高层持续加强。      

夏季两次低槽冷锋型暴雨成因对比分析

利用常规观测资料和NCEP 1°×1°再分析资料,对对流层低层无低涡、无低空急流配置的低槽和冷锋影响下2004年7月29—30日后倾槽和2004年8月3—4日前倾槽两次暴雨过程的成因进行了对比分析,结果表明: 虽然两次过程对流层中低层形势非常相近,但在空间结构上却存在显著差异。后倾槽锋区向冷空气倾斜且成3段锋,其中,第1段锋在850 hPa以下,冷空气虽较弱,但对整个降水过程起抬升触发作用,暴雨区出现在该段锋移动方向的前沿,即地面辐合线呈气旋性弯曲的流线密集处;前倾槽锋区完整,湿斜压锋区向暖区倾斜,暴雨区出现在锋前1～2个纬距处,即地面辐合线右侧偏南气流密集带中。两次过程低层均有强的水汽输送,存在高温高湿区,925 hPa比湿均达15 g〖DK〗·kg-1以上,所不同的是,后倾槽暴雨区位于水汽通量大值区、等〖WTBX〗θe〖WTBZ〗密集线前沿及风场辐合明显的水汽辐合区内,而前倾槽暴雨区则位于水汽通量等值线密集带中的水汽辐合区、〖WTBX〗θe〖WTBZ〗暖舌的舌尖和风场辐合处,但更偏向暖空气一侧。此外,暴雨易发生在山区或海岸线等特殊地形抬升的区域。     

2016年6月赣北两次对流性暴雨过程对比分析

利用常规气象观测资料、NCEP 1°×1°FNL资料和多普勒雷达、卫星TBB等资料,对2016年6月1—2日和18—19日江西省北部两次对流性暴雨过程进行对比分析。结果表明,高空冷涡、西太平洋副热带高压、中低层急流、高空槽等共同作用导致两次暴雨发生。中层有冷空气影响,低层深厚西南急流维持时,更利于降水的对流性特征维持。两次暴雨过程强降水由低质心较强回波的“列车效应”造成。强降水回波带有多单体风暴和强降水超级单体风暴特征时,强降水效率更高。两次过程水汽收支中水汽通量散度项由正转负,水汽垂直输送项由负转正,中低层水汽辐合将低层大量水汽向上输送至中高层,利于强降水的形成。差动涡度平流中心与上升运动中心吻合,其导致垂直动力强迫,促进扰动不稳定和垂直运动的发展。强上升运动区南北两侧垂直经向环流和反环流的形成,为强暴雨的发生维持提供了持续的强水汽水平输送和辐合抬升条件。     

鄂东北春季三类典型极端强降水特征分析

利用1961—2020年降水资料、NCEP／NCAR2.5°×2.5°和ERA5资料,对鄂东北春季极端强降水个例天气系统及物理量的异常度、配置与降水落区的关系进行了分类对比研究。结果发现：(1)鄂东北春季极端强降水有三种典型形势,即地面倒槽型、冷锋前沿型、暖低压型。三类极端强降水过程500 hPa均有南支槽缓慢东移,湖北以东、东北地区到日本有异常强的高压或高压脊,东高西低的形势使降水持续时间长。850 hPa偏南急流异常强盛,鄂东北位于切变辐合区。强降水发生前地面暖低压异常发展,为强降水提供了有利的环境条件。(2)鄂东北大多数春季极端强降水与低层水汽、中低层垂直速度的异常密切相关。鄂东北及周边为低层水汽辐合和垂直速度异常度绝对值之和大值区。     

2003年8月28日至29日铜川区域性暴雨成因分析

对2003-08—28—29铜川市暴雨过程分析发现，暴雨发生在南亚高压由东部型转为西部型的调整过程中，200hPa急流为暴雨区高层辐散提供了动力条件，500hPa陕西处于副高边缘，新疆有冷涡不断分裂冷空气扩散南下，这种形势的稳定，为暴雨区提供了充足的能量和动力作用；700hPa关中及其以南的SW急流和850hPa华北到陕西东部的东风气流，为暴雨过程提供了充足的水汽，铜川处于切变辐合区；物理量场中，铜川处于一个水汽的辐合中心，垂直速度的上升中心，散度场上表现为低层辐合，高层辐散，水汽条件和动力条件配置非常有利。中尺度分离分析表明，暴雨发生过程中，雨区低空700hPa和850hPa有明显的中尺度辐合区存在。     

重庆2014年9月12-19日连阴雨过程分析

利用NCEP1°×1°的再分析资料及重庆地区逐日、逐时降水资料、TBB资料以及雷达回波资料对2014年9月12-19日连阴雨天气及期间两次暴雨过程进行对比分析。结果表明：此次连阴雨期间欧亚大陆中纬度为“一槽”型,暖湿气流沿台风“海鸥”外围以及副高边缘不断将南海及西太平洋的水汽输送至川渝地区,为连阴雨提供良好的水汽输送条件;比湿的演变对连阴雨期间降水的增强及间歇有明显的指示意义,负的整层水汽通量散度大值区与两次暴雨落区一致。连阴雨前期大气为对流不稳定层结,能量条件好,阻塞高压明显,中高纬度的环流经向度较大,有利于冷空气的快速南下,前期的“9.13”大暴雨的对流性特征更明显;后期“9.17”暴雨则表现为稳定性降水,TBB资料在连阴雨期间的强度演变对两次暴雨过程产生的时段和落区的指示意义较好。在整个连阴雨期间近地层多弱冷空气的影响,中层有偏南风,连阴雨天气得以持续,但是两次暴雨过程开始时,冷平流均有明显的增大,表明冷空气有所加强,冷空气的加强触发了暴雨的产生,同时伴有中层偏南暖湿气流的增强。从两次暴雨过程时多普勒雷达垂直风廓线图也能反映出低层冷空气入侵,中层偏南暖湿气流的增强。     

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;