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。     

一次引发川东暴雨的西南涡特征分析

利用NCEP再分析格点资料、常规观测资料、自动站降水资料、0.1°×0.1°的FY-2E云顶亮温资料,对2013年6月29日至7月1日发生在四川东部的大暴雨过程进行分析,结合涡度收支方程重点分析了引发这次大暴雨的西南涡结构。结果表明：在西南低涡发生发展过程中,对低涡发展起直接作用的是水平辐合辐散项和水平平流项,低涡形成前水平辐合辐散项起主要贡献,低涡形成后水平平流项贡献增大,并在对流层中低层以正贡献为主,扭转项贡献最小,而垂直输送项在低涡形成前期以正贡献为主,低涡减弱阶段以负贡献为主;在西南低涡形成前期,对流层高层有位涡大值区向下传输至中层,中高层正位涡叠加在低层负位涡之上,有利于低层低涡的发展及不稳定能量的存储与释放,是低涡维持发展的重要因素。     

一次双台风影响下暴雨过程的中尺度涡旋模拟分析

本文对上海地区一次双台风环境影响的暴雨过程进行数值模拟及分析,探讨了强降水过程中大气中低层的涡旋特征及发展机理。结果表明:(1)暴雨过程处于双台风、大陆高压的共同影响下,中低层伴随有较明显的中尺度低涡发展。(2)与涡旋相关的局地垂直涡度由低层开始发展,先期涡度发展集中于850 hPa以下,之后向大气中上层发展增强,涡旋尺度强度也随之发展,最终形成在对流层下半部具有闭合式气旋性环流的深厚涡旋。(3)影响局地涡度变化的水平平流项、垂直平流项、散度制造项和倾斜项对不同时间、不同高度的涡度作用各不相同,其中散度制造项是中低层涡度的主要来源,垂直平流项的输送作用对中上层的涡度发展有重要作用,倾斜项对涡旋发展移动也有部分贡献。(4)通过敏感性试验考察了对流潜热反馈的贡献,发现潜热释放过程通过加热改变大气温压场结构,从而维持并改变局地涡度倾向的中低层辐合及对流上升运动,对涡旋的发展和移动起了重要影响。     

一次长江中下游梅雨锋暴雨过程的诊断分析

利用NCEP 1°×1°再分析资料、FY-2C卫星云顶亮温(TBB)和中尺度模式WRF输出的15 km高分辨率资料,对2008年影响浙皖赣地区的一次梅雨锋暴雨过程进行了诊断分析.结果表明,青藏高原东侧西风槽和副热带高压之间的相互作用、对流层中低层切变线的维持以及低涡东移、发展是暴雨发生的天气尺度背景.TBB数据显示,在切变线附近不断有中尺度对流云团生成并东移、发展.与暴雨区相对应,在低空西南急流左侧存在多个β中尺度强水汽通量辐合中心,高空西风急流人口区右侧排列着一系列的辐散中心,表明该地区存在较强的水汽辐合上升运动.对流层低层高温高湿、中高层冷空气侵入,导致大气层结处于极不稳定状态.湿位涡的分布与中心位置对暴雨落区及强度具有较好地指示意义.暴雨区附近对流层高、低层都存在较明显的位涡水平平流,导致位涡扰动不断地自上游向下游地区移动.锋区前暖区的对流层中低层存在强垂直位涡柱,引发气旋性环流的发展,从而促进了辐合上升运动.     

高层冷涡的不同结构对台风运动的影响

通过对不同水平和垂直结构的高层冷涡在台风移动中的作用的对比试验，发现高层冷涡的水平和垂直结构变化会影响台风的运动。对总涡度倾向分布和强度的比较分析，发现正压过程主导台风运动的方向，而斜压过程在某些情况下对台风移速有很大影响。通过各动力项对总涡度倾向贡献的讨论，发现涡度平流对总涡度倾向正中心的贡献主要来自引导气流对非对称涡度场的平流。散度场贡献主要来自行星涡度和非对称散度相关场。最后还得到预报性的结论:台风朝对流层上层的辐合中心或对流层中下层辐散中心方向移动。     

由台风低压倒槽引发的山东暴雨过程研究

2004年8月26-28日发生在山东省的大到暴雨过程主要是由"艾莉"台风减弱的低压和西风带冷空气远距离相互作用造成的,台风倒槽的发展与低空东南气流的加强及台风低压外围热量和动量的向北输送密切相关.采用双向三重嵌套网格非静力模式MM5对这一过程进行了数值模拟,研究了台风倒槽的中尺度结构特征,并通过涡度收支探讨了台风倒槽及中尺度低涡发生发展的物理过程.结果表明,强降水是在台风倒槽顶部强风中心与弱风中心之间的强辐合作用下触发的,台风倒槽的增强和中尺度低涡的形成是低空急流及其动力作用的结果,降水的非绝热加热也起着重要作用.涡度方程的收支诊断表明,对流层低层的散度项、对流层中层的水平平流项和铅直输送项是正涡度的主要贡献者,在同一等压面上散度项和水平平流项的作用是相反的.对流层中层铅直输送项的贡献为正,扭转项为负贡献,涡度变化的总趋势是它们相互作用的净结果.等压面上相对涡度的变化趋势并不是均匀的,中尺度低涡的东南象限相对涡度局地变化较强,这是强降水发生在此的重要原因.低层正涡度的增加是由水平辐合引起的,而高层正涡度的增加是涡度由低层向高层垂直输送的结果.因此台风倒槽的发展和中尺度低涡的形成主要是由于低层的涡度制造,另一方面来自中低层涡度的垂直输送.     

冬季一次引发华北暴雪的低涡涡度分析

利用NCEP FNL 1°×1°再分析资料和WRF模式,模拟了2010年1月2～3日我国华北地区的一次由涡旋造成的冬季降雪过程,并采用位涡和涡度方程对引发暴雪的涡旋发展机制进行了诊断分析。结果表明,这次降雪过程中,对流层中层高空浅槽东移、加深及发展,并引导低空和地面系统自西向东移动,高空位涡的下传强迫加强了对流层中低层涡旋的发展。平均通量和涡旋区域的辐合、辐散作用对涡旋涡度的增长贡献最大,扰动通量和类倾斜项的作用较小。在中层涡旋成熟期,环境场的风速小于中层涡旋的移动速度时,环境场相对于涡旋区域为辐散,涡旋涡度减小;当环境场风速大于涡旋的移动速度时,环境场相对于涡旋区域为辐合,涡旋涡度增加。在涡旋衰减期,向涡旋外输送的绝对涡度通量使得涡旋涡度逐渐减弱。这次过程中,高空位涡强迫、低空辐合和涡旋边界平均气流对扰动涡度的输送是涡旋发展的主要机制。     

集合动力因子对登陆台风“莫拉克”(0908)暴雨 落区的诊断与预报研究

“莫拉克”是2009年登陆我国热带气旋中影响范围最广、造成损失最大的台风.“莫拉克”带来的强降水导致台湾南部发生50年来最严重的水灾,福建、浙江等省的部分站点过程雨量超过50年一遇.因此,在台风暴雨(强降水)预报中,能否准确把握其落区就显得尤为重要.本文首先利用中尺度非静力数值模式WRF对台风“莫拉克”进行高分辨率数值模拟(三层嵌套,最高分辨率为2 km).模式较好地再现了台风中心的移动路径、强度;模拟的降水分布区域与实况也较为相符.利用再分析资料及模拟的高分辨率资料对暴雨成因进行诊断分析,表明造成此次强降水过程的水汽主要由西南季风输送,并且垂直运动旺盛,贯穿整个对流层.根据集合动力因子预报方法,运用广义湿位温、对流涡度矢量垂直分量及水汽散度通量对暴雨落区进行了诊断和预报,发现广义湿位温等值线的“漏斗状”区域与暴雨落区对应关系显著;基于NCEP-GFS每日四次的预报场资料,利用对流涡度矢量和水汽散度通量做出的降水落区预报表明,二者对降水落区均有一定的指示意义.强降水主要位于对流层中低层对流涡度矢量垂直积分量的梯度大值区附近,其时间演变与观测降水的演变具有相当高的一致性;水汽通量散度抓住了垂直运动和水汽散度这两个引发暴雨的关键因子,对降水的发生范围和强降水极值中心的判断更为准确.这三个动力因子都可以为“莫拉克”台风暴雨(强降水)落区提供信号,对台风暴雨落区具有一定的诊断和预报意义.     

新疆夏季一次特大暴雨天气成因分析

通过对新疆2007.07.17大暴雨过程的T213产品物理场进行解释分析,根据揭示其动力、热力和水汽场特征,进而找出新疆大暴雨天气预报的指标。结果表明:新疆2007.07.17大暴雨天气发生在对流层中层强烈的上升运动区、对流层中低层Ω型θse高能舌中心附近以及对流层中低层偏东气流与中层偏南气流和高层偏北气流交汇处的重合区内;而对流层中低层东路水汽的输送对大暴雨的贡献最为重要。T213产品的涡度、散度、垂直运动、风场、流场、水汽通量、比湿、T-Td场在预报时效上与暴雨区一致;假相当位温场、水汽通量散度场在预报时效上超前于暴雨。     

双流机场盛夏雷雨的中尺度分析

多个雷雨过程的位涡诊断分析表明:位势涡度的变化比涡度倾向更明显地表征了成都地区夏季雷雨的变化.即雷雨发展时,对流层中低层出现负的变位涡,而在其减弱时为正的变位涡,高层则正好相反.影响位涡变化最主要的物理因子是位涡通量的水平散度作用、水平风速的辐合作用和加热率的垂直差异效应.     

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;