潍坊地区降水性层状云特征及云水资源的估算

一、引言我省自1989年开展飞机人工增雨工作以来,催化对象以降水性层状云为主。飞机人工增雨基地设在潍坊市,我们已就影响其降水天气过程的主要天气系统,作业条件的选择等问题做了初步分析。分析表明:该地区飞机增雨作业季节(3—6,9—10月)以层状云降水为主,各类系统产生层状云降水的日数占各自降水总日数的70—95％,其中黄淮气旋产生层状云降水日数占该系统降水总日数的95％。同时降水性层状云具有维持时间长,范围广,云层稳定少变等特点,适宜飞机人工增雨作业。因此,进一步深入研究降水性层状云的     

大连市火箭人工增雨流动作业技术与业务流程

文章通过统计和分析大连地区1975—2004年30年过程降水≥5 mm的40个天气样本资料,总结了主要降水形势为大槽型、中纬度系统型、北涡型和南涡型4种类型;同时分析了2003年6—10月过程降水在5 mm以上的22个多普勒雷达观测个例,总结得出大连地区主要降水云系为层状云和积层混合云,且积层混合云多于层状云,表明大连地区更适宜开展火箭增雨作业,进而统计和建立了火箭增雨的雷达作业预警、决策判别指标模型;并研究了通过自然降水落区与社会需水综合分析确定作业区域的技术方法;根据雷达预警判别指标权重分析,侧重研究了作业车辆适时调度技术方案及业务流程,为火箭人工增雨科学、高效作业提供保障。     

汉中盆地层状云结构的对比分析

陕西地区主要的降水云系和催化对象,多属系统性层状云。因而,开展层状云的微物理结构观测研究,对拟定飞机人工催化作业的物理设计,天气资源的考察、利用和提出增雨的必备条件、相对潜力、有效途径都有着直接的关系。本文通过对秦岭南坡(汉中盆地)两次催化降水     

陕西省飞机增雨作业典型天气个例分析

在2002～2003年春秋季陕西省14次飞机增雨作业天气分析的基础上,对2002年9月13日飞机增雨作业典型天气个例的环流背景、影响系统以及500 hPaθse、温度、垂直速度、水汽通量散度等物理量场进行了分析,并结合飞机和雷达探测资料,得出飞机增雨作业需要的有利于层状云向降水转化的条件：有稳定的层状云,云系有一定冷层厚度和过冷云水量,供自然冰相降水元和人工催化形成的降水元增长;云系还应有一定厚度的暖层,供下落的自然和人工形成降水元融化再经碰并云水增长成雨滴形成降水.陕西省春秋季具有进行有效飞机增雨作业的天气系统和天气条件.     

基于飞机真实轨迹的一次层状云催化的增雨效果及其作用机制的模拟研究

层状云降水效率通常较低,但却具有较高的云水资源开发潜力,是人工增雨作业的重要对象。随着中国南方地区生态改善、水库增蓄、抗旱等社会需求的增加,针对这些地区降水云系的人工增雨研究显得愈发重要。使用三维中尺度冷云催化模式,对2018年10月21日湖北省一次层状云飞机人工增雨作业过程进行了数值模拟研究,并将模拟结果与卫星、降水和机载云物理观测数据进行了对比。模式合理地模拟出了云和降水的主要宏、微观特征,观测和模拟结果均显示作业云区具有较好的冷云催化条件,在此基础上,按照实际作业中的飞机播撒轨迹,完整地模拟了此次催化作业过程。对数值模拟结果的分析表明:凝结冻结核化和凝华核化是碘化银催化剂的主要核化方式;90%以上碘化银粒子的局地活化比为0.01%—2%,平均活化比为0.07%—0.27%;云系降水是由冷云降水和暖云降水两种机制共同作用的结果,催化作业使两种降水机制均有增强,增雨效果明显;催化后4 h,整个评估区内的累计净增雨量为2.12×108 kg,局地增雨率为?51.1%—306.7%,区域平均增雨率为8.1%;催化作业也使部分地区出现减雨,主要是由于催化过程中的潜热释放引起过冷层动力场扰动,一部分云区的上升气流减弱,从而导致降水粒子的成长减弱,地面出现减雨;在过冷云区,碘化银核化使冰晶浓度升高,导致冰晶-雪、雪-霰的转化过程增强,雪、霰粒子总量增加,更多的雪、霰粒子从冷区落入暖区,在暖区上层产生更多的大雨滴,从而使暖区的云雨粒子碰并过程增强,最终地面降水增加,这是此次催化作业导致增雨的主要微物理链条。      

渭南市人工增雨作业技术指标与判据

利用渭南市1997—2006年10a 46次人工增雨过程的711雷达回波、天气形势、地面观测、实况等资料,结合近2a的多普勒雷达产品资料进行统计、对比、分析,得出渭南市高炮火箭人工增雨作业技术指标和判据。西风槽是主要影响天气系统;增雨时段2—9月,以3—7月为主;作业主要云系为层状云、对流云和混合云;首次分析出层状云0oC层亮带变化规律,强度20~35dB z,厚度0.3~0.5km,亮带高度随季节变化;根据不同云系的回波判据确定作业时机、部位、方式及用弹量;回波的移向移速也是确定作业时机的重要判据;得出多普勒雷达产品在人工增雨应用中的简易指标。     

江苏盛夏飞机人工增雨作业的雷达气象学分析

本文对盛夏季节33次飞机人工增雨作业过程作业前后的天气雷达回波资料、天气背影和地面降水资料进了分析,发现作业效果明显的云系主要是：浓积云或浓积云群;复合型层状云;积层混合云;其中最明显的是积层混合云。通过分析,提出了一套飞机对冷云部位进行人工增雨作业的具体指标和作业方式。     

新一代可移式天气雷达在人工影响天气中的应用研究

在总结“十五”国家科技攻关重大计划项目“人工增雨技术研究及示范”成果的基础上,利用我国可移式新一代天气雷达在青海省河南县和河南省许昌市进行秋(春)季降水系统中尺度结构外场试验观测的方法和技术,对这两个地区云和降水的若干特征进行了分析;另外,使用两步变分方法反演了风场结构,分析了层状云和对流云的中尺度回波强度和动力结构。结果表明:新一代天气雷达可为人工影响天气作业指挥和云物理研究提供更多信息,包括风场中尺度结构、辐合线位置等;青海省河南县及周边地区秋季降水以对流云降水为主,低空辐合是对流云旺盛发展的重要原因;河南省许昌市春季降水既含有对流云降水也含有层状云降水,对流云降水过程伴有低空辐合,层状云内风场比较均匀,但风的垂直切变明显,多为暖平流。     

层状云结构和降水机制研究及人工增雨问题讨论

总结了层状云及其降水物理研究的部分成果。在此基础上, 讨论了层状云人工增雨的几个问题, 提出用常规观测资料判断人工增雨条件的方法。具体结果如下:层状云结构是不均匀的。层状云系在垂直方向上具有分层结构。“催化—供给”云是降水性层状云的典型结构, “催化—供给”云相互作用是导致降水的主要过程。按微观结构可以将降水性层状云分成3 层:冰相层、冰水混合层和液水层。冰相层是催化云, 冰水混合层和液水层是供给云。层状云降水过程研究表明, 对应于层状云或“催化—供给”云的3层宏观结构, 发生着不同的微物理过程, 粒子形成和增长过程也不同。冰相层的冰晶和雪, 凝华是其主要增长方式, 其次是雪与冰晶的聚合过程;雪(或聚合体)落入冰水混合层后, 继续通过凝华增长或贝吉龙过程增长, 同时撞冻过冷云水增长, 有部分冰雪晶通过撞冻增长而转化成霰。在液水层, 雪(或聚合体)霰开始融化, 同时收集云暖区云水增长。冰相粒子的撞冻增长过程和凝华增长过程相比同样重要。层状云各层对降水的贡献不同。一般而言, 对于“催化—供给”云, 催化云对降水的贡献低于30%, 供给云在70%以上。在以上研究的基础上, 讨论了层状云人工增雨的问题。(1)“催化—供给”云结构有利于云水转化成降水, 只有冰相层、冰水混合成和液水层相互“配合”, 才能形成有效降水。可以将“催化—供给”云作为层状云人工增雨催化的结构条件。(2)要选择降水形成以冷云过程为主的层状云催化, 冰面饱和水汽量和过冷水含量要大些。(3)层状云人工增雨原理应该补充。降水形成不但经历贝吉龙-芬德森过程, 冰水混合层的聚合和撞冻增长也是十分重要的过程。过冷水对于降水的形成非常重要, 但冰面饱和水汽量对降水的形成也同样重要。最后, 结合层状云的研究成果, 提出用常规探测资料判别层状云人工增雨催化条件的方法:利用卫星云图和雷达回波判别“催化—供给”云的结构, 用雷达RHI 回波(在距离高度显示器上的回波)判别降水机制和液水层。       

基于雨滴谱的增雨作业微物理特征分析

针对2018年3月16—17日四川盆地西北部一次典型层状云降水天气过程,利用云模式、卫星、雷达、探空等资料,分析了人工增雨作业条件和物理响应。同时,基于雨滴谱观测数据开展了人工增雨作业微物理特征分析,重点研究了粒径速度谱分布特征。结果表明:人工增雨作业后,不同粒径、不同粒速的粒子数变化明显,大滴粒子显著增多,雨滴谱变宽,0.437～1.625mm粒径谱和1.5～5.2m/s速度谱明显变宽;同时,粒径和粒子速度无明显跃变,区间值分布相对集中在粒径0.312～2.125mm、粒速1.1～6.8m/s,人工增雨作业对降水粒子的粒径和粒子速度改变有限。人工增雨作业影响时段,降水强度和雨含水量以及总数浓度和有效粒径,依然保持与自然降水时段各自峰值、谷值、震荡态势的一致性,人工增雨作业不改变各物理量之间的相关性。     

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;