甘南地区一次强对流天气的多普勒雷达特征分析

针对甘南地区2006年7月12日出现的冰雹天气过程,利用兰州CINRAD/CC多普勒雷达回波资料以及MICAPS资料对此次强对流天气过程进行了初步的分析,分析了冰雹天气过程中雷达回波的形态特征、结构和动态特征,发现在此次强对流天气是雷达回波具有明显的超级单体风暴特征,且呈现出三体散射、有界弱回波、钩状回波等特征;雷达回波强度值55dB;回波顶16km;径向速度图上出现较强的气旋性辐合,在中高层辐合中还存在着中尺度气旋;此外,降雹过程前后垂直液态水含量(VIL)变化较大。     

2010年福建一次早春强降雹超级单体风暴对比分析

利用探空、地面资料以及建阳、龙岩、长乐三部新一代天气雷达资料,对2010年3月5日福建中北部地区5cm强降雹的两个超级单体风暴进行了对比分析。结果表明,干暖盖、强垂直风切变、中高层正涡度区及地面中尺度低压为超级单体的形成提供了良好的环境场。两个超级单体都是由多单体合并后发展起来的,在成熟阶段以右移为主,属长寿命右移风暴:第一个超级单体在发展过程中由于地形作用和新单体的并入经历了3次加强过程,低层出现明显的钩状回波、中高层三体散射特征;第二个超级单体经历了多单体风暴—超级单体风暴—多单体风暴3个阶段,成熟阶段低层呈现出明显的倒"V"形回波特征,中高层有明显向右伸展的云帖。两个超级单体风暴的中气旋都是由中层发展起来,随着中气旋强度不断加强和厚度加大,最强切变中心突降时出现冰雹、大风强对流天气。通过对第一个超级单体中气旋流场分析,发现风暴前、后侧的下沉气流与低层入流形成了明显的辐合旋转作用,下沉的干冷气流进一步推动低层的暖湿入流,形成强烈的上升气流,并在风暴顶形成强辐散,使得风暴长时间维持。第二个超级单体在风暴减弱阶段,风暴右侧出现中气旋分裂,之后减弱、消失。产生强对流天气时,中高层维持高反射率因子,出现三体散射现象、风暴顶强烈辐散以及较大的VIL密度等特征。     

一次超级单体的多普勒雷达特征分析

运用长沙多普勒天气雷达的基本反射率因子(R)、径向速度(V)、垂直积分液态含水量(VIL)、垂直剖面等产品,分析了2008年4月8日发生在湘东地区的一次超级单体强对流天气过程。分析表明:这次过程是一次典型的超级单体过程,在反射率因子上出现了钩状回波、三体散射回波、弱回波、高悬垂回波,相应的径向速度图上出现了持久的中气旋,风暴顶表现为强烈的辐散,降雹前相应的液态垂直累积含水量有一个跃增,这些多普勒雷达特征均为超级单体风暴预警提供了有利的临近预报因子。     

2009年9月2日包头市强对流灾害性天气雷达回波特征分析

通过对强雹暴灾害性天气雷达回波形状、有界区域的分析,发现强雹暴云团雷达回波,具有强降水超级单体的特征,加强超级单体、飑线风暴等强对流天气的分析研究,对做好强对流天气的预报、预警,具有重要意义。     

一次强对流系列风暴个例的多普勒天气雷达资料分析

讨论了新一代（多普勒）天气雷达对2002年5月14日19:00至21:00影响湖南常德地区的3个对流风暴的探测情况，其中两个为超级单体，一个为飑线。观测到了与超级单体相联系的中气旋和龙卷式涡旋特征（TVS）。上述强对流系统产生了地面大风、大冰雹和龙卷等强烈天气，与雷达的探测相吻合。特别值得一提的是在中国首次探测到了三体散射（TBSS）和龙卷式涡旋特征（TVS）这两个分别指示大冰雹和龙卷的雷达回波特征，并得到了地面报告的印证。     

一次超级单体风暴的天气学成因及多普勒雷达回波特征研究

对2002年5月27日发生在安徽蚌埠地区的一次超级单体风暴过程的天气形势、卫星云图、雷达回波进行了分析,此次过程为该地区近50 a来发生的最强烈的一次超级单体风暴过程。本文尝试从常规的高空、地面环流形势中寻找特殊的异常信息,结合卫星云图、多普勒雷达资料对超级单体的结构及其演变过程进行分析。发现这次强对流性超级单体风暴过程是在较为有利的大尺度环流形势下,多尺度天气系统相互作用,由中尺度对流云体激发产生的超级单体风暴;文章揭示了该地区超级单体风暴的多普勒雷达回波典型特征,旨在对这类强天气的监测、识别和临近预报提供天气学及雷达回波分析的参考依据。     

关中北部一次冰雹天气不同雷达特征对比分析

利用常规气象观测数据、ERA-5再分析资料、两种波段多普勒雷达产品、FY-2F云顶亮温TBB资料对2020年5月21日陕西一次强对流天气过程进行分析。结果表明：强对流发生在有利天气背景和环境条件下,对流云自北向南迅速移动发展增强,强天气区位于对流云后部TBB等温线密集处;C波段雷达产品在风暴演变过程中识别出了线状回波、三体散射回波特征,风暴单体具有明显的悬垂结构,单体质心短时间内迅速下降对应地面出现大风,三体散射回波在高低仰角依次出现对地面降雹有指示意义;X波段雷达偏振参量在风暴单体发展与成熟阶段有明显差异,发展阶段利用融化层以上的ZDR柱可以对风暴单体发展趋势做出预判,成熟阶段融化层以下RHV在08～09且ZDR和KDP随高度降低不断增大的区域为降落冰雹融化区,可以作为地面降雹的预判。     

2012年4月广东左移和飑线内超级单体的环境条件和结构对比分析

2012年4月开汛后广东省接连出现强对流天气,尤其是冰雹日数更是超过历史同期平均。本文利用常规天气观测资料和雷达、自动站等非常规资料对广东首次观测到的风暴分裂中左移超级单体风暴和飑线内超级单体风暴引发的两次强对流天气过程进行了对比分析。结果表明:"4·10"冰雹和雷雨大风天气是由局地强烈加热产生的"热雷暴"发展成超级单体风暴造成的;"4·12"冰雹、雷雨大风和短时强降水天气由飑线及飑线内超级单体风暴造成的,其产生于切变线、较强冷空气南下过程中的低层暖平流和中层冷槽共同作用的环境条件下,较强的平流过程使垂直风切变明显增大;两次过程中0℃层高度都低于4月当地0℃层高度平均值。风切变矢量随高度的变化决定了左移和右移风暴的发展趋势,"4.10"风切变矢量随高度逆时针变化,使风暴分裂后左移风暴得以发展成超级单体;"4·12"风切变矢量随高度顺时针变化,有利于有组织风暴即飑线和飑线内超级单体的形成和发展,超级单体向承载层平均风的右侧运动。左移超级单体回波具有中反气旋、弱回波区和旁瓣回波及强回波中心位于其移动方向左侧等特点;飑线内超级单体的中气旋、弱回波区和强回波中心位于回波移动方向右侧,三体散射长钉长度和中层辐合厚度都很大,后侧下击暴流产生了31.1 m·s~(-1)地面强风。     

三门峡市2006年两次下击暴流天气过程诊断分析

2006年5月26日和6月25日三门峡辖区均出现了以地面大风、冰雹为主的灾害性天气.利用常规观测资料和非常规加密探测资料以及雷达、卫星云图资料对这两次灾害性天气过程进行诊断分析,结果表明:这两次灾害性天气均是由500 hPa华北冷涡后部的下滑槽或横槽转竖带动北方冷空气急剧南下造成的下击暴流引发的强对流天气.当雷达回波顶高超过10 km、强度≥50 dBz并出现回波悬垂结构的超级对流单体或多单体超级对流风暴移来时,易产生强对流天气;强回波质心在移动过程中不断下降或回波悬垂结构、低层弱回波区出现在风暴后部时,是下击暴流发生的前兆;冰雹指数、风暴跟踪信息、中气旋3个产品叠加对强对流风暴发生、发展、移向的预报具有较好的参考作用.     

一次超极单体的多普勒雷达回波分析

2004年3月17日傍晚出现在黔西南州境内的一次强对流天气，其雷达回波是一次较为典型的多单体风暴，其中的两个超级单体生成和发展造成黔西南州普安县境内的70mm的大冰雹，贞丰县境内50mm的大冰雹和初春极为少见的1h内达32．8mm的强降水。文章仔细分析了这次强对流天气的回波图像，总结出一些在黔西南州出现的强对流天气的多普勒回波特征。     

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.     

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.     

VARIABILITY OF INTER-ANNUAL RELATIONSHIP BETWEEN INDIAN OCEAN DIPOLE AND HUAXI REGION’S AUTUMN PRECIPITATION

The moving-window correlation analysis was applied to investigate the relationship between autumn Indian Ocean Dipole (IOD) events and the synchronous autumn precipitation in Huaxi region, based on the daily precipitation, sea surface temperature (SST) and atmospheric circulation data from 1960 to 2012. The correlation curves of IOD and the early modulation of Huaxi region’s autumn precipitation indicated a mutational site appeared in the 1970s. During 1960 to 1979, when the IOD was in positive phase in autumn, the circulations changed from a “W” shape to an ”M” shape at 500 hPa in Asia middle-high latitude region. Cold flux got into the Sichuan province with Northwest flow, the positive anomaly of the water vapor flux transported from Western Pacific to Huaxi region strengthened, caused precipitation increase in east Huaxi region. During 1980 to 1999, when the IOD in autumn was positive phase, the atmospheric circulation presented a “W” shape at 500 hPa, the positive anomaly of the water vapor flux transported from Bay of Bengal to Huaxi region strengthened, caused precipitation ascend in west Huaxi region. In summary, the Indian Ocean changed from cold phase to warm phase since the 1970s, caused the instability of the inter-annual relationship between the IOD and the autumn rainfall in Huaxi region.     

A COMPARATIVE ANALYSIS OF ATMOSPHERIC AND OCEANIC CONDITIONS BEFORE THE OCCURRENCE OF TWO TYPES OF EL NIO EVENTS

The atmospheric and oceanic conditions before the onset of EP El Ni?o and CP El Ni?o in nearly 30 years are compared and analyzed by using 850 hPa wind, 20℃ isotherm depth, sea surface temperature and the Wheeler and Hendon index. The results are as follows: In the western equatorial Pacific, the occurrence of the anomalously strong westerly winds of the EP El Ni?o is earlier than that of the CP El Ni?o. Its intensity is far stronger than that of the CP El Ni?o. Two months before the El Ni?o, the anomaly westerly winds of the EP El Ni?o have extended to the eastern Pacific region, while the westerly wind anomaly of the CP El Ni?o can only extend to the west of the dateline three months before the El Ni?o and later stay there. Unlike the EP El Ni?o, the CP El Ni?o is always associated with easterly wind anomaly in the eastern equatorial Pacific before its onset. The thermocline depth anomaly of the EP El Ni?o can significantly move eastward and deepen. In addition, we also find that the evolution of thermocline is ahead of the development of the sea surface temperature for the EP El Ni?o. The strong MJO activity of the EP El Ni?o in the western and central Pacific is earlier than that of the CP El Ni?o. Measured by the standard deviation of the zonal wind square, the intensity of MJO activity of the EP El Ni?o is significantly greater than that of the CP El Ni?o before the onset of El Ni?o.     

IMPACT OF ATMOSPHERIC LOW-FREQUENCY OSCILLATION ON THE IMMIGRATION OF NILAPARVATA LUGENS(STL) IN HUNAN AND JIANGXI PROVINCES

Various features of the atmospheric environment affect the number of migratory insects, besides their initial population. However, little is known about the impact of atmospheric low-frequency oscillation(10 to 90 days) on insect migration. A case study was conducted to ascertain the influence of low-frequency atmospheric oscillation on the immigration of brown planthopper, Nilaparvata lugens(Stl), in Hunan and Jiangxi provinces. The results showed the following:(1) The number of immigrating N. lugens from April to June of 2007 through 2016 mainly exhibited a periodic oscillation of 10 to 20 days.(2) The 10-20 d low-frequency number of immigrating N. lugens was significantly correlated with a low-frequency wind field and a geopotential height field at 850 h Pa.(3) During the peak phase of immigration, southwest or south winds served as a driving force and carried N. lugens populations northward, and when in the back of the trough and the front of the ridge, the downward airflow created a favorable condition for N. lugens to land in the study area. In conclusion, the northward migration of N. lugens was influenced by a low-frequency atmospheric circulation based on the analysis of dynamics. This study was the first research connecting atmospheric low-frequency oscillation to insect migration.     

全球贸易隐含碳变化及其影响分析

基于最新的GTAP8 (Global Trade Analysis Project）数据库,使用投入产出法,分析了2004年到2007年全球贸易变化下南北集团贸易隐含碳变化及对全球碳排放的影响。结果显示,随着发展中国家进出口规模扩张,全球贸易隐含碳流向的重心逐渐向发展中国家转移。2004年到2007年,发达国家高端设备制造业和服务业出口以及发展中国家资源、能源密集型行业及中低端制造业出口的趋势加强,该过程的生产转移导致全球碳排放增长4.15亿t,占研究时段全球贸易隐含碳增量的63%。未来发展中国家的出口隐含碳比重还将进一步提高。贸易变化带来的南北集团隐含碳流动变化对全球应对气候变化行动的影响日益突出,发达国家对此负有重要责任。     

Retrieval of outgoing longwave radiation from COMS narrowband infrared imagery

Hourly outgoing longwave radiation(OLR) from the geostationary satellite Communication Oceanography Meteorological Satellite(COMS) has been retrieved since June 2010. The COMS OLR retrieval algorithms are based on regression analyses of radiative transfer simulations for spectral functions of COMS infrared channels. This study documents the accuracies of OLRs for future climate applications by making an intercomparison of four OLRs from one single-channel algorithm(OLR12.0using the 12.0 μm channel) and three multiple-channel algorithms(OLR10.8+12.0using the 10.8 and 12.0 μm channels; OLR6.7+10.8using the 6.7 and 10.8 μm channels; and OLR All using the 6.7, 10.8, and 12.0 μm channels). The COMS OLRs from these algorithms were validated with direct measurements of OLR from a broadband radiometer of the Clouds and Earth's Radiant Energy System(CERES) over the full COMS field of view [roughly(50°S–50°N, 70°–170°E)] during April 2011.Validation results show that the root-mean-square errors of COMS OLRs are 5–7 W m-2, which indicates good agreement with CERES OLR over the vast domain. OLR6.7+10.8and OLR All have much smaller errors(～ 6 W m-2) than OLR12.0and OLR10.8+12.0(～ 8 W m-2). Moreover, the small errors of OLR6.7+10.8and OLR All are systematic and can be readily reduced through additional mean bias correction and/or radiance calibration. These results indicate a noteworthy role of the6.7 μm water vapor absorption channel in improving the accuracy of the OLRs. The dependence of the accuracy of COMS OLRs on various surface, atmospheric, and observational conditions is also discussed.     

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;