一次冰雹天气过程的多源资料观测分析

利用地基微波辐射计、风廓线雷达和雨滴谱仪等观测资料,对2015年4月28日发生在南京的一次冰雹天气进行了分析,探讨新型探测资料在冰雹监测预警中的应用。结果表明:(1)华北冷涡后部冷空气南下,与低层暖湿气流交汇,是产生这次冰雹的天气背景;高空冷平流叠加在低层暖湿气流之上,使得对流层中低层形成不稳定层结;地面辐合中心及辐合线是降雹的触发机制。(2)微波辐射计监测显示,降雹期间冰雹云中上升气流将底层空气的感热和潜热向上输送,导致2 km以上大气有明显升温,由于低层水汽聚集及冰雹在近地层融化造成降雹时近地层相对湿度、水汽密度增大。冰雹发生在云液态水含量快速增长的波峰上,对冰雹的发生具有较好指示意义。(3)对比南京3站风廓线雷达资料表明各站上空环境风场存在一定差异,六合地区降雹前6 km高度高空急流有利于六合上空形成有利的辐散形势,降雹时0~6 km存在较深厚的垂直风切变,配合地面中尺度低压,降雹最为强烈;南京站降雹时,对流层中下层有一槽过境,而高淳地区冰雹由近地面垂直风切变激发。(4)六合站、高淳站雨滴谱仪分析表明不同降水相态对应的滴谱特征有差异,两站雨滴谱型分别呈指数型、多峰型分布。高淳站雨滴谱仪监测到直径达到15 mm的冰雹粒子,六合站冰雹直径最大为5 mm。两站速度谱大致为单峰型,在较强降水时刻,粒子下落峰值速度在3~4 m·~(-1)。(5)影响六合的超级单体存在钩状回波、回波悬垂、三体散射等雷达回波中尺度特征,地面中尺度低压系统、中低层的中气旋及高层的辐散环流配置造成了雹云中维持较强的旋转上升气流,有利于出现大冰雹。     

呼伦贝尔市雾的时空分布特征及天气分型

应用1960—2012年呼伦贝尔市雾日统计资料,对呼伦贝尔市雾日的时空分布特征进行了详细分析,并总结了雾的天气类型。结果表明:(1)呼伦贝尔市雾日的时间分布特征表现为,年际变化在均值附近波动,且呈现减少趋势;雾日季节性分布不均匀性明显,夏季7、8月是雾日最多的月份,秋、冬季节次之,春季4、5月是雾日最少的月份。(2)雾日的空间分布随地形、地貌差异很大,林区雾日明显多于牧区和农区,林区北部根河市年均日数4.14d,为最多,牧区西部的新巴尔虎右旗年平均日数仅1.69d,为最少。(3)雾的天气分型以高空影响系统分为7种类型,其中高压脊型、槽前型和冷涡(槽)底部型为雾天气的主要类型。     

张家界大雾的气候特征及影响因子分析

本文通过对1958～2008年张家界地区的永定、桑植、慈利三个地面观测站51年雾日变化的研究,发现其都存在6年左右的年际变化周期;一年中大雾天气最多出现在冬季和秋末;张家界地区全天都有出现大雾的可能,但主要出现在早上,到了13h出现大雾的几率开始变得非常小;不同的地理环境也使三个站的雾日分布不均.晴朗辐射降温、微风、近地面水汽充沛和出现逆温层是形成辐射雾的有利条件.     

北京地区一次持续性雾霾天气分析

2015年11月27日至12月1日,北京地区出现了一次十分严重的雾、霾天气过程。综合分析此次雾、霾天气过程的天气形势、加密自动站、探空、风廓线雷达以及连续观测的PM2.5资料,结果表明:本次雾、霾过程,能见度的恶化与天气形势、PM2.5持续性波动增长、相对湿度增加、逆温出现频率高、近地层风速小及近地面偏南风输送有密切关系:(1)此次雾、霾过程期间,华北地区较长时间被地面高压后部的弱气压场和低压辐合区控制,地面风速和近地层风速较小,北京大部分地区处于弱的偏南风或偏东风控制中,很不利于污染物的水平扩散;(2)地面增湿趋势明显,低层偏南和偏东气流将水汽和上游污染物向北京地区输送,加之29日气温明显回升,导致地面积雪融化,近地面相对湿度增加,有时接近饱和;(3)边界层逆温一直存在,很不利于污染物的垂直扩散。     

1961~2005年中国大雾天气气候特征

利用1961～2005年中国541个地面台站观测的能见度和相对湿度资料,分析了中国大雾时空分布特征和趋势变化特征.结果表明:中国大部分地区冬半年大雾日数明显偏多.夏半年明显偏少.其中11月最多,6月最少.在空间分布上,中国东部降水量较多的平原和丘陵年均大雾日数较多,而内蒙古大部和中国西部大部分地区年均大雾日数较少,多在1天以下.长江中下游和黄淮地区一些省市,是大雾天气多发的地区,并且具有明显正变化趋势,年大雾天气日数呈波动增多的趋势,波动的周期大约为1.5年.1982、1987、1989～2000年和2002年是大雾日数较多的年份,而1967年则是大雾日数明显偏少的年份.     

我国大雾的时空分布特征及其发生的环流形势

根据1971～2005年35年来714站大雾资料,统计了我国大雾的时空分布特征和环流形势.结果表明:年平均大雾最多的地区主要集中在四川盆地、重庆、云南南部、湖南和江南东部;雾日有明显的季节和月际变化,春、夏季雾的范围较小,秋、冬季雾的范围较大,内陆雾主要为(秋)冬季正态分布型,东北的雾夏季偏多,沿海雾春、夏季较多.雾通常开始于晚上20时(北京时间,下同)至次日早晨8时(以6～7时为最多),结束于8～12时,持续时间大多在1～10 h,持续3h的雾出现的频数最高.近35年雾日的线性趋势表明:江南、华南的雾日变化不明显,其余大部分地区的雾日都呈递减趋势,不同能见度的雾日在1985年前后基本上都呈相反的变化趋势,并且能见度越低的雾日变化越明显.主要考虑地面天气形势我国大范围大雾发生的环流形势可分为均压型和锋前型两大类型.     

韶关近18年霾的气候特征和典型天气类型

利用1991～2008年韶关市曲江站地面气象观测资料和NCEP 1°×1°的6 h再分析资料,分析韶关市霾的气候特征和典型天气类型。结果表明,近18年韶关市年霾日总体呈明显上升趋势,变化过程分为3个阶段,2006年开始的上升趋势是一种突变现象;韶关市11月霾天气最多,6月最少,并表现出秋冬季霾日多、春夏季少的分布特征。统计出现霾时的天气形势发现,地面风弱和气压场减弱都有利于形成霾,韶关市出现霾时以NW风最多;冷高压控制下的天气形势最有利于形成霾,其次是低压槽和热带气旋外围下沉气流。     

中国大陆1951—2005年雾与轻雾的长期变化

雾的记录有明确的天气指示意义。通过分析1951—2005年中国大陆743个地面气象站的资料, 对中国大陆雾、轻雾的长期变化趋势有如下认识：我国大陆雾日地理分布基本气候特征呈现东南部多西北部少的特点, 冬半年雾日数多夏半年少。各年代的差异在不同地区不尽一致。西南地区是我国雾日最多的地区，四川盆地一年有雾日20余天；华北平原和东北平原在冬春季节会出现严重的持续性雾天气。长江以南各省的轻雾日数明显多于长江以北地区，而且1980年代以后轻雾日有明显增加；西南地区是我国轻雾日最多的地区，四川盆地一年有轻雾日100余天。     

2009年南京冬季一次平流雾成因分析

利用常规气象观测资料和NCEP 1°×1°的再分析资料,对2009年12月1—2日南京地区一次浓雾天气过程产生的大尺度天气背景、气象要素及各种物理量进行分析,结果表明:高空弱脊和地面弱高压的控制有效抑制对流的发展,为这次浓雾的形成提供有利的环流形势;地面弱冷空气的影响,低层弱的辐合上升及中高层下沉增温作用,促使多层逆温存在,为雾的形成提供有利的层结条件;前期降水条件、近地层偏东风场及暖干盖作用为雾的形成提供了丰富的水汽条件;另外污染物集聚,也为雾的形成提供了丰富的凝结核。     

1960-2004年南京市风速变化及其成因研究

利用南京、高淳、江宁、江浦、六合、溧水六个地面站1960—2004年逐日平均风速数据资料,分析了南京市平均风速的时空变化特征及其可能原因。其结果表明:近45年来南京市年平均风速为2.61m/s,最大平均风速出现在3月,为3.08m/s,十月的平均风速最小,为2.45m/s。一年四季中,春季的平均风速最大,为2.93m/s,秋季平均风速最小,为2.50m/s,呈现出"春季大,秋季小"的季节分布类型。近45年间各季节与年平均风速的年代际变化趋势基本一致,都呈现出波动性变化及总体减弱的趋势。在空间上,南京市平均风速呈现出"南大北小"的地理分布格局。风速的变化,主要是由于全球气候变暖下中国乃至亚洲大气环流的变化而引起的局地环流的变化。此外,强冷空气南下次数和强度的减小、台风的减少、观测环境的影响、仪器性能的差异以及统计时次的变更等也是引起南京市风速变化的原因。     

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.     

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.     

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.     

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;