榆林市辐射型浓雾天气成因及维持机制个例分析

利用逐5 min地面观测资料、探空资料、风云四号卫星云图以及NCEP 1°×1°再分析资料,分析2020年2月1—2日出现在榆林市的一次浓雾天气成因及维持机制。结果表明：此次浓雾属于辐射雾,发生在500 hPa为较平直纬向气流,700 hPa和850 hPa盛行弱偏北风,地面处于均压场中的大尺度环流背景下。大雾出现前雾区有降雪,降雪后空气湿度达到饱和,地面维持3 m/s以下弱偏北风,夜间辐射降温,气温下降至露点温度,饱和水汽凝结成小水珠,大雾得以形成和发展;雾区上空850 hPa上逆温层稳定存在,影响动量的垂直交换,使得水汽在近地层长时间集聚,是浓雾得以维持12 h的主要原因;日出后地面气温回升,近地面动量下传和冷空气入侵,垂直扩散增强,浓雾得以快速消散。分析浓雾期间动力和水汽条件发现,大雾出现前,水汽在雾区上空辐合,为大雾的形成提供了水汽基础;大雾维持阶段,雾区上空层结稳定,近地面有逆温层存在;大雾消散阶段,逆温层被破坏,低层转为辐散气流,浓雾快速消散。     

基于多种资料对成都辐射平流雾生消机理分析

本文利用风廓线雷达、地基微波辐射计、地面自动站、秒级探空和NCEP/NCAR(1°×1°)再分析资料,对2016年12月成都的3次辐射平流雾过程进行生消机理分析。结果表明:①高空500hPa受西北气流控制,700hPa干层和1.5km下弱风速的维持,利于近地面快速辐射冷却形成逆温层,为雾的形成提供了热力条件。②垂直方向上无运动或微弱运动,为雾的形成和发展提供了动力条件;贴地层弱西南暖湿气流(≤4m/s)携带的水汽在成都地区辐合,为强浓雾的形成提供了充足的水汽条件。③贴地逆温层和逆露点温度层(高度低于逆温层高度)同时存在,对雾的预报有较好的指示意义。④午后,双逆温层的维持,近地面风速接近静风,是辐射平流雾维持的重要原因。⑤西南暖湿气流消失或水汽辐合移出本地之后,近地面逆温层消失或雾顶上部干空气的卷入,是导致辐射平流雾最终消散的原因。     

陕西冬季一次大雾天气生消机制的数值模拟

利用非静力平衡中尺度模式WRF、NCEP 1°×1°再分析资料及常规观测资料,对2005年12月30～31日发生在陕西的大雾天气过程进行了数值模拟,分析了大雾天气过程形成的主要原因及雾的生消机制。结果表明,WRF模式能较好地模拟出雾的水平分布特征、强度和生消过程,反映出实际雾的生消变化规律。适当提高模式水平分辨率能较明显地改进模拟效果。这次大雾为平流辐射雾,长波辐射冷却是大雾形成和发展的主要原因。逆温层的发展、维持和近地面层较高的相对湿度对雾的产生和发展起着重要作用。近地面层有弱的水汽辐合是大雾发展和维持的主要原因之一。大雾形成和发展阶段,900 hPa以下的辐合上升运动和900 hPa以上的辐散下沉运动有利于在上升和下沉运动区的界面层中形成逆温层,逆温层的形成有利于低层水汽的积累。随着高空转为辐合上升运动,900 hPa以下为辐散下沉运动,接着日出后,太阳短波辐射增温等的共同作用,使逆温减弱直至被破坏。中高云的存在影响了近地面层逆温的形成和加强,推迟了雾的形成和消散。暖平流的输入有利于逆温层的形成发展。     

江西一次大雾天气诊断分析和ECMWF集合预报产品释用

利用常规气象资料,对2013年11月15日出现在江西省北部地区的一次大雾天气过程进行了诊断分析,并利用ECMWF集合预报产品对该大雾天气过程的预报进行了解释应用。诊断结果表明,该次大雾天气过程是一次典型的辐射雾。14日傍晚到夜间,江西省北部地区转处冷高压控制,阴转晴,冷空气和地面辐射共同造成的冷却作用明显;大雾发生时的逆温层高度大约在981 hPa;1—3 m/s的风速有利于形成较厚的冷却层;地面相对湿度大,水汽充足。通过对ECMWF集合预报的气温、地面湿度、地面风速和天空总云量预报产品的释用,可以在大雾出现的前日判断出江西省西北部地区同时满足辐射雾出现4个条件(水汽、晴空辐射冷却、微风、近地层的稳定层结)的概率最大,因此出现大雾的可能性最大;江西其他地区不能同时满足4个条件,出现大雾天气的可能性很小。随着对集合预报产品的不断深入挖掘,可以进一步提高集合预报对大雾等灾害性天气的预报能力。     

北京地区一次典型大雾天气的空气污染过程物理量分布特征

2002年11月30至12月4日，北京持续4天大雾天气，空气污染物在持续的稳定层结条件下，空气质量连续3天达5级以上。文中分析了大雾天气各主要污染物的变化特征，以及此次过程中天气形势的特点及演变，并对造成大雾日空气污染天气的物理量分布特征进行分析。结果表明：高空WNW气流、稳定性持续增加、逆温层结持续存在、低空风速较小、相对湿度大，导致局地污染物不能及时随大气扩散；1000～700hPa有弱的上升气流形成和维持，与500hPa高空下沉气流之间在低空的某层高度上形成稳定层结(逆温层)，导致大雾及重污染的形成；850hPa为暖区，850～500hPa为冷平流，有利于大雾的形成和加重。     

内蒙古兴安盟一次大雾天气过程特征

2013年10月21—22日内蒙古兴安盟大部分地区出现了能见度不足1000m的雾,本文利用MICAPS系统下常规资料、探空资料以及NCEP2.5°×2.5°再分析资料对这次大雾过程做了分析。结果表明:此次大雾天气过程共持续2天,在21日白天和22日白天强度最强,能见度最差,最小能见度不足100m。大雾是发生在500hPa西南气流,850hPa暖脊中,地面则表现为低压前部和弱高压场中。东南气流的暖湿空气输送和逆温层的稳定存在为大雾的维持提供了有利条件。高空冷涡前部强上升运动区,使近地面层的湿度条件减弱是大雾消散的主要原因。     

2012年河南省中东部一次大雾成因分析

利用气象台站观测资料和NCEP 1°×1°再分析资料,对2012年1月10日河南中东部一次较大范围的大雾天气过程进行分析,以揭示大雾的成因。结果表明: 1)高层环流平直,中低层受高压脊前弱偏北气流控制,地面均压场及弱冷空气活动,是此次大雾形成的环流背景条件。2)T-lnp图上,各项不稳定指数及状态曲线与层结曲线显示,大雾区整层大气处于稳定层结状态,且t( 925 hPa -地面)≥2℃、湿层（t-td≤4℃）顶部高度高于1000hPa (即海拔高度250m以上)。3)边界层暖平流的输入有利于逆温层结的建立以及大雾的加强与维持,冷暖平流间的零平流区能较好区分大雾区与非雾区。4）T639预报场中,近地层逆温区、地面风速≤4 m/s区域、地面相对湿度超过90%区域与近地面微弱上升运动区的重合区即为大雾易发区域。应用数值预报可较好预报区域性大雾。     

山东省一次大雾气象条件分析

应用常规资料对2009年11月30日-12月2日发生在山东省的一次大雾天气进行分析,结果发现:高空弱的偏西-西南气流及中低层弱的暖湿气流,近地面附近有逆温层,地面存在地形槽或均压场,为有利于大雾形成的环流形势场.t-td≤2℃,近地层相对湿度80％以上,地面风速0～3 m/s,500 hPa以下有弱辐合区,上升速度为0×10-3 hPa·s-1,假相当位温处于低值区等是大雾形成的主要原因.当风速加大、上升速度增强、湿度增加、有降水产生时,或者风速加大、上升速度较小、湿度迅速降低时,均会促使大雾消散.     

成都冬季雾在夜间探空资料上的反映

本文利用成都机场1985～1990年冬季近600个逐日实况及19点探空资料对成都冬季雾的统计分析发现,夜间存在近地面逆温层使出雾时间提前4～15分钟,大雾形成时间推迟20分钟左右;夜间存在中空逆温层时对大雾减弱成中雾的时间影响不大,而使中雾消散或减弱,为轻雾的时间推迟50分钟～2小时20分钟;夜间少云时,第二天成雾率为85～90％;夜间多云时,当空间(900—600hPa)平均最小(T—T_d)<1℃时,第二天出大雾;当(T—T_d)≥2℃时,第二天出中雾。其结果可为成都冬季雾的定量化预报提供参考。     

乌鲁木齐近31年大雾天气气候特征分析

统计分析了1976-2006年乌鲁木齐大雾天气的气候特征,乌鲁木齐大雾多出现在夜间,持续时间以3小时之内为多.31年来年平均大雾日为29.5天,一年中大雾主要出现在冬季11月至次年3月;冬季大雾的持续时间最长.31年来大雾日随着年代的推移总体呈逐渐减少的趋势,平均以1.9天/10年的速度减少,1990年代大雾日明显偏少,进入21世纪后大雾日又迅速增多.当气温为0～-10℃、相对湿度85%～95%、风速0～3m·s-1、气压910～925hPa时,出现大雾的频率最高;冬季大雾天气出现在逆温层底部距地高度低、逆温层厚度大、强度强的低空温度层结条件中.     

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;