河北省霾日变化及成因

利用1981-2010年河北省142个气象站地面观测资料,按照霾的观测和预报等级行业标准,分析河北省霾日空间分布特征.以石家庄为例对多霾日成因进行探讨。结果表明：河北省霾日主要分布在太行山东麓地区,霾日高频次中心随时间范围在扩大,频次有增加趋势;典型代表站霾日随时间呈现增加、减少和平稳变化三种态势,单站间变化差异明显;河北省霾日主要以能见度大于5km轻微霾为主,代表站中度、重度霾日季节变化显著,64%以上出现在秋冬季节;霾的形成是多种因素综合作用的结果,区域分布主要与地理环境有关,气候上逆温层、风速、相对湿度、年降水日数与霾的形成有关,另外地方经济发展、人类社会活动对大气污染物的排放起到一定作用。     

广州地区低能见度事件变化特征分析

使用2004—2007年广州地区番禺、东山和南沙站3套能见度仪和3套自动气象站的逐时能见度、相对湿度等气象要素资料,通过对比研究,分析了3地低能见度事件的年、季和日变化等变化规律。研究表明,近年来广州地区轻雾(雾)出现频率总体较低且日数偏少,霾天气高发期在10月—次年4月、7月极少出现,年变化特征明显。番禺低能见度事件以5～8 km的情况居多,东山和南沙则以8～10 km的轻度视程障碍为主;但同期番禺≥10 km的日数百分比增长最显著,意味着当地能见度有明显好转。低能见度事件多见于70%≤RH90%的中高相对湿度范围,RH30%的低相对湿度情况下未曾出现视程障碍现象;番禺和东山的低能见度事件有随RH减少而增多的趋势,南沙则大致相反。对比分析有代表性的旱、雨季发现,(极端)低能见度事件多在早晚发生,日间能见度低值区则一般出现在正午前后,日变化特征明显,且旱季更显著,同时以番禺最具代表性。总体上广州地区的低能见度事件呈逐年减少的趋势。     

基于地基微波辐射计观测的东营地区低能见度天气指示性分析

针对东营市2016—2017 年出现的27 个低能见度天气过程,利用 MP -3000A 型地基微波辐射计二级数据,计算过程影响期间逐10 min 的逆温层温差、逆温层厚度和低层相对湿度等物理量。 结合空气质量和能见度变化情况,按照雾和霾、雾、降水三类天气对 27 次过程进行分类研究,总结地基微波辐射计观测的温湿度量对低能见度天气的指示意义和参考指标。结果表明:(1)雾和霾共同影响导致的低能见度天气出现在冬半年,PM2. 5浓度越大通常对应的能见度越低;逆温层温差和逆温层厚度与能见度的相关系数分别为-0. 39 和-0. 45,逆温层温差增大指示能见度降低,逆温层厚度减小指示能见度升高。低层相对湿度在 90%以上时,能见度受雾影响通常小于 2 km;低层相对湿度在80%以下时,能见度受霾影响仍然维持在5 km 以下。(2)雾影响导致的低能见度天气多出现在冬半年,与 PM2. 5浓度无关;逆温层温差和逆温层厚度与能见度的相关系数分别为-0. 54和-0. 45。在逆温层生成和破坏阶段,逆温层温差变化幅度大,对能见度的指示性更强,而逆温层厚度变化幅度相对较小,多维持在 300 ~ 400 m 之间。低层相对湿度 90%以上时能见度通常小于5 km,当低层湿层消失后能见度升高至 5 km 以上。(3)降水影响导致的低能见度天气出现在夏季,多伴随短时强降水出现,强降水时段逆温层温差达到8 ℃ 以上,逆温层厚度为 500 m;强降水结束后,逆温消失,能见度转好。     

盖州市大气能见度与气象条件相关分析

利用2011—2012年盖州市大气能见度和地面气象要素(相对湿度、风速、气温、气压)的观测资料,分析了盖州地区大气能见度月和日的变化特征及大气能见度与气象要素的相关性。结果表明:盖州市大气高能见度事件多出现在3月和10月,低能见度事件多出现在6—8月;夏季能见度最低,14时能见度最大,20时能见度比08时略小。大气能见度与相对湿度相关性最大,与风速和气温相关性次之,与气压相关性最差;当相对湿度80.0%时,能见度最低值为10.4±3.2km,大气能见度与气压、气温、相对湿度的相关系数分别为-0.52、0.51和-0.52;其中较高的气温、较大的相对湿度、较小的风速及较低的气压是盖州地区低能见度(10km)事件发生的主要气象条件。     

大连地区能见度影响因子及个例分析

为探讨大连市大气能见度特征及其影响因子,揭示低能见度天气成因,利用2010—2012年大连地区大气能见度与地面气象要素(相对湿度、风速、气温、气压)日均值的统计资料,分析了大连地区大气能见度与气象要素的相关性。进一步结合PM_(10)质量浓度的变化特征,分析了两次低能见度事件中的天气成因。结果表明:2010—2012年大连地区年均能见度分别约为13.5 km、13.2 km和13.9 km,高能见度事件多出现在10月—次年2月,低能见度事件多出现在每年6—8月,大连地区低能见度事件每年7月较多,1月较少,2010—2012年大连地区低能见度事件分别出现169、157 d和163 d;2010—2012年PM_(10)质量浓度分别为57.8μg·m~(-3)、67.4μg·m~(-3)和65.9μg·m~(-3),PM_(10)质量浓度高值多出现在每年的4—5月和9—12月,PM_(10)质量浓度低值多出现在1—2月;大气能见度和相对湿度和气温的相关性较好,随着相对湿度的增加,能见度与PM_(10)质量浓度的相关性逐渐减小,当相对湿度大于90%时,能见度与PM_(10)质量浓度相关系数减小至-0.23;两次低能见度事件过程中,2011年10月31日一次辐射平流雾过程中的水汽输送来自西南风气流,2012年4月28日一次浮尘事件过程中的沙尘来自西北方向的沙源。该研究可为空气质量预报提供科学依据参考。     

太行山东麓焚风天气的统计特征和机理分析Ⅰ:统计特征

利用2007-2008年京津冀区自动站资料,根据小时变温,对太行山东麓焚风天气进行了详细的统计和分析。结果表明,焚风具有明显的季节变化和日变化,其分布特征在夜间表现明显,强焚风一般出现在太行山东侧50km内,而弱焚风则可到达太行山以东100km范围内;太行山北段的东南侧和南段的东侧呈现出两个焚风中心,而中段附近焚风出现的相对较少;焚风出现的位置与风向的关系密切,西北风造成的焚风主要出现在太行山北段东侧,偏西风主要影响太行山南段,而西南风主要影响中段;焚风强度与风速大小有一定的对应关系,具有明显的阵性特征;在红外云图中焚风常表现为一条明显的暗带;太行山东麓焚风造成的加热区可向东或东南方向移动,影响河北平原。     

华北地区雷暴大风的时空分布及物理量统计特征分析

利用2011—2015年4—9月华北地区主要区域(北京、天津、河北、山西)的重要天气报和雷暴观测资料,统计分析了该地区雷暴大风的时空分布等特征。结果表明,华北地区雷暴大风出现最多的月份为6—7月,最多的时次为下午到前半夜,大范围雷暴大风天气过程起始时间多为13:00(北京时,下同)-15:00,持续时间为4~8 h,高海拔地区出现雷暴大风的频次大于低海拔地区。在将华北地区站点分为高海拔站点和低海拔站点的基础上,使用2011—2013年4—9月的NCEP物理量分析场对雷暴大风过程的指示性进行统计分析,结果表明:多数常用的热力指标需考虑季节因素;下沉对流有效位能阈值基本不随季节变化,并对高海拔和低海拔区域的雷暴大风的出现及其范围均有一定的指示性;对流抑制能量、0~3 km垂直风切变、低层散度、500 hPa风场、整层可降水量、500 hPa相对湿度08:00—14:00变化等物理量在一些具体方面对于雷暴大风的出现及范围有一定的指示性。主要发生在高海拔地区的雷暴大风天气过程,850 hPa的相对湿度均在50%以下;主要发生在低海拔地区的雷暴大风天气过程,850 hPa的相对湿度基本在50%以上;850 hPa相对湿度较大的大范围雷暴大风天气过程,850 hPa和500 hPa的温差在24~28℃,850 hPa相对湿度较小的大范围雷暴大风天气过程,850 hPa和500 hPa的温差则常常达到30℃或以上。     

河北省致电网事故的气象灾害特征及风险评价

利用1983~2008年河北省灾情直报数据,对造成河北电网安全事故的气象灾害进行统计。大风、暴雨、雷电是造成河北省电网安全事故的主要气象灾害,占事故总数的98.5%。致电网安全事故的气象灾害主要出现在夏半年5~9月,且年变化显著。应用气象灾害风险指数方法,对与电网事故有关的气象灾害进行风险评价,分为高、中、低3个风险等级。结果表明:致电网事故的气象灾害高风险区集中在燕山南麓的唐山和太行山东麓北段的保定;中度风险区出现在太行山东麓中南段的邢台、邯郸,还有沿海的沧州地区。中高风险区主要集中在受地形影响局地强对流天气较多的区域,还有沿海多大风的地区。     

一次持续性雾霾天气过程的阶段性特征及影响因子分析

应用常规与非常规气象观测资料及PM2.5浓度监测资料,对2013年1月20~24日山西区域一次持续性雾霾天气过程进行分析。研究发现：（1）本次雾霾天气过程具有明显的阶段性特征。2013年1月20日14时至23日11时,由于相对湿度的变化导致了3次轻雾转大雾过程;23日14~20时,由于PM2.5浓度的增大经历了1次轻雾转霾的天气过程。（2）地面弱的气压场和较小的风速以及PM2.5浓度的上升和相对湿度的增大为本次持续性雾霾天气过程的形成和发展提供了有利条件。（3）边界层逆温的存在是雾霾低能见度过程形成的必要条件,边界层有逆温层而不出现雾霾天气的条件是：相对湿度〈50%,PM2.5日均值浓度〈75μg·m-3;逆温层下相对湿度的大小是区别雾和霾天气的指标。（4）相对湿度和PM2.5是决定能见度大小的关键因子,其对能见度的影响体现出明显的阶段性特征,当相对湿度〈90%时,PM2.5浓度对能见度的作用强于相对湿度,是影响能见度变化的主要因子,但随着相对湿度的增大,其对能见度的影响相对增强,当能见度降至1 km以下时,相对湿度成为影响能见度变化的主要因子。     

京津冀及周边地区冬季能见度与PM2.5浓度和环境湿度的多元回归分析

2013年至今,中国冬季与雾霾相伴的低能见度事件频发,京津冀及周边地区尤为严重。PM_2.5浓度与环境湿度是导致低能见度的最关键影响因素。为了深入研究PM_2.5浓度与环境湿度对大气能见度的影响,利用2017年1月京津冀及周边地区MICAPS气象数据与PM_2.5观测数据,运用天气学诊断分析方法讨论了不同相对湿度下PM_2.5浓度、环境湿度对冬季能见度变化的相对贡献,按照地理环境与污染程度差异将京津冀及周边地区划分为北京-天津地区与河北-山东地区,建立了PM_2.5浓度与环境湿度（由露点温度、温度代表）对能见度的多元回归方程,并对2015、2016、2018、2019年冬季能见度进行了回算检验。结果显示:相对湿度低于70%、PM_2.5浓度低于75 μg/m3时,北京-天津地区与河北-山东地区能见度多高于10 km,PM_2.5浓度升高是此时能见度迅速降低的主导因素;相对湿度从70%上升至85%和PM_2.5浓度从75 μg/m3升高200 μg/m3的共同作用导致了能见度降低到10 km至5 km;能见度进一步从5 km下降至2 km则更多依赖于相对湿度进一步从85%升高至95%,PM_2.5浓度与此时能见度相关减弱;能见度降低至2 km甚至更低主要是由于水汽近饱和状态下（相对湿度95%以上）的雾滴消光引起,与PM_2.5浓度的变化关系不大。与不分组直接拟合相比,以相对湿度85%为界线,分别拟合能见度能够很大程度优化多元回归模型,相对湿度高于85%时能见度拟合值的均方根误差从9.2和5.2 km下降至0.5和0.7 km,5 km以下拟合能见度的误差大幅度减小。按相对湿度85%将数据分组所得的拟合方程对2015、2016、2018、2019年1月能见度估算结果较好,观测值与拟合值相关系数均高于0.91,为雾-霾数值预报系统提供了新的能见度参数化算法。      

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.     

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     

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.     

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.     

Information for Authors

正AIMS AND SCOPE Atmospheric and Oceanic Science Letters (AOSL) publishes short research letters on all disciplines of the atmosphere sciences and physical oceanography.     

Information for Authors

AIMS AND SCOPE
Atmospheric and Oceanic Science Letters （AOSL） publishes short research letters on all disciplines of the atmosphere sciences and physical oceanography. Contributions from all over the world are welcome.     

Information for Authors

AIMS AND SCOPE Atmospheric and Oceanic Science Letters （AOSL） pub- lishes short research letters on all disciplines of the atmos- phere sciences and physical oceanography. Contributions from all over the world are welcome.     

INFORMATION FOR AUTHORS

正Aims Scope Advances in Atmospheric Sciences(AAS)is an international journal on the dynamics,physics,and chemistry of the atmosphere and ocean with papers across the full range of the atmospheric sciences,co-published bimonthly by Science Press and Springer.The journal includes Articles,Note and Correspondence,and Letters.Contributions from all over the world are welcome.