气象参证站的选取对AERMOD模型结果的影响分析

选取南昌经济技术开发区规划大气环境影响预测工作为案例,分析了当选取不同气象参证站,输入同期两套不同的气象数据参数对AERMOD模型计算结果的影响,探究气象条件的差异对大气扩散的影响程度。分析结果表明,气象参证站的选取对大气环境影响预测的结果有较大的影响,使用两个距离相近的地面气象站进行预测,污染物浓度计算结果相差可达数倍以上。对比风速、风向、气温、湿度、压力、云量这些单个的气象因子,稳定边界层(SBL)湍流高度和莫宁-奥布霍夫长度这些边界层参数更能综合地反映当时的气象扩散条件,对污染扩散模型的响应更为直接。翔实的有代表性的气象数据是环境影响评价至关重要的基础。在日常的大气环境影响评价中,应全面调查了解项目周边的地面气象站的数量、类型、距离、地形特征,经过专业的气象分析,选取最能代表评价项目所在地气象条件的气象站作为气象参证站,用于该项目的环评和浓度预测。各地气象部门还应加强城市与郊区大气边界层垂直结构变化规律的观测与研究,从而为提高各地空气质量预报的精度,改善大气污染治理措施和优化城市布局提供更为科学的依据。     

西安世园区化学危害品泄漏应急响应系统

介绍西安世园区化学危害品泄漏应急响应系统,该系统运用城市小区尺度气象和污染物扩散模式,利用高分辨率的街区建筑物布局及其高度、朝向和地表利用类型等GIS资料,在假定的气象条件和污染源强情景下,模拟计算出在该气象环境条件下的污染物质量浓度分布。     

2014年邯郸大气环境特征及气象条件影响因子分析

利用2013—2014年邯郸市环境监测站环保资料、邯郸气象站地面观测资料及邢台站探空资料,分析了邯郸大气环境特征及影响污染物扩散的气象条件,结果表明：空气重污染主要发生在秋冬季和初春季节;2014年影响污染物扩散的气象条件与2013年的大体相当,但2014年邯郸重污染日数减少,达标日数增加,污染物浓度较2013年的下降,重污染天气持续时间较2013年的明显减少,表明邯郸市实施的一系列减排措施效果明显。     

城市小区建筑物和屋顶绿化对小区气象环境影响的数值模拟研究

为了研究城市小区建筑物和屋顶绿化对小区气象环境影响,利用USDSM(城市小区气象与污染扩散数值模式)对杭州市一临钱塘江小区进行敏感性试验。结果表明:(1)小区建筑物的拖曳、阻尼和摩擦作用造成敏感小区内10 m高度平均水平风速减小0.56 m·s-1;对风速的影响范围:迎风向为500 m,背风向为500~600 m,侧风向为200 m,影响高度达到建筑物群平均高度的两倍以上。(2)小区建筑物存在导致低层污染扩散能力减弱,平均污染物浓度达到初始浓度0.01的时间延迟7.6 min。(3)100%的屋顶绿化面积可造成敏感小区平均地表温度下降0.56℃;5 m高度平均气温下降0.70℃;小区平均建筑物高度处气温下降0.94℃。敏感小区内10 m高度平均水平风速增加0.14 m·s-1;小区建筑物下游水平风速显著减小,影响范围达到了600~800 m。     

北京大兴国际机场不利气象条件下飞机尾气排放对大气环境影响的模拟研究北大核心CSCD

北京大兴国际机场作为吞吐量巨大的超大型国际枢纽,其运营过程中飞机尾气排放量大,对机场周边地区大气环境的影响不容忽视。为研究不利气象条件下飞机尾气排放对大气环境的影响,根据北京市环境空气数据筛选出2020年度不利气象条件时段,基于北京大兴国际机场2025年规划相关基础数据,采用排放与扩散模型系统(EDMS)建立飞机尾气排放源清单,开展不利气象条件下大气污染扩散模拟预测分析。气象分析结果表明不利气象条件时段存在小静风、高湿、厚度薄而强度大的接地逆温等特征,该时段北京地区经历以PM_(2.5)为首要污染物的重污染过程,并伴随着低能见度天气。模式预测结果显示在不利气象条件下摩擦速度和混合层高度等参数均处于较小的水平,大气污染扩散条件较差,飞机尾气对周边环境产生较大程度的影响,且污染物落地浓度存在昼间较低、夜间较高的时间分布规律。分析不利气象条件下的污染气象特征和飞机尾气污染物落地浓度分布特征对开展机场大气污染控制措施研究工作具有一定的参考价值。     

兰州市新区规划气候条件评估分析

利用1978-2007年兰州市永登县气象站观测资料及周围部分区域观测站资料,对兰州新区各项气象要素进行了分析,并采用RAMS模式,模拟了兰州地区在不同气象扩散条件(有利于扩散、一般扩散条件及不利于扩散)天气背景情况下的污染情况.结果表明,兰州污染最严重的地区在西固区,兰州西部的红古区和北部的中川、皋兰是污染较轻的地区,而位于南部的榆中则易受污染物的影响.利用HYSPLIYT模型,对各代表点采用相同的污染源,分析了由于气象条件不同造成的各代表点污染扩散的差异,在轻重两种典型的污染天气背景条件下确定了各代表点在24 h内的大气扩散能力与污染气团传输路线,发现兰州北部的大气扩散能力好于南部,西部好于东部,因此兰州老城区不宜规划污染企业;而位于兰州新区永登县秦川镇附近区域较位于皋兰县西岔镇附近区域更适于规划重工业企业.     

成都市大气污染及气象条件影响分析

本文分析了成都市大气污染现状及其趋势,比较在不同气象条件下,大气污染物对城市大气环境的污染影响.利用污染气象参数,提供城市大气污染的预测预警服务,为优化城市局部和整体规划提供科学决策,实现生态环境和社会经济的协调发展.     

2010年广州亚运限行减排对大气环境的影响

通过对亚运会、亚残运会期间限行减排各个阶段的广州和相邻城市监测到的污染物浓度以及同期的天气背景进行分析,探讨污染源、城市的地理位置、天气条件和限行减排对亚/亚残运会期间广州大气环境的影响,结果表明:由于人为限行减排、城市的地理位置和气象扩散条件等各种因素综合作用,使得亚运会期间广州统计平均的能见度基本大于10 km;机动车的限行减排对PM10的削减作用不如PM2.5和PM1明显;限行减排对广州O3浓度控制"不敏感",但是会导致浓度峰值时间相对广东东莞出现1小时左右时间"滞后";珠江三角洲(简称珠三角)的大气环境问题呈现区域性,而且污染程度不容乐观,将来对该区域的大气环境治理需要进行长期持续的联防联控。     

基于多元数据的城市工业布局气候适宜性评估——以淄博为例

本文以淄博市城市发展战略规划（2017—2030年）为例，开展了基于多元数据的城市工业布局气候适宜性评估。基于气象、遥感、环保等多元资料，对城市规划密切相关的风环境、热环境、大气环境等进行了综合分析。结果表明：研究区主导风向和风速大小存在一定空间差异，北部的桓台和临淄的主导风路径多以东—西为主，其余地区以南—北路径为主，临淄的东部地区和淄川的中部地区为常年风速大值区，而周村、张店的大部分地区和博山的南部区域为风速小值区；城市热岛范围随时间扩张明显，以两轴区域为核心，呈蔓延式发展，较强以上等级热岛面积由2009年的405 km2发展至2016年的918 km2，其中张店区的增幅速度最快；敏感性模拟试验显示，易在山前出现污染汇聚带，PM2.5污染物高浓度主要分布在淄博的中北部平原地区，而市域北部和东部区域的污染扩散条件相对较好，各区域工业排放大气污染物的扩散特征主要受不同区域的主导风影响，其中淄川北部与张店南部的污染物扩散易对整个中心城区产生较大影响。综合考虑以上风环境、热环境、大气环境的分析结果，将规划区进行大气环境敏感区等级划分，明确了5类区域的工业布局气候适宜性，从改善局地气候与大气环境角度提出规划建议，为合理配置土地功能、优化工业布局提供了科学支撑。     

成都市大气污染及气象条件影响分析

本文分析了成都市大气污染现状及其趋势．比较在不同气象条件下．大气污染物对城市大气环境的污染影响。利用污染气象参数，提供城市大气污染的预测预警服务．为优化城市局部和整体规划提供科学决策．实现生态环境和社会经济的协调发展。     

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.     

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.     

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.     

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.     

Our Warmest Thanks to AAS Reviewers 2014

正The editorial office of Advances in Atmospheric Sciences(AAS),on behalf of all AAS editors,would like to publicly acknowledge the people listed below who served as reviewers for the journal daring 1 September 2013 to 24 August 2014.We recognize that the time and work of the reviewers is the most important resource in academic publishing.The quality of our journal depends in a crucial way upon the reviewing process and therefore all reviewers'time and efforts taken to sustain the quality of the journal are greatly appreciated.     

Data selection using support vector regression

Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection(thinning)methods to preserve essential information. Satellites, such as Wind Sat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression(SVR), is developed and tested. To manage large on-line satellite data streams, observations from Wind Sat are formed into subsets by Voronoi tessellation and then each is thinned by SVR(TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods(random selection, averaging and Barnes filtering), producing a 10% thinning rate(90% data reduction), low mean absolute errors(MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s-1and correlations 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.     

Temporal vulnerability in hazardscapes: Flood memory-networks and referentiality along the North Carolina Neuse River (USA)

Social models of population vulnerability to disasters increasingly include the notion that vulnerability has a strong temporal component. While this temporality is typically conceptualized as objective (making vulnerability “dynamic,” “multiscalar,” and/or “historical”), it consistently fails to acknowledge that among stakeholders managing hazardscapes temporality is also a social process in which subjective experience of time may play a role in creating situations of population vulnerability. This paper proposes that the temporal situatedness of a population relative to past hazard events and the quality with which stakeholders engage hazard memory-chains combine to significantly influences its vulnerability to natural hazards. It is proposed that this temporal vulnerability is characterized by shared, population level potential for surprise and can be evaluated by exploration of time-series depth and temporal reference points in historical ecological narratives and documents. Based on ethnohistoric research conducted from 2002 to 2006 in flood-prone eastern North Carolina (USA), it is illustrated how temporal vulnerability was relatively higher in the Neuse River watershed located at the City of Kinston than surrounding watersheds. Due to the combination of factors including the damming of the Neuse River in the 1980s, outdated official floodplain maps, relatively unmonitored floodplain development, the stochastic timing of flood events (placing the last major flood more than a generation away), technological optimism, and turnover of floodplain officials and residents, local stakeholders were seriously misinformed about the space-time risks involved both before and after the disaster of Hurricane Floyd (1999) happened. To deal with this inconsistency, the temporal rarity of Hurricane Floyd as a “500-year event” has been motivated and embraced by many in an effort to continue life-as-is. The paper proposes that the concept of temporal vulnerability is further explored and used as key dimension in the vulnerability sciences.     

An assessment of the predictability of the East Asian Subtropical Westerly Jet based on TIGGE data

The predictability of the position, spatial coverage and intensity of the East Asian subtropical westerly jet(EASWJ) in the summers of 2010 to 2012 was examined for ensemble prediction systems(EPSs) from four representative TIGGE centers,including the ECMWF, the NCEP, the CMA, and the JMA. Results showed that each EPS predicted all EASWJ properties well, while the levels of skill of all EPSs declined as the lead time extended. Overall, improvements from the control to the ensemble mean forecasts for predicting the EASWJ were apparent. For the deterministic forecasts of all EPSs, the prediction of the average axis was better than the prediction of the spatial coverage and intensity of the EASWJ. ECMWF performed best, with a lead of approximately 0.5–1 day in predictability over the second-best EPS for all EASWJ properties throughout the forecast range. For probabilistic forecasts, differences in skills among the different EPSs were more evident in the earlier part of the forecast for the EASWJ axis and spatial coverage, while they departed obviously throughout the forecast range for the intensity. ECMWF led JMA by about 0.5–1 day for the EASWJ axis, and by about 1–2 days for the spatial coverage and intensity at almost all lead times. The largest lead of ECMWF over the relatively worse EPSs, such as NCEP and CMA, was approximately 3–4 days for all EASWJ properties. In summary, ECMWF showed the highest level of skill for predicting the EASWJ, followed by JMA.