从中期天气过程看近几年长江中下游梅雨偏少的原因

长江中下游地区自2000年到2007年连续八年梅雨期降水偏少。本文从中期天气过程的角度分析了这八年不利于长江中下游梅雨的主要因子, 有东亚高空急流中准定常波动、 西太平洋暖池强对流活动和西北太平洋热带气旋活动。西太平洋副热带高压受这些因子的直接影响, 在中期时间尺度上副高环流形势发生变异, 从而造成长江中下游梅雨期的降水异常减少。在这八年的梅雨期中, 这些因子的特别异常, 更主要的由于它们的组合作用是导致近年来长江中下游梅雨偏少的直接原因。并且, 同样是长江中下游梅雨偏少, 不同因子的组合方式也影响着长江中下游梅雨偏少的降水异常分布背景。本文还初浅地讨论了在季节内预测长江中下游梅雨时对中期天气过程的参考。     

中高纬Rossby波活动对盛夏东亚/太平洋事件中期演变过程的影响

在中期时间尺度上探讨了Rossby波活动对盛夏东亚太平洋(EAP)事件的影响。正负EAP事件的形成过程不是简单的反位相过程, 但这一时期的气候平均流波导结构决定了两者在Rossby波能量频散特征上有许多相似之处。在对流层中上层, 源自上游东北大西洋或东欧平原的Rossby波能量经巴尔喀什湖附近向东北方向频散, 它对东北亚异常中心的形成和维持起重要作用。由于盛夏东亚急流位置偏北, 我国华北[CD*2]东北地区成为波导区, Rossby波能量从贝加尔湖附近向该波导区频散, 形成了EAP事件的东亚中纬度异常中心。在EAP事件的三个异常中心中, 东北亚异常中心最先形成, 西太平洋副热带异常中心则最后出现。在对流层上下层波导结构有明显差异, 它导致了Rossby波能量在东亚沿岸中高纬地区的反向经向传播特征。在负事件的对流层低层, 西太平洋副热带异常中心和中纬度两个异常中心间的经向Rossby波链接较为明显。在东亚地区, 盛夏月平均尺度上EAP型的Rossby能量频散特征与中期EAP事件基本一致。     

Responses of Vertical Structures in Convective and Stratiform Regions to Large-Scale Forcing during the Landfall of Severe Tropical Storm Bilis （2006）

The responses of vertical structures,in convective and stratiform regions,to the large-scale forcing during the landfall of tropical storm Bilis(2006) are investigated using the data from a two-dimensional cloud-resolving model simulation.An imposed large-scale forcing with upward motion in the mid and upper troposphere and downward motion in the lower troposphere on 15 July suppresses convective clouds,which leads to ～100% coverage of raining stratiform clouds over the entire model domain.The imposed forci...     

基于多指标综合指数的灾害性天气过程预评估方案

通过分析给出了一种基于多指标综合指数的灾害性天气过程预评估方案,提出预估应包含对灾害性天气过程本身程度预估和灾害影响预估两个部分。通过建立综合指数来描述灾害性天气过程本身的程度,利用历史和实时气象资料对综合指数进行计算,分别对灾害性天气过程进行历史分析和实时监测;通过列表法,对收集的历史和实时灾害影响资料进行解析,综合指数中组合要素的差异,可用于分析灾害的不同特征;最后利用历史统计情景、相似指数年份类比和中短期预报等三种方案,对灾害性天气进程和影响发展程度进行预估。并以湖北省2008年初持续低温雨雪冰冻过程为例进行具体说明,此次过程综合指数值位居历史第二位,约70年一遇,直接经济损失超过110亿元,是1951年以来冬季损失最大的一次气象灾害。     

肇州降水集中度和集中期的气候特征分析

对近50年来肇州降水的集中度和集中期进行了计算分析,结果表明:近50年来,降水的集中程度有下降的趋势,集中期有提前的趋势。集中度和集中期分别存在着11a和14a左右的年代际尺度周期变化。肇州的年降水量呈下降趋势,肇州降水集中度和集中期与年降水量有很好的正相关性,年降水总量比较大的年份,降水集中程度相对较高。     

Improvement and Application of the Similarity Saltation Model: Wind-Tunnel Experimental Investigation and Numerical Simulation of the Vertical Sand Mass Flux Distribution in the Saltation Layer

Based upon comparisons between published experimental data and simulated results on the vertical sand flux distribution in the saltation layer, Shao’s similarity saltation model has been greatly improved by correcting the average vertical particle lift-off velocity and using a more suitable universal roughness length. By the improved model, the vertical sand flux profile over the bare, dry and loose uniform sandy surface, which is quite representative of real desert surfaces, can be reproduced very well. Meanwhile, the surface transport rate and the characteristic and average saltation heights have been simulated and analyzed in detail, disclosing their relationships with friction velocity, particle size and roughness length, and the possible underlying mechanisms. Besides, the average particle lift-off velocity and the average mean vertical aerodynamic action upon the ascending particle, which determine the saltation process, are explicitly expressed by parameters involved in the similarity model, and their relationships with friction velocity, particle size and roughness length are also described concisely. The corrected average particle lift-off velocity makes it possible to investigate the characteristic particle trajectory, whose initial velocity equals the average lift-off velocity, so as to estimate the average particle against surface impacting velocity and the average aerodynamic action upon the saltation process.     

Source identification of PM2.5 particles measured in Gwangju, Korea

The UNMIX and Chemical Mass Balance (CMB) receptor models were used to investigate sources of PM_2.5 aerosols measured between March 2001 and February 2002 in Gwangju, Korea. Measurements of PM_2.5 particles were used for the analysis of carbonaceous species (organic (OC) and elemental carbon (EC)) using the thermal manganese dioxide oxidation (TMO) method, the investigation of seven ionic species using ion chromatography (IC), and the analysis of twenty-four metal species using Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES)/ICP-Mass Spectrometry (MS). According to annual average PM_2.5 source apportionment results obtained from CMB calculations, diesel vehicle exhaust was the major contributor, accounting for 33.4% of the measured PM_2.5 mass (21.5 μg m^− 3), followed by secondary sulfate (14.6%), meat cooking (11.7%), secondary organic carbon (8.9%), secondary nitrate (7.6%), urban dust (5.5%), Asian dust (4.4%), biomass burning (2.8%), sea salt (2.7%), residual oil combustion (2.6%), gasoline vehicle exhaust (1.9%), automobile lead (0.5%), and components of unknown sources (3.4%). Seven PM_2.5 sources including diesel vehicles (29.6%), secondary sulfate (17.4%), biomass burning (14.7%), secondary nitrate (12.6%), gasoline vehicles (12.4%), secondary organic carbon (5.8%) and Asian dust (1.9%) were identified from the UNMIX analysis. The annual average source apportionment results from the two models are compared and the reasons for differences are qualitatively discussed for better understanding of PM_2.5 sources.Additionally, the impact of air mass pathways on the PM_2.5 mass was evaluated using air mass trajectories calculated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory model. Source contributions to PM_2.5 collected during the four air mass patterns and two event periods were calculated with the CMB model and analyzed. Results of source apportionment revealed that the contribution of diesel traffic exhaust (47.0%) in stagnant conditions (S) was much higher than the average contribution of diesel vehicle exhaust (33.4%) during the sampling period. During Asian dust (AD) periods when the air mass passed over the Korean peninsula, Asian dust and secondary organic carbon accounted for 25.2 and 23.0% of the PM_2.5 mass, respectively, whereas Asian dust contributed only 10.8% to the PM_2.5 mass during the AD event when the air mass passed over the Yellow Sea. The contribution of biomass burning to the PM_2.5 mass during the biomass burning (BB) event equaled 63.8%.     

Source attribution of urban smog episodes caused by coal combustion

Stable weather conditions together with extensive use of coal combustion often lead to severe smog episodes in certain urban environments, especially in Eastern Europe. In order to identify the specific sources that cause the smog episodes in such environments, and to better understand the mixing state and atmospheric processing of aerosols, both single particle and bulk chemical characterization analysis of aerosols were performed in Krakow, Poland, during winter 2005.Real-time measurements of the bulk PM10 aerosol during a severe smog episode (PM10 mass > 400 µg m^− 3) showed a stable concentration of black carbon in the aerosol, and an increase in the sulphate and chlorine mass contributions towards the end of the episode. Chemical characterization of single particles further helped to identify residential coal burning as the main source that caused this severe smog episode, consisting of single particles with major signals for carbon with simultaneous absence of sulphate, chlorine and calcium. Particles from industrial coal combustion gained importance towards the end of that episode, after residential coal combustion was switched off, indicated by an increase of the percentage of sulphate and chlorine containing particles. Traffic was not a significant source during the severe smog episode. During a lighter smog episode, residential and industrial coal combustion was still predominant, with an increased contribution of traffic and processed/aged aerosols. On a clean day, particle classes containing nitrate were the most abundant. In addition, the aerosol was more internally mixed showing that there were more sources contributing to the total aerosol population.     

北京市奥运期间气象灾害风险承受与控制能力分析

针对北京市奥运会期间的7种主要气象灾害(雷电、冰雹、大风、高温、暴雨、大雾和霾灾害),建立了气象灾害风险承受能力与风险控制能力评价的指标体系.经过专家评分,获取7种气象灾害的评价指标所对应的分值.利用层次分析法,计算评价指标的权重系数.最后得到7种气象灾害评价指标的加权平均值作为其风险承受能力与风险控制能力系数.利用灾害模数、经济易损模数、生命易损模数3个指标进行北京市奥运期间18个区县空间易损度区划分析.结果表明:北京市奥运会期间,高温灾害和暴雨灾害的风险承受能力与风险控制能力最弱;雷电灾害和大雾灾害的承受与控制能力中等;冰雹灾害和霾灾害较强;大风灾害最强.易损度空间差异分析表明,城区(东城区、西城区、崇文区和宣武区)、朝阳区和海淀区为高易损性区域;丰台区、石景山区、房山区、昌平区、顺义区和大兴区为中易损性区域;门头沟区、通州区、平谷区、怀柔区、密云县和延庆县为低易损性区域.     

奥运气象服务社会经济效益评估的AHP/BCG组合分析

综合应用层次分析法(AHP方法)和波士顿矩阵(BCG矩阵)相结合的思路,将气象服务用户群对服务效益评估这个复杂系统的思维过程数学化、系统化,建立了定量的气象服务期望度/满意度组合矩阵分析模型.以2007年"好运北京"青岛国际帆船赛调查问卷为例进行实证分析,从用户反馈信息来考核对用户需求的了解和满足、气象服务时间和时效、气象服务宣传服务手段、服务人员综合能力、预报准确度和用户未来意向等6个主要项目,应用该模型来较为客观地评估奥运气象信息服务当前的四象限结构现状,掌握和判断其所处位置,发现问题进行改进,并预测该项目的未来发展变化.