Assessment of the Extreme Rainfall Event at Nashville,TN and the Surrounding Region on May 1–3, 2010

This paper analyzes the May 1–3, 2010 rainfall event that affected the south‐central United States, including parts of Mississippi, Tennessee, and Kentucky. The storm is evaluated in terms of its synoptic setting, along with the temporal distributions, and spatial patterns of the rainfall. In addition, the recurrence interval of the storm is assessed and the implications for hydrologic structure designs are discussed. The event was associated with an upper‐level trough and stationary frontal boundary to the west of the rainfall region, which remained quasi‐stationary for a period of 48 h. Heavy rainfall was produced by two slow‐moving mesoscale convective complexes, combined with abundant atmospheric moisture. Storm totals exceeding 330 mm occurred within a large elongated area extending from Memphis to Nashville. Isolated rainfall totals over 480 mm were reported in some areas, with NEXRAD weather radar rainfall estimates up to 501 mm. An extreme value analysis was performed for one‐ and two‐day rainfall totals at Nashville and Brownsville, Tennessee, as well as for gridded rainfall estimates for the entire region using the Storm Precipitation Analysis System. Results suggest maximum rainfall totals for some durations during the May 1–3, 2010 event exceeded the 1,000‐year rainfall values from National Oceanic and Atmospheric Administration Atlas 14 for a large portion of the region and reached up to 80% of the probable maximum precipitation values for some area sizes and durations.     

基于K-means聚类分析法的肇庆市干季PM2.5污染天气分型研究

通过分析肇庆市2013—2018年国控大气环境监测站的PM_(2.5)连续监测数据,发现肇庆市区PM_(2.5)浓度在干季(10月—次年4月)明显高于其余月份,轻度以上污染基本发生在干季,且PM_(2.5)浓度对年总浓度贡献达70.8%.基于Era-interim再分析资料采用K-means聚类分析法对2013—2018年干季逐日的海平面气压和10 m水平风进行分型,揭示了肇庆市易出现PM_(2.5)污染的6种大气环流形势,包括冷锋前部(CF)、变性高压脊(THR)、脊后槽前型(BRFT)、高压底后部(HSW)、弱冷高压脊(HR)和台风外围型(TP).2013—2016年易污染天气型影响天数呈明显减少趋势,2017—2018年呈增加趋势.不同天气型PM_(2.5)浓度与局地气象要素相关性不一致,其中CF、HR、HSW、TP天气型与湿度相关性最好,THR与风速、BRFT与气压相关性最好.PM_(2.5)污染除BRFT天气型主要以本地排放累积影响为主,其余易污染天气型存在不同尺度的外来输送影响,HSW、HR主要来自广州、清远、韶关, CF主要来自佛山、中山,THR来自广州、清远、佛山.同一污染天气型在不同月份的污染影响差异较大,其中HSW、THR污染型主要影响1月和10月,CF为1月和12月,HR为2月和12月,TP为10月,BRFT为1月和10—11月.不同年份的同一月份造成不同程度的PM_(2.5)污染除了排放影响,还与天气环流类型和同一天气型下的局地气象要素密切相关.     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

北京市秋季大气气溶胶质量浓度谱分布特征的观测研究

为了研究不同天气形势和气象要素对不同粒径段的大气气溶胶质量浓度谱分布的影响,2010年秋季在北京市采用空气动力学粒径仪对0.5 ～20 μm的大气颗粒物进行了为期2个月的连续观测.结果表明,在不同天气形势和气团运动下,质量浓度谱分布差异明显.受到西北气流影响,在质量浓度较小的清洁天气,谱分布主要集中在粗粒径段(清洁天气).当天气以静稳为主,颗粒物质量浓度值中等偏高时,粗细粒径气溶胶分布中出现谱值相近的高峰(中等浑浊天气).在受到西南气流主要影响的高质量浓度的观测日中,细粒径气溶胶在谱分布中占主要地位(高浑浊天气).在相同气团影响的天气下,昼夜间质量浓度谱分布的谱型变化趋势一致.清洁天气保持了粗粒径段的单峰分布,中等浑浊和高浑浊天气呈双峰分布.清洁天气细粒径段谱值受光化学反应影响在日间略高于夜间.中等浑浊天气中质量浓度的谱值在夜间受相对湿度和温度变化影响有显著增加.高浑浊天气下,昼夜温湿差别小,谱分布未出现明显变化.     

海峡西岸沿海城市群大气污染物浓度特征及其与天气形势的关系

将海峡西岸沿海主要城市分成北部、中部和南部3个城市群,分别取其组成城市2006年1月-2010年12月大气污染物(SO2、NO2、PM10)日均浓度的平均值进行时空分布特征分析,结果表明:南部城市群的总体污染水平是最高的,SO2和PM10平均浓度值比中部、北部城市群高,NO2浓度略低于中部城市群;3个城市群主要都以PM10为首要污染物;SO2、NO2、PM10浓度年变化、月变化特征相类似;各城市群大气污染物都没有表现出周末效应;分析不同天气形势对大气污染物浓度的影响,结果表明:变性冷高压、高空槽、暖区辐合3种天气形势为不利于大气污染物扩散的天气型,低涡切变、副热带高压及边缘、台风(热带辐合带)、台风(热带辐合带)外围、冷高压脊5种天气形势为有利于大气污染物扩散的天气型.     

COVID-19管控期间气象条件变化对京津冀PM2.5浓度影响

2020年初新冠肺炎疫情（COVID-19）暴发后,中国多地实施了严格的管控措施,导致污染物排放量明显下降.但在减排实施的情况下,京津冀PM_2.5等污染物浓度较过去5 a同期却明显增长,出现了两次PM_2.5重度污染事件.利用欧洲中心ERA5再分析资料分析发现,相对于过去5 a, COVID-19管控期间京津冀地区的气象场表现为偏高的相对湿度、偏低的边界层高度和边界层内异常的辐合上升运动,有利于颗粒物的吸湿增长和二次转化,不利于污染物垂直方向上的扩散.此外,利用WRF-Chem模式开展敏感性试验发现,在京津冀中部地区气象场的变化导致2020年管控期间ρ(PM_2.5)升高了20～55μg·m^-3,升高比例高达60%～170%.进一步利用过程诊断分析法得出,增强的气溶胶化学过程和不利的湍流扩散条件是2020年COVID-19管控期间PM_2.5浓度升高的主要原因.在当今减排的大背景下,边界层高度和相对湿度的变化可能成为预报预测京津冀地区PM_2.5污染事件的重要指标...     

辽宁中部地区灰霾天气气候环境特征

利用的1961—2007年辽宁中部地区6个城市的地面常规气象资料以及2002—2007年的东亚地面和高空天气形势资料,2005—2007污染监测资料,统计分析了辽宁中部地区气候环境特征对灰霾天气形成的有利形势与不利的地面天气形势,对比颗粒物资料与实况资料分析了灰霾天气常见的天气形势。结果表明：由于特殊的环境特征与气象条件,灰霾易发时期为秋冬春季节。不利于大气扩散易形成灰霾天气的地面天气形势可分为高压类、低压类、均压类。蒙古高压、东北地形槽以及长白山小高压等局地不利的天气形式对灰霾天气产生有重要影响。常见灰霾天气形势为冷高压型、占65%左右,低压槽型、占13.5%,冷锋过境型,冷锋前部型以及长白山小高压等。     

福建沿海城市霾天气特征

利用2006年1月—2010年12月福建省沿海6个城市的能见度、相对湿度、降水、天气形势和大气污染物资料,对霾天气时空变化特征、霾与天气形势的关系进行了分析. 结果表明:宁德市每年霾日数最少,为12～26 d;漳州市霾日数最多,为74～118 d;而福州、厦门和泉州三市的霾日数相近.6个城市霾天气主要出现在12月—次年6月,7—11月霾的出现率极低且均以轻微霾为主,所占比例在85.5%以上;持续时间短暂的霾较多,半天和全天的霾较少. 在暖区辐合、高空槽和变性冷高压3种天气形势下霾出现频率均较高,分别达到21.2%～49.5%、9.1%～42.3%、6.1%～47.5%. ρ(PM₁₀)与能见度的相关系数最高,为-0.44~-0.33,并且各城市霾天气下的ρ(PM₁₀)均明显高于非霾天气;ρ(SO₂)除莆田市外,ρ(NO₂)除漳州市外,其他城市ρ(SO₂)和ρ(NO₂)均为霾天气高于非霾天气.      

气象水文因子对呼伦湖湿地区域植物气候生产潜力的影响

利用呼伦湖湿地50年的气象资料、水文资料和生态环境监测资料,采用迈阿密模型及回归统计分析方法,分析了气象水文因子对呼伦湖湿地区域植物气候生产潜力的影响,结果表明,①50年来该湿地植物气候生产潜力变化趋势总体为下降趋势,减少的气候倾向率为每10年157.7 kg·km-2,峰值时段为1970—1990年,两个谷值时段为1961—1969年、1991—2010年,最大值和最小值与降水量出现的最多值和最少值一一对应。②在15个气象水文因子中有年降水量、年蒸散量、生长季径流量、春季大风日数、水位、春季平均风速、年大风日数、水域面积8个因子达到显著性检验（P〈0.150～0.001）,上述8个因子的排序也是与湿地区域植物气候生产潜力相关程度由大到小的排序。综述表明,呼伦湖湿地区域呈暖干化趋势,并且干旱灾害比较严重,是限制气候生产潜力的重要原因。③气象水文因子协同作用对该湿地区域植物气候生产潜力的影响较大,复相关系数为-0.997,年蒸散量与年平均气温、年降水量、生长季径流量和水位因子对湿地区域植物气候生产潜力的贡献相反,随其减少或增加,湿地区域植物气候生产潜力变化率增加或减少149.7 kg·hm-2。可以看出,该湿地气象水文因子的匹配并不理想,暖干化趋势依然是制约该区光能利用率低下的重要原因。④该湿地区域植物现实的生产力远未达到气候生产潜力,约有近60%的潜力可以开发;光能利用率较小的主要原因：一是现有的生态保护、修复技术及管理水平还比较落后,二是该区域暖干化趋势显著;而提高气候资源利用率的有效途径是加大保护生态环境力度和积极实施人工增雨、节水灌溉工程。     

中山市2000~2014年霾天气特征及气象影响因子

利用中山市2000~2014年气象资料及2013~2014年环境监测站资料,分析中山市霾特征及气象影响因子,结果表明,中山市霾日数年际变化明显,最少为11d,出现在2005年;最多为134d,出现在2008年.霾天气主要发生在秋冬季节,霾日数最多的月份是1月,平均为10.5d.霾日PM_2.5的平均浓度是非霾日的2.26倍,PM_2.5是霾天气的重要污染物.中山市霾日典型天气形势有7种:大陆高压型、海上高压型、均压场型、冷锋前部型、台风外围下沉气流型、槽前脊后型、低压槽型.其中以大陆高压型占比例最高,为52.03%,冷锋前部型造成的能见度最低.气流轨迹聚类分析表明,影响中山的气流轨迹有7类,主要来源于东北方向的大陆和偏东方向的沿海;在东北方向气流轨迹影响下,污染物浓度较高;在东部沿海的气流轨迹下,能见度较低,表明中山市的霾天气受区域传输影响显著.