合肥冬半年降水及所处天气型对大气颗粒物浓度的影响

利用合肥市2015—2017年冬半年环境监测站和自动气象站数据,以及高空、地面天气图资料,运用常规统计和天气学方法分析了降水强度及不同降水天气系统对PM_2.5、PM₁₀浓度的影响.结果表明：冬半年降水日PM_2.5、PM₁₀平均浓度较无降水日分别下降18.1μg·m^-3（23.9%）、38.2μg·m^-3（37.8%）;小于5 mm的日降水量对颗粒物清除效果不明显,且有28%样本PM_2.5和PM₁₀浓度不降反升;当日降水量大于10 mm,位于“优”等级的PM_2.5和PM₁₀浓度比例分别为54%和80%,显著上升.连续降水期间PM_2.5、PM₁₀日均浓度中位值和均值逐日下降,降水第2日PM_2.5、PM₁₀日均浓度降幅最大.合肥冬半年降水天气系统可以分为切变线Ⅰ型、切变线Ⅱ型、低槽冷锋型和...     

武汉市PM_(10)污染日变化及其高污染时段特征

利用武汉市环境监测中心站2004～2005年部分逐时PM10监测资料和相对应的气象资料,分析了城区PM10浓度的日变化特征,及其与气象条件的关系,特别是对四季PM10日变化中高污染时段的形成与天气系统的关系进行了分析。武汉市城区PM10的日变化与季节、天气系统的关系密切,与气象要素有一定的相关性。     

GENERAL-CIRCULATION-MODEL SIMULATIONS OF FUTURE SNOWPACK IN THE WESTERN UNITED STATES1

ABSTRACT: April 1 snowpack accumulations measured at 311 snow courses in the western United States (U.S.) are grouped using a correlation-based cluster analysis. A conceptual snow accumulation and melt model and monthly temperature and precipitation for each cluster are used to estimate cluster-average April 1 snowpack. The conceptual snow model is subsequently used to estimate future snowpack by using changes in monthly temperature and precipitation simulated by the Canadian Centre for Climate Modeling and Analysis (CCC) and the Hadley Centre for Climate Prediction and Research (HADLEY) general circulation models (GCMs). Results for the CCC model indicate that although winter precipitation is estimated to increase in the future, increases in temperatures will result in large decreases in April 1 snowpack for the entire western U.S. Results for the HADLEY model also indicate large decreases in April 1 snowpack for most of the western US, but the decreases are not as severe as those estimated using the CCC simulations. Although snowpack conditions are estimated to decrease for most areas of the western US, both GCMs estimate a general increase in winter precipitation toward the latter half of the next century. Thus, water quantity may be increased in the western US; however, the timing of runoff will be altered because precipitation will more frequently occur as rain rather than as snow.     

VARIABILITY CHARACTERISTICS OF MONTHLY PRECIPITATION IN CENTRAL OKLAHOMA1

ABSTRACT: To fully take advantage of regional climate forecast information for agricultural applications, the relationship between divisional and station scale precipitation characteristics must be quantified. The spatial variability of monthly precipitation is assumed to consist of two components: a systematic and a random component. The systematic component is defined by differences in long-term mean precipitation between stations within a climate division, and the random component by differences between station and divisional standardized values. For the Central Climate Division of Oklahoma, the systematic component has a positive precipitation gradient from west to east with a slope ranging between 3 to 16 mm of precipitation per 100 km depending on the month of the year. On the other hand, the random component ranges between 27 to 48 percent of the mean temporal variation of the monthly precipitation. This significant random spatial variability leads to large localized departures from divisional values, and clearly demonstrates the critical influence of the random component in the utilization of divisional climate forecasts for local agricultural applications. The results of this study also provide an uncertainty range for local monthly precipitation projections that are derived from divisional climate information.     

ESTIMATES OF RUNOFF USING WATER-BALANCE AND ATMOSPHERIC GENERAL CIRCULATION MODELS1

ABSTRACT: The effects of potential climate change on mean annual runoff in the conterminous United States (U.S.) are examined using a simple water-balance model and output from two atmospheric general circulation models (GCMs). The two GCMs are from the Canadian Centre for Climate Prediction and Analysis (CCC) and the Hadley Centre for Climate Prediction and Research (HAD). In general, the CCC GCM climate results in decreases in runoff for the conterminous U.S., and the HAD GCM climate produces increases in runoff. These estimated changes in runoff primarily are the result of estimated changes in precipitation. The changes in mean annual runoff, however, mostly are smaller than the decade-to-decade variability in GCM-based mean annual runoff and errors in GCM-based runoff. The differences in simulated runoff between the two GCMs, together with decade-to-decade variability and errors in GCM-based runoff, cause the estimates of changes in runoff to be uncertain and unreliable.     

PRECIPITATION DIFFERENCES AMONGST GCMs USED FOR THE U.S. NATIONAL ASSESSMENT1

ABSTRACT: Two general circulation models (GCMs) used in the U.S. national assessment of the potential consequences of climate variability and change (CGCM1 and HadCM2) show a large increase in precipitation in the future over the southwestern U.S., particularly during winter. This precipitation increase is an extension of a larger region of increased precipitation in the Pacific Ocean off the west coast of North America that is associated with a deepened and southward-shifted Aleutian Low, a weaker subtropical high, and warmer sea surface temperatures (SSTs). The models differ in their simulation of precipitation anomalies over the southeastern U.S., with CGCM1 showing drier conditions and HadCM2 showing wetter conditions in the future. While both models show decreased frequency of Atlantic storms, consistent with decreased meridional and land/sea temperature gradients, the more coastal position of the storm track in CGCM1 results in less precipitation than modern along the eastern seaboard of the U.S. During summer, differences in land surface models within the two GCMs sometimes lead to differences in soil moisture that feed back to the precipitation over land due to available moisture.     

WATER LEVEL FLUCTUATIONS IN AN URBAN POND: CLIMATIC OR ANTHROPOGENIC IMPACT?1

ABSTRACT: In 1996, the Illinois State Geological Survey began an investigation of fluctuating water levels in a pond in Cary, Illinois. The cause of the fluctuations appeared to be ground water discharge into a storm sewer recently installed by the Illinois Department of Transportation. However, analysis of climatic data provided an equally likely explanation of the fluctuations. Distinguishing the effect of climatic variations from the effect of the storm sewer was hampered by the lack of antecedent ground water and surface water data. In similar settings, it is recommended that ground water and surface water data be collected prior to initiating any infrastructure improvements.     

北京市夏季不同O3和PM2.5污染状况研究

从天气背景场、气象要素、前体物和PM_(2.5)化学组分、气团运动轨迹以及大气氧化性等方面对北京市夏季两种不同的O_3和PM_(2.5)污染状况进行了分析.结果表明,O_3达到中度污染而PM_(2.5)浓度优良(O_3和PM_(2.5)一高一低)污染状况的天气形势场为:高空为偏西北气流,地面受高压后部控制;而O_3和PM_(2.5)同时达到中度污染(O_3和PM_(2.5)两高)的天气形势场为:高空为偏西气流,地面受低压控制.与O_3和PM_(2.5)一高一低污染状况相比,O_3和PM_(2.5)两高时的气象要素特征为:偏南风更为明显和相对湿度更高.O_3和PM_(2.5)两高时污染物浓度演变特征为,O_3和PM_(2.5)的起始浓度较高,PM_(2.5)日变化特征更为明显,而O_3平均浓度却低于O_3和PM_(2.5)一高一低的污染状况.前体物、大气氧化性以及PM_(2.5)化学组分分析的结果表明,较高的起始浓度在不利气象条件下的积累和吸湿增长以及当天较大偏南风造成的区域传输可能是造成O_3和PM_(2.5)两高污染状况中PM_(2.5)浓度达到四级中度污染的主要原因.     

气象因素对环境空气质量达标的影响分析

以天津市为例,分析地面风场、大气层结及天气形势等主要污染气象参量的特征及其与环境空气质量的相关关系;估算高、低污染潜势的天气背景对大气污染物浓度的影响幅度;提出在天气气候分类的基础上,建立气象条件标准化分级,评估气象条件对环境空气质量影响水平的方法。研究表明:天津市采暖季高污染的天气形势出现的频率约为34%,而非采暖季高污染的天气形势出现的频率约为16%,ISCLT3模型模拟结果显示相应于相同的污染源排放数据库由于采暖季和非采暖季气象条件不同引起的SO2全市平均浓度的差异约在10%以上。污染源源强相对稳定的条件下,高污染潜势的天气背景对空气污染的加剧作用大于低污染潜势的天气背景对空气污染的减轻作用。     

2013—2015年北京污染频发期细颗粒物重污染成因与天气形势关系的研究

我国重污染呈现愈演愈烈态势,重污染事件在供暖季节（污染频发期）尤为频发.本文利用北京2013—2015年采暖期逐小时PM_2.5浓度数据、再分析资料、气团后向轨迹、气溶胶雷达数据以及探空数据综合分析了北京地区重污染状况,概括了重污染发生时常见的天气形势,探讨了重污染形成原因与天气形势的关系.研究结果表明：2013—2015年采暖期北京发生重污染（日均PM_2.5浓度大于150 μg·m^-3）的天数分别为36、28及35 d,即北京采暖期21.9%的天数受重污染天气影响.2月份重污染事件最为频发,发生频次为27.3%.北京发生重污染事件时,地面被高压控制时,高空500 hPa多东移的槽脊,当位于脊后槽前时,为上升运动,西南风,850 hPa多暖平流,西南风输送暖湿气流,湿度较大,地面偏南风,可能会存在污染物的输送;地面为低压控制时,500 hPa一般为稳定的西风气流或西北气流,低空850 hPa可能存在暖平流,地面常伴随弱的风场辐合,导致污染物累积;当地面为均压场时,高空500 hPa多为脊后槽前的形势,低空无明显冷暖平流,地面等压线稀疏或无等压线,静风天气.这3类结构引发的重污染天数分别占总重污染天数的47.3%、18.2%及34.5%.进一步分析重污染成因与天气形势关系表明：北京地面受高压系统控制时,污染时间持续最长,也最为频发（47.3%）,PM_2.5平均浓度最高可达258.8 μg·m^-3,且常伴随来自西南方向的污染物输送,北京上空1 km附近存在逆温和逆湿.对污染传输路径研究发现：主要存在3条输送通道,①天津-廊坊-北京、②沧州-廊坊-北京、③石家庄-保定-北京.鉴于目前数值模式对天气形势的预报较为成熟,本文对区域重污染过程与天气形势之间的关系研究,有助于为北京地区空气质量的精准预报预警提供科学支持.