Case Study of Fog Predictability for an Event with Cold-Front Synoptic Pattern

Fog has recently become a frequent high-impact weather phenomenon along the coastal regions of North China. Accurate fog forecasting remains challenging due to limited understanding of the predictability and mechanism of fog formation associated with synoptic-scale circulation. One frequent synoptic pattern of fog formation in this area is associated with cold front passage(cold-front synoptic pattern, CFSP). This paper explored the predictability of a typical CFSP fog event from the perspective of analyzing key characteristics of synoptic-scale circulation determining fog forecasting performance and the possible mechanism. The event was ensemble forecasted with the Weather Research and Forecasting model. Two groups of ensemble members with good and bad forecasting performance were selected and composited. Results showed that the predictability of this case was largely determined by the simulated strengths of the cold-front circulation(i.e., trough and ridge and the associated surface high). The bad-performing members tended to have a weaker ridge behind a stronger trough, and associated higher pressure over land and a weaker surface high over the sea, leading to an adverse impact on strength and direction of steering flows that inhibit warm moist advection and enhance cold dry advection transported to the focus region. Associated with this cold dry advection, adverse synoptic conditions of stratification and moisture for fog formation were produced, consequently causing failure of fog forecasting in the focus region. This study highlights the importance of accurate synoptic-scale information for improved CFSP fog forecasting, and enhances understanding of fog predictability from perspective of synoptic-scale circulation.

Impacts of Aerosol−Radiation Interactions on the Wintertime Particulate Pollution under Different Synoptic Patterns in the Guanzhong Basin,China

The effects of aerosol-radiation interactions(ARI)are not only important for regional and global climate,but they can also drive particulate matter(PM)pollution.In this study,the ARI contribution to the near-surface fine PM(PM_(2.5))concentrations in the Guanzhong Basin(GZB)is evaluated under four unfavorable synoptic patterns,including“northlow”,“transition”,“southeast-trough”,and“inland-high”,based on WRF-Chem model simulations of a persistent heavy PM pollution episode in January 2019.Simulations show that ARI consistently decreases both solar radiation reaching down to the surface(SWDOWN)and surface temperature(TSFC),which then reduces wind speed,induces sinking motion,and influences cloud formation in the GZB.However,large differences under the four synoptic patterns still exist.The average reductions of SWDOWN and daytime TSFC in the GZB range from 15.2%and 1.04°C in the case of the“transition”pattern to 26.7%and 1.69°C in the case of the“north-low”pattern,respectively.Furthermore,ARI suppresses the development of the planetary boundary layer(PBL),with the decrease of PBL height(PBLH)varying from 18.7%in the case of the“transition”pattern to 32.0%in the case of the“north-low”pattern.The increase of daytime near-surface PM_(2.5)in the GZB due to ARI is 12.0%,8.1%,9.5%,and 9.7%under the four synoptic patterns,respectively.Ensemble analyses also reveal that when near-surface PM_(2.5)concentrations are low,ARI tends to lower PM_(2.5)concentrations with decreased PBLH,which is caused by enhanced divergence or a transition from divergence to convergence in an area.ARI contributes 15%-25%toward the near-surface PM_(2.5)concentrations during the severe PM pollution period under the four synoptic patterns.

Low-Level Temperature Inversions and Their Effect on Aerosol Condensation Nuclei Concentrations under Different Large-Scale Synoptic Circulations

Knowledge of the statistical characteristics of inversions and their effects on aerosols under different large-scale synoptic circulations is important for studying and modeling the diffusion of pollutants in the boundary layer. Based on results gen- erated using the self-organizing map （SOM） weather classification method, this study compares the statistical characteristics of surface-based inversions （SBIs） and elevated inversions （EIs）, and quantitatively evaluates the effect of SBIs on aerosol condensation nuclei （CN） concentrations and the relationship between temperature gradients and aerosols for six prevailing synoptic patterns over the the Southern Great Plains （SGP） site during 2001-10. Large-scale synoptic patterns strongly influ- ence the statistical characteristics of inversions and the accumulation of aerosols in the low-level atmosphere. The activity, frequency, intensity, and vertical distribution of inversions are significantly different among these synoptic patterns. The verti- cal distribution of inversions varies diurnally and is significantly different among the different synoptic patterns. Anticyclonic patterns affect the accumulation of aerosols near the ground more strongly than cyclonic patterns. Mean aerosol CN con- centrations increase during SBIs compared to no inversion cases by 16.1%, 22.6%, 24.5%, 58.7%, 29.8% and 23.7% for the six synoptic patterns. This study confirms that there is a positive correlation between temperature gradients and aerosol CN concentrations near the ground at night under similar large-scale synoptic patterns. The relationship is different for different synoptic patterns and can be described by linear functions. These findings suggest that large-scale synoptic patterns change the static stability of the atmosphere and inversions in the lower atmosphere, thereby influencing the diffusion of aerosols near the ground.

The spring Yellow Sea fog：synoptic and air–sea characteristics associated with different airflow paths

The fog occurs frequently over the Yellow Sea in spring（April–May）, a climatical period of Asian monsoon transition. A comprehensive survey of the characteristic weather pattern and the air-sea condition is provided associated with the fog for the period of 1960–2006. The sea fog is categorized by airflow pathways of backward trajectory cluster analysis with the surface observations derived from international comprehensive oceanatmosphere dataset（I_COADS） I_COADS datasets and contemporaneous wind fields from the National Centers for Environmental Prediction（NCEP）/National Center for Atmospheric Research（NCAR） reanalysis. On the basis of the airflow paths, the large-scale lower-tropospheric circulation patterns and the associated surface divergence,the distribution of a vertical humidity, the horizontal water vapor transportation and the air-sea temperature difference are investigated and the major findings are summarized as follows.（1） Four primary clusters of the airflow paths that lead to spring sea fog formation are identified. They are originated from the northwest, east,southeast and southwest of the Yellow Sea, respectively.（2） Springtime Yellow Sea fog occurs under two typical weather patterns： the Yellow Sea high（YSH） and cyclone and anticyclone couplet（CAC）. Each pattern appears by about equal chance in April but the YSH occurrence drops to around one third and the CAC rises to around two third of chance in May.（3） The common feature in the two types of synoptic conditions is that surface divergence center is located over the Yellow Sea.（4） For the YSH type of fog, water vapor comes mainly from local evaporation with a well-defined dry layer present in the lower atmosphere; for the CAC type of fog, however, water vapor comes mainly from areas outside the Yellow Sea with a thick surface layer of high humidity.（5） With the differences in weather patterns and its associated vertical distribution of the humidity and the transportation of water vapor, there are two types of sea fogs. Most fogs of the CAC types are ＂warm＂ fog, while fogs of YSH type have nearly equal chance to be ＂warm＂ and ＂cold＂ fog.

On the Blocking Flow Patterns in the Euro–Atlantic Sector:A Simple Model Study

The flow patterns of Euro-Atlantic blocking events in winter are investigated by dividing the sector into three sub- regions： 60°-30°W （Greenland region）; 20°W-30°E [eastern Atlantic-Europe （EAE） region]; and 50°-90°E （Ural region）. It is shown that blocking events in winter are extremely frequent in the three sub-regions. Composite 500-mb geopotential height fields for intense and long-lived blocking events demonstrate that the blocking fields over Greenland and Ural regions exhibit southwest-northeast （SW-NE） and southeast-northwest （SE-NW） oriented dipole-type patterns, respectively, while the composite field over the EAE region exhibits an Ω-type pattern. The type of composite blocking pattern seems to be related to the position of the blocking region relative to the positive center of the climatological stationary wave （CSW） anomaly existing near 10°W. The physical cause of why there are different composite blocking types in the three sub-regions is identified using a nonlinear multiscale interaction model. It is found that when the blocking event is in almost the same position as the positive CSW anomaly, the planetary-scale field can exhibit an Ω-type pattern due to the enhanced positive CSW anomaly. Neverthe- less, a SW-NE （SE-NW） oriented dipole-type block can occur due to the reduced positive CSW anomaly as it is farther in the west （east） of the positive CSW anomaly. The total fields of blocking in the three regions may exhibit a meandering flow comprised of several isolated anticyclonic and cyclonic vortices, which resembles the Berggren-Bolin-Rossby meandering jet type.

从气候学角度理解“21.7”河南特大暴雨的形成机理

2021年7月19—22日,河南省遭遇历史罕见特大暴雨,破纪录降水引发了严重的洪涝灾害。目前,“21.7”河南特大暴雨的研究大多针对降水个例,引发极端降水的气候学机制仍不明确。本文基于谱聚类方法,利用中国气象局多年历史站点降水资料和NCEP/NCAR再分析数据研究了1960—2021年华北地区夏季区域性极端降水事件的主控环流天气型,系统揭示了“21.7”河南特大暴雨的发生过程。结果表明,“21.7”河南特大暴雨发生在罕见天气型下,即西太平洋上空增强的远距离台风配合异常偏西北的西太平洋副热带高压与异常偏东北的南亚高压。该天气型虽然仅贡献了华北地区夏季区域性极端降水事件的5.97%,却易导致更强的极端降水事件。台风和副高之间的盛行东南风将水汽从西太平洋输送到河南,对流层高低层强烈的风切变引发的垂直上升运动导致了“21.7”河南暴雨的发生。河南及周边地区对流层低层更加强烈的上升运动与非绝热加热之间的正反馈效应使得“21.7”河南暴雨相对同一天气型下的其他特大暴雨事件的极端性更强。本研究为理解和准确预测类似“21.7”河南暴雨的破纪录区域性极端降水提供了全新的气候学视角。

海陀山冬季降水天气分型及冬奥预报应用

基于2019—2021年1月1日至3月15日北京冬奥会延庆赛区(以下简称海陀山)降水观测资料和ERA5再分析资料,对期间34次降水过程进行天气分型,并对各天气型下不同海拔的降水实况特征开展统计分析。研究结果表明:冬季海陀山降水根据天气系统及地形影响可分为偏北气流型、偏东气流型、低涡低槽型、回流低涡低槽型四种天气型。不同天气型下海陀山地形高度以下主要气流方向和强度、水汽垂直分布等条件,以及与地形相互作用使得不同海拔之间降水量、持续时间等呈现显著差异。偏北气流型受500 hPa槽后整层强偏北气流控制,形成越山气流,降水集中在高海拔地区;偏东气流型受低层偏东气流影响,降水集中在低海拔地区,以上两种天气型无天气尺度系统配合,由地形强迫作用主导,降水量不大、持续时间相对较短。低涡低槽型受高空东移低涡低槽作用,配合低层西南气流,高海拔降水量更多,同时该型也是海陀山冬季最主要的降水天气型;回流低涡低槽型受高空东移低涡低槽影响,配合降水前东风回流对低层增湿并起到冷垫作用,低海拔降水量更多,以上两种天气型均存在天气尺度系统,并叠加海陀山地形作用,降水量显著且持续时间长,会对赛事运行造成较大影响。上述特征统计结果在2022年北京冬奥会期间一次强降雪预报服务中得到验证和应用,证明上述结果可以在冬季海陀山复杂地形降水预报中发挥作用。

Dominant Synoptic Patterns and Their Relationships with PM2.5 Pollution in Winter over the Beijing–Tianjin–Hebei and Yangtze River Delta Regions in China

This paper concerns about the episodes of PM2.5 pollution that frequently occur in China in winter months.The severity of PM2.5 pollution is strongly dependent on the synoptic-scale atmospheric conditions.We combined PM2.5 concentration data and meteorological data with the Hybrid Single Particle Lagrangian Integrated Trajectory model(HYSPLIT4)to investigate the dominant synoptic patterns and their relationships with PM2.5 pollution over the Beijing–Tianjin–Hebei(BTH)and Yangtze River Delta(YRD)regions in the winters of 2014–17.The transport of PM2.5 from the BTH to YRD regions was examined by using cluster analysis and HYSPLIT4.It is found that the level of PM2.5 pollution over the BTH region was higher than that over the YRD region.The concentration of PM2.5 in the atmosphere was more closely related to meteorological factors over the BTH region.The episodes of PM2.5 pollution over the BTH region in winter were related to weather patterns such as the rear of a high-pressure system approaching the sea,a high-pressure field,a saddle pressure field,and the leading edge of a cold front.By contrast,PM2.5 pollution episodes in the YRD region in winter were mainly associated with the external transport of cold air,a high-pressure field,and a uniform pressure field.Cluster analysis shows that the trajectories of PM2.5 were significantly different under different weather patterns.PM2.5 would be transported from the BTH to the YRD within 48 h when the PM2.5 pollution episodes were associated with three different kinds of weather patterns:the rear of a highpressure system approaching the sea,the high-pressure field,and the leading edge of a cold front over the BTH region.This suggests a possible method to predict PM2.5 pollution episodes based on synoptic-scale patterns.

京津及河北平原凌晨强降水形势和环境条件

北京、天津及河北平原大部(37°~41°N,115°~119.5°E)(简称BTH-P)为夏季强降水夜发性较突出的区域。基于2010—2021年国家级地面气象站逐小时降水数据和CFSR再分析数据集,对该区域6—8月42个凌晨时段强降水个例的天气形势进行了分类,统计了不同天气形势下强降水的环境物理量特征。结果表明:BTH-P区域凌晨强降水的天气形势可分为热带气旋类、低涡切变类、低槽冷锋类和副高边缘弱天气尺度强迫类,且低涡切变类凌晨强降水最为频发。统计不同类天气形势的降水环境物理量场特征分布发现,水汽条件都非常充沛,热带气旋类凌晨强降水水汽条件最优,其大气可降水量的中位数达72 mm;热带气旋类热力和不稳定能量条件不如其他类突出,对流有效位能、K指数均最小,而副高边缘弱天气尺度强迫类的上述指数显著强于其他3类;热带气旋类降水相比其他类降水凌晨低层的辐合更为明显、垂直涡度也更大,低涡切变和低槽冷锋类降水的0~6 km垂直风切变最大、850 hPa与500 hPa温差显著大于其他类,表明其对流层中低层具有更强的斜压性。总体来看,该区域凌晨时段强降水的主要天气形势为低涡切变、低槽冷锋和热带气旋,具有特别充沛的水汽条件、不太强的条件不稳定和中等偏弱的0~6 km垂直风切变环境条件等。

Identification of synoptic patterns for extreme rainfall events associated with landfalling typhoons in China during 1960–2020

Extreme rainfall associated with landfalling typhoon(ERLTC)can cause severe disasters and economic impacts throughout China.Improving the accuracy of ERLTC forecasts is therefore crucial in disaster prevention and mitigation.The top 26 ERLTC events in China during 1960–2020 are investigated based on multi-source datasets.These ERLTC events are categorized into five main types according to the geographical location of the extreme precipitation and its position relative to the tropical cyclone(TC)center,namely:the typhoon inner-core rainfall in Taiwan(TWIC),typhoon inverted trough rainfall in Taiwan(TWIT),weak typhoon rainfall in Hainan(HNWK),strong typhoon rainfall in Zhejiang(ZJST)and inland typhoon remnant rainfall(ILRM).All the ERLTC events occurred in the weakening stage of TC after reaching its lifetime maximum intensity in convective cloud(TBB≤−32℃)regions over complex local terrain.The translational speeds of 20 TCs(76.9%of the total)were smaller than the climatological average(20.6 km h^(−1))during the extreme precipitation events.The differences are as follows:the TWIC and TWIT types are featured with different season,track and water vapor channel although both occurred in Taiwan.The other three types are distinguished by spinning track and strong convective cloud for HNWK type,strong TC intensity and binary TC interactions for ZJST type;and stagnation and strong westerly trough activity for ILRM type,respectively.These results are expected to provide useful clues for an in-depth understanding of ERLTC events over China.

潮州市城区PM_(2.5)-O_(3)复合污染特征及其发生的天气型分析

利用广东省潮州市区2014—2020年空气质量逐小时浓度数据,分析了PM_(2.5)、O_(3)浓度及复合污染的年、月、日变化特征,利用再分析资料对天气形势进行分型,结合相应时段潮州国家气象观测站资料,分析PM_(2.5)-O_(3)与天气型及气象因子的关系。结果表明:2014—2020年潮州市区年平均ρ(PM_(2.5))、ρ(O_(3)-8h)及复合污染出现日数均呈波动下降趋势,月平均ρ(PM_(2.5))最高出现在3月,月平均ρ(O_(3)-8h)最高出现在10月,两种污染物最低均出现在6月。复合污染出现较多的是10月—次年4月。PM_(2.5)、O_(3)污染具有一定相互作用,当其中一种污染物日均浓度较高时另一种污染物的浓度相应较高,同时污染物浓度的日较差也会相应增大;污染物峰谷值出现时间表现为空气污染较严重时,O_(3)峰值出现时间在15时,PM_(2.5)峰值出现在19—20时。造成潮州复合污染的天气型主要为变性高压脊(DR)、反气旋(AC)、副高+变性高压脊(SD)、均压场(UP)天气型,主要出现在秋、冬、春季,少数出现在初夏。4种天气型出现的季节不同,但总体来说晴朗干燥是出现复合污染的主要气象因素。

华北区域大气污染过程中天气型和输送路径分析

以2003年10月2—12日一次大气污染过程为例,分析华北区域大气污染过程中主要天气型及污染物输送路径.结果表明:区域内大部分城市API上升阶段,以西南输送路径为主;API下降阶段,受纬向锋区影响,以东北气流为主,污染物从北向南扩散,区域内各城市空气质量好转.统计2003年9—12月7次大气污染过程发现,当华北地区为大尺度高气压控制时,若高压中心位于山西南部,河北为弱的低气压区(地形槽)时,将导致西南风气流盛行该区域.受地形和天气型控制,西南方向的输送通道是引起北京大气污染过程的主要通道.

天气类型对天津大气PM_(10)污染的影响分析

对天津地区出现的天气系统进行分类,利用后向轨迹模型对到达天津地区的气团轨迹进行模拟,并按照不同天气类型进行聚类,分析不同天气类型下到达天津地区的气团传输路径.结果表明,到达天津地区的气团主要来自西北和东南方向.对采暖期和非采暖期不同天气类型的出现频率及其对大气中ρ(PM10)的影响进行了研究.结果表明,天气类型是影响天津地区ρ(PM10)变化的重要因素.2008年采暖期的有利天气类型出现频率为84.2%,比非采暖期(66.9%)多17.3%;同期不利天气类型出现频率为15.8%,比非采暖期(32.2%)低16.4%.虽然采暖期颗粒物排放源强有所增加,但是有利天气类型出现频率明显高于非采暖期,而不利天气类型出现频率较低,这是2008年采暖期ρ(PM10)比非采暖期略低的重要原因.

造成北京PM10重污染的二类典型天气形势

利用北京空气质量监测资料和NCEP再分析资料,分析了北京发生PM10重污染的天气形势。研究表明:1)虽然北京地区PM10重污染(API指数3级以上)每年只有10 d左右,但与之关联的轻微或轻度空气污染(API指数3级)天数,却可能占全年3级污染总天数的40%-50%。因此,分析研究造成北京PM10重污染的天气形势,对于空气污染的预警预报以及污染源的控制和管理,都具有十分重要意义。2)通过海平面气压场的主观分析,确定了二类北京PM10重污染的典型天气形势,即高压南下东移阻滞型和与北上台风(或热带低压)相关联的弱高压控制型,并指出了后者在2008年奥运会期间,对开展北京空气污染预报和污染控制的指导作用。

典型时段区域污染过程分析及系统聚类法的应用

应用系统聚类法对2008年12月4─17日全国86个城市由空气污染指数(API)反算的ρ(PM10)进行聚类分析.结果表明:86个城市可以分为7个分区;根据ρ(PM10)的区域同步性特征及实况天气背景分析对7个分区进行了调整.在12月4─17日的区域污染过程中,华北和华东2个地区为全国ρ(PM10)的高值区.对华北和华东地区城市群ρ(PM10)的区域环境过程同步演变特征进行了对比分析,并根据天气型演变规律对形成原因进行了研究,建立了城市及区域环境污染过程与天气型背景的关系概念模型,揭示出天气型组合系统、分布、尺度、移动和演变是造成大气环境质量区域性、过程性特征和ρ(PM10)峰谷值出现的主要原因.用聚类分析方法和所提出的概念模型可对我国城市和区域环境污染分区控制和成因进行深入研究.

连云港温带风暴潮及可能最大温带风暴潮的计算

用 4 6a资料首次对连云港温带风暴潮进行了统计分析 ,计算了不同重现期的温带风暴潮 (增、减水 )值 ,并划分引起温带风暴潮的天气类型 ;进而首次构造引起连云港可能最大温带风暴潮 (增、减水 )的天气系统 ;最后 ,采用经过典型温带风暴潮过程数值模拟检验的风暴潮数学模型 ,计算了连云港可能最大温带风暴潮 ,计算结果已被江苏田湾 (连云港 )

上海市一次罕见的连续11天空气污染过程的特征及成因分析

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