雷达雨量计联合估算降水在城市内涝模型中的应用

为了满足城市暴雨内涝模型对面雨量精细化的需求,在天津城市暴雨内涝模型的基础上,将雷达估算降水产品应用到模型面雨量计算中,针对2012年7月25—26日天津的大暴雨过程,考察4种雷达估算降水产品和两种插值方法计算的内涝模型面雨量。经过对比发现,利用变分方法计算的雷达估算降水产品VAR用曲面插值方法计算内涝模型的面雨量整体效果最好。     

影响辽东半岛两个台风Meari和Muifa暴雨环流特征的对比分析

Meari(1105)和Muifa(1109)是两个路径相似并在辽东半岛产生强降水的热带气旋。但Meari强降水持续时间长,而Muifa时间短。利用中国气象局热带气旋年鉴、FY_2D(0.1°×0.1°)云顶亮温资料、大连地区逐时自动气象站降雨量资料、常规观测资料和NCEP/NCAR再分析资料,对两个台风影响辽东半岛的降水过程进行了对比分析。结果表明:(1)Meari影响辽东半岛时强度较弱,但与低空东南急流相连获得丰富水汽供应,衰减缓慢;Muifa影响时强度较强但与低空急流水汽通道快速断开而衰减迅速。(2)两个台风均进入西风槽区而变性,但两者影响辽东半岛时处于不同的变性阶段。Meari为半冷半暖结构,其北侧中尺度对流活动发展旺盛,非对称结构明显;Muifa低层环流已变性为冷中心,云系结构孤立且无发展。(3)两次过程中辽东半岛位于台风不同的对流发展区域是其降水强度差异的原因之一。辽东半岛位于Meari北侧,低层的水平辐合较强,水平风垂直切变较大,深厚的上升运动维持;Muifa影响下辽东半岛位于其西侧,受偏北风下沉气流控制,不稳定度和动力抬升条件减弱。     

夏季云顶高于对流层顶事件对东亚天气型及上对流层-下平流层大气结构的影响

采用A-Train系列卫星的AURA/MLS水汽、温度资料,CALIPSO/CALIOP云物理资料,结合ECMWF气象再分析资料,分析了东亚地区云顶高于对流层顶事件(Cloud Top Above the Tropopause,CTAT)的区域分布,及其对上对流层-下平流层(Upper Troposphere and Lower Stratosphere,UTLS)水汽和温度结构的影响。结果表明:亚洲季风区的夏季CTAT发生率是30%~55%,为全球最强区域;东北亚的夏季CTAT发生率是15%~20%,为中纬度最强分布区。以CTAT为指标的合成结果表明:15~30°N的东亚-西太平洋UTLS,水汽呈"上干下湿"的异常分布,温度呈"上冷下暖"的异常分布,该结构与该区域热带气旋合成的结果一致,说明热带气旋是该区域CTAT形成的主要天气系统;35~50°N的东北亚UTLS,水汽呈"上干下湿"的异常分布,温度呈"上暖下冷"的异常分布,该结构与该区域温带气旋合成的结果一致,说明温带气旋是该区域CTAT形成的主要天气系统。     

北京“7.21”特大暴雨云降水结构及云雨转化特征

利用多普勒雷达资料、FY-2E静止卫星和MODIS极轨卫星反演产品,研究2012年7月21日北京特大暴雨的云降水结构及云雨转化特征。结果表明:降水过程三阶段的云降水垂直结构不同。1)在暖区对流降水阶段,降水以暖雨机制启动,雨滴在暖区存在深厚的碰并增长过程,暖雨过程对降水起主要贡献。随着云体的发展,冷雨过程加剧。T-Re分析表明,-10℃层以下云滴凝结碰并显著,-10℃层以上为深厚的冰相增长带,云顶以冰相大粒子为主,云水向雨水转化迅速。2)在锋面对流降水阶段,降水系统为高度组织化的"低质心"强降水液态MCC(Mesoscale Convective Complex)系统。回波强度在冰水混合层增长较快,冻结层是此阶段成雨微物理的关键层。降水粒子在暖云区碰并增长较快,而蒸发或破碎过程并不显著。3)在锋后降水阶段,0℃层附近冰晶粒子与云水的碰并增长较为明显。前期降水存在明显的雨滴蒸发过程。随着云体的发展,暖区云水含量较少,降水粒子不能有效碰并增长。     

Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) in predicting station-scale rainfall in South China(SC) in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output(DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.     

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.     

Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Niño3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Niño-Southern Oscillation.     

Mesoscale dynamics and its application in torrential rainfall systems in China

Progress over the past decade in understanding moisture-driven dynamics and torrential rain storms in China is reviewed in this paper. First, advances in incorporating moisture effects more realistically into theory are described, including the development of a new parameter, generalized moist potential vorticity(GMPV) and an improved moist ageostrophic Q vector(Qum). Advances in vorticity dynamics are also described, including the adoption of a "parcel dynamic" approach to investigate the development of the vertical vorticity of an air parcel; a novel theory of slantwise vorticity development, proposed because vorticity develops easily near steep isentropic surfaces; and the development of the convective vorticity vector(CVV)as an effective new tool. The significant progress in both frontal dynamics and wave dynamics is also summarized, including the geostrophic adjustment of initial unbalanced flow and the dual role of boundary layer friction in frontogenesis, as well as the interaction between topography and fronts, which indicate that topographic perturbations alter both frontogenesis and frontal structure. For atmospheric vortices, mixed wave/vortex dynamics has been extended to explain the propagation of spiral rainbands and the development of dynamical instability in tropical cyclones. Finally, we review wave and basic flow interaction in torrential rainfall, for which it was necessary to extend existing theory from large-scale flows to mesoscale fields, enriching our knowledge of mesoscale atmospheric dynamics.     

2013年7月1日河北宁晋极端短时强降水成因研究

2013年7月1日午后至夜间,华北出现一次区域性暴雨和局地大暴雨过程。局地极端降水出现在河北省邢台市宁晋县四芝兰镇,过程雨量409 mm,其中当日17—19时连续2 h雨量超过100 mm。利用常规高空和地面观测资料、NCEP再分析资料和石家庄新一代天气雷达资料,探讨了宁晋极端短时强降水的形成原因。主要结论是:(1)低槽、冷锋、副热带高压及其外围低涡切变线为其主要影响系统,海南附近台风远距离影响加强了水汽自南向北的输送,半定常的地面辐合切变线对新生对流的触发和已有对流的维持及加强起到重要作用;(2)宁晋最强降水期间,其上空具有较强的垂直风切变,有利于高度组织化的对流系统发展;(3)对流系统的后向传播使回波主体移动缓慢、持续时间长,而回波强度大和雨强很强,则导致四芝兰镇极端强降水,此外,具有弱中气旋的超级单体相对较长时间的影响使其对四芝兰镇强降水具有重要贡献;(4)产生极端降水的对流系统属于高质心发展强烈的大陆强对流型,而非更易导致强降水的低质心系统。同时,针对众多学者研究北京"7.21"特大暴雨得到的一些结论进行了进一步探讨和验证。     

地形对台风“海葵”降水增幅影响的研究

使用NCEP GFS资料和WRF V3.4模式对2012年第11号台风"海葵"(1211)引发的安徽强降水过程进行数值模拟,通过改变模式中安徽省大别山区和皖南山区的地形高度,设计一组敏感性试验,对"海葵"降水的地形增幅效应进行研究。结果表明:(1)WRF模式对台风"海葵"降水过程有较好的模拟能力。(2)大别山区和皖南山区地形对"海葵"移动路径、强度以及降水分布、强度均有不同程度的影响;不同地形高度下模拟的台风路径及降水分布差异较大,且降水中心强度与地形高度相关性较好,地形对暴雨增幅作用明显。(3)山区地形有利于中尺度辐合线和低涡生成、发展,并有强水汽辐合中心与之相对应;有地形时对流层低层上升运动比无地形时明显加强,对安徽中南部强降水增幅作用显著。