闪电和固、液态降水关系差异的数值模拟

为了进一步认识闪电和固、液态降水的关系,本文利用三维雷暴云动力-电耦合数值模式,通过设置敏感性试验组,模拟了一次雷暴过程,分析雷暴中闪电和降水的特征,以及闪电和固、液态降水对垂直风速的依赖关系,探讨闪电与固、液态降水的时空分布关系和单次闪电表征的降水量(RPF:rainyields per flash)。结果表明:对流云降水中,液态降水占主要部分,但固态降水比液态降水对于垂直风速的依赖性更强。随着对流的增强,固态降水在总降水中占的比重越来越大。首次放电时间不断提前,闪电峰值落后垂直风速峰值,总闪数一开始随对流的增强而增加,对流一旦增强到一定程度,总闪数则逐渐减小。固态降水和液态降水的开始时间和峰值时间均随着对流的增强而不断提前,而液态降水出现时间和峰值时间均提前于固态降水。雷暴云首次放电的时间滞后于液态降水,而闪电峰值提前固态降水峰值或与固态降水峰值同时产生。雷暴云中的放电活动集中在强降水区域前缘的较弱降水区,强降水区对应的闪电较少,对流的增强会使降水区域面积、降水量和降水强度增加。由于液态降水总量远大于固态降水总量,固、液态RPF的数值相差达到一个量级,但单位时间内固态降水和液态降水增加的速率相近。在单位时间内闪电次数越多,RPF则越小,而固态RPF和闪电次数的线性相关性明显好于液态RPF,所以利用固态降水可以更好地预报闪电。这些结果有助于进一步认识闪电和降水的关系,并可为闪电预报提供新的思路。     

The Relationship between the Interdecadal Variation of Summer Precipitation and Its Interannual Variability over the Middle and Lower Reaches of the Yangtze River Valley

The intensity of summer precipitation interannual variability is strongest over the middle and lower reaches of the Yangtze River Valley(MLYRV). The variability is larger than 1.5 mm d–1 and up to 35%–40% of the climatological mean summer precipitation. The relationship between the interdecadal change in the intensity of summer precipitation and its interannual variability over this area is investigated, by analyzing five gauged and re-constructed precipitation datasets. The relationship is found to be very weak over the MLYRV, with a correlation coefficient of only approximately 0.10. The Pacific Decadal Oscillation influences the western North Pacific subtropical high, which is responsible for the interdecadal change in summer precipitation over the MLYRV. However, the precipitation interannual variability is closely related to the ENSO events in the preceding winter due to its impact on the meridional displacement of the East Asian westerly jet. Different physical mechanisms cause different interdecadal variation in the intensity of summer precipitation and its interannual variability, and thus result in a poor relationship.     

南京市降水化学特征及其来源研究

为了解南京江北地区降水化学特征,分析了2011年3—6月共25个降水日的109个降水样品中的主要水溶性离子,并利用后向轨迹模式探讨了降水气团来源.结果表明:1)南京地区3—6月降水主要受南、北2种气团影响,北方气团降水的主要离子浓度高于南方气团降水.2)海盐示踪法和相关性分析显示,降水中NO₃^-和SO₄^2-主要来自燃煤、工业排放和汽车尾气;Ca²⁺主要来自地壳源;Cl^-主要来自海洋;海洋源和陆源对Mg²⁺和K⁺都有贡献,Mg²⁺的陆源贡献大于海洋源贡献,K⁺受海洋源的影响程度要低于Mg²⁺.3)南、北气团初期降水的各离子浓度高于总降水的各离子浓度,且初期降水的主要离子的富集系数高于总降水.这说明在降水初始阶段,雨水对南京大气中污染物(气态污染物和颗粒物)的云下冲刷去除作用较强,降水的离子浓度最高,局地源对降水离子的贡献较明显.     

Impacts of Two Ice Parameterization Schemes on the Cloud Microphysical Processes and Precipitation of a Severe Storm in Northern China

A severe storm that occurred over Beijing in northern China on 23 June 2011 was simulated with two different ice crystal parameterization schemes(the DeMott scheme and Meyers scheme) by using the Regional Atmospheric Modeling System. Compared with the DeMott scheme, the simulation results with the Meyers scheme have the following characteristics:(1) Updrafts are stronger and more numerous;(2) The cloud is better organized and contains a greater peak of ice-phase hydrometeor mixing ratios;(3) Cloud water and hail mixing ratios increase while graupel mixing ratios decrease;(4) The surface precipitation is initially greater. However, at the end of the simulation, less precipitation is produced. In short, the differences between the two schemes are not obvious, but the De Mott scheme has a relatively more reasonable result.     

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。     

A diagnostic analysis on the effect of the residual layer in convective boundary layer development near Mongolia using 20th century reanalysis data

Although the residual layer has already been noted in the classical diurnal cycle of the atmospheric boundary layer,its effect on the development of the convective boundary layer has not been well studied. In this study, based on 3-hourly20 th century reanalysis data, the residual layer is considered as a common layer capping the convective boundary layer. It is identified daily by investigating the development of the convective boundary layer. The region of interest is bounded by(30°–60° N, 80°–120° E), where a residual layer deeper than 2000 m has been reported using radiosondes. The lapse rate and wind shear within the residual layer are compared with the surface sensible heat flux by investigating their climatological means, interannual variations and daily variations. The lapse rate of the residual layer and the convective boundary layer depth correspond well in their seasonal variations and climatological mean patterns. On the interannual scale, the correlation coefficient between their regional averaged(40°–50°N, 90°–110° E) variations is higher than that between the surface sensible heat flux and convective boundary layer depth. On the daily scale, the correlation between the lapse rate and the convective boundary layer depth in most months is still statistically significant during 1970–2012. Therefore, we suggest that the existence of a deep neutral residual layer is crucial to the formation of a deep convective boundary layer near the Mongolian regions.     

Deep convective clouds over the northern Pacific and their relationship with oceanic cyclones

Based on combined Cloud Sat/CALIPSO detections, the seasonal occurrence of deep convective clouds(DCCs) over the midlatitude North Pacific(NP) and cyclonic activity in winter were compared. In winter, DCCs are more frequent over the central NP, from approximately 30°N to 45°N, than over other regions. The high frequencies are roughly equal to those occurring in this region in summer. Most of these DCCs have cloud tops above a 12 km altitude, and the highest top is approximately 15 km. These wintertime marine DCCs commonly occur during surface circulation conditions of low pressure, high temperature, strong meridional wind, and high relative humidity. Further, the maximum probability of DCCs,according to the high correlation coefficient, was found in the region 10°–20° east and 5°–10° south of the center of the cyclones. The potential relationship between DCCs and cyclones regarding their relative locations and circulation conditions was also identified by a case study. Deep clouds were generated in the warm conveyor belt by strong updrafts from baroclinic flows. The updrafts intensified when latent heat was released during the adjustment of the cyclone circulation current. This indicates that the dynamics of cyclones are the primary energy source for DCCs over the NP in winter.     

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.     

Synergistic contribution of precipitation anomalies over northwestern India and the South China Sea to high temperature over the Yangtze River Valley

This study explores the characteristics of high temperature anomalies over eastern China and associated influencing factors using observations and model outputs. Results show that more long-duration (over 8 days) high temperature events occur over the middle and lower reaches of the Yangtze River Valley (YRV) than over the surrounding regions, and control most of the interannual variation of summer mean temperature in situ. The synergistic effect of summer precipitation over the South China Sea (SCS) region (18°-27°N, 115°-124°E) and the northwestern India and Arabian Sea (IAS) region (18°-27°N, 60°-80°E) contributes more significantly to the variation of summer YRV temperature, relative to the respective SCS or IAS precipitation anomaly. More precipitation (enhanced condensational heating) over the SCS region strengthens the western Pacific subtropical high (WPSH) and simultaneously weakens the westerly trough over the east coast of Asia, and accordingly results in associated high temperature anomalies over the YRV region through stimulating an East Asia-Pacific (EAP) pattern. More precipitation over the IAS region further adjusts the variations of the WPSH and westerly trough, and eventually reinforces high temperature anomalies over the YRV region. Furthermore, the condensational heating related to more IAS precipitation can adjust upper-tropospheric easterly anomalies over the YRV region by exciting a circumglobal teleconnection, inducing cold horizontal temperature advection and related anomalous descent, which is also conducive to the YRV high temperature anomalies. The reproduction of the above association in the model results indicates that the above results can be explained both statistically and dynamically.     

中国区域逐日融合降水数据集与国际降水产品的对比评估

中国国家气象信息中心基于2400多个国家级台站观测日降水量和CMORPH卫星反演降水产品,采用概率密度匹配和最优插值相结合的两步数据融合方法,研制了中国区域1998年以来的0.25°×0.25°分辨率的逐日融合降水产品(CMPA_Daily)。通过该数据集与广泛应用于中国天气气候领域的两种国际上降水融合产品TRMM 3B42(Tropical Rainfall Measuring Mission, 3B42)和GPCP(Global Precipitation Climatology Project, 1 degree daily)的对比评估,考察CMPA_Daily产品的质量,评价其能否合理体现中国降水的天气气候特征。首先利用2008—2010年5—9月独立检验数据定量对比了CMPA_Daily、TRMM 3B42和GPCP 三种降水产品的误差,结果表明,在误差的时间变化和空间分布上,CMPA_Daily均具有最高的相关系数和最小的平均偏差及均方根误差,TRMM 3B42其次,GPCP的误差相对较大。CMPA_Daily只低估了大暴雨,TRMM 3B42低估了大雨以上量级的降水,而GPCP低估了除小雨以外的所有降水。CMPA_Daily产品因融入了更多的站点观测信息,不论在中国东部沿海,还是中西部地形复杂区,其精度均优于TRMM 3B42和GPCP产品,即使在站点稀疏的青藏高原地区,CMPA_Daily降水量也更加接近站点观测,呈现明显的高相关。CMPA_Daily与独立检验数据的高相关在地形起伏时效果也较稳定,TRMM和GPCP的相关系数则随着地形变化幅度陡变而非常明显地降低。进一步通过对比分析各降水产品1998—2012年的气候平均降水特征表明,3种资料对中国区域气候平均降水量、降水强度、频率分布以及年际变化的总体描述基本一致,因有效融入了更多的中国站点观测信息,不论降水空间分布还是降水量,CMPA_Daily与地面观测均最为接近,在中国的中东部大部分地区对降水的估计精度明显更高,而在站点分布较稀疏的青藏高原地区,CMPA_Daily的降水分布型与TRMM、GPCP卫星融合资料类似,较地面站点插值产品更能体现出合理的降水分布。对中国强降水事件监测对比表明,CMPA_Daily产品可以更加准确地描述降水的强度变化,细致刻画降水空间分布,在把握降水小尺度特征上具有明显的优势,体现出高分辨率、高精度降水产品的特点。