苏沪浙地区短时强降水与冰雹天气分布及物理量特征对比分析

利用1971-2006年气象记录月报表A文件资料及1999-2009年自计、自动站降水资料对苏浙沪地区短时强降水与冰雹天气时空分布特征进行统计分析基础上,对华北冷涡背景条件下区域性冰雹与3小时降水量大于100 mm的极端降水过程环境场条件差异进行了对比.归纳了两种强对流天气的物理量阈值.结果表明:冰雹年发生频率先递减后略增,30～50mm·h-1降水天气日数缓慢增加,高发区均位于江苏省北部.强降水较冰雹天气华北冷涡浅薄位置偏南,冷空气强度较弱,伴随低空急流,深厚湿对流明显;冰雹天气时高空急流强盛且偏南,上千下湿呈干对流风暴特征,两者均由低层不连续线触发.统计表明,0℃层高度、△T850-500、K指数、可降水量和高空风切变等参数冰雹与强降水天气分别平均相差-1700m、7℃、8℃、-37 mm和1.63×10-3s-1,这些物理量用来区分对流天气类型较好.     

引发广西两次严重山洪地质灾害的暴雨过程分析

利用常规观测、卫星云图、雷达探测以及自动站雨量等资料,对2010年6月27—28日和2012年5月20—22日桂西北两次严重山洪地质灾害的气象条件进行了对比分析,结果表明：(1) 强降雨发生在桂西北暴雨区,最大过程雨量＞350 mm,过程最大中尺度雨团和致灾区最大中尺度雨团值分别＞100 mm·h-1和＞70 mm·h-1,集中降雨时段为02—06时。灾害开始于后半夜,发生在最大过程雨量和地质条件脆弱区,不同的地质状况对应不同的灾害;灾害性天气具有区域小、降雨时段集中、过程雨量大、强度强及引发灾害重等特征。(2) 暴雨发生在欧亚地区500 hPa呈两脊一槽型、200 hPa南亚高压脊线贯穿广西上空及季风云系活跃的背景下。高空为槽或低涡、地面为干线或锋面,属低涡暴雨型。(3) 高空要素变化为雨前降压升温、后降温。不稳定能量及层结、低层辐合、中低层涡旋、整层大气的上升运动、高温高湿及水汽强烈辐合是物理量特征。(4) 云图上对流云团生成、合并对强降雨有指示意义,暴雨发生在云团合并发展阶段;TBB值＜200 K可以作为强降雨的指标。低质心强雷达回波产生的列车效应或回波停滞和地形作用是造成强降雨的重要因素,低层辐合、高层辐散导致了强烈的上升运动,有利于强对流的发展与维持。     

对流云盐粉催化的数值模拟研究

利用三维对流云盐粉催化模式对一次对流云降水进行了盐粉催化模拟试验。模式中考虑了盐粉与云雨滴和冰相粒子间的相互作用。模拟结果显示,当采用20μm粒径的盐粉进行单次催化时,催化效果较好,地面总雨量可增加20%,催化次数的增多会导致降水的减少。霰融化成雨是雨滴增长的主要机制,霰碰并过冷云水是霰增长的主要机制,催化后霰融化成雨和霰碰并过冷云水的速率均有提高。催化会引起水成物云滴、冰晶、霰和雨滴的比质量变化。霰和云滴的粒径也在催化后出现了明显的增大。催化后短时间内动力过程出现了变化。     

山西地区一次层状云降水过程的微观特征观测分析

针对山西省2010年5月27日一次层状云降水过程,利用机载DMT探头和Parsivel激光降水粒子谱仪进行探测试验,分析了云微物理特征,并对空中和地面雨滴谱进行比较。结果显示：空中云垂直和水平结构分布不均匀,CDP、CIP探测最大粒子浓度分别为165．20、1．08cm^（-3）。地面雨滴微物理量的平均值说明本次降水是典型的层状云降水,雨强主要由雨滴数密度决定,雨滴微物理参量随时间分布不均匀。建立地面雨强,与雷达反射率因子Z、雨水含量W、雨滴数浓度N、Gamma分布的谱参数眠、A的相关关系,Z-I、W-I,相关性很好,N-I、N_0-I、λ-I,相关性较差。地面平均雨滴谱较空中平均雨滴谱窄、谱型陡。结合粒子图像和雨滴特征量分析空中雨滴谱随高度的分布发现,本次降水是冷云和暖云降水共存。     

青藏高原与东亚地区暖季MCSs统计特征的对比分析

利用1996-2010年逐时红外云顶亮温(TBB)数据,对青藏高原(下称高原)和东亚地区暖季(3-9月)中尺度对流系统(MCSs)进行了普查,获得MCSs各项特征数据集;结合CMORPH降水资料对MCSs降水特征进行了分析,在此基础上又对高原和东亚地区的MCSs特征进行了对比分析.结果表明:(1)高原是东亚地区MCSs高频发生区域,高原MCSs的平均发生频次远高于东亚25°N以北地区,是东亚25°N以北地区唯一的MCSs活动高频区.高原腹地是高原地区MCSs发生的高频区,在31°N,88°E附近MCSs的发生频次最高.(2)高原地区 MCSs分布有明显的月际变化,春季主要出现在高原北部,夏季主要出现在高原中东部和南部.高原地区MCSs的月际变化特征与东亚地区基本一致,东亚夏季风是其月际变化的重要影响因子.(3)高原地区MCSs存在明显的单峰型日变化特征,但春、夏季日变化特征略有不同,高原MCSs的日变化较东亚地区更为显著.(4)高原地区MCSs的降水频次为5.6％,降水贡献率为10.1％,最大降水频次为12％,最大降水贡献率为27％;与东亚地区相比,高原的MCSs降水偏小.(5)高原地区的MCSs大多为向东移动且移速缓慢以及短生命史的MβCS,平均生命史为4.6h,平均面积约为11.2×10-1km2,平均移速为31.5 km·h-1,东移的MCSs占59.4％;与整个东亚地区的MCSs相比,高原的MCSs面积和尺度都较小,生命史略短且移速慢,云顶平均TBB和平均最低TBB均偏高.     

VARIATIONS OF CLOUD FRACTION OVER EAST ASIA UNDER GLOBAL WARMING CONDITIONS IN THE PAST 20 YEARS

Based on the variation of cloud fraction revealed by D2 Cloud Climatic Data of the International Satellite Cloud Climatology Project and trend analysis methods, the trend of different types of cloud fraction over East Asia during 1984-2006 is obtained. The analysis focuses on the relationship between temperature and different cloud fraction under the background of globe warming. The result shows a fluctuating decreasing tendency in the total cloud fraction, high-level cloud and low-level cloud over East Asia with the decrement being 2.24%, 1.65% and 1.68%, respectively, while the mid-level cloud increases by 1.07%. In addition, there are great regional differences in cloud fraction. Temperature and water-vapor content variation caused by the greenhouse effects over East Asia is the primary reason for the variation of cloud fraction. Over the Tibetan Plateau, the Bay of Bengal and the Intertropical Convergence Zone, the temperature is negatively correlated with high-level cloud, but positively correlated with mid- and low-level cloud. However, over the West Pacific and the ocean east and north of Japan, the temperature is negatively correlated with low-level cloud but positively correlated with high-level cloud.     

Shortwave cloud radiative forcing on major stratus cloud regions in AMIP-type simulations of CMIP3 and CMIP5 models

Cloud and its radiative effects are major sources of uncertainty that lead to simulation discrepancies in climate models. In this study, shortwave cloud radiative forcing (SWCF) over major stratus regions is evaluated for Atmospheric Models Intercomparison Project (AMIP)-type simulations of models involved in the third and fifth phases of the Coupled Models Intercomparison Project (CMIP3 and CMIP5). Over stratus regions, large deviations in both climatological mean and seasonal cycle of SWCF are found among the models. An ambient field sorted by dynamic (vertical motion) and thermodynamic (inversion strength or stability) regimes is constructed and used to measure the response of SWCF to large-scale controls. In marine boundary layer regions, despite both CMIP3 and CMIP5 models being able to capture well the center and range of occurrence frequency for the ambient field, most of the models fail to simulate the dependence of SWCF on boundary layer inversion and the insensitivity of SWCF to vertical motion. For eastern China, there are large differences even in the simulated ambient fields. Moreover, almost no model can reproduce intense SWCF in rising motion and high stability regimes. It is also found that models with a finer grid resolution have no evident superiority than their lower resolution versions. The uncertainties relating to SWCF in state-of-the-art models may limit their performance in IPCC experiments.     

Relationship between cloud characteristics and radar reflectivity based on aircraft and cloud radar co-observations

Cloud properties were investigated based on aircraft and cloud radar co-observation conducted at Yitong, Jilin, Northeast China. The aircraft provided in situ measurements of cloud droplet size distribution, while the millimeter-wavelength cloud radar vertically scanned the same cloud that the aircraft penetrated. The reflectivity factor calculated from aircraft measurements was compared in detail with simultaneous radar observations. The results showed that the two reflectivities were comparable in warm clouds, but in ice cloud there were more differences, which were probably associated with the occurrence of liquid water. The acceptable agreement between reflectivities obtained in water cloud confirmed that it is feasible to derive cloud properties by using aircraft data, and hence for cloud radar to remotely sense cloud properties. Based on the dataset collected in warm clouds, the threshold of reflectivity to diagnose drizzle and cloud particles was studied by analyses of the probability distribution function of reflectivity from cloud particles and drizzle drops. The relationship between reflectivity factor (Z) and cloud liquid water content (LWC) was also derived from data on both cloud particles and drizzle. In comparison with cloud droplets, the relationship for drizzle was blurred by many scatter points and thus was less evident. However, these scatters could be partly removed by filtering out the drop size distribution with a large ratio of reflectivity and large extinction coefficient but small effective radius. Empirical relationships of Z-LWC for both cloud particles and drizzle could then be derived.