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941.
梅雨锋云系的结构特征及其成因分析 总被引:7,自引:9,他引:7
利用逐时卫星遥感观测资料和地面测站的降水资料,分析了江淮流域2003年6月22~26日暴雨过程中梅雨锋云系的演变、结构特征和形成原因。结果表明,梅雨锋云系为一条TBB的低值带,稳定少动,其上分布着中尺度对流系统(MCS),而中尺度对流系统是由不同尺度、不同强度.的对流单体(包括中β和中γ尺度对流单体)组成的,从而使得梅雨锋云系产生不均匀的降水分布(包括时间上和空间上)。在该暴雨过程中,梅雨锋云系充分体现了中尺度对流系统中所包括的3类组织结构形式。梅雨锋云系与中高纬度云系或热带辐合带云系之间的相互作用与暴雨过程关系密切,梅雨锋云系的维持和发展与强大的黄淮气旋云系直接相关,它是江淮流域上空冷暖气流交汇的结果。 相似文献
942.
利用24个CMIP6全球气候模式的逐日降水模拟资料,基于广义极值分布(GEV)模型,研究了全球增暖1.5/2℃下我国20、50和100 a重现期极端降水的未来风险变化。可以发现,相对于历史时期(1995—2014年),全球升温1.5和2℃下极端降水发生概率风险空间分布相近,总体上呈现增加趋势,但额外增暖0.5℃将导致更高的风险。如50 a重现期极端降水,在增暖1.5/2℃下其重现期将分别变为17/14 a,极端降水将变得更加频繁。不同区域对气候变暖的响应存在区域差异,其中中国西部长江黄河中上游和青藏高原地区、中国东部长江黄河中下游及其以南地区,极端降水发生概率比达到3以上,局部更是达到5以上,为我国极端降水气候变化响应高敏感区域。进一步,基于概率分布函数从理论角度探讨了位置和尺度参数对发生概率风险的影响与贡献度量,并用于探讨极端降水气候平均态和变率变化对极端降水发生风险的影响,结果显示:位置和尺度参数的增量变化、风险变化率存在着显著的东西部差异,从而导致极端降水发生风险的影响因素存在差异。如中国西部尽管极端降水气候平均态和变率变化幅度不大,但因风险变化率较高,从而导致该区域的发生风险大... 相似文献
943.
944.
饱和粉土液化特性的大型振动台模型试验研究 总被引:3,自引:0,他引:3
京沪高速铁路徐沪段路基的粉土粘粒含量少于1.5%、粉粒含量约为80%,在强烈地震作用下存在着液化可能性.为充分研究这一饱和粉土地层的液化特性,本文作者利用大型地震模拟振动台,进行了模拟自由场地饱和粉土的地震液化模型试验,试验结果再现了自然地震触发的粉土液化的各种宏观震害现象,揭示了饱和粉土的地震液化规律和特征。试验结果为京沪高速铁路徐沪段路基的抗震设计提供了参考依据。 相似文献
945.
946.
纵横波独立成像的弹性波逆时偏移方法 总被引:1,自引:1,他引:1
本文采用逆时递推,在空间域完成纵横波分离,同时实现了位移场成像和纵、横波的独立成像。模拟数据与实际资料试算表明,对纵波和横波分别成像不仅有理论意义,而且有明显的应用价值。文中还就偏移速度对成像结果的影响和实际资料的预处理问题进行了讨论。 相似文献
947.
948.
DING Guoan CHAN Chuenyu GAO Zhiqiu YAO Wenqing LI Yoksheung CHENG Xinghong MENG Zhaoyang YU Haiqing WONG Kamhang WANG Shufeng MIAO Qiuju 《中国科学D辑(英文版)》2005,48(Z2)
The vertical structures and their dynamical character of PM2.5 and PM10 over Beijing urban areas are revealed using the 1 min mean continuous mass concentration data of PM2.5 and PM10 at 8, 100, and 320 m heights of the meteorological observation tower of 325 m at Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP CAS tower hereafter) on 10―26 August, 2003, as well as the daily mean mass concentration data of PM2.5 and PM10 and the continuous data of CO and NO2 at 8, 100 (low layer), 200 (middle layer), and 320 m (high layer) heights, in combination with the same period meteorological field observation data of the meteorological tower. The vertical distributions of aerosols observed on IAP CAS tower in Beijing can be roughly divided into two patterns: gradually and rapidly decreasing patterns, I.e. The vertical distribution of aerosols in calm weather or on pollution day belongs to the gradually decreasing pattern, while one on clean day or weak cold air day belongs to the rapidly decreasing pattern. The vertical distributive characters of aerosols were closely related with the dynamical/thermal structure and turbulence character of the atmosphere boundary layer. On the clean day, the low layer PM2.5 and PM10 concentrations were close to those at 8 m height, while the concentrations rapidly decreased at the high layer, and their values were only one half of those at 8 m, especially, the concentration of PM2.5 dropped even more. On the clean day, there existed stronger turbulence below 150 m, aerosols were well mixed, but blocked by the more stronger inversion layer aloft, and meanwhile, at various heights, especially in the high layer, the horizontal wind speed was larger, resulting in the rapid decrease of aerosol concentration, I.e. Resulting in the obvious vertical difference of aerosol concentrations between the low and high layers. On the pollution day, the concentrations of PM2.5 and PM10 at the low, middle, and high layers dropped successively by, on average, about 10% for each layer in comparison with those at 8 m height. On pollution days, in company with the low wind speed, there existed two shallow inversion layers in the boundary layer, but aerosols might be, to some extent, mixed below the inversion layer, therefore, on the pollution day the concentrations of PM2.5 and PM10 dropped with height slowly; and the observational results also show that the concentrations at 320 m height were obviously high under SW and SE winds, but at other heights, the concentrations were not correlated with wind directions. The computational results of footprint analysis suggest that this was due to the fact that the 320 m height was impacted by the pollutants transfer of southerly flow from the southern peripheral heavier polluted areas, such as Baoding, and Shijiazhuang of Hebei Province, Tianjin, and Shandong Province, etc., while the low layer was only affected by Beijing's local pollution source. The computational results of power spectra and periods preliminarily reveal that under the condition of calm weather, the periods of PM10 concentration at various heights of the tower were on the order of minutes, while in cases of larger wind speed, the concentrations of PM2.5 and PM10 at 320 m height not only had the short periods of minute-order, but also the longer periods of hour order. Consistent with the conclusion previously drawn by Ding et al., that air pollutants at different heights and at different sites in Beijing had the character of "in-phase" variation, was also observed for the diurnal variation and mean diurnal variation of PM2.5 and PM10 at various heights of the tower in this experiment, again confirming the "in-phase" temporal/spatial distributive character of air pollutants in the urban canopy of Beijing. The gentle double-peak character of the mean diurnal variation of PM2.5 and PM10 was closely related with the evident/similar diurnal variation of turbulent momentum fluxes, sensible heat fluxes, and turbulent kinetic energy at various heights in the urban canopy. Besides, under the condition of calm weather, the concentration of PM2.5 and PM10 declined with height slowly, it was 90% of 8 m concentration at the low layer, a little lesser than 90% at the middle layer, and 80% at the high layer, respectively. Under the condition of weak cold air weather, the concentration remarkably dropped with height, it was 70% of 8 m concentration at the low layer, and 20%―30% at the middle and high layers, especially the concentration of PM2.5 was even lower. 相似文献
949.
针对当前单模式系统臭氧(O3)预报的不确定性问题,提出了一种基于活动区间的多模式超级集成的、高效的预报方法。本研究基于长江三角洲(长三角)地区多模式空气质量预报系统,将改进后的超级集成预报方法(AR-SUP)运用到2015年长三角地区的O3预报中,并与滑动训练期的超级集成预报(R-SUP)、多模式集成平均预报(EMN)、消除偏差的集成平均预报(BREM)对比,结果表明AR-SUP对预报效果的改善最明显,其在暖季和冷季的均方根误差(RMSE)较最优单模式平均下降了20%和23%。将AR-SUP运用到48 h和72 h预报中发现,当预报时效增加时该方法依旧保持较高的预报技巧。多项统计数据均证明AR-SUP在研究时段内所有站点均能显著减小O3预报误差、提高整体相关性和一致性,有效提高当前短期(三天)预报准确率。 相似文献
950.