冬春季切变类冰雹发生条件的对比分析

本文以 1996年 12月 31日和 1981年 5月 1日为例 ,对冬、春季节发生在江苏的较大范围的切变类冰雹天气过程作了对比分析。结果指出 ,无论冬季或春季当高原东部有深槽东移 ,冷暖空气在江淮地区交汇 ,地面抬升系统为暖切 ,并有大气层结不稳定 (Δθse( 50 0 - 850 ) <0℃ =中心和较强的风向和风速垂直切变、85 0hPa西南急流轴、85 0hPa最大水汽通量轴线、5 0 0和 85 0hPa正涡度中心等相配置时 ,就可能导致江苏地区较大范围强对流天气的发生。     

MODIS遥感监测滇池蓝藻水华分布

以中分辨率的MODIS数据作为遥感影像源,运用蓝藻水华在蓝波段、红波段和近红外波段的光谱特征,使用假彩色合成法（RGB：6-2-1）和归一化植被指数法对滇池的蓝藻水华进行遥感监测。通过星地同步试验,证明了该两种方法的正确性。其中假彩色合成法通过色彩差异表现蓝藻水华,具有视觉效果较好的优点,归一化植被指数法则以数值大小的方式区别水华浓度,该方法建立反演模型后可用于定量研究。     

鄱阳湖流域径流模型

流域径流是鄱阳湖主要来水,建立鄱阳湖流域径流模型对揭示湖泊水量平衡及其受流域自然和人类活动的影响具有重要意义.针对鄱阳湖-流域系统的特点:流域面积大(16.22×104km2)、多条入湖河流、湖滨区坡面入湖径流等,研究了相应的模拟方法,建立了考虑流域土壤属性和土地利用空间变化的鄱阳湖流域分布式径流模型.采用6个水文站1991-2001年的实测河道径流对模型进行了率定和验证.结果显示,模型整体模拟精度较高.其中,赣江、信江和饶河均取得了较好的模拟结果,月效率系数为0.82-0.95;抚河和修水模拟精度略低,为0.65-0.78.模型揭示了研究时段内年平均入湖径流总量为1623×108m3,其中,赣江最多,占47%,其次为信江和抚河,分别占13%和12%,湖滨区坡面入湖径流约占4%,其余24%来自饶河、修水以及其它入湖支流.模型将用于评估流域下垫面或气候变化引起的入湖水量变化,为湖泊水量平衡计算提供依据.     

统计降尺度方法在青海省冬季最低温度预测中的应用

利用2003-2007年国家气象中心T213L31全球中期数值预报模式逐日输出产品与青海地区25个气象站的观测数据作为试验资料, 利用相关系数和逐步回归进行因子选择, 并以单隐层神经网络和多元回归作为降尺度方法进行对比研究, 用2003-2006年间的11月1日～次年3月1日的资料作为训练样本, 以数值预报产品和前一日观测的最低温度作为因子, 建立青海省25个气候站的冬季最低温度的24, 48, 72 h预报模型, 并且以2006年12月和2007年的1、 2月作为24, 48, 72 h逐日最低温度预报试验时段。试验表明, 对于青海地区来说, 青海北部地区的预报命中率总体好于南部高原地区; 在4种对比方案中, 以选择数值预报资料结合前一日地面观测的最低温度作为主要因子的方法相对较优, 随着预报时效的延长, 24 h历史实况的作用逐渐减弱; 对于所有台站来说, 这4种方案各有优缺点, 没有一种方案可以完全代替其他所有方案; 在实际业务运行中, 对不同的台站应采用不同的预报方案进行实际业务预报。     

Vertical structures of PM10 and PM 2.5 and their dynamical character in low atmosphere in Beijing urban areas

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.     

A CLOUD-RESOLVING MODELING STUDY OF SURFACE RAINFALL PROCESSES ASSOCIATED WITH LANDFALLING TYPHOON KAEMI(2006)

The detailed surface rainfall processes associated with landfalling typhoon Kaemi(2006) are investigated based on hourly data from a two-dimensional cloud-resolving model simulation. The model is integrated for 6 days with imposed large-scale vertical velocity, zonal wind, horizontal temperature and vapor advection from National Center for Environmental Prediction (NCEP) / Global Data Assimilation System (GDAS) data. The simulation data are validated with observations in terms of surface rain rate. The Root-Mean-Squared (RMS) difference in surface rain rate between the simulation and the gauge observations is 0.660 mm h^-1, which is smaller than the standard deviations of both the simulated rain rate (0.753 mm h^-1) and the observed rain rate (0.833 mm h^-1). The simulation data are then used to study the physical causes associated with the detailed surface rainfall processes during the landfall. The results show that time averaged and model domain-mean P_s mainly comes from large-scale convergence (Q_WVF) and local vapor loss (positive Q_WVT). Large underestimation (about 15%) of P_s will occur if Q_WVT and Q_CM (cloud source/sink) are not considered as contributors to P_s. Q_WVF accounts for the variation of P_s during most of the integration time, while it is not always a contributor to P_s. Sometimes surface rainfall could occur when divergence is dominant with local vapor loss to be a contributor to P_s. Surface rainfall is a result of multi-timescale interactions. Q_WVE possesses the longest time scale and the lowest frequency of variation with time and may exert impact on P_s in longer time scales. Q_WVF possesses the second longest time scale and lowest frequency and can explain most of the variation of P_s. Q_WVT and Q_CM possess shorter time scales and higher frequencies, which can explain more detailed variations in P_s. Partitioning analysis shows that stratiform rainfall is dominant from the morning of 26 July till the late night of 27 July. After that, convective rainfall dominates till about 1000 LST 28 July. Before 28 July, the variations of in rainfall-free regions contribute less to that of the domain-mean Q_WVT while after that they contribute much, which is consistent to the corresponding variations in their fractional coverage. The variations of Q_WVF in rainfall regions are the main contributors to that of the domain-mean Q_WVF, then the main contributors to the surface rain rate before the afternoon of 28 July.     

黄土高原半干旱区非均一下垫面粗糙度分析

利用2007年4月17日-2008年4月16日兰州大学半干旱气候与环境观测站边界层气象塔的风速、 风向、 温度、 气压、 湿度等观测资料, 采用经典的廓线法和风速、 风向标准差法, 分别计算了中性大气层结下观测站下垫面粗糙度长度, 并得到了具有黄土高原地理特征的地表粗糙度及其时空变化特征。计算结果表明, 季节变化对粗糙度的影响幅度可达0.159 m, 空间非均一性对粗糙度的影响幅度可达0.155 m。测站附近粗糙度春季为0.017 m, 夏季为0.062 m, 秋季为0.065 m, 冬季为0.018 m。测站西北方向上游粗糙度春季为0.17 m, 夏季为0.22 m, 秋季为0.34 m, 冬季为0.05 m。测站东南方向上游粗糙度春季为0.11 m, 夏季为0.17 m, 秋季为0.19 m, 冬季为0.05 m。该站下垫面粗糙度计算宜选用风速为6±1.5 m·s-1, 风向变化30°范围内的数据。     

北京雷暴大风气候特征及短时临近预报方法

北京地区雷暴大风的预报准确率低而且时效短.为了提高对这种灾害性天气的预警能力,在气候统计的基础上,研究了潜势预报方法和临近预报算法.对1998-2007年134个雷暴大风过程的统计结果表明,北京地区绝大多数的雷暴大风具有下击暴流特征,而且冰雹的落区附近也是大风的爆发区之一.因此,负浮力的作用和对冰雹具有指示性意义的因子是研究雷暴大风预报方法应主要考虑的因素.500hPa环流背景分析表明,尽管绝大多数雷暴大风爆发时对流层中层有干空气侵入,但是还有少数个例产生在偏南暖湿气流中.目前,对后一类大风产生的机制仍然不清楚.研究表明,当对流层中层有干空气侵入时,有利于雷暴大风出现的环境条件是:下沉气流具有较大的不稳定性,同时对流层低层环境大气的温度直减率较大.此外,还讨论了经验指数--大风指数在北京地区的应用.基于上述的研究,形成了北京地区雷暴大风短时潜势预报方法,还使用相关分析和多元回归分析技术建立了基于雷达观测和环境条件的雷暴大风临近预报方程.个例分析表明,临近预报方程对于飑线和弓形回波等带来的地面大风具有一定的预报能力.     

水库对河流营养盐滞留效应研究进展

综述了国内外水库对河流营养盐滞留效应的主要进展,尽管在大型水库是否显著改变原有河流输送营养盐的问题上存在诸多争议,反映在滞留效率的估算上,存在前后矛盾的结果,但大量的文献仍支持筑坝显著改变了河流原有输送营养盐的特性,并对下游、河口产生深远的影响.本文将"滞留"的主要过程分为泥沙过滤作用和生物过滤作用,并列举了若干经典的估算滞留效率的方法和部分水库对营养盐的滞留效率.     

Uncertainty analysis of CO_2 flux components in subtropical evergreen coniferous plantation

We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different ...