数字调绘在DLG数据更新中的应用

随着测绘技术的迅猛发展,内业处理已全面进入数字化阶段,但传统的外业调绘方式已经成为地理信息数据快速更新的“瓶颈”,不能完全满足现代测绘的需要。本文总结了数字调绘系统与传统调绘方法的各自优缺点,设计了一种数字调绘系统。该系统作为一个易操作的平台,降低了劳动强度,提高了数据精度,节约了生产成本。数字调绘为地理信息数据的快速更新提取提供了一种较好的技术手段,必将是今后航测外业的发展方向。     

地图数据缩编更新中的道路自动选取研究

总结道路选取的研究现状,分析在道路的缩编更新过程中,道路要素选取需要考虑的要素指标。介绍一种道路选取方法,综合考虑道路的等级、长度、网眼面积、区域道路密度、是否是悬挂链等几何、语义和拓扑信息,模拟人类在缩编更新时的思维过程,实现道路的自动选取,并进行试验,验证方法的有效性。     

The Mesoscale Predictability of a Heavy Precipitation Event

Using the mesoscale model MM5, the development of initial condition uncertainties at different scales and amplitudes and their influences on the mesoscale predictability of the ＂0185＂ Shanghai heavy precipitation event are investigated. It is found that different initial conditions obtained from different globe model analyses lead to large variations in the simulated location and strength of the heavy precipitation, and the scales and amplitudes of the initial condition perturbations significantly influence the model error growth. The power spectrum evolution of the difference total energy （DTE） between a control simulation and a sensitivity experiment indicates that the error growth saturates after 12 h, which is the predictable time limit of the heavy precipitation event. The power spectrum evolution of the accumulated precipitation difference between the control and sensitivity simulations suggests a loss of the mesoscale predictability for precipitation systems of scales smaller than 300 kin, i.e., the predictable space for the heavy precipitation event is beyond 300 km. The results also show that the initial uncertainties at larger scales and amplitudes generally result in larger forecast divergence than the uncertainties at smaller scales and amplitudes. The predictable forecasting time and space can be expanded （e.g., from 12 to 15 h, and from beyond 300 kin to beyond 200 km） under properly prescribed initial perturbations at smaller scales and amplitudes.     

Effects of the low-frequency zonal wind variation on the high frequency atmospheric variability over the tropics

Recently, there is increasing evidence on the interaction of atmospheric high-frequency (HF) variability with climatic low-frequency (LF) variability. In this study, we examine this relationship of HF variability with large scale circulation using idealized experiments with an aqua-planet Atmospheric GCM (with zonally uniform SST), run in different zonal momentum forcing scenarios. The effect of large scale circulation changes to the HF variability is demonstrated here. The HF atmospheric variability is enhanced over the westerly forced region, through easterly vertical shear. Our study also manifests that apart from the vertical wind shear, strong low-level convergence and horizontal zonal wind shear are also important for enhancing the HF variance. This is clearly seen in the eastern part of the forcing, where the HF activity shows relatively maximum increase, in spite of similar vertical shear over the forced regions. The possible implications for multi-scale interaction (e.g. MJO–ENSO interaction) are also discussed.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

与广东持续性干旱事件有关的两类海温异常型

利用1961\_2005年广东省86个常规地面观测站降水资料、 ERSST全球月平均海温资料以及NCEP/NCAR 大气多要素再分析资料, 分析了广东跨季节持续性干旱事件(下称广东干旱)与海温外强迫之间的关系及其物理过程。结果表明, 与广东干旱相关的海温异常有赤道东太平洋海温负异常型（即La Nia型）、 赤道中太平洋海温正异常型两种类型。约在1975年之前, 广东干旱基本上属于La Nia型; 在1975年之后, 除了La Nia型外, 赤道中太平洋海温正异常型干旱出现概率明显增加。可见, 在全球气候变暖的背景下, 广东干旱成因更复杂。近几年来广东干旱频繁发生, 与赤道中太平洋海温正异常型出现概率增加有关。La Nia型、 赤道中太平洋海温正异常型与广东干旱之间都存在物理上的联系, 不同的海温异常型对广东干旱影响的物理过程有所不同, 即使同一种海温异常型对不同季节的影响过程也有所不同, 然而所有物理过程最终都是通过改变和削弱降水所需的动力条件或水汽条件, 从而造成局地干旱的发生。     

沙尘输送模式的不确定性分析

利用一个远距离输送的沙尘模式估计了由于参数化过程 (干沉降速度) 和输入资料 (源强和水平风场) 的误差造成沙尘模拟的不确定性。通过对以上参数分别进行敏感性试验, 分析了模式对2002年3月15～24日期间中国东部地区两次主要沙尘过程模拟的不确定性。结果显示, 源区的潜在源强和气象水平风场的不确定性对模拟结果的影响最大, 而干沉降速度的影响相对较小。同时, 对不同区域 ［西部 (＜95°E)、中部 (95°E～110°E) 和东部 (＞110°E)］ 的潜在源强和干沉降速度参数进行敏感性试验发现, 中部区域的参数设置对模拟结果的影响最大, 而西部和东部区域的参数变化对模拟结果的影响很小。此外, 不同高度的风场影响也不一样: 地面风速影响最大, 中层的影响较小, 而高层 (约6 km高度以上) 的风场几乎没有影响。     

2006年东亚夏季风活动特征与我国东部雨带分布

利用1979—2006年NCEP/NCAR大气月平均资料、OLR对流和CMAP降水日平均资料,从季节平均、月平均、季节内振荡不同时间尺度分析了2006年东亚夏季风活动特征及其与雨带分布之间的联系。结果表明,2006年东亚夏季风爆发时间正常,而夏季风推进过程存在异常：前沿在华南地区和华北地区维持时间偏长、在长江流域维持时间偏短,夏季风极端北界偏北,这种推进异常与南海夏季风强度偏强有关。南海夏季风强度偏强及推进过程异常导致东亚夏季(6—8月)降水为Ⅰ类雨带分布,即长江流域降水偏少,华南、黄淮流域-朝鲜半岛-日本中南部降水偏多。从月时间尺度看,2006夏季各月都具有南海地区夏季风强度偏强、前沿位置偏北和偏东的异常趋势;西太平洋副热带高压6月偏西偏南,7、8月偏北,在这种异常形势下,长江流域6、7、8各月降水都偏少,华南地区各月以偏多为主,黄淮流域7、8月降水偏多。从季节内振荡看,2006年大气季节内振荡(ISO)纬向传播与气候平均相比具有特殊性,长江流域纬度带西传波和静止波偏弱,华南地区纬度带东传波和静止波偏强,ISO这种异常与夏季长江流域降水偏少、华南降水偏多有关。     

2009年7月8-9日泰安市暴雨成因分析

2009年7月8-9日发生在泰安的暴雨天气过程主要是在副高西进北抬、副高边缘西南暖湿气流与高空低槽东移南压相结合的大尺度环流下,由黄河北部的低层中尺度切变线和鲁中地区的小低涡以及低空西南急流共同作用造成的.低空西南急流为大暴雨的产生输送了充足的水汽,低涡加大了辐合上升运动和水汽辐合.850 hPa低空大气散度辐合中心正处于泰安,垂直速度强上升区也在鲁中地区,为暴雨产生提供了足够的动力条件,低层850 hPa假相当位温θse＞75 ℃的高能舌为这次暴雨提供了不稳定能量.