大气本底站反应性气体观测数据处理系统功能设计及实现

基于反应性气体多年观测的较为成熟的数据处理和质量控制方法,采用模块化功能结构设计,通过应用Visual Studio开发平台并结合MAP WinGIS等空间和时间显示控件,对反应性气体观测数据处理系统进行了设计和开发.系统实现了观测数据在空间和时间上的可视化显示,实际业务应用表明,具有良好的人机交互和数据处理能力,具备一定的数据统计分析功能.实现了对大气本底站反应性气体观测数据的统一管理和综合处理,已成为大气成分观测数据质量控制业务系统的一个重要组成部分.     

加密自动站资料在强对流天气分析预报中的应用

利用常规分析资料、雷达产品和太原地面加密自动站资料,对2008年6月28日出现在太原地区的一次强对流天气过程进行了分析,结果发现:①高空冷涡和中低空暖湿气团配置为此次强对流天气提供了有利的环流背景条件。②雷达回波的形状、结构、VIL均显示出强对流特征,速度辐合区与地面中尺度辐合线相对应,垂直方向存在强烈的风切变。③逐小时变温场显示出冷空气入侵路径、强度,负变温中心未来成为强对流天气区。④地面中尺度辐合线是强对流发生的触发机制,逐小时变压场的负中心与随后出现的灾害性天气区有效对应。⑤对流发生前,单站气象要素出现显著的不连续变化,气压和湿度呈同位相变化,与气温变化则反位相,3者在对流发生前1h同步出现谷(峰)值,灾害天气出现在之后的要素陡升(降)时段内。     

2009年天津地区首场降雪过程分析

基于NCEP再分析资料、多普勒天气雷达产品与风云卫星云参数反演产品,对天津地区2009年的首场降雪过程进行了分析,研究表明:①造成此次降雪的主要天气系统是东移高空槽和地面倒槽;②降雪回波具备典型层云稳定性降水回波的特点,最强回波不超过35 dBz,伴随着降雪结束,回波顶高有所下降;③降雪过程云粒子有效半径数值维持在20μm,云体过冷层厚度、云顶高度较大,云顶温度在-30℃左右.随着降雪结束,云粒子有效半径、云体过冷层厚度和云顶高度数值逐渐减小,云顶温度则有所升高;④地面降水量和云粒子有效半径、云顶高度、云体过冷层厚度呈现正相关,与云顶温度呈现负相关.     

三维GIS建筑物和道路实体模型与地形模型匹配方法

以建筑物和道路实体模型为例,对其与地形模型的匹配方法进行研究与试验。结果表明,顾及建筑物与道路实体模型特征的地形模型匹配结果更符合实际建模要求,为构建逼真的三维数字环境提供了基础。     

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.     

WAP Push在江西气象预警信息发布平台中的应用

阐述了WAP Push技术的体系结构、工作流程和WAP Push技术在气象预警信息发布中的优势;介绍了江西气象预警信息发布平台的整体框架,该平台采用了分布式的3层结构,分别为信息获取层、信息编辑/存储层和信息发布层,具有较强的安全性、稳定性和可维护性;重点介绍了WAP Push信息发布模块的工作流程和主要功能,该模块具有发送图文并茂的WAP Push消息或普通短信息,支持群发信息,返回信息发送状态等功能;最后探讨了WAP Push技术在气象业务中的应用及前景。     

近50年黄河中游三花区间降水特征分析

利用黄河中游三花区间(三门峡到花园口)近50 a(1955-2004年)气候资料,分析了三花区间降水量的时空分布特征,结果表明:区间汛期降水量的年代变化趋势与非汛期大多相反;不论区间还是分区,20世纪90年代降水量较常年偏少都十分显著.近期(1986-2004年)年降水量较常年偏少1成左右.其中,汛期降水量偏少1～2成;非汛期区间和伊洛河偏少,沁河和干流为正常或略偏多.     

山西离柳集团化肥厂雷击原因分析及防雷方案

离柳集团化肥厂几乎每年均遭受雷害侵袭造成较大损失。本文对雷击原因（特别是DCS系统雷击原因）进行了系统分析并提出综合防雷方案。该方案2007年实施后取得良好的防雷效果。     

初始场中尺度信息对暴雨预报的影响

由于观测资料分辨率与模式分辨率的不同,利用高分辨率模式对暴雨进行预报时,常规观测资料形成的初始场不能直接分辨出中尺度系统,这种中尺度系统特征的缺少可以认为是初始场的一种信息误差。利用中尺度天气分析的尺度分离方法可以提取这种中尺度信息。通过分析初始场中尺度信息的结构、演变特征及其对暴雨预报影响的机理,发现初始场中尺度信息的结构在主要雨带的对流敏感区具有明确的天气学意义,包含了有利暴雨产生的信息;其能量随时间也是增长的,特别是在积分12小时以后,能量迅速增长然后趋于稳定,超过了初始随机扰动的能量增长。利用减弱和增强初始场中中尺度信息的两种初始场作暴雨预报,其结果反映了初始场中尺度信息对暴雨预报的重要性,特别是对雨团位置和强度的预报,这些信息会直接影响暴雨的精细预报。