基于地闪数据的雷电流幅值累积频率公式探讨

利用2007年和2008年浙江省气象部门闪电定位系统的地闪监测数据,应用Matlab数学软件中的曲线拟合工具箱,以最小二乘法原理对IEEE推荐公式和我国规程推荐公式进行最优化拟合,得出前者拟合效果优于后者的结论。通过分析IEEE推荐公式计算结果与实际值之间的相对误差,发现正闪雷电流幅值累积频率在(1kA,270kA)范围内相对误差绝对值较小,最大不超过10%;而负闪雷电流幅值累积频率在(-1kA,-300kA)范围内相对误差绝对值较大,最大值约为38%。针对上述情况,利用数学软件拟合出负闪(-1kA,-300kA)相对误差曲线的近似函数,修正了原累积频率公式,大幅度减小了其相对误差。其适用范围也从原来的(2kA,200kA)放宽至正闪(1kA,270kA)、负闪(-1kA,-300kA)。     

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.     

基于BP神经网络的地质灾害细网格预报模型

地质灾害成因复杂,其中以气象因素、地质地貌因素引发的地质灾害最为常见.以金华地区为例,通过对金华市地质地貌条件及其对地质灾害点的调查,将全区划分为4个地质灾害隐患风险等级的网格区域.在此基础上利用金华中尺度气象资料,采用BP神经网络模型,建立地质灾害细网格预报模型,对该模型进行模拟和预报试验.结果表明,合理的隐患风险等级分区能使预报模型更符合科学规律,而采用分布较细的中尺度资料作为预报因子能进一步提高预报精度.模型的预报结果达到一定的可信度,为防灾减灾工作提供了科学依据.     

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。     

十堰市气候干旱分析及预报指标

利用十堰市7个代表站1953-2006年的降水资料和气温资料,用降水距平百分率（R指标）这一传统的干旱等级划分方法,并考虑到气温引入了P指标对干旱等级进行了划分,揭示了该区域干旱发生的基本特征。结果表明：十堰是我国中部地区干旱频发地区之一,干旱存在自身的周期性和连续发生的规律,地理分布是北部重于南部。造成十堰干旱的主要天气系统是东亚大槽、副热带高压和青藏高压（大陆高压）。这3个系统交替影响并控制该市：春季主要是东亚大槽稳定;夏季副热带高压控制为主、大陆高压控制次之;秋季大陆高压控制为主、副热带高压控制次之。根据不同季节和不同类型的干旱,给出了具体的预报指标。