一维扩散方程整体观测资料下的初值变分同化反演

本文考虑一维扩散方程的反问题,利用变分同化方法通过观测资料来确定方程中的未知初值,通过分析观测误差对于初值误差的影响,证明变分同化初值收敛于原问题的真实参数,并得到了参数的收敛精度。同时将得到的初值代入预报模式中,得到预报解,并分析了预报解的收敛性和预报误差。     

煤炭资源勘查误差理论研究

对煤炭资源勘查资源量与客观物质量之间差值的误差理论探讨,提出误差由真值误差、系统误差和离差3个基本部分组成,其中真值误差是不可消除的观测误差、系统误差是控制网度所决定的准确度标准、而离差是由地质构造及矿床形态的多解性产生的资源量波动。在此基础上,构筑了一个关于误差的数学模型,最后应用误差理论对现行勘查工作中常见的错误进行分析,并就如何避免错误及减少误差提出了见解。     

CPⅢ测量数据处理系统开发若干关键技术研究

本文首先介绍了无砟轨道的相关内容,指出CPⅢ测量数据处理是整个建设中的关键,提出系统开发的总体目标与要求且设计了系统总体结构,详细阐述了系统开发中涉及的关键技术与基本理论,文中最后结合具体工程实例验证系统开发成果。     

改进相似性测度的高光谱影像FCM聚类分析

高光谱遥感将反映目标辐射属性的光谱信息与反映目标空间几何关系的图像信息有机地结合在一起,能够实现地面目标的精细分类识别。FCM是一种有效的聚类算法,但存在相似性测度模型单一、分类精度的提高受到限制等问题。文中结合高光谱影像的技术特点,综合考虑光谱曲线的形状、地物辐射亮度及其权重,提出可以更好描述光谱向量之间的相似性的距离测度,并将其引入到FCM聚类模型中。聚类分析试验结果表明：通过改进和优化相似性测度的FCM,可以显著提高高光谱影像聚类精度。     

Verification of the motion fields over Southeast Asia by the Florida State University (FSU) superensemble forecast

The Florida State University (FSU) multimodel superensemble forecast is evaluated against several other operational weather models for the Southeast Asia region. The superensemble technique has demonstrated its exceptional skills in forecasting precipitation, motion and mass fields compared to either individual global operational or ensemble mean forecasts. The motion field investigation for the season of 2001 reveals that the superensemble forecasts are closer to the observed data compared to the other global member operational models through its low systematic errors at the 850 hPa level. The FSU multimodel superensemble forecasts exhibit the lowest root mean square errors (RSMEs), the highest correlation against the best observed data and the lowest systematic errors compared to the other operational model members. These forecasts have the potential to provide better daily weather predictions over the Southeast Asia region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asia region.     

The Earthquake Readiness Scale: The Development of a Valid and Reliable Unifactorial Measure

Preparedness is a key dependent variable in many studies examining people’s response to disasters such as earthquakes. A feature of many studies on this issue, however, is the lack of attention given to psychometric issues when constructing measures of preparedness. With regard to earthquake preparation, for example, many studies could be greatly improved by the use of a valid and reliable measure of preparedness. This research developed such a measure that assessed both low-level preparedness, such as having an emergency kit, and high-level preparedness, such as altering home structures to mitigate damage. Studies of Wellington (New Zealand) residents using two samples totalling n=652 showed that 23 items measuring these different aspects of earthquake preparation could be combined into a reliable, valid, unifactorial scale. This brief scale should have utility in multivariate studies of earthquake preparation, either as a dependent variable, where preparation is the outcome variable of primary interest, or as one of several independent variables, where preparation and other measures predict another outcome variable.     

奎屯河流域冰雹天气发生规律及防御措施

根据1989～2003年奎屯河流域中小尺度强对流冰雹天气资料,统计分析了其发生规律,给出了雹暴发生源地、移动路径和基本类型,探讨了地形和下垫面的作用,并提出了综合防御措施.     

施放氢气球时存在的安全隐患及防范措施

针对采用球服务中的安全隐患，提出有效的防范措施，对开展此项工作的同志有很好的借鉴意义。     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS

In the context of tower measured radiation datasets.following the correction principle meetinga diagnostic equation in data quality control and in terms of a technique for model construction ondata and ANN(artificial neural network)retrieval for BP correction of radiation measurementswith rough errors available,a BP model is presented.Evidence suggests that the developed modelworks well and is superior to a convenient multivariate linear regression model,indicating its wideapplications.     

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS*

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.