三种电离层延迟多频修正算法的比较

电离层延迟多频修正算法在修正电离层延迟的同时会放大观测噪声等伪距误差的影响。分析了电离层延迟双频修正、三频一阶修正和三频二阶修正三种修正算法及观测噪声的影响。以Galileo系统的四个载波频率为例,对不同频率组合下三种修正算法进行了比较。结果表明电离层延迟多频修正中,并不是观测量的频点数越多或修正掉的高阶项越多修正精度越高,还与观测伪距噪声的大小以及采用的修正方法有关。为多频测距系统的电离层延迟修正算法和最佳频率组合设计提供了一种可行的分析方法。     

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。     

GPS单频精密单点定位方法与实践

讨论了单频精密单点定位的解算方法与思路,编写了单频精密动态单点定位的程序,实现了事后动态单频精密单点定位.     

The ionospheric eclipse factor method (IEFM) and its application to determining the ionospheric delay for GPS

A new method for modeling the ionospheric delay using global positioning system (GPS) data is proposed, called the ionospheric eclipse factor method (IEFM). It is based on establishing a concept referred to as the ionospheric eclipse factor (IEF) λ of the ionospheric pierce point (IPP) and the IEF’s influence factor (IFF) . The IEF can be used to make a relatively precise distinction between ionospheric daytime and nighttime, whereas the IFF is advantageous for describing the IEF’s variations with day, month, season and year, associated with seasonal variations of total electron content (TEC) of the ionosphere. By combining λ and with the local time t of IPP, the IEFM has the ability to precisely distinguish between ionospheric daytime and nighttime, as well as efficiently combine them during different seasons or months over a year at the IPP. The IEFM-based ionospheric delay estimates are validated by combining an absolute positioning mode with several ionospheric delay correction models or algorithms, using GPS data at an international Global Navigation Satellite System (GNSS) service (IGS) station (WTZR). Our results indicate that the IEFM may further improve ionospheric delay modeling using GPS data.     

Group Time Delay in the Reflection of P-Polarized Electromagnetic Plane Waves at a Stratigraphic Interface

The group time delay (GTD) formulae of quasi-total reflection of inhomogeneous P-polarized electromagnetic waves (P-PEW, whose electric field parallels to the plane of incidence), caused by the Goos–Hänchen effect, is derived by the phase shift of the wave. A numerical example where the frequency equals to 1 GHz is given. The curves of calculating results show that there are three discontinuous points at the critical angle of phase shift vector, the critical angle of attenuation vector and 90^○. When the angle of incidence is equal to one of these three angles, the GTD will become infinite, and the electromagnetic wave will propagate along the interface. The GTD will be very large, when the angle of incidence is close to one of these three angles. The results indicate that there is an important relationship between the two critical angles and the conductivity and permittivity of the two strata, and that the GTD has an important relationship with the angle of incidence. These results can be used to determine the lithology of the strata and to delimit the interfaces of strata more effectively. It is suggested that this research may prove useful in electromagnetic logging analysis and, perhaps, in the design of logging instruments.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

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.     

青藏高原地面-对流层系统的能量收支

利用CCM3中的辐射模式CRM，计算了1月和7月地－气系统、地面－对流层系统和地面辐射能收支，研究了青藏高原地面－对流层系统辐射能收支的冬、夏季节特征及其与地面和地－气系统辐射能收支的关系，并与东部平原地区和高原北侧干旱地区比较。文中还讨论了云和高原冬季地面积雪对辐射能收支的影响，比较了大气辐射加热和地面感热通量对夏季高原对流层大气加热的贡献。     

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.     

长江日流量混沌变化特性研究——Ⅰ相空间嵌入滞时的确定

为了以新途径探索长江水量变化规律并在此基础上做出预测,全面而系统地研究了宜昌站日流量混沌变化特性并建立了新预测模型.重点论述相空间嵌入滞时的确定.分析了传统的自相关函数法和真实矢量场法确定嵌入滞时的优劣,并提出以广义相关函数合理确定嵌入滞时的方法.研究结果表明广义相关函数法是一个合理又可操作的途径.