GPS在轨卫星原子钟的稳定性分析

导航卫星星载原子钟的相位或频率数据,作为导航系统应用研究的基础,将直接影响导航系统时间尺度建立以及定位的精度和准确性。本文针对由IGS官网提供的四种GPS卫星钟的钟差数据,采用修正阿伦方差进行了稳定性分析,得到了一些有益的结论。     

一种确定联合反演中相对权比的两步法

提出确定联合反演中相对权比的两步法。第1步是不考虑相对权比,建立两类或多类数据的联合反演模型,通过赫尔墨特方差分量估计使得两类或多类观测数据的单位权方差相等（或单位权方差的比值接近1）,从而理论上确定观测数据合理的权阵,同时对不同种类数据进行归一化;第2步是在获得合理的观测数据权阵之后,建立顾及相对权比的联合反演模型,...     

A Comparison Study of the Contributions of Additional Observations in the Sensitive Regions Identified by CNOP and FSV to Reducing Forecast Error Variance for the Typhoon Morakot

The sensitive regions of conditional nonlinear optimal perturbations (CNOPs) and the first singular vector (FSV) for a northwest Pacific typhoon case are reported in this paper. A large number of probes have been designed in the above regions and the ensemble transform Kalman filter (ETKF) techniques are utilized to examine which approach can locate more appropriate regions for typhoon adaptive observations. The results show that, in general, the majority of the probes in the sensitive regions of CNOPs can reduce more forecast error variance than the probes in the sensitive regions of FSV. This implies that adaptive observations in the sensitive regions of CNOPs are more effective than in the sensitive regions of FSV. Furthermore, the reduction of the forecast error variance obtained by the best probe identified by CNOPs is twice the reduction of the forecast error variance obtained by FSV. This implies that dropping sondes, which is the best probe identified by CNOPs, can improve the forecast more than the best probe identified by FSV. These results indicate that the sensitive regions identified by CNOPs are more appropriate for adaptive observations than those identified by FSV.     

影像不同空间尺度对鲁甸植被分析的影响

针对植被指数和植被覆盖度与遥感影像空间尺度的关系对高分辨率影像在植被分析中的运用具有重要影响的问题,该文提出一种确定最佳分辨率的方法。以云南鲁甸县茨院乡为研究区,先对原始影像(Worldview-2)进行重采样处理,生成一系列低分辨率影像;计算13组数据的NDVI并利用像元二分模型反演出植被覆盖度;运用信息熵理论和按照局部方差的思路等两种方法,定量分析该区植被分析的最佳分辨率。实验结果表明,随着空间分辨率的降低,NDVI值域区间呈现收敛趋势;植被覆盖度分级图斑斑块数量急剧减少,而所占面积比则保持相对平稳;信息熵中的信息量也随之减少。分析结果认为,该区进行基于Worldview-2影像植被分析的最佳空间分辨率为12m。     

Spatial distribution and influencing factors of interprovincial terrestrial physical geographical names in China

The interprovincial terrestrial physical geographical entities are the key areas of regional integrated management. In this paper, we analyzed the spatial patterns of the interprovincial terrestrial physical geographical names (ITPGN) from three aspects: numerical features, spatial variance and spatial agglomeration. The influencing factors of the distribution of ITPGN and the implications for the regional management were further discussed. GIS technology was used to visualize the distribution of ITPGN, analyze the spatial agglomeration and the influencing factors of ITPGN. A total of 11,325 ITPGN, including 4243 water ITPGN and 7082 terrain ITPGN, were extracted from the database of “China’s Second National Survey of Geographical Names (2014-2018)”, and the mountain geographical names were the largest type in ITPGN. Hunan Province had the largest number of the names in China, and Shanghai had the smallest number of the names. The spatial variance of the terrain ITPGN was larger than that of the water ITPGN, and the ITPGN showed a significant agglomeration phenomenon in the southern part of China. In addition, the relative elevation and the population had an impact on the distribution of the ITPGN. The largest number of the geographical names occurred in the regions where the relative elevation was between 1000-2000 meters, and where the population was between 40-50 million. Based on the analysis, it was suggested that the government should take the ITPGN as management units, optimize management strategies based on the characteristics of different types of ITPGN, strengthen the naming of unnamed interprovincial terrestrial physical geographical entities and balance the interests in the controversial ITPGN. This study demonstrated that GIS and spatial analysis techniques were useful for the research of ITPGN and the results could provide targeted management suggestions to realize coordinated development in the interprovincial regions.     

基于自适应时序剖分与KNN的短时交通流量预测

在智能交通系统中,准确和高效的短时交通流量预测是交通诱导、管理和控制的前提。由于交通流量动态变化中表现出的时变性和非平稳性特征,其预测难度较大,是交通领域中亟待解决的难题。为提高短时交通流量的预测精度,本文设计与实现了基于自适应时序剖分与KNN（A-TS-KNN）的短时交通流量预测算法。① 基于动态时间规整（Dynamic Time Warping,DTW）动态剖分单日时序为不同的交通模式;② 在不同交通模式,采用互信息法求解每个预测时刻时间延迟的最大阈值,构造不同时间延迟的状态向量,生成交通流量历史数据库;③ 采用十次十折交叉验证的方法求解每个时刻不同时间延迟与不同K值的正交误差结果分布,提取误差最小的正交结果,得到自适应时间延迟与K值的参数组合;④ 采用K个最相似的近邻的距离倒数加权值作为预测结果。对比K近邻（K-nearest neighbors, KNN）、支持向量回归（Support vector regression,SVR）、长短期记忆神经网络（Long-short term memory neural network,LSTM）以及门控递归单元神经网络（Gate recurrent unit neural network,GRU）共4种主流预测模型,A-TS-KNN算法预测精度显著提升;将A-TS-KNN算法用于福州市城市路网中其他交叉路口的短时交通流量预测,结果表现出良好的泛化能力。     

自动与人工雨量计观测降水量的差异分析

通过对东胜国家基准气候站自动与人工两年观测数据资料的实时对比,分析判断出自动观测雨量计相对人工观测雨量计来说,误差率比较稳定。     

地下介质横向变化的地震多尺度边缘检测技术

本文提出的地震多尺度边缘检测技术以坎尼最佳边缘检测准则和小波变换的多分辨分析为理论基础,能在地震记录上较方便而可靠地识别地下介质的横向变化。这些横向变化包括不能用肉眼在地震同相轴上直接发现而又实际存在的隐蔽特征。对地层尖灭,透镜体、岩性变化和孔洞、裂缝发育带均可进行识别。模型计算和实际地震资料的处理结果显示该方法具有实际应用价值     

A Note on the Flux-Variance Similarity Relationships for Heat and Water Vapour in the Unstable Atmospheric Surface Layer

Atmospheric surface layer (ASL) experiments over the past 10 years demonstrate that the flux-variance similarity functions for water vapour are consistently larger in magnitude than their temperature counterpart. In addition, latent heat flux calculations using the flux-variance method do not compare as favorably to eddy- correlation measurements when compared to their sensible heat counterpart. These two findings, in concert with measured heat to water vapour transport efficiencies in excess of unity, are commonly used as evidence of dissimilarity between heat and water vapour transport in the unstable atmospheric surface layer. In this note, it is demonstrated that even if near equality in flux-profile similarity functions for heat and water vapour is satisfied, the flux-variance similarity functions for water vapour are larger in magnitude than temperature for a planar, homogeneous, unstably-stratified turbulent boundary-layer flow.     

一定分布模式下的最优Lp估计

观测数据在一定的分布模式下,需从理论上解决究竟用多大的ｐ值进行Ｌｐ估计（最优ｐ值的确定）,本文基于渐近方差整体最小的原则,给出了最优Ｌｐ估计的定义。观测数据中含有粗差（异常值）的扰动,其误差分布可视为污染分布,作者分析了最优ｐ值的大小,结果表明,若观测数据中含有不同大小、数量的粗差,为使Ｌｐ估计差最小,则ｐ将取［１．２,１．５］中的某一定值,Ｌｐ估计结果最优,此结论对测量数据处理具有一定的参考价值