Ekman模型时间变化风应力系数的伴随参数估计

本文基于时间分布参数设置,利用伴随同化方法,反演了Ekman模型中随时间变化的风应力拖曳系数,并在孪生实验和实际实验中对该方法进行了验证。在孪生实验中,研究了参数反演结果对不同影响因素的响应,包括:风速分布、风应力系数分布、风应力系数初始猜测值、风应力系数独立变量个数、观测数据误差和观测的深度。孪生实验结果验证了伴随同化方法反演Ekman模型中时变风应力系数的有效性,具体包括如下五个方面结论:1)不同风速分布下均能成功反演出不同风应力拖曳系数分布; 2)反演结果对初始猜测值较为敏感,风应力系数初始猜测值越接近给定值,反演结果越好;3)风应力系数独立点个数的选取会显著影响反演结果,合理的选择有利于提高反演效率及减小观测数据误差;4)观测误差能够影响反演结果,观测数据误差在20%以下时能取得合理的反演结果; 5)反演结果对观测数据的表层和次表层流速更为敏感,这是由Ekman流的物理性质决定的。实际实验,利用百慕大锚系试验平台的风速和流速数据,去除周期性潮流和地转流成分后得到Ekman流成分,并作为观测输入到该同化模型,反演出了适用于该区域和该时段的随时间变化的风应力系数。通过比较模拟流速和观测流速,证明利用伴随同化方法能从实测数据中反演出合理的时变风应力系数,对于海洋模型风应力系数的确定是一项有益的尝试。     

Large-time response of ice cover to a load moving along a frozen channel

Unsteady response of an ice cover in a channel with vertical walls is studied for large times. The ice deflection is caused by a load moving along the frozen channel at a constant speed. The ice cover is modelled as a thin elastic plate clamped to the walls of the channel. The time-dependent problem is solved by using the Fourier transform along the channel and the method of separating variables. In the system moving along the channel together with the load, the large-time deflection of the ice cover consists of steady deflection and standing waves in front and behind the load. The number of waves, their frequencies and wavenumbers depend on the speed of the load and the values of the critical speeds for the channel. The number of the waves and their amplitudes are calculated for a given load and its speed. The maximum stress in the ice as a function of the load speed is estimated.     

基于EMD方法的地心运动时间序列分析

针对时间序列混有的高频信息会影响地心运动规律分析的问题,采用网平移法对IGS提供的GNSS周解进行解算,得到2007-2017年地心运动时间序列,对其进行分解重构,剔除高频项,并利用重构时序对地心运动规律作进一步分析探讨。结果表明：本文解算的地心运动在T_x、T_y和T_z方向的精度均为毫米级。EMD方法重构的时序保留了原序列的基本信息,且抑制了高频项的影响,提高了周期贡献率,3个方向的贡献率分别提高了12.3%、16.7%及6.3%。通过分析重构后的时序发现,周年项振幅为各周期对应振幅的最大值,分别为2.32、1.89和2.07 mm;T_y和T_z方向长期变化趋势较T_x更为明显,分别为0.13和-0.27 mm/a;半年项较小,且在T_x和T_y方向上具有时变性。此外,还发现了一些其他较小的年际变化。     

Cartographic Design and the Space–Time Cube

This paper reports on the results of an empirical evaluation that aimed to define the effectiveness and efficiency of different visual variables in depicting the Space–Time Cube’s (STC) content. Existing STC applications demonstrate that the most used visual variables are size and colour hue. Less is known, however, about their usability metrics. The research sets design criteria for STC contents, such as space–time paths, based on the cartographic design theory. The visual variables colour hue, colour value, colour saturation, size and orientation have been applied in two different use case studies. Besides, to support the three-dimensional visual environment, depth cues such as shading and transparency were considered too. User tests have been executed based on real-world problems with particular attention for the visualization strategy and data complexity. The outcomes revealed the most efficient and effective visual variables to represent data of various complexities in the STC.     

Cartographic Symbol Design Considerations for the Space–Time Cube

The cartographic representation of geographic phenomena in the space–time cube comes with special challenges and opportunities when compared with two-dimensional maps. While the added dimension allows the display of attributes that vary with time, it is difficult to display rapidly varying temporal data given the limited display height. In this study, we adapt 2D cyclic point symbols to construct 3D surfaces designed along a helical path for the space–time cube. We demonstrate how these complex?3D helical surfaces can display detailed data, including data reported daily over 100 years and data reported in four-hour intervals over a year. To create the point symbols, each value is plotted along the curve of a helix, with each turn of the helix representing one year or week, respectively. The model is modified by varying the radii from the time axis to all points using the attribute value, in these cases maximum daily temperature and four-hourly ridership, and then creating a triangulated surface from the resulting points. Using techniques common to terrain representation, we apply hue and saturation to the surface based on attribute values, and lightness based on relief shading. Multiple surfaces can be displayed in a space–time cube with a consistent time interval facing the viewer, and the surfaces or viewer perspective can be rotated to display synchronized variations. We see this method as one example of how cartographic design can refine or enhance operations in the space–time cube.     

Detection of spatio-temporal evolutions on multi-annual satellite image time series: A clustering based approach

The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.     

Status of Satellite Orbit Determination and Time Synchronization Technology for Global Navigation Satellite Systems

In the form of satellite ephemerides and clock parameters, the space datum and system time information of one global navigation satellite system (GNSS) is transferred to users. With the continuous updating in the satellite payload such as the high-precision atomic clock, the monitoring and tracking technique such as the inter-satellite link, and in the data processing technique, the accuracy and real-time performance of the satellite ephemeris and clock error products are steadily improved. Starting from December 27th, 2018, the BeiDou Navigation System 3, or BDS-3, has provided the accurate and reliable basic positioning, navigation, and timing (PNT) service for the users in the countries within the “one belt and one road”. This paper has summarized the faced challenges of the precise orbit determination and time synchronization from the regional BDS-2 system to the BDS-3 global system, and the specific solutions at the control segment. In addition, this paper has compared the BDS with other GNSS systems in terms of technical characteristics. Finally, aiming at a higher accuracy and more reliable PNT service, the road map of precise orbit determination and time synchronization technique for the next generation navigation systems is discussed, which will provide a reference for developing the global navigation satellite systems with an even higher accuracy.     

实时卫星钟差基准精化方法

在基于精密单点定位(PPP)的授时方法中,卫星钟差产品的高精度时间基准至关重要.针对实时卫星钟差产品时间基准不够稳定的问题,本文采用一组具有原子钟外部输入的国际全球卫星导航系统(GNSS)服务(IGS)跟踪站建立了顾及原子钟变化特性的基准精化方法.该方法首先采用阿伦方差对不同的IGS跟踪站外接原子钟进行稳定度分析,挑选出一组稳定度高的原子钟用以精化时间基准.在此基础上,利用阿伦方差分析各台原子钟的噪声参数特征,并确定不同原子钟之间的权比关系.最终,建立时间基准改正量的随机模型,并计算出精化后的时间基准.通过实例验证表明:与IGS事后精密钟差产品定义的时间基准比较,改正后的实时钟差基准单天内的标准差(STD)优于0.1 ns,相比于改正前最高提升了93%.同时,基准改正后的天内万秒稳达到10^-15量级,实现了一个量级的提高.此外,通过相对钟差精度的分析,表明钟差基准修正不影响PPP的定位精度.     

基于支持向量机的GNSS时间序列预测

在深度学习的理论框架下,针对预测全球卫星导航系统(GNSS)时间序列,传统的经验风险最小化预测模型误差大精度低,泛化性能差且对历史数据的经验依赖大的问题.提出一种采用结构风险最小化原则的基于支持向量机(SVM)的时间序列预测模型.通过和多层的BP神经网络预测模型预测效果比较,结果证明SVM预测模型拥有更好的时间序列预测效果.     

Re-examining urban region and inferring regional function based on spatial–temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents’ needs. Understanding the changes of regional structure and dynamics of city function based on the residents’ movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T)?×?week (W)?×?origin (O)?×?destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.