基于卡尔曼动态频率的ECMWF降水预报订正

利用2019年1月至2020年2月ECMWF细网格模式降水预报和388个自动气象站降水观测资料,以及国家气象信息中心三源网格降水量融合分析产品,在降水频率客观分析检验的基础上,采用卡尔曼动态频率匹配方法对ECMWF网格降水预报进行订正,所得结论如下:ECMWF模式小雨以上量级降水预报频率较观测明显偏多,暴雨偏少;模式预...     

河蟹生态养殖池浮游植物功能类群的结构特征

为探究河蟹生态养殖池浮游植物群落的结构及动态,2013年4-10月对上海松江泖港地区河蟹养殖池的浮游植物进行了调查,分析其种类构成、密度、生物量和生物多样性.共检测到浮游植物105种,其中河蟹生态养殖池优势种11种,分别属于B、D、J、LO、X1、WO、X2、S1和MP功能群.河蟹养殖池浮游植物平均密度为282.58×10~4cells/L,平均生物量为1.59 mg/L,Shannon-Wiener多样性指数变化范围为2.11~3.09.水源浮游植物平均密度为175.13×104cells/L,平均生物量为0.99 mg/L,Shannon-Wiener多样性指数变化范围为1.70~2.79.河蟹养殖池浮游植物平均密度分别于5月14日-6月14日和7月29日-8月29日与水源差异显著;河蟹养殖池浮游植物平均生物量分别于5月30日、6月14日和7月29日-8月29日与水源差异显著;河蟹养殖池浮游植物Shannon-Wiener生物多样性指数分别于5月14日-6月14日和8月14日-9月15日与水源差异显著.河蟹养殖池浮游植物的平均密度、平均生物量和生物多样性指数与水源水体变化趋势基本一致,均高于水源,群落稳定.     

Influence of the GLONASS inter-frequency bias on differential code bias estimation and ionospheric modeling

GPS Solutions - Due to the application of frequency division multiple access, the signals of GLONASS satellites suffer from code and carrier phase inter-frequency biases (IFBs). In this study, the...     

Characteristics of inter-frequency clock bias for Block IIF satellites and its effect on triple-frequency GPS precise point positioning

GPS Solutions - The latest generation of GPS satellites, termed Block IIF, provides a new L5 signal. Multi-frequency signals open new prospects for precise positioning and fast ambiguity resolution...     

天线相位中心改正模型对PPP参数估计的影响

比较了IGS发布的相对天线相位中心改正模型与绝对天线相位中心改正模型,分析了两种不同模型对精密单点定位（PPP）参数估计的影响。结果表明,采用不同的天线相位中心改正模型,天顶对流层延迟（ZPD）的估值存在5mm左右的差异,接收机钟差参数存在3ns左右的差异,估计的测站坐标高程方向有1cm左右的差异。使用绝对天线相位中心模型估计得到的ZPD精度优于5mm,高程方向定位精度约为1cm,接收机钟差估计的精度达0.1ns。     

Real-time precise point positioning regional augmentation for large GPS reference networks

An increasing number of GNSS reference stations are installed around the world to provide real-time precise positioning services. In most of the current services, a full network solution is required for the precise determination of biases. Such a network solution is time consuming and difficult to achieve for very large regions such as Europe or China. Therefore, we developed a multi-layer processing scheme for precise point positioning (PPP) regional augmentation to avoid processing large networks. Furthermore, we use L1 and L2 raw observations and estimate atmospheric delays, which were properly constrained to the atmospheric corrections derived from the reference stations. Therefore, inaccurate representation of atmospheric delays due to temporal and/or spatial atmospheric fluctuations in the processing can be compensated. The proposed scheme of PPP regional augmentation was implemented into the operational real-time PPP service system at GFZ for validation. The real-time orbit and clock corrections, the uncalibrated phase delays, and regional augmentation corrections are generated by this system. The augmentation corrections from the regional network are investigated and the positioning performance in terms of positioning accuracy and time for fixed solution is demonstrated in real-time. Our results indicate that a reliable fixing is possible after 5 s on average. The positioning accuracy is about 12, 10, and 25 mm in east, north, and vertical direction, respectively.     

基于图优化的紧耦合双目视觉/惯性/激光雷达SLAM方法

基于单一传感器的同时定位与地图构建技术已经逐渐不能满足移动机器人、无人机及自动驾驶车辆等智能移动载体日益复杂的应用场景。为了进一步提升移动载体在复杂环境下的定位与建图性能,基于多传感器融合的SLAM技术成为目前研究的热点内容。本文提出了一种基于图优化的紧耦合双目视觉/惯性/激光雷达SLAM方法(S-VIL SLAM),该方法在视觉惯性系统中引入激光雷达原始观测,基于滑动窗口实现了IMU量测、视觉特征及激光点云特征的多源数据联合非线性优化。利用视觉与激光雷达的互补特性设计了视觉增强的激光雷达闭环优化算法,进一步提升了多源融合SLAM系统的全局定位与建图精度。为了验证本文算法的性能,利用自主搭建的集成多传感器的硬件采集平台在室外场景下进行了车载试验。试验结果表明,本文提出的紧耦合双目视觉/惯性/激光雷达里程计相比于紧耦合双目视觉惯性里程计和激光雷达里程计定位定姿性能显著提升,视觉增强的激光雷达闭环优化算法能够在大尺度场景下有效探测出轨迹中的闭环信息,并实现高精度的全局位姿图优化,经过闭环优化的点云地图具有良好的分辨率和全局一致性。     

Precise orbit determination for LEO satellites:single-receiver ambiguity resolution using GREAT products

In recent years, the large Low Earth Orbit (LEO) constellations have become a hot topic due to their great potential to improve the Global Navigation Satellite ...     

Using stable isotopes of surface water and groundwater to quantify moisture sources across the Yellow River source region

Characterization of stable isotope compositions (δ²H and δ¹⁸O) of surface water and groundwater in a catchment is critical for refining moisture sources and establishing modern isotope–elevation relationships for paleoelevation reconstructions. There is no consensus on the moisture sources of precipitation in the Yellow River source region during summer season. This study presents δ²H and δ¹⁸O data from 111 water samples collected from tributaries, mainstream, lakes, and groundwater across the Yellow River source region during summertime. Measured δ¹⁸O values of the tributaries range from ?13.5‰ to ?5.8‰ with an average of ?11.0‰. Measured δ¹⁸O values of the groundwater samples range from ?12.7‰ to ?10.5‰ with an average of ?11.9‰. The δ¹⁸O data of tributary waters display a northward increase of 1.66‰ per degree latitude. The δ¹⁸O data and d‐excess values imply that moisture sources of the Yellow River source region during summertime are mainly from the mixing of the Indian Summer Monsoon and the Westerlies, local water recycling, and subcloud evaporation. Analysis of tributary δ¹⁸O data from the Yellow River source region and streamwater and precipitation δ¹⁸O data from its surrounding areas leads to a best‐fit second‐order polynomial relationship between δ¹⁸O and elevation over a 4,600 m elevation range. A δ¹⁸O elevation gradient of ?1.6‰/km is also established using these data, and the gradient is in consistence with the δ¹⁸O elevation gradient of north and eastern plateau. Such relationships can be used for paleoelevation reconstructions in the Yellow River source region.