基于GPT3模型的ZTD及PWV反演精度分析

使用亚洲区域18个IGS测站和中国区域内16个探空站2016~2018年的数据,研究GPT3模型反演天顶对流层延迟（ZTD）和大气可降水量（PWV）的精度,并与其他GPT系列模型进行对比。结果表明,GPT3-1模型估计的ZTD的bias均值和最大值均最小,分别为1.34 mm和14.06 mm;GPT3模型整体精度略优于GPT2w模型,优于GPT2模型。探空站处GPT3模型反演的PWV的bias和RMSE均表现出较强的季节性特征;由GPT3模型反演的PWV的月均值可知,GPT3-1模型比GPT3-5模型具有更高的精度和稳定性。     

Observation of Filterable Bromine Variabilities During Arctic Tropospheric Ozone Depletion Events in High (1hour) Time Resolution

The halogen ions Br^- and Cl^- together with NO₃ ^-, SO₄ ⁼, MSA^- (methane sulfonate), Na⁺ and NH₄ ⁺ were analysed by ion chromatography in extracts of more than 800 aerosol cellulose filter samples taken at Ny Ålesund, Svalbard (79°N, 12°E) in spring 1996 (March 27 - May 16) within the European Union project ARCTOC (Arctic Tropospheric Ozone Chemistry). Anticorrelated variations between f-Br (filterable bromine, i.e. water soluble bromine species that can be collected by aerosol filters) and ozone within the arctic troposphere were evaluated at a resolution of 1 or 2 hours for periods with depleted ozone and 4 hours at normal ozone. A mean f-Br concentration of 11 ng m^-3 (0.14 nmol m^-3) was observed for the whole campaign, while maximum concentrations of 80 ng m^-3 (1 nmol m^-3) were detected during two total O₃-depletion events (O₃ drop to mixing ratios below the detection limit of < 2 ppb). Anticorrelation between f-Br and O₃ was also seen during minor O₃-depletion episodes (sudden drop in O₃ by at least 10 ppb, but O₃ still exceeding the detection limit) and even for ozone variations near its background level (40-50 ppb). A time lag of about 10 hours between the change of ozone and of f-Br concentrations could only be found during a total ozone depletion event, when f-Br reached its maximum values several hours after ozone was totally destroyed. Bromine oxide (BrO) concentrations, measured by DOAS (Differential Optical Absorption Spectroscopy), and f-Br showed a coincident variability during almost the entire campaign (except in the case of total O₃-loss). Frequently enhanced anthropogenic nitrate and sulphate concentrations were observed during O₃-depletion periods. At O₃ concentrations < 10 ppb sulphate and nitrate exceed their typical mean level by 54% and 77%, respectively. This may indicate a possible connection between acidity and halogen release.     

基于改进的BP神经网络构建区域精密对流层延迟模型

利用神经网络算法挖掘海量数据的规律已成为科技发展的一种趋势,本文针对卫星信号的天顶对流层延迟进行建模.对流层延迟是影响卫星定位精度的重要因素之一,建立精密区域对流层模型对高精度定位有着重要的意义.对区域测站对流层延迟数据的分析,考虑到实时建模中传统BP（Back Propagation）神经网络计算量大,易出现"过拟合"现象、不稳定等因素,通过改进的BP神经网络建立了区域精密对流层模型.详细介绍了新模型的建立过程,并与常用的对流层区域实时模型进行了对比.还讨论了建模测站数目对预报精度的影响.相比现有的其他对流层延迟模型,基于改进的BP神经网络构建的区域精密对流层延迟模型无论在拟合和预报方面都有较好的精度,且随着测站数目的增加模型精度趋于平稳.改进的模型参数较少,可以进行实时的区域精密对流层延迟改正;需要播发的信息量小,适用于连续运行参考站系统（Continuously Operating Reference Stations,CORS）的应用.研究表明：改进的BP神经网络模型能够更好的充分利用大规模历史数据描述卫星信号对流层延迟的空间分布情况,适用于实时大区域精密对流层建模.基于日本地区2005年近1000多个测站的NCAR（National Center Atmospheric Research）对流层数据进行区域对流层延迟建模,结果表明改进的BP神经网络模型在拟合和预报精度上都有较大提升,RMSE（Root Mean Square Error）分别为：7.83 mm和8.52 mm,而四参数模型拟合、预报RMSE分别18.03 mm和16.60 mm.     

利用CORS数据计算重庆InSAR对流层延迟

利用了重庆市CORS数据,计算了该地区InSAR对流层延迟,分析了重庆地区对流层延迟对InSAR技术的影响。     

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卫星钟差采样间隔对高频PPP-ZTD精度的影响

高频对流层延迟（ZTD）的提取对于反映水汽含量的高时间分辨率瞬时变化及其在暴雨短临预报中的应用具有重要意义。基于精密单点定位技术（PPP）分析了不同采样间隔的卫星钟差对PPP-ZTD精度的影响。结果表明,卫星钟差的时间间隔小于30 s时,所获得的PPP-ZTD(RMS<4 mm)比5 min间隔的（RMS<6 mm）精度要高;而5 s与30 s采样间隔的卫星钟差所获得的ZTD精度相当。     

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对流层映射函数对陆态网解算精度的影响

对流层映射函数是卫星高度角及其他一些因素的函数,它的好坏直接影响对流层延迟改正的效果。本文介绍了NMF、VMF1及GMF 3种模型,采用2016年陆态网及周边IGS站数据,通过GAMIT软件计算对比分析了3种映射函数对陆态网解算精度的影响。结果表明,在进行陆态网解算时,3种映射函数模型精度差别不大,截止高度角为10°时精度最佳。     

大范围网络RTK基准站间整周模糊度实时快速解算

网络RTK是目前实现高精度实时动态定位的重要手段之一,而网络RTK高精度定位的关键问题是基准站间整周模糊度的实时快速准确固定。对于大范围网络RTK,由于基准站间距离的增加,电离层延迟误差、对流层延迟误差和卫星轨道误差相关性降低,导致基准站间整周模糊度不能快速准确地固定,因此本文提出了一种大范围网络RTK基准站间整周模糊度固定算法。该算法首先利用L1、L2载波相位观测值和P1、P2伪距观测值解算基准站间的双差宽巷模糊度;然后采用Saastamoinen模型和Chao映射函数模型相结合解算双差对流层延迟误差,并将双差宽巷模糊度作为L1、L2双差载波相位整周模糊度的约束关系来确定L1、L2双差载波相位整周模糊度;最后采用CORS站的实测数据进行试验,并将本文的试验结果同GAMIT软件的解算结果进行比对,结果表明该算法可以快速准确地实现单历元双差载波相位整周模糊度的固定。     

利用神经网络建立GPS网络RTK的双差对流层误差模型

为减小对流层误差改正数中系统偏差的影响以提高对流层改正精度,提出了基于神经网络的顾及空间的对流层误差建模模型,该模型的对流层延迟误差改正在网内外精度均达5 cm。